Generating capacity prediction method and device of cascade hydropower station, computer equipment, readable storage medium and program product

Abstract

The invention relates to a generating capacity prediction method and device of a cascade hydropower station, computer equipment, a readable storage medium and a program product, and relates to the technical field of electric power. The method comprises the following steps: in response to a generating capacity prediction instruction for a target cascade hydropower station, acquiring historical rainfall data of the target cascade hydropower station under the condition that the target cascade hydropower station has rainfall data in a historical time period; performing water flow velocity prediction on the target cascade hydropower station based on the historical rainfall data to obtain a target water flow velocity of the target cascade hydropower station; and according to the target water flow velocity, generating capacity prediction is performed on the target cascade hydropower station to obtain target generating capacity of the target cascade hydropower station. By adopting the method, the generating capacity prediction accuracy of the cascade hydropower station can be improved.

Description

Technical Field

[0001] The present application relates to the field of electric power technology, and in particular to a method, device, computer equipment, computer-readable storage medium, and computer program product for predicting power generation of a cascade hydropower station. Background Art

[0002] With rapid economic development, the demand for energy continues to grow. Hydropower, as a clean, renewable energy source, has enormous development potential. Therefore, building cascade hydropower stations can fully utilize water resources and provide a stable power supply.

[0003] When dispatching power from cascade hydropower stations, it is necessary to predict their power generation. Traditional cascade hydropower station power generation predictions rely on fixed and single empirical parameters, which have poor adaptability and easily lead to inaccurate power generation predictions for cascade hydropower stations. Summary of the Invention

[0004] Based on this, it is necessary to provide a method, device, computer equipment, computer-readable storage medium and computer program product for predicting power generation of cascade hydropower stations, which can improve the accuracy of power generation prediction of cascade hydropower stations in order to address the above technical problems.

[0005] In a first aspect, the present application provides a method for predicting power generation of a cascade hydropower station, comprising: in response to a power generation prediction instruction for a target cascade hydropower station, obtaining historical rainfall data of the target cascade hydropower station when rainfall data exists for the target cascade hydropower station within a historical time period; based on the historical rainfall data, predicting the water flow velocity of the target cascade hydropower station to obtain a target water flow velocity of the target cascade hydropower station; based on the target water flow velocity, predicting the power generation of the target cascade hydropower station to obtain a target power generation of the target cascade hydropower station.

[0006] In one embodiment, a cascade hydropower station includes multiple sub-hydropower stations; based on historical rainfall data, a water flow velocity of a target cascade hydropower station is predicted to obtain a target water flow velocity of the target cascade hydropower station, including: based on historical rainfall data, a water flow velocity of the target cascade hydropower station is predicted to obtain a first water flow velocity of the target cascade hydropower station; for each adjacent target hydropower station among the multiple sub-hydropower stations, distance information between each target hydropower station is obtained; based on the distance information, the first water flow velocity is updated to obtain the target water flow velocity of the target cascade hydropower station.

[0007] In one embodiment, the historical rainfall data includes the average rainfall of the target cascade hydropower station during the historical time period; based on the distance information, the first water flow velocity is updated to obtain the target water flow velocity of the target cascade hydropower station, including: based on the distance information, the first water flow velocity is updated to obtain the second water flow velocity of the target cascade hydropower station; when the average rainfall is greater than the rainfall threshold, the water level analysis is performed on the target cascade hydropower station to obtain the target water level of the target cascade hydropower station; based on the water pressure data that matches the target water level, the second water flow velocity is updated to obtain the target water flow velocity of the target cascade hydropower station.

[0008] In one embodiment, based on water pressure data that matches the target water level, the second water flow velocity is updated to obtain the target water flow velocity of the target cascade hydropower station, including: based on water pressure data that matches the target water level, the second water flow velocity is updated to obtain the third water flow velocity of the target cascade hydropower station; obtaining a rainfall duration that matches the average rainfall, and when the rainfall duration is greater than a duration threshold, performing a riverbed height analysis on the target cascade hydropower station to obtain the target riverbed height of the target cascade hydropower station; based on water pressure data that matches the target riverbed height, the third water flow velocity is updated to obtain the target water flow velocity of the target cascade hydropower station.

[0009] In one embodiment, the third water flow velocity is updated based on water pressure data that matches the target riverbed height to obtain a target water flow velocity of the target cascade hydropower station, including: updating the third water flow velocity based on water pressure data that matches the target riverbed height to obtain a fourth water flow velocity of the target cascade hydropower station; performing a discharge analysis on the target cascade hydropower station based on average rainfall to obtain discharge data of the target cascade hydropower station; and updating the fourth water flow velocity based on water pressure data that matches the discharge data to obtain the target water flow velocity of the target cascade hydropower station.

[0010] In one embodiment, the power generation of a target cascade hydropower station is predicted based on the target water flow velocity to obtain the target power generation of the target cascade hydropower station, including: predicting the power generation of the target cascade hydropower station based on the target water flow velocity to obtain a first power generation of the target cascade hydropower station; and updating the first power generation based on the power generation matching the discharge data to obtain the target power generation of the target cascade hydropower station.

[0011] In a second aspect, the present application also provides a power generation prediction device for a cascade hydropower station, comprising: a rainfall data acquisition module for responding to a power generation prediction instruction for a target cascade hydropower station, and obtaining historical rainfall data of the target cascade hydropower station when rainfall data exists for the target cascade hydropower station within a historical time period; a water flow velocity prediction module for predicting the water flow velocity of the target cascade hydropower station based on the historical rainfall data, and obtaining a target water flow velocity of the target cascade hydropower station; and a power generation prediction module for predicting the power generation of the target cascade hydropower station based on the target water flow velocity, and obtaining a target power generation of the target cascade hydropower station.

[0012] On the third aspect, the present application also provides a computer device, including a memory and a processor, the memory storing a computer program, and the processor implementing the following steps when executing the computer program: in response to a power generation prediction instruction for a target cascade hydropower station, when there is rainfall data for the target cascade hydropower station within a historical time period, obtaining historical rainfall data of the target cascade hydropower station; based on the historical rainfall data, predicting the water flow velocity of the target cascade hydropower station to obtain a target water flow velocity of the target cascade hydropower station; based on the target water flow velocity, predicting the power generation of the target cascade hydropower station to obtain a target power generation of the target cascade hydropower station.

[0013] In a fourth aspect, the present application also provides a computer-readable storage medium having a computer program stored thereon, which implements the following steps when the computer program is executed by a processor: in response to a power generation prediction instruction for a target cascade hydropower station, when there is rainfall data for the target cascade hydropower station within a historical time period, obtaining historical rainfall data of the target cascade hydropower station; based on the historical rainfall data, predicting the water flow velocity of the target cascade hydropower station to obtain a target water flow velocity of the target cascade hydropower station; based on the target water flow velocity, predicting the power generation of the target cascade hydropower station to obtain a target power generation of the target cascade hydropower station.

[0014] In a fifth aspect, the present application also provides a computer program product, comprising a computer program, which, when executed by a processor, implements the following steps: in response to a power generation prediction instruction for a target cascade hydropower station, obtaining historical rainfall data of the target cascade hydropower station when there is rainfall data for the target cascade hydropower station within a historical time period; based on the historical rainfall data, predicting the water flow velocity of the target cascade hydropower station to obtain a target water flow velocity of the target cascade hydropower station; based on the target water flow velocity, predicting the power generation of the target cascade hydropower station to obtain a target power generation of the target cascade hydropower station.

[0015] The aforementioned method, apparatus, computer device, computer-readable storage medium, and computer program product for predicting power generation at cascade hydropower stations, in response to a power generation prediction instruction for a target cascade hydropower station, obtains historical rainfall data for the target cascade hydropower station, if rainfall data exists for the target cascade hydropower station within a historical time period. Based on this historical rainfall data, the water flow velocity of the target cascade hydropower station is predicted to obtain a target water flow velocity for the target cascade hydropower station. Furthermore, based on the target water flow velocity, the power generation of the target cascade hydropower station is predicted to obtain a target power generation for the target cascade hydropower station. Thus, this solution takes into account the impact of rainfall data on power generation at cascade hydropower stations, as well as the fact that water flow velocity directly reflects water kinetic energy and is closely related to power generation. Therefore, by first predicting the water flow velocity of a cascade hydropower station using historical rainfall data and then predicting the power generation of the cascade hydropower station based on the water flow velocity, the accuracy of power generation prediction for cascade hydropower stations can be effectively improved. This facilitates rational decision-making by power dispatching departments, effectively ensuring power system supply and demand balance, power generation operation safety, and resource utilization efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following briefly introduces the drawings required for use in the embodiments of the present application or related technical descriptions. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying any creative work.

[0017] Figure 1 This is a diagram illustrating an application environment of a method for predicting power generation of a cascade hydropower station in one embodiment;

[0018] Figure 2 1 is a flow chart of a method for predicting power generation of a cascade hydropower station in one embodiment;

[0019] Figure 3 1 is a flow chart of updating the water flow velocity for the first time in one embodiment;

[0020] Figure 4 1 is a flow chart of updating the water flow velocity for the second time in one embodiment;

[0021] Figure 5 1. A schematic diagram of a process for updating the water flow velocity for the third time in one embodiment;

[0022] Figure 6 1 is a flow chart of updating the water flow velocity for the fourth time in one embodiment;

[0023] Figure 7 1. A schematic diagram of a process for updating power generation in one embodiment;

[0024] Figure 8 A schematic diagram of a power generation prediction process for a cascade hydropower station in a specific embodiment;

[0025] Figure 9 1. It is a structural block diagram of a power generation prediction device for a cascade hydropower station in one embodiment;

[0026] Figure 10 is a structural diagram of a power generation prediction device for a cascade hydropower station in another embodiment;

[0027] Figure 11 FIG. 1 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0028] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0029] With rapid economic development, the demand for energy continues to grow. Hydropower, as a clean, renewable energy source, holds enormous development potential. Due to natural and technical reasons, river development must be carried out in sections. This involves constructing a series of hydropower stations, one section at a time, in a stepped pattern, starting from the upper reaches of the river. This development approach is known as cascade development. A series of hydropower stations built through this cascade development approach is known as cascade hydropower. Cascade hydropower stations fully utilize hydropower resources and provide a stable power supply. Power generation forecasting for cascade hydropower stations facilitates informed decision-making by power dispatchers. Traditional power generation forecasting for cascade hydropower stations is relatively simple and relies heavily on fixed, single empirical parameters, resulting in poor adaptability. This can lead to inaccurate power generation forecasts for cascade hydropower stations, significantly impacting subsequent power dispatch.

[0030] In order to solve the above problems, the present invention provides a method for predicting the power generation of a cascade hydropower station, which can be applied to Figure 1 In the application environment shown, terminal 102 communicates with server 104 via a network. A data storage system can store data that server 104 needs to process. The data storage system can be integrated with server 104 or placed in the cloud or on other network servers. Terminal 102 can be, but is not limited to, various personal computers, laptops, smartphones, tablets, and IoT devices. Server 104 can be a standalone physical server, a server cluster or distributed system consisting of multiple physical servers, or a cloud server providing cloud computing services.

[0031] Specifically, in response to a power generation forecast instruction initiated by terminal 102 for a target cascade hydropower station, server 104 obtains historical rainfall data for the target cascade hydropower station, if rainfall data exists for the target cascade hydropower station within a historical time period. Based on this historical rainfall data, server 104 predicts the water flow velocity for the target cascade hydropower station, obtaining a target water flow velocity for the target cascade hydropower station. Furthermore, server 104 predicts the power generation for the target cascade hydropower station based on the target water flow velocity, obtaining a target power generation for the target cascade hydropower station.

[0032] In an exemplary embodiment, Figure 2 As shown in the figure, a method for predicting the power generation of cascade hydropower stations is provided. Figure 1 Taking the server 104 in the example as an example, the following steps are included:

[0033] Step S202 : in response to a power generation prediction instruction for a target cascade hydropower station, if rainfall data exists for the target cascade hydropower station within a historical time period, obtaining historical rainfall data for the target cascade hydropower station.

[0034] The target cascade hydropower station may refer to a cascade hydropower station for which a power generation forecast is required. The power generation forecast instruction may refer to a forecast of the target cascade hydropower station's power generation within a certain future timeframe, such as the average monthly power generation over the medium to long term. The medium to long term includes both the mid-term and long-term. The mid-term can be understood as a period of time between the short-term and long-term, such as a quarter. The long-term is a relatively long period of time, such as an annual period. The historical timeframe may refer to a certain past timeframe, such as the past year or two, and the specific period can be determined based on actual needs. Rainfall data refers to relevant data related to rainy weather, such as at least one of the following: total rainfall, average rainfall, rainfall intensity, and rainfall duration (i.e., the time interval from the start to the end of rainfall). Historical rainfall data refers to rainfall data for the target cascade hydropower station over a historical timeframe, such as the monthly average rainfall for each quarter or each year in previous years.

[0035] For example, upon receiving a power generation forecast instruction for a target cascade hydropower station, the server may first determine whether rainfall data exists for the target cascade hydropower station within a historical time period. If rainfall data exists, the server may obtain historical rainfall data for the target cascade hydropower station within the historical time period. If rainfall data does not exist, the server may extend the historical time period and further determine whether rainfall data exists for the target cascade hydropower station within the extended historical time period, until historical rainfall data for the target cascade hydropower station is obtained.

[0036] In some embodiments, whether there is rainfall data for a target cascade hydropower station within a historical time period can be determined by searching for weather information for the region where the target cascade hydropower station is located within the historical time period. Specifically, the target cascade hydropower station is located within the historical time period. If there is rainfall in the region where the target cascade hydropower station is located within the historical time period, then it can be determined that there is rainfall data for the target cascade hydropower station within the historical time period.

[0037] Step S204 : predicting the water flow velocity of the target cascade hydropower station based on the historical rainfall data to obtain the target water flow velocity of the target cascade hydropower station.

[0038] The target water flow rate can refer to the water flow rate per unit time at a target cascade hydropower station. It can be understood that when a cascade hydropower station generates electricity through water flow, the greater the rainfall, the faster the water flow into the power station due to the hydraulic pressure, and the greater the power generation. Therefore, the water flow rate of a cascade hydropower station can be predicted based on rainfall, thereby predicting the power generation of the cascade hydropower station.

[0039] For example, after obtaining historical rainfall data, the server can predict the water flow velocity of a target cascade hydropower station based on the historical rainfall data. Specifically, the water flow velocity of the target cascade hydropower station can be calculated based on the average rainfall amount in the historical rainfall data, a rainfall threshold, and the maximum pressure-resistant water flow velocity of the target cascade hydropower station.

[0040] In some embodiments, the water flow velocity is calculated as:

[0041]

[0042] Where V represents the water velocity, represents the average rainfall, represents the rainfall threshold, Indicates the water velocity at which the target cascade hydropower station can withstand the maximum pressure.

[0043] In some embodiments, the expression for average rainfall is:

[0044]

[0045] in, represents the total rainfall in a quarter, Indicates the total rainfall for the whole year.

[0046] Step S206 , predicting the power generation of the target cascade hydropower station according to the target water flow velocity, and obtaining the target power generation of the target cascade hydropower station.

[0047] The target power generation refers to the power generation of the target cascade hydropower station within a certain time range in the future, and can specifically be the medium- and long-term power generation of the target cascade hydropower station in the future, including the medium-term power generation of the target cascade hydropower station, such as quarterly power generation, and long-term power generation, such as annual power generation.

[0048] For example, after calculating the target water flow velocity for a target cascade hydropower station, the server can then predict the power generation of the target cascade hydropower station based on the target water flow velocity, thereby obtaining the power generation of the target cascade hydropower station within a certain future timeframe. Specifically, the prediction can be based on the target water flow velocity, the total power generation efficiency of the target cascade hydropower station, the water head height, and the cross-sectional area of the water flow.

[0049] In some embodiments, the target power generation amount is calculated as follows:

[0050]

[0051] Where P represents the target power generation, represents the total power generation efficiency, A represents the cross-sectional area of the water flow, V represents the water flow velocity, and H represents the water head height. It can be seen that the more rainfall, the more power generation.

[0052] In this embodiment, in response to a power generation prediction instruction for a target cascade hydropower station, if rainfall data exists for the target cascade hydropower station within a historical time period, historical rainfall data for the target cascade hydropower station is obtained. Based on this historical rainfall data, a water flow velocity prediction is performed for the target cascade hydropower station to obtain a target water flow velocity for the target cascade hydropower station. Furthermore, based on the target water flow velocity, a power generation prediction is performed for the target cascade hydropower station to obtain a target power generation for the target cascade hydropower station. In this way, this embodiment takes into account the impact of rainfall data on power generation at cascade hydropower stations, as well as the fact that water flow velocity directly reflects water kinetic energy and is closely related to power generation. Therefore, using historical rainfall data to first predict the water flow velocity at a cascade hydropower station and then predicting the power generation of the cascade hydropower station based on the water flow velocity can effectively improve the accuracy of power generation predictions for cascade hydropower stations. This facilitates rational decision-making by power dispatching departments, effectively ensuring supply and demand balance, power generation operation safety, and resource utilization efficiency in the power system.

[0053] In an exemplary embodiment, Figure 3 As shown in FIG, based on the historical rainfall data, the water flow velocity of the target cascade hydropower station is predicted to obtain the target water flow velocity of the target cascade hydropower station, including:

[0054] Step S302 : Based on historical rainfall data, a water flow velocity prediction is performed on the target cascade hydropower station to obtain a first water flow velocity of the target cascade hydropower station.

[0055] A cascade hydropower station consists of multiple sub-stations constructed sequentially from upstream to downstream. The first water flow velocity refers to the predicted initial water flow velocity. It is understood that in the actual power generation scenario of a cascade hydropower station, the water flow velocity is not fixed and varies with various influencing factors. Therefore, in this embodiment, the predicted water flow velocity is continuously updated and optimized to ensure that the water flow velocity better matches the actual power generation status of the cascade hydropower station, thereby improving the accuracy of the water flow velocity and, consequently, the accuracy of the power generation prediction.

[0056] Step S304 : for each adjacent target hydropower station among the plurality of sub-hydropower stations, obtain distance information between each target hydropower station.

[0057] Step S306: Based on the distance information, the first water flow velocity is updated to obtain a target water flow velocity of the target cascade hydropower station.

[0058] Adjacent target hydropower stations are those that are geographically adjacent or relatively close to each other and connected by water flow. These hydropower stations are typically located upstream and downstream of a river, with a water flow connection between them. Distance information can be understood as the location distance between target hydropower stations, specifically the height difference in the river section between them.

[0059] For example, for a cascade hydropower station, the height difference between the various sub-stations in the river is one factor that affects water flow velocity. The greater the height difference, the faster the water flow. Therefore, in this embodiment, the height difference between river sections is used to update and optimize the water flow velocity. Specifically, the height difference between target hydropower stations that are geographically adjacent or relatively close and fluidly connected is obtained. Based on this height difference, the calculated first water flow velocity is updated to obtain the target water flow velocity for the target cascade hydropower station.

[0060] In some embodiments, the expression for updating the water flow velocity based on the height difference of the river section is:

[0061]

[0062] in, is the height difference of the river section between the target hydropower stations, Indicates the maximum value of the preset river height difference. It is the water velocity updated based on the height difference of the river section.

[0063] In this embodiment, considering that cascade hydropower stations are composed of several sub-stations arranged in a stepped pattern, the height difference between the river sections between the power stations directly affects the water flow velocity. Specifically, the greater the height difference, the faster the water flow during impact. Therefore, updating the water flow velocity based on the height difference between the power stations can improve the accuracy of the water flow velocity, thereby making the power generation data predicted based on the water flow velocity more accurate, which is beneficial for subsequent power dispatch decisions.

[0064] In an exemplary embodiment, Figure 4 As shown, based on the distance information, the first water flow velocity is updated to obtain the target water flow velocity of the target cascade hydropower station, including:

[0065] Step S402: Based on the distance information, the first water flow velocity is updated to obtain a second water flow velocity of the target cascade hydropower station.

[0066] The second water flow velocity refers to the water flow velocity obtained after updating the water flow velocity based on the height difference of the river section. It is understood that in addition to the height difference of the river section affecting the water flow velocity, rainfall also affects the water flow velocity. As rainfall increases, the water level of the reservoir rises, the generated water pressure increases, and thus the water flow velocity increases. Therefore, in this embodiment, the water flow velocity is further updated and optimized based on rainfall, or the water level affected by rainfall.

[0067] Step S404: When the average rainfall is greater than the rainfall threshold, water level analysis is performed on the target cascade hydropower station to obtain a target water level of the target cascade hydropower station.

[0068] The rainfall threshold refers to a pre-set standard rainfall value, or the standard monthly average rainfall value. When the rainfall threshold is exceeded, it is considered that rainfall has increased, and the water level will rise. When the rainfall threshold is equal to or less than the threshold, the rainfall is considered normal, and the water level will not rise. The target water level refers to the water level at the target cascade hydropower station if the average rainfall exceeds the rainfall threshold.

[0069] For example, after updating the water flow velocity based on the height difference of the river section, the server can further determine whether the monthly average rainfall in the rainfall data exceeds the preset rainfall standard value to assess whether the rainfall will cause the water level to rise. If the average rainfall is greater than the rainfall threshold, it is considered that the rainfall is large and will cause the water level to rise. In this case, the increased water level of the target cascade hydropower station can be calculated. Specifically, the inflow flow, that is, the flow entering the reservoir, can be calculated based on the average rainfall. The increased water level is then calculated based on the inflow and outflow. If the average rainfall is less than or equal to the rainfall threshold, it can be considered that the rainfall is normal and will not cause the water level to rise. The water flow velocity at this time remains at the second water flow velocity.

[0070] Step S406: Based on the water pressure data that matches the target water level, the second water flow velocity is updated to obtain the target water flow velocity of the target cascade hydropower station.

[0071] The water pressure data refers to the water pressure value that matches the target water level. Of course, in addition to the water pressure value, the water pressure data may also include data such as the water pressure change and the water pressure change range.

[0072] For example, after calculating the elevated water level of the target cascade hydropower station, the server can determine the water pressure value that matches this elevated water level. Specifically, the server can pre-store a correlation between water level and water pressure and, based on this correlation, retrieve the water pressure value that matches this elevated water level. As the water level rises, the water pressure also increases accordingly. The server further obtains the corresponding increase in water flow velocity associated with this increase in water pressure, and based on this water flow velocity, updates the second water flow velocity to obtain the target water flow velocity for the target cascade hydropower station.

[0073] In some embodiments, when When , the expression for updating the water flow velocity based on the water level is:

[0074]

[0075] in, is the water flow velocity based on the water level update, It means that when the water level rises, the water pressure increases and the water flow rate increases.

[0076] In some embodiments, when hour, .

[0077] In this embodiment, as rainfall increases, the reservoir water level rises, generating a relatively higher water pressure, which in turn leads to a further increase in water velocity. Therefore, updating the water velocity secondary to the water level can further improve the accuracy of the water velocity, making the power generation data predicted based on the water velocity more accurate, which is beneficial for subsequent power dispatch decisions.

[0078] In an exemplary embodiment, Figure 5 As shown, based on the water pressure data matching the target water level, the second water flow velocity is updated to obtain the target water flow velocity of the target cascade hydropower station, including:

[0079] Step S502: Based on the water pressure data matching the target water level, the second water flow velocity is updated to obtain a third water flow velocity of the target cascade hydropower station.

[0080] The third water flow velocity refers to the water flow velocity obtained after updating the water flow velocity based on the water level. It is understood that heavy rainfall can produce sediment, which can cause the riverbed to rise. This reduces the reservoir's water storage capacity, which in turn reduces the water pressure and water flow velocity. Therefore, this embodiment further optimizes the water flow velocity based on the riverbed height.

[0081] Step S504: Obtain a rainfall duration that matches the average rainfall. When the rainfall duration is greater than a duration threshold, analyze the riverbed height of the target cascade hydropower station to obtain a target riverbed height of the target cascade hydropower station.

[0082] The duration of rainfall refers to the duration of rainfall. When the average rainfall is greater than the rainfall threshold, the rainfall will continue to increase with the duration of rainfall. After heavy rainfall, the sediment in the riverbed increases, causing the riverbed to rise. The target riverbed height refers to the amount of riverbed rise.

[0083] For example, after updating the water flow velocity based on the water level, the server can further obtain the rainfall duration of the average rainfall. If the rainfall duration exceeds a duration threshold, the riverbed will rise due to the sediment brought by the rainfall. The duration threshold can be a pre-set threshold for rainfall duration. The server calculates the amount of riverbed rise. Specifically, the server can calculate the ratio of the sediment accumulation volume to the riverbed area of the sediment accumulation zone. This ratio is the riverbed rise.

[0084] Step S506: Based on the water pressure data that matches the target riverbed height, the third water flow velocity is updated to obtain the target water flow velocity of the target cascade hydropower station.

[0085] Among them, water pressure data refers to the water pressure value that matches the target riverbed height. Of course, in addition to the water pressure value, water pressure data can also include data such as water pressure changes and water pressure change range.

[0086] For example, after calculating the riverbed elevation of the target cascade hydropower station, the server can determine the water pressure value that matches this riverbed elevation. Specifically, the server can pre-store a correlation between riverbed elevation and water pressure, and based on this correlation, it can query the water pressure value that matches this riverbed elevation. As the riverbed rises, the water pressure and water flow velocity decrease accordingly. The server further obtains the corresponding decrease in water flow velocity due to the riverbed elevation, and based on this water flow velocity, updates the third water flow velocity to obtain the target water flow velocity for the target cascade hydropower station.

[0087] In some embodiments, when When , the expression for updating the water flow velocity based on the riverbed rise is:

[0088]

[0089] in, represents the water velocity updated based on the riverbed elevation, Indicates the reduction in water flow velocity caused by the rise of the riverbed.

[0090] In some embodiments, when hour, .

[0091] In this embodiment, as rainfall increases, ground sediment is washed into the reservoir by rainwater, leading to silt accumulation in the riverbed and a rise in the riverbed. This reduces the reservoir's water storage capacity, resulting in lower water pressure and flow velocity. Therefore, by updating the flow velocity three times based on the riverbed rise, we can more accurately predict the flow velocity during periods of heavy rainfall, thereby ensuring the accuracy of power generation predictions.

[0092] In an exemplary embodiment, Figure 6 As shown, based on the water pressure data matching the target riverbed height, the third water flow velocity is updated to obtain the target water flow velocity of the target cascade hydropower station, including:

[0093] Step S602: Based on the water pressure data matching the target riverbed height, the third water flow velocity is updated to obtain a fourth water flow velocity of the target cascade hydropower station.

[0094] The fourth water flow velocity refers to the water flow velocity obtained after updating the water flow velocity based on the riverbed rise. It is understood that in the event of heavy rainfall, gradient power stations need to release water to prevent dam collapse. As the frequency of water release increases, the water level in the reservoir continues to decrease, the water pressure decreases again, and the water flow velocity also decreases. Therefore, in this embodiment, the water flow velocity is further updated and optimized based on the water release data.

[0095] Step S604: performing discharge analysis on the target cascade hydropower station according to the average rainfall to obtain discharge data of the target cascade hydropower station.

[0096] The discharge data may refer to data generated when the target cascade hydropower station discharges water, including but not limited to at least one of discharge frequency, discharge time, and discharge flow rate.

[0097] For example, after updating the water flow velocity based on the riverbed rise, the server can further analyze the discharge of the target cascade hydropower station based on average rainfall to determine the discharge frequency of the target cascade hydropower station. Specifically, the discharge frequency of the target cascade hydropower station can be predicted based on the historical discharge data of the target cascade hydropower station. Alternatively, based on the correlation between average rainfall and discharge frequency, a discharge frequency that matches the average rainfall can be determined.

[0098] Step S606: Based on the water pressure data that matches the discharge data, the fourth water flow velocity is updated to obtain a target water flow velocity of the target cascade hydropower station.

[0099] The water pressure data refers to the water pressure value that matches the discharge data. Of course, in addition to the water pressure value, the water pressure data may also include data such as the water pressure change and the water pressure change range.

[0100] For example, after calculating the discharge frequency of the target cascade hydropower station, the server can determine the water pressure value that matches the discharge frequency. Specifically, the server can pre-store the correlation between discharge frequency and water pressure, and based on this correlation, it can query the water pressure value that matches the discharge frequency. As the discharge frequency increases, the water pressure and water flow velocity decrease accordingly. The server further obtains the corresponding decrease in water flow velocity when the discharge frequency increases, and based on this water flow velocity, updates the fourth water flow velocity to obtain the target water flow velocity for the target cascade hydropower station.

[0101] In some embodiments, when When , the expression for updating the water flow velocity based on the riverbed rise is:

[0102]

[0103] in, represents the water flow velocity updated based on the discharge frequency, Indicates the decrease in water flow velocity as the discharge frequency increases.

[0104] In some embodiments, when hour, .

[0105] It should be noted that after each water flow velocity update, the power generation of the target gradient hydropower station can be updated synchronously.

[0106] In this embodiment, the frequency of water discharge increases with increasing rainfall, leading to a gradual decrease in water storage and water level, significantly reducing water pressure and flow velocity. Therefore, the water flow velocity is updated again based on the frequency of water discharge to significantly reduce the predicted flow velocity. This avoids overestimating and inaccurately predicting power generation, thereby ensuring the accuracy of power generation predictions.

[0107] In an exemplary embodiment, Figure 7 As shown in FIG, based on the target water flow velocity, the power generation of the target cascade hydropower station is predicted to obtain the target power generation of the target cascade hydropower station, including:

[0108] Step S702: predicting the power generation of the target cascade hydropower station according to the target water flow velocity to obtain a first power generation of the target cascade hydropower station.

[0109] Step S704: Based on the power generation that matches the discharge data, the first power generation is updated to obtain the target power generation of the target cascade hydropower station.

[0110] The first power generation refers to the predicted initial power generation. It is understood that discharges from cascade hydropower stations can increase power generation time, leading to a dramatic increase in power generation. Therefore, in this embodiment, the predicted power generation is updated based on discharge data to ensure the accuracy of the power generation forecast.

[0111] For example, after obtaining the target water flow velocity, the server can predict the power generation of the target cascade hydropower station based on the target water flow velocity, thereby obtaining the initial power generation of the target cascade hydropower station. As rainfall continues to increase, the target cascade hydropower station will need to frequently release water to prevent dam collapse. Frequent releases increase power generation, so the initial power generation is updated based on this increased power generation, thereby obtaining the final power generation of the target cascade hydropower station.

[0112] In some embodiments, when When , the updated expression of power generation is:

[0113]

[0114] in, Indicates the increase in power generation due to leakage. Indicates the updated power generation based on leakage data.

[0115] In some embodiments, when hour, .

[0116] In this embodiment, the impact of discharge on power generation is taken into account, and the increased power generation due to discharge is used to update the initially predicted power generation to obtain the final power generation of the target cascade hydropower station, thereby ensuring the accuracy of the power generation prediction.

[0117] In some embodiments, for each sub-hydropower station in the target cascade hydropower station, the sub-power generation of each sub-hydropower station can be calculated based on the content of the above embodiment. The server adds up these sub-power generation to obtain the total power generation of the target cascade hydropower station, which is expressed as:

[0118]

[0119] Where P represents the total power generation of the target cascade hydropower station, 、 ... They represent each sub-hydropower station, and N represents the number of sub-hydropower stations.

[0120] In a specific embodiment, Figure 8 The power generation forecast flow chart of cascade hydropower stations is shown, including:

[0121] S1: Obtain the monthly average rainfall of each quarter and each year in previous years for cascade hydropower stations;

[0122] S2: Based on the average rainfall, the water flow velocity of the cascade hydropower station is predicted to obtain the water flow velocity of the cascade hydropower station. And based on the water flow velocity, the power generation of the cascade hydropower station is predicted to obtain the power generation of the cascade hydropower station;

[0123] S3: Optimize the water flow velocity based on the height difference of the river sections between the sub-power stations to obtain the optimized water flow velocity and update the power generation at the same time;

[0124] S4: When the average rainfall is greater than the rainfall threshold, the water flow velocity obtained in S3 is optimized twice based on the rising water level to obtain the second optimized water flow velocity, and the power generation is updated at the same time;

[0125] S5: Based on the riverbed elevation, the water flow velocity obtained in S4 is optimized three times to obtain the optimized water flow velocity, and the power generation is updated at the same time;

[0126] S6: Based on the discharge frequency, the water flow velocity obtained in S5 is optimized four times to obtain the water flow velocity after four optimizations, and the target water flow velocity is obtained. At the same time, the power generation is updated based on the discharge frequency;

[0127] S7: The power generation of each sub-power station is aggregated to obtain the total power generation of the cascade power station.

[0128] In this embodiment, in response to a power generation prediction instruction for a target cascade hydropower station, if rainfall data exists for the target cascade hydropower station within a historical time period, historical rainfall data for the target cascade hydropower station is obtained. Based on this historical rainfall data, a water flow velocity prediction is performed for the target cascade hydropower station to obtain a target water flow velocity for the target cascade hydropower station. Furthermore, based on the target water flow velocity, a power generation prediction is performed for the target cascade hydropower station to obtain a target power generation for the target cascade hydropower station. In this way, this embodiment takes into account the impact of rainfall data on power generation at cascade hydropower stations, as well as the fact that water flow velocity directly reflects water kinetic energy and is closely related to power generation. Therefore, using historical rainfall data to first predict the water flow velocity at a cascade hydropower station and then predicting the power generation of the cascade hydropower station based on the water flow velocity can effectively improve the accuracy of power generation predictions for cascade hydropower stations. This facilitates rational decision-making by power dispatching departments, effectively ensuring supply and demand balance, power generation operation safety, and resource utilization efficiency in the power system.

[0129] It should be understood that, although the various steps in the flowcharts involved in the various embodiments described above are displayed in sequence according to the instructions of the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps can be executed in other orders. Moreover, at least a portion of the steps in the flowcharts involved in the various embodiments described above can include multiple steps or multiple stages, and these steps or stages are not necessarily executed and completed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a portion of steps or stages in other steps.

[0130] Based on the same inventive concept, embodiments of the present application also provide a device for predicting power generation for a cascade hydropower station, which is used to implement the aforementioned method for predicting power generation for a cascade hydropower station. The solution provided by this device is similar to the solution described in the aforementioned method. Therefore, the specific limitations of the embodiments of the device for predicting power generation for one or more cascade hydropower stations provided below can be found in the aforementioned limitations of the method for predicting power generation for a cascade hydropower station, and will not be further elaborated here.

[0131] In an exemplary embodiment, Figure 9As shown, a power generation prediction device for a cascade hydropower station is provided, including: a rainfall data acquisition module 902, for responding to a power generation prediction instruction for a target cascade hydropower station, and obtaining historical rainfall data of the target cascade hydropower station when rainfall data exists for the target cascade hydropower station within a historical time period; a water flow velocity prediction module 904, for predicting the water flow velocity of the target cascade hydropower station based on the historical rainfall data, and obtaining a target water flow velocity of the target cascade hydropower station; and a power generation prediction module 906, for predicting the power generation of the target cascade hydropower station according to the target water flow velocity, and obtaining a target power generation of the target cascade hydropower station.

[0132] In some embodiments, Figure 10 Another architecture diagram of a power generation prediction device for a cascade hydropower station is shown. It includes: a water flow velocity prediction unit, a height difference determination unit, a water level prediction unit, a riverbed height prediction unit, a discharge prediction unit, a power generation prediction unit, and a power generation integration unit. The water flow velocity prediction unit is used to predict water flow velocity. The height difference determination unit is used to determine the height difference between river sections between sub-power stations. The water level prediction unit is used to predict the water level of the reservoir. The riverbed height prediction unit is used to predict the riverbed height. The discharge prediction unit is used to predict the discharge frequency. The power generation prediction unit is used to predict the power generation of a power station. The power generation integration unit is used to integrate the power generation of each power station.

[0133] In some embodiments, the water flow velocity prediction unit is used to predict the water flow velocity of the target cascade hydropower station based on historical rainfall data to obtain a first water flow velocity of the target cascade hydropower station; the height difference determination unit is used to obtain distance information between each target hydropower station that is adjacent to each other in a plurality of sub-hydropower stations; the water flow velocity optimization unit is used to update the first water flow velocity based on the distance information to obtain a target water flow velocity of the target cascade hydropower station.

[0134] In some embodiments, the water flow velocity optimization unit is further used to update the first water flow velocity based on the distance information to obtain the second water flow velocity of the target cascade hydropower station; the water level prediction unit is used to perform water level analysis on the target cascade hydropower station when the average rainfall is greater than the rainfall threshold to obtain the target water level of the target cascade hydropower station; the water flow velocity optimization unit is further used to update the second water flow velocity based on the water pressure data that matches the target water level to obtain the target water flow velocity of the target cascade hydropower station.

[0135] In some embodiments, the water flow velocity optimization unit is further used to update the second water flow velocity based on water pressure data that matches the target water level to obtain a third water flow velocity of the target cascade hydropower station; the riverbed height prediction unit is used to obtain a rainfall duration that matches the average rainfall, and when the rainfall duration is greater than a duration threshold, perform a riverbed height analysis on the target cascade hydropower station to obtain a target riverbed height of the target cascade hydropower station; the water flow velocity optimization unit is further used to update the third water flow velocity based on water pressure data that matches the target riverbed height to obtain a target water flow velocity of the target cascade hydropower station.

[0136] In some embodiments, the water flow velocity optimization unit is further used to update the third water flow velocity based on water pressure data that matches the target riverbed height to obtain a fourth water flow velocity of the target cascade hydropower station; the discharge prediction unit is used to perform a discharge analysis on the target cascade hydropower station based on the average rainfall to obtain the discharge data of the target cascade hydropower station; the water flow velocity optimization unit is further used to update the fourth water flow velocity based on water pressure data that matches the discharge data to obtain the target water flow velocity of the target cascade hydropower station.

[0137] In some embodiments, the power generation prediction module 906 is also used to: predict the power generation of the target cascade hydropower station based on the target water flow velocity to obtain a first power generation of the target cascade hydropower station; and update the first power generation based on the power generation matching the discharge data to obtain a target power generation of the target cascade hydropower station.

[0138] Each module in the aforementioned cascade hydropower station power generation prediction device can be implemented in whole or in part via software, hardware, or a combination thereof. Each module can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a computer device memory in the form of software, so that the processor can call and execute the corresponding operations of each module.

[0139] In an exemplary embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as shown in FIG. Figure 11As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, abbreviated as I / O) and a communication interface. The processor, memory and input / output interface are connected via a system bus, and the communication interface is connected to the system bus via the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The database of the computer device is used to store power generation prediction data of cascade hydropower stations. The input / output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, a power generation prediction method for cascade hydropower stations is implemented.

[0140] Those skilled in the art will understand that Figure 11 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0141] In an exemplary embodiment, a computer device is provided, comprising a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the following steps when executing the computer program: in response to a power generation prediction instruction for a target cascade hydropower station, if rainfall data exists for the target cascade hydropower station within a historical time period, obtaining historical rainfall data for the target cascade hydropower station; based on the historical rainfall data, predicting the water flow velocity of the target cascade hydropower station to obtain a target water flow velocity for the target cascade hydropower station; and predicting the power generation of the target cascade hydropower station based on the target water flow velocity to obtain a target power generation of the target cascade hydropower station.

[0142] In one embodiment, when the processor executes the computer program, it further implements the following steps: based on historical rainfall data, predicting the water flow velocity of the target cascade hydropower station to obtain a first water flow velocity of the target cascade hydropower station; for each adjacent target hydropower station among multiple sub-hydropower stations, obtaining distance information between each target hydropower station; based on the distance information, updating the first water flow velocity to obtain a target water flow velocity of the target cascade hydropower station.

[0143] In one embodiment, when the processor executes the computer program, it further implements the following steps: based on the distance information, updating the first water flow velocity to obtain the second water flow velocity of the target cascade hydropower station; when the average rainfall is greater than the rainfall threshold, performing a water level analysis on the target cascade hydropower station to obtain the target water level of the target cascade hydropower station; based on the water pressure data that matches the target water level, updating the second water flow velocity to obtain the target water flow velocity of the target cascade hydropower station.

[0144] In one embodiment, when the processor executes the computer program, it also implements the following steps: based on the water pressure data that matches the target water level, the second water flow velocity is updated to obtain the third water flow velocity of the target cascade hydropower station; the rainfall duration that matches the average rainfall is obtained, and when the rainfall duration is greater than the duration threshold, the riverbed height of the target cascade hydropower station is analyzed to obtain the target riverbed height of the target cascade hydropower station; based on the water pressure data that matches the target riverbed height, the third water flow velocity is updated to obtain the target water flow velocity of the target cascade hydropower station.

[0145] In one embodiment, when the processor executes the computer program, it further implements the following steps: based on water pressure data that matches the target riverbed height, updating the third water flow velocity to obtain a fourth water flow velocity of the target cascade hydropower station; based on the average rainfall, performing a discharge analysis on the target cascade hydropower station to obtain discharge data of the target cascade hydropower station; based on water pressure data that matches the discharge data, updating the fourth water flow velocity to obtain a target water flow velocity of the target cascade hydropower station.

[0146] In one embodiment, when the processor executes the computer program, it further implements the following steps: predicting the power generation of the target cascade hydropower station based on the target water flow velocity to obtain a first power generation of the target cascade hydropower station; and updating the first power generation based on the power generation matching the discharge data to obtain a target power generation of the target cascade hydropower station.

[0147] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the following steps are implemented: in response to a power generation prediction instruction for a target cascade hydropower station, when rainfall data exists for the target cascade hydropower station within a historical time period, historical rainfall data of the target cascade hydropower station is obtained; based on the historical rainfall data, a water flow velocity is predicted for the target cascade hydropower station to obtain a target water flow velocity for the target cascade hydropower station; and based on the target water flow velocity, a power generation prediction is performed for the target cascade hydropower station to obtain a target power generation of the target cascade hydropower station.

[0148] In one embodiment, when the computer program is executed by the processor, the following steps are also implemented: based on historical rainfall data, the water flow velocity of the target cascade hydropower station is predicted to obtain a first water flow velocity of the target cascade hydropower station; for each adjacent target hydropower station among multiple sub-hydropower stations, the distance information between each target hydropower station is obtained; based on the distance information, the first water flow velocity is updated to obtain a target water flow velocity of the target cascade hydropower station.

[0149] In one embodiment, when the computer program is executed by the processor, the following steps are further implemented: based on the distance information, the first water flow velocity is updated to obtain the second water flow velocity of the target cascade hydropower station; when the average rainfall is greater than the rainfall threshold, the water level of the target cascade hydropower station is analyzed to obtain the target water level of the target cascade hydropower station; based on the water pressure data that matches the target water level, the second water flow velocity is updated to obtain the target water flow velocity of the target cascade hydropower station.

[0150] In one embodiment, when the computer program is executed by the processor, the following steps are also implemented: based on the water pressure data that matches the target water level, the second water flow velocity is updated to obtain the third water flow velocity of the target cascade hydropower station; the rainfall duration that matches the average rainfall is obtained, and when the rainfall duration is greater than the duration threshold, the riverbed height of the target cascade hydropower station is analyzed to obtain the target riverbed height of the target cascade hydropower station; based on the water pressure data that matches the target riverbed height, the third water flow velocity is updated to obtain the target water flow velocity of the target cascade hydropower station.

[0151] In one embodiment, when the computer program is executed by the processor, the following steps are further implemented: based on water pressure data that matches the target riverbed height, the third water flow velocity is updated to obtain a fourth water flow velocity of the target cascade hydropower station; based on the average rainfall, a discharge analysis is performed on the target cascade hydropower station to obtain discharge data of the target cascade hydropower station; based on water pressure data that matches the discharge data, the fourth water flow velocity is updated to obtain a target water flow velocity of the target cascade hydropower station.

[0152] In one embodiment, when the computer program is executed by the processor, the following steps are further implemented: based on the target water flow velocity, the power generation of the target cascade hydropower station is predicted to obtain a first power generation of the target cascade hydropower station; based on the power generation that matches the discharge data, the first power generation is updated to obtain a target power generation of the target cascade hydropower station.

[0153] In one embodiment, a computer program product is provided, comprising a computer program that, when executed by a processor, implements the following steps: in response to a power generation prediction instruction for a target cascade hydropower station, obtaining historical rainfall data for the target cascade hydropower station, if rainfall data exists for the target cascade hydropower station within a historical time period; predicting a water flow velocity for the target cascade hydropower station based on the historical rainfall data to obtain a target water flow velocity for the target cascade hydropower station; and predicting a power generation for the target cascade hydropower station based on the target water flow velocity to obtain a target power generation for the target cascade hydropower station.

[0154] In one embodiment, when the computer program is executed by the processor, the following steps are also implemented: based on historical rainfall data, the water flow velocity of the target cascade hydropower station is predicted to obtain a first water flow velocity of the target cascade hydropower station; for each adjacent target hydropower station among multiple sub-hydropower stations, the distance information between each target hydropower station is obtained; based on the distance information, the first water flow velocity is updated to obtain a target water flow velocity of the target cascade hydropower station.

[0155] In one embodiment, when the computer program is executed by the processor, the following steps are further implemented: based on the distance information, the first water flow velocity is updated to obtain the second water flow velocity of the target cascade hydropower station; when the average rainfall is greater than the rainfall threshold, the water level of the target cascade hydropower station is analyzed to obtain the target water level of the target cascade hydropower station; based on the water pressure data that matches the target water level, the second water flow velocity is updated to obtain the target water flow velocity of the target cascade hydropower station.

[0156] In one embodiment, when the computer program is executed by the processor, the following steps are also implemented: based on the water pressure data that matches the target water level, the second water flow velocity is updated to obtain the third water flow velocity of the target cascade hydropower station; the rainfall duration that matches the average rainfall is obtained, and when the rainfall duration is greater than the duration threshold, the riverbed height of the target cascade hydropower station is analyzed to obtain the target riverbed height of the target cascade hydropower station; based on the water pressure data that matches the target riverbed height, the third water flow velocity is updated to obtain the target water flow velocity of the target cascade hydropower station.

[0157] In one embodiment, when the computer program is executed by the processor, the following steps are further implemented: based on water pressure data that matches the target riverbed height, the third water flow velocity is updated to obtain a fourth water flow velocity of the target cascade hydropower station; based on the average rainfall, a discharge analysis is performed on the target cascade hydropower station to obtain discharge data of the target cascade hydropower station; based on water pressure data that matches the discharge data, the fourth water flow velocity is updated to obtain a target water flow velocity of the target cascade hydropower station.

[0158] In one embodiment, when the computer program is executed by the processor, the following steps are further implemented: based on the target water flow velocity, the power generation of the target cascade hydropower station is predicted to obtain a first power generation of the target cascade hydropower station; based on the power generation that matches the discharge data, the first power generation is updated to obtain a target power generation of the target cascade hydropower station.

[0159] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0160] Those skilled in the art will understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. In particular, any reference to memory, database, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The databases involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the various embodiments provided herein may be, but are not limited to, general-purpose processors, central processing units (CPUs), graphics processing units (GPUs), digital signal processors (DSPs), programmable logic devices (PLDs), quantum computing-based data processing logic devices, artificial intelligence (AI) processors, and the like.

[0161] The technical features of the above embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0162] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present application. It should be noted that a person of ordinary skill in the art may make various modifications and improvements without departing from the spirit of the present application, and these modifications and improvements fall within the scope of protection of the present application. Therefore, the scope of protection of the present application shall be determined by the appended claims.

Claims

1. A method for predicting power generation of a cascade hydropower station, characterized in that: The method comprises: In response to a power generation prediction instruction for a target cascade hydropower station, if rainfall data exists for the target cascade hydropower station within a historical time period, acquiring historical rainfall data for the target cascade hydropower station; Based on the historical rainfall data, predicting the water flow velocity of the target cascade hydropower station to obtain a target water flow velocity of the target cascade hydropower station; The target power generation of the target cascade hydropower station is predicted according to the target water flow velocity to obtain the target power generation of the target cascade hydropower station.

2. The method according to claim 1, characterized in that The cascade hydropower station includes a plurality of sub-hydropower stations; and the water flow velocity prediction for the target cascade hydropower station based on the historical rainfall data to obtain a target water flow velocity for the target cascade hydropower station includes: Based on the historical rainfall data, predicting the water flow velocity of the target cascade hydropower station to obtain a first water flow velocity of the target cascade hydropower station; For each adjacent target hydropower station among the plurality of sub-hydropower stations, obtaining distance information between the target hydropower stations; Based on the distance information, the first water flow velocity is updated to obtain a target water flow velocity of the target cascade hydropower station.

3. The method according to claim 2, characterized in that The historical rainfall data includes the average rainfall of the target cascade hydropower station in a historical time period; The updating of the first water flow velocity based on the distance information to obtain the target water flow velocity of the target cascade hydropower station includes: Based on the distance information, the first water flow velocity is updated to obtain a second water flow velocity of the target cascade hydropower station; When the average rainfall is greater than the rainfall threshold, performing water level analysis on the target cascade hydropower station to obtain a target water level of the target cascade hydropower station; The second water flow velocity is updated based on the water pressure data matching the target water level to obtain the target water flow velocity of the target cascade hydropower station.

4. The method according to claim 3, characterized in that The updating of the second water flow velocity based on the water pressure data matching the target water level to obtain the target water flow velocity of the target cascade hydropower station includes: updating the second water flow velocity based on water pressure data matching the target water level to obtain a third water flow velocity of the target cascade hydropower station; Obtaining a rainfall duration that matches the average rainfall amount, and when the rainfall duration is greater than a duration threshold, performing a riverbed height analysis on the target cascade hydropower station to obtain a target riverbed height of the target cascade hydropower station; The third water flow velocity is updated based on the water pressure data that matches the target riverbed height to obtain the target water flow velocity of the target cascade hydropower station.

5. The method according to claim 4, characterized in that The updating of the third water flow velocity based on the water pressure data matching the target riverbed height to obtain the target water flow velocity of the target cascade hydropower station includes: updating the third water flow velocity based on water pressure data matching the target riverbed height to obtain a fourth water flow velocity of the target cascade hydropower station; performing a discharge analysis on the target cascade hydropower station according to the average rainfall to obtain discharge data of the target cascade hydropower station; The fourth water flow velocity is updated based on the water pressure data that matches the discharge data to obtain the target water flow velocity of the target cascade hydropower station.

6. The method according to claim 5, characterized in that The step of predicting the power generation of the target cascade hydropower station according to the target water flow velocity to obtain the target power generation of the target cascade hydropower station includes: Predicting the power generation of the target cascade hydropower station according to the target water flow velocity to obtain a first power generation of the target cascade hydropower station; Based on the power generation that matches the discharge data, the first power generation is updated to obtain the target power generation of the target cascade hydropower station.

7. A power generation prediction device for a cascade hydropower station, characterized in that: The device comprises: a rainfall data acquisition module, configured to, in response to a power generation prediction instruction for a target cascade hydropower station, acquire historical rainfall data for the target cascade hydropower station if rainfall data exists for the target cascade hydropower station within a historical time period; a water flow velocity prediction module, configured to predict the water flow velocity of the target cascade hydropower station based on the historical rainfall data, and obtain a target water flow velocity of the target cascade hydropower station; The power generation prediction module is used to predict the power generation of the target cascade hydropower station according to the target water flow velocity, so as to obtain the target power generation of the target cascade hydropower station.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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