Method, device and apparatus for adjusting gate opening

Abstract

The application provides a gate opening adjustment method, device and equipment, water conservancy data of a target hydropower station is acquired; a flow value of the target hydropower station is determined according to flow rate data in the water conservancy data; if a difference between the determined flow value and a preset flow value is greater than or equal to a first preset threshold, the flow value is input into a preset prediction model to obtain a predicted flow value; and the gate opening of the gate of the target hydropower station is adjusted according to the predicted flow value. The application can predict the flow data of the target hydropower station, dynamically adjust the gate opening of the gate of the hydropower station according to the predicted flow, and thus achieve the purpose of intelligent operation of the hydropower station and reduce the operation and maintenance difficulty of the operation and maintenance personnel.

Description

Technical Field

[0001] This application relates to the field of water conservancy operation and maintenance technology, and in particular to a method, device and equipment for adjusting the gate opening. Background Technology

[0002] Water conservancy projects are now being built on a larger scale to meet people's water resource needs. In order to control the flood discharge or diversion operations of dams and ensure the safe and reliable operation of hydropower stations, it is necessary to adjust the opening of the hydropower station gates in a timely manner.

[0003] In existing technologies, various parameters and status data of water conservancy are detected by detection equipment, data analysis is performed, and the gate opening is adjusted in a timely manner based on the analysis results.

[0004] However, in the above methods, the water conservancy data changes in real time, making it impossible to dynamically adjust the opening of the hydropower station gates, and thus failing to reduce the operation and maintenance difficulty for maintenance personnel. Summary of the Invention

[0005] This application provides a method, apparatus, and equipment for adjusting the gate opening, in order to solve the problem that the gate opening of a hydropower station cannot be dynamically adjusted due to real-time changes in water conservancy data.

[0006] In a first aspect, this application provides a method for adjusting the opening degree of a gate, the method comprising:

[0007] Obtain the water conservancy data of the target hydropower station; wherein, the water conservancy data is the water conservancy data of the target hydropower station within a preset time period;

[0008] The flow rate of the target hydropower station is determined based on the flow velocity data in the water conservancy data; wherein the flow velocity data is the water flow velocity of the target hydropower station within a preset time period, and the flow rate value represents the water flow rate of the target hydropower station within the preset time period.

[0009] If the difference between the flow value and the preset flow value is determined to be greater than or equal to a first preset threshold, then the flow value is input into the preset prediction model to obtain the predicted flow value; wherein, the predicted flow value represents the predicted water flow of the target hydropower station in the current time period;

[0010] Based on the predicted flow rate, adjust the gate opening of the target hydropower station.

[0011] In one example, after obtaining the hydraulic data of the target hydropower station, the process also includes:

[0012] Obtain motor data from the water conservancy data; wherein, the motor data is the motor data of the gate motors of the target hydropower station within a preset time period;

[0013] Based on the motor data, the gate opening value is determined; wherein, the gate opening value represents the gate opening of the target hydropower station within the preset time period;

[0014] If the difference between the gate opening value and the preset gate opening value is greater than or equal to the second preset threshold, then the gate opening value is input into the preset prediction model to obtain the predicted gate opening value; wherein, the predicted gate opening value represents the gate opening of the target hydropower station in the current time period.

[0015] Based on the predicted gate opening value, adjust the gate opening of the target hydropower station.

[0016] In one example, before adjusting the gate opening of the target hydropower station based on the predicted flow rate, the method further includes: determining a control mode; wherein the control mode is the control mode of the gate of the target hydropower station.

[0017] Adjusting the gate opening of the target hydropower station based on the predicted flow rate includes:

[0018] Based on the control method and the predicted flow rate, adjust the gate opening of the target hydropower station.

[0019] In one example, the control method is any of the following: local electric control, remote electric control, or local robotic hand crank control.

[0020] In one example, the method further includes:

[0021] Send the working status information of the target hydropower station; wherein, the working status information represents the working status of the target hydropower station; the working status of the target hydropower station includes the hydropower station's hydraulic data, gate opening degree and motor operation status; the motor operation status represents whether the gate motor of the target hydropower station is in a normal or abnormal state.

[0022] In one example, the hydraulic data includes flow velocity data and motor data.

[0023] Secondly, this application provides a model training method for adjusting gate opening, the method comprising:

[0024] Acquire training data for the target hydropower station; wherein, the training data is the hydraulic data of the target hydropower station within a preset time period, and the hydraulic data includes flow data and gate opening data;

[0025] The training data is tested to obtain the tested training data;

[0026] Based on the tested training data, the initial model is trained to obtain the preset prediction model; wherein, the preset prediction model is used to process the water conservancy data in the method described in the first aspect, and adjust the gate opening after obtaining the predicted water conservancy data.

[0027] In one example, the training data is validated to obtain validated training data, including:

[0028] Based on the training data, determine the autocorrelation coefficient and partial autocorrelation coefficient corresponding to the training data; wherein, the autocorrelation coefficient characterizes the degree of correlation between data corresponding to any two different times in the training data; the partial autocorrelation coefficient characterizes the degree of correlation between data corresponding to any two adjacent times in the training data;

[0029] The training data is tested based on the autocorrelation coefficient and the partial autocorrelation coefficient to obtain the tested training data.

[0030] In one example, the training data is tested based on the autocorrelation coefficient and the partial autocorrelation coefficient to obtain the tested training data, including:

[0031] If the training data fails the test, a differential transformation is performed on the training data to obtain the transformed training data; and the transformed training data is determined as the tested training data.

[0032] In one example, the training data includes actual water conservancy data; wherein, the actual water conservancy data is the actual water conservancy data of the target hydropower station;

[0033] Based on the tested training data, the initial model is trained to obtain the preset prediction model, including:

[0034] The flow data in the tested training data is input into the initial model to obtain the predicted water conservancy data and the trained initial model; wherein, the predicted water conservancy data is the predicted water conservancy data of the target hydropower station;

[0035] Based on the predicted water conservancy data and the actual water conservancy data, residual data is determined; wherein, the residual data includes the difference between the predicted water conservancy data and the actual water conservancy data;

[0036] Based on the residual data, the trained initial model is optimized to obtain the preset prediction model.

[0037] Thirdly, this application provides a gate opening adjustment device, comprising:

[0038] The first acquisition unit is used to acquire water conservancy data of the target hydropower station; wherein, the water conservancy data is the water conservancy data of the target hydropower station within a preset time period;

[0039] The first determining unit is used to determine the flow rate value of the target hydropower station based on the flow velocity data in the water conservancy data; wherein the flow velocity data is the water flow velocity of the target hydropower station within a preset time period, and the flow rate value represents the water flow rate of the target hydropower station within the preset time period.

[0040] The first prediction unit is configured to input the flow value into a preset prediction model to obtain a predicted flow value if the difference between the flow value and the preset flow value is greater than or equal to a first preset threshold; wherein the predicted flow value represents the predicted water flow of the target hydropower station in the current time period.

[0041] The first adjustment unit is used to adjust the gate opening of the target hydropower station according to the predicted flow value.

[0042] In one example, after the first acquisition unit acquires the hydraulic data of the target hydropower station, the method further includes:

[0043] The second acquisition unit is used to acquire motor data from the water conservancy data; wherein, the motor data is the motor data of the gate motors of the target hydropower station within a preset time period;

[0044] The second determining unit is used to determine the gate opening value based on the motor data; wherein the gate opening value represents the gate opening of the target hydropower station within the preset time period;

[0045] The second prediction unit is used to input the gate opening value into the preset prediction model to obtain the predicted gate opening value if the difference between the gate opening value and the preset gate opening value is greater than or equal to a second preset threshold; wherein, the predicted gate opening value represents the gate opening of the target hydropower station in the current time period.

[0046] The second adjustment unit is used to adjust the gate opening of the target hydropower station according to the predicted gate opening value.

[0047] In one example, before the first adjustment unit adjusts the gate opening of the target hydropower station based on the predicted flow value, the system further includes a third determining unit for determining a control mode; wherein the control mode is the control mode of the gate of the target hydropower station.

[0048] The first adjustment unit includes:

[0049] The adjustment module is used to adjust the gate opening of the target hydropower station according to the control method and the predicted flow value.

[0050] In one example, the control method is any of the following: local electric control, remote electric control, or local robotic hand crank control.

[0051] In one example, the device further includes:

[0052] The sending unit is used to send the working status information of the target hydropower station; wherein, the working status information represents the working status of the target hydropower station; the working status of the target hydropower station includes the hydropower station's hydraulic data, gate opening degree and motor operating status; the motor operating status represents whether the gate motor of the target hydropower station is in a normal or abnormal state.

[0053] In one example, the hydraulic data includes flow velocity data and motor data.

[0054] Fourthly, this application provides a model training device for adjusting the opening degree of a gate, comprising:

[0055] The acquisition unit is used to acquire the training data of the target hydropower station; wherein, the training data is the water conservancy data of the target hydropower station within a preset time period, and the water conservancy data includes flow data and gate opening data;

[0056] The verification unit is used to verify the training data to obtain the verified training data.

[0057] The training unit is used to train the initial model based on the tested training data to obtain the preset prediction model; wherein the preset prediction model is used to process the water conservancy data in the device as described in the third aspect, and adjust the gate opening after obtaining the predicted water conservancy data.

[0058] In one example, the testing unit includes:

[0059] The first determining module is used to determine the autocorrelation coefficient and partial autocorrelation coefficient corresponding to the training data based on the training data; wherein, the autocorrelation coefficient characterizes the degree of correlation between data corresponding to any two different times in the training data; and the partial autocorrelation coefficient characterizes the degree of correlation between data corresponding to any two adjacent times in the training data.

[0060] The verification module is used to verify the training data based on the autocorrelation coefficient and the partial autocorrelation coefficient to obtain the verified training data.

[0061] In one example, the verification module includes:

[0062] The transformation submodule is used to perform differential transformation on the training data if it is determined that the training data fails the test, so as to obtain the transformed training data.

[0063] The determination submodule is used to determine the transformed training data as the verified training data.

[0064] In one example, the training data includes actual water conservancy data; wherein, the actual water conservancy data is the actual water conservancy data of the target hydropower station;

[0065] The training unit includes:

[0066] The generation module is used to input the flow data in the verified training data into the initial model to obtain the predicted water conservancy data and the trained initial model corresponding to the initial model; wherein, the predicted water conservancy data is the predicted water conservancy data of the target hydropower station;

[0067] The second determining module is used to determine residual data based on the predicted water conservancy data and the actual water conservancy data; wherein, the residual data includes the difference between the predicted water conservancy data and the actual water conservancy data;

[0068] The training module is used to optimize the trained initial model based on the residual data to obtain the preset prediction model.

[0069] Fifthly, this application provides an electronic device, including: a processor, and a memory communicatively connected to the processor;

[0070] The memory stores computer-executed instructions;

[0071] The processor executes computer execution instructions stored in the memory to implement the methods described in the first and second aspects.

[0072] In a sixth aspect, this application provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the methods described in the first and second aspects.

[0073] In a seventh aspect, this application provides a computer program product comprising: a computer program stored in a readable storage medium, wherein at least one processor of an electronic device can read the computer program from the readable storage medium, and the at least one processor executes the computer program to cause the electronic device to perform the methods described in the first and second aspects.

[0074] This application provides a method, apparatus, and equipment for adjusting the gate opening, which acquires hydraulic data of a target hydropower station. The hydraulic data is the hydraulic data of the target hydropower station within a preset time period. Based on the flow velocity data in the hydraulic data, the flow rate of the target hydropower station is determined. The flow velocity data is the flow velocity of the target hydropower station within the preset time period, and the flow rate represents the flow rate of the target hydropower station within the preset time period. If the difference between the determined flow rate and the preset flow rate is greater than or equal to a first preset threshold, the flow rate is input into a preset prediction model to obtain a predicted flow rate. The predicted flow rate represents the predicted flow rate of the target hydropower station within the current time period. Based on the predicted flow rate, the gate opening of the target hydropower station is adjusted. By collecting the flow velocity data of the target hydropower station over a period of time and using a deep learning model trained with artificial intelligence technology, the current flow rate data of the target hydropower station is predicted. Based on the predicted flow rate, the gate opening of the hydropower station is dynamically adjusted, thereby achieving intelligent operation of the hydropower station and reducing the operation and maintenance difficulty for maintenance personnel. Attached Figure Description

[0075] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0076] Figure 1 A flowchart illustrating a method for adjusting gate opening according to an embodiment of this application;

[0077] Figure 2 A flowchart illustrating another method for adjusting the gate opening provided in this application embodiment;

[0078] Figure 3 This is a schematic diagram of the structure of a water conservancy terminal device provided in an embodiment of this application;

[0079] Figure 4 This is a schematic diagram of a water conservancy terminal control scheme provided in an embodiment of this application;

[0080] Figure 5 A flowchart illustrating a model training method for adjusting gate opening provided in an embodiment of this application;

[0081] Figure 6A flowchart illustrating another model training method for adjusting gate opening provided in this application embodiment;

[0082] Figure 7 A schematic diagram of a gate opening adjustment device provided in an embodiment of this application;

[0083] Figure 8 A schematic diagram of another gate opening adjustment device provided in an embodiment of this application;

[0084] Figure 9 A schematic diagram of the structure of a model training device for adjusting gate opening provided in an embodiment of this application;

[0085] Figure 10 A schematic diagram of another model training device for adjusting gate opening provided in an embodiment of this application;

[0086] Figure 11 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application;

[0087] Figure 12 This is a block diagram illustrating an electronic device according to an exemplary embodiment.

[0088] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0089] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0090] With the improvement of my country's infrastructure, water conservancy projects that benefit the people are becoming increasingly large-scale, such as the Three Gorges Dam, which effectively prevents floods and meets people's water resource needs. In order to control the dam's flood discharge or diversion operations and ensure the safe and reliable operation of hydropower stations, it is necessary to adjust the opening of the hydropower station gates in a timely manner.

[0091] In existing technologies, various parameters and status data of water conservancy are detected by detection equipment, data analysis is performed, and the gate opening is adjusted in a timely manner based on the analysis results.

[0092] One example provides a telemetry terminal system based on intelligent water conservancy sensing, including multiple telemetry terminals. Each telemetry terminal is wirelessly connected to multiple servers, which are deployed in different monitoring centers. Each telemetry terminal is also connected to: multiple data acquisition devices for collecting water conservancy data; monitoring cameras for capturing on-site conditions; solenoid valves for controlling the presence or absence of lubricating water; limit switches for collecting the opening and closing status of gates; a Global Positioning System (GPS) module installed next to the telemetry terminal; and a display connected to each server. By detecting various water conservancy parameters and status data, the system performs safety monitoring for water level status, foreign objects, etc., analyzes the data through the system where the terminal is located, and promptly switches the equipment status of the hydropower station.

[0093] However, in the above methods, when water conservancy data changes in real time, it is impossible to dynamically adjust the opening of the hydropower station gates, which in turn cannot reduce the operation and maintenance difficulty for operation and maintenance personnel and cannot achieve intelligent operation and maintenance of hydropower stations.

[0094] This application provides a method, apparatus, and device for adjusting the gate opening, aiming to solve the above-mentioned technical problems of the prior art.

[0095] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.

[0096] Figure 1 A flowchart illustrating a method for adjusting gate opening according to an embodiment of this application is shown below. Figure 1 As shown, the method includes:

[0097] S101. Obtain the water conservancy data of the target hydropower station; wherein, the water conservancy data is the water conservancy data of the target hydropower station within a preset time period.

[0098] For example, the executing entity of this embodiment can be an electronic device, a server, a terminal device, or other apparatus or device capable of executing this embodiment. This embodiment uses an electronic device as an example for description.

[0099] Based on electronic devices, such as water conservancy terminals, they can be used for gate control, protection control, water level and volume monitoring, pressure detection, etc. of hydropower stations. For a target hydropower station determined by the user, a control unit of the water conservancy terminal can be set up to obtain the water conservancy data of the target hydropower station within a preset time period.

[0100] S102. Determine the flow rate of the target hydropower station based on the flow velocity data in the water conservancy data; wherein, the flow velocity data is the water flow velocity of the target hydropower station within a preset time period, and the flow rate value represents the water flow rate of the target hydropower station within the preset time period.

[0101] For example, based on the water conservancy data of the target hydropower station within a preset time period, the water flow velocity data of the target hydropower station within the preset time period is extracted from these water conservancy data. Based on the flow velocity data, the water flow rate of the target hydropower station within the preset time period is obtained through analysis, processing and calculation by the data acquisition unit in the water conservancy terminal.

[0102] In one example, the water conservancy terminal acquires water conservancy data from a flow meter or flow plate. The flow meter or flow plate is installed at a location where the water flow is stable downstream of the gate. Based on the flow closed-loop control mode of the water conservancy terminal, the data acquisition task of the water conservancy terminal is to collect the instantaneous flow velocity of the water flow through the acquisition plate, or the stable flow velocity of the water flow within the stable time after the initial adjustment, and then calculate the real-time instantaneous flow rate, i.e. the flow value of the target hydropower station, according to the algorithm under different conditions.

[0103] S103. If the difference between the determined flow value and the preset flow value is greater than or equal to the first preset threshold, the flow value is input into the preset prediction model to obtain the predicted flow value; wherein, the predicted flow value represents the predicted water flow of the target hydropower station in the current time period.

[0104] For example, at the beginning of the adjustment of the gates of a hydropower station, a preset flow rate of the target hydropower station within the current time period is set based on human experience, i.e., the preset flow rate value, and a pre-designed flow control error value, i.e., the first preset threshold, is also set. Based on the calculated flow rate of the target hydropower station within the preset time period, i.e., the flow rate value, the difference between the flow rate value and the preset flow rate value is calculated. The absolute value of the difference is taken and compared with the first preset threshold. If it is determined that the absolute value of the difference is greater than or equal to the first preset threshold, it means that the current flow rate of the hydropower station needs to be controlled. In order to adjust the gate opening more accurately and timely to control the flow rate, a preset prediction model is needed, such as a time-series neural network model. The flow rate of the target hydropower station within the current time period, i.e., the flow rate value, is input into the preset prediction model. The prediction model analyzes and compares the input flow data and outputs the predicted flow rate value of the target hydropower station, which can predict the flow rate of the target hydropower station within the current time period.

[0105] S104. Adjust the gate opening of the target hydropower station based on the predicted flow rate.

[0106] For example, based on the predicted water flow of the target hydropower station in the current time period, and based on the recorded relationship between gate position and flow change, the gate opening value of the target hydropower station in the current time period is calculated. Based on the predicted gate opening value of the target hydropower station in the current time period, the gate opening value of the target hydropower station can be manually set via the touch screen of the water conservancy terminal or automatically set by the water conservancy terminal system. The gate control module set by the water conservancy terminal controls the raising, lowering, or stopping of the gate of the target hydropower station according to the set gate opening value, thereby adjusting the gate opening value of the target hydropower station, achieving the purpose of dynamically adjusting the gate opening value, carrying out intelligent operation and maintenance of the hydropower station, and reducing the operation and maintenance difficulty for maintenance personnel.

[0107] In this embodiment, hydraulic data of the target hydropower station is acquired; wherein, the hydraulic data is the hydraulic data of the target hydropower station within a preset time period; the flow rate value of the target hydropower station is determined based on the flow velocity data in the hydraulic data; wherein, the flow velocity data is the water flow velocity of the target hydropower station within the preset time period, and the flow rate value represents the water flow rate of the target hydropower station within the preset time period; if the difference between the determined flow rate value and the preset flow rate value is greater than or equal to a first preset threshold, the flow rate value is input into a preset prediction model to obtain a predicted flow rate value; wherein, the predicted flow rate value represents the predicted water flow rate of the target hydropower station within the current time period; the gate opening of the target hydropower station is adjusted according to the predicted flow rate value. By collecting the water flow velocity data of the target hydropower station within a certain period of time, and using a deep learning model trained with artificial intelligence technology, the current water flow rate data of the target hydropower station is predicted. Based on the predicted water flow rate, the gate opening of the hydropower station is dynamically adjusted, thereby achieving the purpose of intelligent operation of the hydropower station and reducing the operation and maintenance difficulty for maintenance personnel.

[0108] Figure 2 A flowchart illustrating another method for adjusting the gate opening provided in this application embodiment is shown below. Figure 2 As shown, the method includes:

[0109] S201. Obtain the water conservancy data of the target hydropower station; wherein, the water conservancy data is the water conservancy data of the target hydropower station within a preset time period.

[0110] In one example, the hydraulic data includes flow velocity data and motor data.

[0111] For example, the executing entity of this embodiment can be an electronic device, a server, a terminal device, or other apparatus or device capable of executing this embodiment. This embodiment uses an electronic device as an example for description.

[0112] Based on electronic devices, such as water conservancy terminals, they can be used for gate control, protection control, water level and volume monitoring, pressure detection, etc. of hydropower stations. For a target hydropower station determined by the user, a data acquisition unit of the water conservancy terminal can be set up to acquire the water conservancy data of the target hydropower station within a preset time period. The water conservancy data includes the flow velocity data and motor data of the hydropower station.

[0113] In one example, Figure 3 This is a structural schematic diagram of a water conservancy terminal device provided in an embodiment of this application, as shown below. Figure 3 As shown, based on electronic devices, such as water conservancy terminals, including gate control units, water level and flow acquisition units, gate opening detection units, solar power units, 5G communication units, display units, alarm units, etc., the gate control unit is mainly responsible for the core control of the gate; the water level and flow acquisition unit collects the channel flow in real time and uploads it to the host computer; the gate opening detection unit collects the gate lifting height in real time; the solar power unit adopts energy-saving and environmentally friendly solar power supply and high-performance maintenance-free batteries of 65AH or above (adaptable to various environments from -40℃ to 70℃), which greatly saves the power investment cost and provides a stable power supply for the system; the 5G communication unit replaces the original Some fiber optic communications reduce investment costs, resulting in extremely low communication costs and timely and reliable wireless data transmission. The alarm unit includes indicator lights and alarms; the display unit is primarily a screen; and the system can be used for water level and flow monitoring. For user-defined target hydropower stations, this water conservancy terminal uses the μC / OS-II operating system to deploy data acquisition tasks, primarily collecting information from water level gauges, acquisition boards, solar controllers, and motor encoders. Based on the water level and flow acquisition unit and the gate opening detection unit, it acquires water conservancy data for the target hydropower station within a preset time period. This water conservancy data includes flow velocity data and motor data. It is worth noting that this water conservancy terminal prioritizes safety, employing solar power and high-performance polymer batteries to provide reliable power to the gates, allowing the system to remain in standby mode 24 hours a day. It features low and high power warning functions, triggering an alarm and prohibiting gate operation upon warning. The system protects the gates through upper and lower limit protection, jamming protection, and motor torque protection.

[0114] After step S201, step S202 or step S206 can be executed.

[0115] S202. Obtain motor data from the water conservancy data; wherein, the motor data is the motor data of the gate motors of the target hydropower station within a preset time period.

[0116] For example, after step S201, the motor data of the gate motor of the target hydropower station within the preset time period is extracted from the hydraulic data of the target hydropower station within the preset time period.

[0117] In one example, based on the gate opening detection unit of the water conservancy terminal, the target hydropower station's water conservancy data within a preset time period is obtained. Based on the gate opening closed-loop mode of the water conservancy terminal, the motor data of the gate motor of the target hydropower station within the preset time period is extracted from these water conservancy data. For example, the encoder value of the motor is collected, including the magnetic pole position measured by the encoder and the rotation angle and speed of the servo motor.

[0118] S203. Determine the gate opening value based on the motor data; wherein, the gate opening value represents the gate opening of the target hydropower station within a preset time period.

[0119] For example, based on the motor data of the gate motor of the target hydropower station within a preset time period, and based on the analysis, processing and calculation of the data acquisition unit in the water conservancy terminal, the gate opening value of the motor gate of the target hydropower station within the preset time period is obtained, that is, the gate opening value.

[0120] In one example, the water conservancy terminal uses the motor data of the gate motor of the target hydropower station within a preset time period to calculate the real-time opening value based on the water conservancy terminal's closed-loop control mode. In this mode, the water conservancy terminal's data acquisition task acquires the motor encoder value and calculates the real-time opening value.

[0121] S204. If the difference between the gate opening value and the preset gate opening value is greater than or equal to the second preset threshold, the gate opening value is input into the preset prediction model to obtain the predicted gate opening value; wherein, the predicted gate opening value represents the gate opening of the target hydropower station in the current time period.

[0122] For example, at the initial adjustment of the gates of a hydropower station, a preset gate opening value for the target hydropower station within the current time period is set based on manual experience, i.e., the preset gate opening value, and a pre-designed gate opening control error value, i.e., the second preset threshold, is also established. Based on the calculated gate opening value of the target hydropower station within the preset time period, the difference between the gate opening value and the preset gate opening value is calculated. The absolute value of this difference is then compared with the second preset threshold. If the absolute value of the difference is determined to be... If the value is greater than or equal to the second preset threshold, it indicates that the gate opening value of the hydropower station needs to be controlled. In order to adjust the gate opening more accurately and timely to control the water flow, it is necessary to retrieve a preset prediction model, such as a time-series neural network model, and input the gate opening value of the target hydropower station in the current time period into the preset prediction model. The prediction model analyzes and compares the input gate opening value and outputs the predicted gate opening value of the target hydropower station. It can predict the gate opening value of the target hydropower station in the current time period.

[0123] S205. Adjust the gate opening of the target hydropower station based on the predicted gate opening value.

[0124] For example, based on the predicted gate opening value of the target hydropower station within the current time period, the gate opening value of the target hydropower station can be manually set via the touch screen of the water conservancy terminal or automatically set by the water conservancy terminal system. The gate control module set by the water conservancy terminal controls the raising, lowering, or stopping of the gate of the target hydropower station according to the set gate opening value, thereby adjusting the gate opening value of the target hydropower station, achieving the purpose of dynamically adjusting the gate opening value, carrying out intelligent operation and maintenance of the hydropower station, and reducing the operation and maintenance difficulty for maintenance personnel.

[0125] S206. Determine the flow rate of the target hydropower station based on the flow velocity data in the water conservancy data; wherein, the flow velocity data is the water flow velocity of the target hydropower station within a preset time period, and the flow rate value represents the water flow rate of the target hydropower station within the preset time period.

[0126] For example, after step S201, based on the water conservancy data of the target hydropower station within the preset time period, the water flow velocity data of the target hydropower station within the preset time period is extracted from these water conservancy data, i.e., the flow velocity data. Based on the flow velocity data, the water flow rate of the target hydropower station within the preset time period is obtained through analysis, processing and calculation by the data acquisition unit in the water conservancy terminal.

[0127] S207. If the difference between the determined flow value and the preset flow value is greater than or equal to the first preset threshold, the flow value is input into the preset prediction model to obtain the predicted flow value; wherein, the predicted flow value represents the predicted water flow of the target hydropower station in the current time period.

[0128] For example, this step can be referred to step S103 above, and will not be repeated here.

[0129] S208. Determine the control method; wherein, the control method is the control method of the gate of the target hydropower station.

[0130] In one example, the control method is any of the following: local electric control, remote electric control, or local robotic hand crank control.

[0131] For example, the opening and closing of the gates of the target hydropower station supports one of three control methods: local electric control, remote electric control, and local mechanical hand-crank control. The control method of the gates of the target hydropower station is determined according to the user's needs.

[0132] In one example, Figure 4 This is a structural schematic diagram of a water conservancy terminal control scheme provided in an embodiment of this application, as shown below. Figure 4As shown, according to user needs, the terminal defaults to remote control after being powered on. At this time, the water conservancy terminal control scheme includes a management and application system and a water conservancy terminal. The management and application system is the water conservancy terminal control system platform, which can remotely control the gate through the system platform and is responsible for collecting and processing on-site data. The water conservancy terminal, such as the water core series terminal, can perform gate control and other operations on-site, summarize on-site data, and then send it to the system platform.

[0133] S209. Adjust the gate opening of the target hydropower station according to the control method and the predicted flow value.

[0134] For example, based on the predicted water flow of the target hydropower station in the current time period, and based on the recorded relationship between gate position and flow change, the gate opening value of the target hydropower station in the current time period is calculated. Based on the predicted gate opening value of the target hydropower station in the current time period, the gate opening value of the target hydropower station can be manually set via the touch screen of the water conservancy terminal or automatically set by the water conservancy terminal system. Based on the determined control method of the hydropower station gate, such as remote control, the set gate opening value is sent to the gate control module of the water conservancy terminal. According to the remote control instructions of the module, the gate of the target hydropower station is controlled to rise, fall, or stop, thereby adjusting the gate opening value of the target hydropower station gate, achieving the purpose of dynamically adjusting the gate opening value, carrying out intelligent operation and maintenance of the hydropower station, and reducing the operation and maintenance difficulty for maintenance personnel.

[0135] In one example, based on the predicted water flow rate of the target hydropower station in the current time period, a target flow rate is set based on remote control. The gate automatically identifies and controls the target flow rate to rise, fall, or stop, thereby adjusting the gate opening value of the target hydropower station. This achieves the purpose of dynamically adjusting the gate opening value, enabling intelligent operation and maintenance of the hydropower station and reducing the operation and maintenance difficulty for maintenance personnel.

[0136] S210. Send the working status information of the target hydropower station; wherein, the working status information represents the working status of the target hydropower station; the working status of the target hydropower station includes the hydropower station's hydraulic data, gate opening degree and motor operation status; the motor operation status represents whether the gate motor of the target hydropower station is in a normal or abnormal state.

[0137] For example, based on a water conservancy terminal device, in order to monitor the working status of a target hydropower station, the application software in the device can deploy a display task. This display task is applied to the display unit within the water conservancy terminal device. Based on the working status information of the target hydropower station obtained by the water conservancy terminal device, including the water conservancy data, gate opening, and motor operating status of the target hydropower station acquired by the data acquisition unit of the water conservancy terminal device, the working status information of the target hydropower station is sent to the display unit in the water conservancy terminal device, or to the user application software connected to the water conservancy terminal device. The motor operating status indicates whether the gate motor of the target hydropower station is in a normal or abnormal state. Based on the working status information displayed by the display unit, the user can determine whether the hydropower station is operating abnormally. If an abnormality occurs, an alarm can be issued based on the alarm unit in the water conservancy terminal device.

[0138] In this embodiment, based on the above embodiment, the hydraulic data of the target hydropower station is obtained; wherein, the hydraulic data is the hydraulic data of the target hydropower station within a preset time period; the motor data in the hydraulic data is obtained; wherein, the motor data is the motor data of the gate motors of the target hydropower station within the preset time period; based on the motor data, the gate opening value is determined; wherein, the gate opening value represents the gate opening of the target hydropower station within the preset time period; if the difference between the determined gate opening value and the preset gate opening value is greater than or equal to a second preset threshold, then the gate opening value is... The data is input into a preset prediction model to obtain the predicted gate opening value. This predicted gate opening value represents the predicted gate opening of the target hydropower station within the current time period. Based on the predicted gate opening value, the gate opening of the target hydropower station is adjusted. The operating status information of the target hydropower station is then sent. This operating status information represents the operating status of the target hydropower station, including its hydraulic data, gate opening, and motor operating status. The motor operating status indicates whether the gate motors of the target hydropower station are in a normal or abnormal state. By collecting motor data from the gate motors of the target hydropower station within a recent time period, and analyzing the data based on the preset prediction model, the gate opening of the target hydropower station within the current time period is predicted. Based on the prediction results, the gate opening value of the target hydropower station is adjusted, achieving the purpose of dynamically adjusting the gate opening value, enabling intelligent operation and maintenance of the hydropower station, and reducing the operation and maintenance difficulty for maintenance personnel.

[0139] Figure 5 This application provides a flowchart illustrating a model training method for adjusting gate opening, as shown in the embodiments below. Figure 5 As shown, the method includes:

[0140] S301. Obtain the training data of the target hydropower station; wherein, the training data is the water conservancy data of the target hydropower station within a preset time period, and the water conservancy data includes flow data and gate opening data.

[0141] For example, the executing entity of this embodiment can be an electronic device, a server, a terminal device, or other apparatus or device capable of executing this embodiment. This embodiment uses an electronic device as an example for description.

[0142] Based on electronic devices and network devices, such as water conservancy terminals, they can be used for gate control, protection control, water level and flow monitoring, pressure detection, etc. of hydropower stations. For a target hydropower station determined by the user, a data acquisition unit of the water conservancy terminal can be set up to acquire the water conservancy data of the target hydropower station within a preset time period, i.e., the training data. The water conservancy data includes flow data and gate opening data.

[0143] S302. Validate the training data to obtain the validated training data.

[0144] For example, in order to obtain accurate data, white noise testing is performed on the collected hydraulic data of the target hydropower station to be trained, and the tested hydraulic data is used for model training.

[0145] S303. Based on the tested training data, train the initial model to obtain a preset prediction model; wherein, the preset prediction model is used to process the water conservancy data in the method of adjusting the gate opening, and then adjust the gate opening after obtaining the predicted water conservancy data.

[0146] For example, a pre-set initial prediction model is invoked, and the tested water conservancy data to be trained is output to the initial prediction model. The initial prediction model is trained to obtain a preset prediction model, which is used to process the water conservancy data in the gate opening adjustment method. After obtaining the predicted water conservancy data, the gate opening is adjusted.

[0147] In this embodiment, training data for the target hydropower station is acquired. This training data consists of hydraulic data of the target hydropower station within a preset time period, including flow rate data and gate opening data. The training data is then validated to obtain validated training data. Based on the validated training data, an initial model is trained to obtain a preset prediction model. This preset prediction model is used to process the hydraulic data in methods such as gate opening adjustment, obtaining predicted hydraulic data before adjusting the gate opening. By collecting hydraulic data from the target hydropower station to train the model, the trained prediction model is used to predict the current hydraulic data of the target hydropower station, and then the gate opening is adjusted based on the prediction results.

[0148] Figure 6 A flowchart illustrating another model training method for adjusting gate opening provided in this application embodiment is shown below. Figure 6 As shown, the method includes:

[0149] S401. Obtain the training data of the target hydropower station; wherein, the training data is the water conservancy data of the target hydropower station within a preset time period, and the water conservancy data includes flow data and gate opening data.

[0150] For example, this step can be referred to step S301, and will not be repeated here.

[0151] S402. Based on the training data, determine the autocorrelation coefficient and partial autocorrelation coefficient corresponding to the training data; wherein, the autocorrelation coefficient represents the degree of correlation between data corresponding to any two different times in the training data; the partial autocorrelation coefficient represents the degree of correlation between data corresponding to any two adjacent times in the training data.

[0152] For example, based on the collected hydraulic data of the target hydropower station to be trained, the autocorrelation coefficient and partial autocorrelation coefficient of the data are calculated, and the autocorrelation plot and partial autocorrelation plot are drawn. This allows us to know the degree of correlation between the data corresponding to any two different times in the hydraulic data to be trained, as well as the degree of correlation between the data corresponding to any two adjacent times in the hydraulic data to be trained, and to perform data verification.

[0153] S403. Based on the autocorrelation coefficient and partial autocorrelation coefficient, the training data is tested to obtain the tested training data.

[0154] In one example, step S403 includes: if it is determined that the training data fails the test, then the training data is subjected to differential transformation to obtain the transformed training data; and the transformed training data is determined as the tested training data.

[0155] For example, based on the calculated autocorrelation coefficient and partial autocorrelation coefficient of the hydraulic data to be trained, autocorrelation plots and partial autocorrelation plots are drawn. The main purpose is to observe whether the distribution of the sequence always fluctuates around a constant through the sequence autocorrelation plot and partial autocorrelation plot, thereby determining whether it is a stationary non-white noise sequence. If the hydraulic data to be trained is a stationary sequence, it is determined that the hydraulic data to be trained has passed the test, and the tested data is obtained. If the data has passed the test but is a non-stationary data sequence, it is first transformed into a stationary sequence by differencing to obtain the tested hydraulic data to be trained, which is used for further model training.

[0156] S404. Input the flow data from the tested training data into the initial model to obtain the predicted water conservancy data and the trained initial model; wherein, the predicted water conservancy data is the predicted water conservancy data of the target hydropower station.

[0157] In one example, the training data consists of actual water conservancy data; where actual water conservancy data refers to the actual water conservancy data of the target hydropower station.

[0158] For example, based on the collected water conservancy data of the target hydropower station within a preset time period, the actual water conservancy data, i.e., the actual water conservancy data of the target hydropower station, can be analyzed.

[0159] Furthermore, the pre-set initial network quality prediction model is invoked, and when selecting a model, the model type and order are determined by autocorrelation plot and partial autocorrelation plot, as shown in Table 1 below.

[0160] Table 1 Model Selection Parameters

[0161] Autocorrelation plot Partial autocorrelation plot Select Model Trail p-order truncation AR(p) q-order truncation Trail MA(q) Trail Trail ARMA(p, q)

[0162] The processed feature data to be trained is output to the initial prediction model, such as the Autoregressive Moving Average (ARMA) model, to obtain the predicted water conservancy data of the target hydropower station.

[0163] S405. Based on the predicted water conservancy data and the actual water conservancy data, determine the residual data; wherein, the residual data includes the difference between the predicted water conservancy data and the actual water conservancy data.

[0164] For example, in order to obtain an accurate prediction model, the difference between the predicted water conservancy data and the actual water conservancy data is calculated based on the obtained predicted water conservancy data and the actual water conservancy data to obtain residual data, and then the model is optimized.

[0165] S406. Based on the residual data, optimize the initial model after training to obtain the preset prediction model.

[0166] For example, the residual data corresponding to the predicted and actual water conservancy data are used to optimize the initial identification model. The parameters of the initial prediction model are optimized, for example, based on the Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC) of the model. This can compensate for the subjectivity of the order determination of the autocorrelation plot and partial autocorrelation plot. The AIC criterion is a weighted function of fitting accuracy and the number of parameters: AIC = 2k - 2lnL; the BIC criterion is: BIC = ln * k - 2lnL; where n is the number of data points, k is the number of parameters in the model; n and k are integers greater than or equal to 1; and L is the maximum likelihood function of the model. The smaller the values ​​of AIC and BIC, the better. This yields the optimal preset prediction model, which is used to process the water conservancy data in the gate opening adjustment method. After obtaining the predicted water conservancy data, the gate opening is adjusted.

[0167] In this embodiment, based on the above embodiments, the autocorrelation coefficient and partial autocorrelation coefficient corresponding to the training data are determined according to the training data. The autocorrelation coefficient characterizes the correlation between data corresponding to any two different times in the training data; the partial autocorrelation coefficient characterizes the correlation between data corresponding to any two adjacent times in the training data. The training data is then tested based on the autocorrelation coefficient and partial autocorrelation coefficient to obtain tested training data. The flow data in the tested training data is input into the initial model to obtain the predicted water conservancy data and the trained initial model. The predicted water conservancy data is the predicted water conservancy data of the target hydropower station. Residual data is determined based on the predicted water conservancy data and the actual water conservancy data. The residual data includes the difference between the predicted water conservancy data and the actual water conservancy data. The trained initial model is optimized based on the residual data to obtain a preset prediction model. By verifying and transforming the collected water conservancy data to be trained, the data quality of the data to be trained is further improved, thereby improving the accuracy of the prediction model. Based on the data to be trained, the initial prediction model is trained and optimized multiple times to obtain a preset prediction model, which is used to predict the water conservancy data of the current target hydropower station. Then, the gate opening is adjusted according to the prediction results.

[0168] Figure 7 This is a schematic diagram of the structure of a gate opening adjustment device provided in an embodiment of this application, as shown below. Figure 7 As shown, the device 500 includes:

[0169] The first acquisition unit 501 is used to acquire the water conservancy data of the target hydropower station; wherein, the water conservancy data is the water conservancy data of the target hydropower station within a preset time period.

[0170] The first determining unit 502 is used to determine the flow rate of the target hydropower station based on the flow velocity data in the water conservancy data; wherein the flow velocity data is the water flow velocity of the target hydropower station within a preset time period, and the flow rate value represents the water flow rate of the target hydropower station within the preset time period.

[0171] The first prediction unit 503 is used to input the flow value into the preset prediction model to obtain the predicted flow value if the difference between the determined flow value and the preset flow value is greater than or equal to the first preset threshold; wherein the predicted flow value represents the predicted water flow of the target hydropower station in the current time period.

[0172] The first adjustment unit 504 is used to adjust the gate opening of the target hydropower station based on the predicted flow value.

[0173] The apparatus in this embodiment can execute the technical solutions in the above method. Its specific implementation process and technical principles are the same, and will not be repeated here.

[0174] Figure 8 A schematic diagram of another gate opening adjustment device provided in an embodiment of this application is shown below. Figure 8 As shown, the device 600 includes:

[0175] The first acquisition unit 601 is used to acquire the water conservancy data of the target hydropower station; wherein, the water conservancy data is the water conservancy data of the target hydropower station within a preset time period.

[0176] The first determining unit 602 is used to determine the flow rate of the target hydropower station based on the flow velocity data in the water conservancy data; wherein the flow velocity data is the water flow velocity of the target hydropower station within a preset time period, and the flow rate value represents the water flow rate of the target hydropower station within the preset time period.

[0177] The first prediction unit 603 is used to input the flow value into the preset prediction model to obtain the predicted flow value if the difference between the determined flow value and the preset flow value is greater than or equal to the first preset threshold; wherein the predicted flow value represents the water flow of the target hydropower station in the current time period.

[0178] The first adjustment unit 604 is used to adjust the gate opening of the target hydropower station based on the predicted flow value.

[0179] In one example, after the first acquisition unit 601 acquires the hydraulic data of the target hydropower station, it also includes:

[0180] The second acquisition unit 605 is used to acquire motor data from water conservancy data; wherein, the motor data is the motor data of the gate motors of the target hydropower station within a preset time period.

[0181] The second determining unit 606 is used to determine the gate opening value based on the motor data; wherein the gate opening value represents the gate opening of the target hydropower station within a preset time period.

[0182] The second prediction unit 607 is used to input the gate opening value into the preset prediction model if the difference between the determined gate opening value and the preset gate opening value is greater than or equal to the second preset threshold, so as to obtain the predicted gate opening value; wherein, the predicted gate opening value represents the gate opening of the target hydropower station in the current time period.

[0183] The second adjustment unit 608 is used to adjust the gate opening of the target hydropower station based on the predicted gate opening value.

[0184] In one example, before the first adjustment unit 604 adjusts the gate opening of the target hydropower station according to the predicted flow value, the system further includes a third determining unit 609 for determining the control mode; wherein the control mode is the control mode of the gate of the target hydropower station.

[0185] The first adjustment unit 604 includes:

[0186] The adjustment module 6041 is used to adjust the gate opening of the target hydropower station according to the control method and the predicted flow value.

[0187] In one example, the control method is any of the following: local electric control, remote electric control, or local robotic hand crank control.

[0188] In one example, device 600 also includes:

[0189] The sending unit 610 is used to send the working status information of the target hydropower station; wherein, the working status information represents the working status of the target hydropower station; the working status of the target hydropower station includes the hydropower station's hydraulic data, gate opening degree and motor operation status; the motor operation status represents whether the gate motor of the target hydropower station is in a normal or abnormal state.

[0190] In one example, the hydraulic data includes flow velocity data and motor data.

[0191] The apparatus in this embodiment can execute the technical solutions in the above method. Its specific implementation process and technical principles are the same, and will not be repeated here.

[0192] Figure 9 This application provides a schematic diagram of the structure of a model training device for adjusting gate opening, as shown in the embodiments of the present application. Figure 9 As shown, the device 700 includes:

[0193] The acquisition unit 701 is used to acquire the training data of the target hydropower station; wherein, the training data is the water conservancy data of the target hydropower station within a preset time period, and the water conservancy data includes flow data and gate opening data.

[0194] The testing unit 702 is used to test the training data to obtain the tested training data.

[0195] The training unit 703 is used to train the initial model based on the tested training data to obtain a preset prediction model; wherein, the preset prediction model is used to process the water conservancy data in the device such as the third aspect, and adjust the gate opening after obtaining the predicted water conservancy data.

[0196] The apparatus in this embodiment can execute the technical solutions in the above method. Its specific implementation process and technical principles are the same, and will not be repeated here.

[0197] Figure 10 A schematic diagram of another model training device for adjusting gate opening provided in this application embodiment is shown below. Figure 10 As shown, the device 800 includes:

[0198] The acquisition unit 801 is used to acquire the training data of the target hydropower station; wherein, the training data is the water conservancy data of the target hydropower station within a preset time period, and the water conservancy data includes flow data and gate opening data.

[0199] The testing unit 802 is used to test the training data to obtain the tested training data.

[0200] The training unit 803 is used to train the initial model based on the tested training data to obtain a preset prediction model; wherein, the preset prediction model is used to process the water conservancy data in the device such as the third aspect, and adjust the gate opening after obtaining the predicted water conservancy data.

[0201] In one example, the inspection unit 802 includes:

[0202] The first determining module 8021 is used to determine the autocorrelation coefficient and partial autocorrelation coefficient corresponding to the training data based on the training data; wherein, the autocorrelation coefficient represents the degree of correlation between data corresponding to any two different time points in the training data; and the partial autocorrelation coefficient represents the degree of correlation between data corresponding to any two adjacent time points in the training data.

[0203] The testing module 8022 is used to test the training data based on the autocorrelation coefficient and the partial autocorrelation coefficient to obtain the tested training data.

[0204] In one example, the verification module 8022 includes:

[0205] The transformation submodule is used to perform differential transformation on the training data if it is determined that the data to be trained fails the test, so as to obtain the transformed training data.

[0206] The determination submodule is used to determine the transformed training data as the validated training data.

[0207] In one example, the training data consists of actual water conservancy data; where actual water conservancy data refers to the actual water conservancy data of the target hydropower station.

[0208] Training unit 803 includes:

[0209] The generation module 8031 ​​is used to input the flow data in the tested training data into the initial model to obtain the predicted water conservancy data corresponding to the initial model and the trained initial model; wherein, the predicted water conservancy data is the predicted water conservancy data of the target hydropower station;

[0210] The second determining module 8032 is used to determine residual data based on the predicted water conservancy data and the actual water conservancy data; wherein, the residual data includes the difference between the predicted water conservancy data and the actual water conservancy data;

[0211] The training module 8033 is used to optimize the initial model after training based on the residual data to obtain the preset prediction model.

[0212] The apparatus in this embodiment can execute the technical solutions in the above method. Its specific implementation process and technical principles are the same, and will not be repeated here.

[0213] Figure 11 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application, such as... Figure 11 As shown, the electronic device 900 includes: a memory 91 and a processor 92; the memory 91 is a memory used to store instructions executable by the processor 92.

[0214] The processor 92 is configured to perform the methods provided in the embodiments described above.

[0215] The terminal device also includes a receiver 93 and a transmitter 94. The receiver 93 is used to receive instructions and data sent by other devices, and the transmitter 94 is used to send instructions and data to external devices.

[0216] Figure 12 This is a block diagram illustrating an electronic device according to an exemplary embodiment. The device may be a mobile phone, computer, digital broadcasting terminal, messaging device, game console, tablet device, medical device, fitness device, personal digital assistant, etc.

[0217] Electronic device 1000 may include one or more of the following components: processing component 1002, memory 1004, power supply component 1006, multimedia component 1008, audio component 1010, input / output interface 1012, sensor component 1014, and communication component 1016.

[0218] Processing component 1002 typically controls the overall operation of electronic device 1000, such as operations associated with display, telephone calls, data communication, camera operation, and recording operations. Processing component 1002 may include one or more processors 1020 to execute instructions to perform all or part of the steps of the methods described above. Furthermore, processing component 1002 may include one or more modules to facilitate interaction between processing component 1002 and other components. For example, processing component 1002 may include a multimedia module to facilitate interaction between multimedia component 10010 and processing component 1002.

[0219] Memory 1004 is configured to store various types of data to support the operation of electronic device 1000. Examples of this data include instructions for any application or method operating on electronic device 1000, contact data, phonebook data, messages, pictures, videos, etc. Memory 1004 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory, electrically erasable programmable read-only memory, erasable programmable read-only memory, programmable read-only memory, read-only memory, magnetic storage, flash memory, magnetic disk, or optical disk.

[0220] Power supply component 1006 provides power to various components of electronic device 1000. Power supply component 1006 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power to electronic device 1000.

[0221] Multimedia component 1008 includes a screen that provides an output interface between electronic device 1000 and a user. In some embodiments, the screen may include a liquid crystal display and a touch panel. If the screen includes a touch panel, the screen may be implemented as a touchscreen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touches, swipes, and gestures on the touch panel. The touch sensors may sense not only the boundaries of touch or swipe actions but also the duration and pressure associated with the touch or swipe operation. In some embodiments, multimedia component 10010 includes a front-facing camera and / or a rear-facing camera. When electronic device 1000 is in an operating mode, such as a shooting mode or a video mode, the front-facing camera and / or rear-facing camera may receive external multimedia data. Each front-facing camera and rear-facing camera may be a fixed optical lens system or have focal length and optical zoom capabilities.

[0222] Audio component 1010 is configured to output and / or input audio signals. For example, audio component 1010 includes a microphone configured to receive external audio signals when electronic device 1000 is in an operating mode, such as call mode, recording mode, and voice recognition mode. The received audio signals may be further stored in memory 1004 or transmitted via communication component 1016. In some embodiments, audio component 1010 also includes a speaker for outputting audio signals.

[0223] Input / output interface 1012 provides an interface between processing component 1002 and peripheral interface modules, which may be keyboards, click wheels, buttons, etc. These buttons may include, but are not limited to, home buttons, volume buttons, start buttons, and lock buttons.

[0224] Sensor assembly 1014 includes one or more sensors for providing state assessments of various aspects of electronic device 1000. For example, sensor assembly 1014 may detect the on / off state of electronic device 1000, the relative positioning of components such as the display and keypad of electronic device 1000, changes in position of electronic device 1000 or a component of electronic device 1000, the presence or absence of user contact with electronic device 1000, orientation or acceleration / deceleration of electronic device 1000, and temperature changes of electronic device 1000. Sensor assembly 1014 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact. Sensor assembly 1014 may also include a light sensor, such as an image sensor, for use in imaging applications. In some embodiments, sensor assembly 1014 may also include an accelerometer, gyroscope, magnetometer, pressure sensor, or temperature sensor.

[0225] Communication component 1016 is configured to facilitate wired or wireless communication between electronic device 1000 and other devices. Electronic device 1000 can access wireless networks based on communication standards. In one exemplary embodiment, communication component 1016 receives broadcast signals or broadcast-related information from an external broadcast management system via a broadcast channel. In one exemplary embodiment, communication component 1016 also includes a near-field communication module to facilitate short-range communication. For example, the near-field communication module may be implemented based on radio frequency identification (RFID), infrared data association (IRA) technology, ultra-wideband (UWB) technology, Bluetooth technology, and other technologies.

[0226] In an exemplary embodiment, the electronic device 1000 may be implemented by one or more application-specific integrated circuits, digital signal processors, digital signal processing devices, programmable logic devices, field-programmable gate arrays, controllers, microcontrollers, microprocessors, or other electronic components to perform the methods described above.

[0227] In an exemplary embodiment, a non-transitory computer-readable storage medium including instructions is also provided, such as a memory 1004 including instructions, which can be executed by a processor 1020 of an electronic device 1000 to perform the above-described method. For example, the non-transitory computer-readable storage medium may be a random access memory, magnetic tape, floppy disk, or optical data storage device, etc.

[0228] This application also provides a non-transitory computer-readable storage medium, which, when the instructions in the storage medium are executed by the processor of an electronic device, enables the electronic device to perform the above-described method.

[0229] This application also provides a computer program product, which includes: a computer program stored in a readable storage medium, at least one processor of an electronic device can read the computer program from the readable storage medium, and the at least one processor executes the computer program to cause the electronic device to perform the solution provided in any of the above embodiments.

[0230] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this application are indicated by the following claims.

[0231] It should be understood that this application is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this application is limited only by the appended claims.

Claims

1. A method of adjusting gate opening, characterized by, The method comprises: obtaining water conservancy data of a target hydropower station; wherein the water conservancy data is water conservancy data of the target hydropower station in a preset time period; determining a flow value of the target hydropower station according to flow rate data in the water conservancy data; wherein the flow rate data is the flow rate of the water flow of the target hydropower station in the preset time period, and the flow value represents the flow of the water flow of the target hydropower station in the preset time period; if it is determined that the absolute value of the difference between the flow value and a preset flow value is greater than or equal to a first preset threshold, inputting the flow value into a preset prediction model to obtain a predicted flow value; wherein the predicted flow value represents the predicted flow of the water flow of the target hydropower station in a current time period, and the preset flow value is the preset flow of the water flow of the target hydropower station in the current time period; adjusting the gate opening of the gate of the target hydropower station according to the predicted flow value; the training method of the preset prediction model comprises: obtaining training data of a target hydropower station; wherein the training data is water conservancy data of the target hydropower station in a preset time period, and the water conservancy data comprises flow data and gate opening data; determining autocorrelation coefficients and partial autocorrelation coefficients corresponding to the training data according to the training data; wherein the autocorrelation coefficients represent the correlation degree between data corresponding to any two different time points in the training data; and the partial autocorrelation coefficients represent the correlation degree between data corresponding to any two adjacent time points in the training data; drawing autocorrelation graphs and partial autocorrelation graphs according to the autocorrelation coefficients and the partial autocorrelation coefficients; determining whether the training data is a stationary non-white noise sequence according to the autocorrelation graphs and the partial autocorrelation graphs; if the training data is the stationary non-white noise sequence, determining that the training data passes the test to obtain tested training data; training an initial model according to the tested training data to obtain the preset prediction model.

2. A method of adjusting the gate opening, characterized by The method comprises: obtaining motor data in the water conservancy data; wherein the motor data is motor data of a gate motor of a target hydropower station in a preset time period; determining a gate opening value according to the motor data; wherein the gate opening value represents the gate opening of the gate of the target hydropower station in the preset time period; if it is determined that the absolute value of the difference between the gate opening value and a preset gate opening value is greater than or equal to a second preset threshold, inputting the gate opening value into a preset prediction model to obtain a predicted gate opening value; wherein the predicted gate opening value represents the predicted gate opening of the gate of the target hydropower station in a current time period, and the preset gate opening value is the preset gate opening value of the target hydropower station in the current time period; adjusting the gate opening of the gate of the target hydropower station according to the predicted gate opening value; the training method of the preset prediction model comprises: Obtaining training data of a target hydropower station; wherein, the training data is water conservancy data of the target hydropower station in a preset time period, and the water conservancy data includes flow data and gate opening data; According to the training data, determining autocorrelation coefficients and partial autocorrelation coefficients corresponding to the training data; wherein, the autocorrelation coefficients represent the correlation degree between data corresponding to any two different time points in the training data; the partial autocorrelation coefficients represent the correlation degree between data corresponding to any two adjacent time points in the training data; According to the autocorrelation coefficients and the partial autocorrelation coefficients, drawing autocorrelation graphs and partial autocorrelation graphs; According to the autocorrelation graphs and the partial autocorrelation graphs, determining whether the training data is a stationary non-white noise sequence; If the training data is the stationary non-white noise sequence, it is determined that the training data passes the test, and the tested training data is obtained; According to the tested training data, training an initial model to obtain the preset prediction model.

3. The method of claim 1, wherein, Before adjusting the gate opening of the gate of the target hydropower station according to the predicted flow value, the method further includes: determining a control mode; wherein, the control mode is the control mode of the gate of the target hydropower station; According to the predicted flow value, adjusting the gate opening of the gate of the target hydropower station, includes: According to the control mode and the predicted flow value, adjusting the gate opening of the gate of the target hydropower station.

4. The method of claim 3, wherein, The control mode is any one of the following: local electric control mode, remote electric control mode, local mechanical manual control mode.

5. The method according to any one of claims 1-4, characterized in that, The method further includes: Sending working state information of the target hydropower station; wherein, the working state information represents the working state of the target hydropower station; the working state of the target hydropower station includes water conservancy data, gate opening and motor running state of the target hydropower station; the motor running state represents that the gate motor of the target hydropower station is in a normal state or an abnormal state.

6. The method according to any one of claims 1-4, characterized in that, The water conservancy data includes flow rate data and motor data.

7. The method of claim 1, wherein, The method further includes: If it is determined that the training data does not pass the test, performing difference conversion processing on the training data to obtain converted training data; and determining the converted training data as the tested training data.

8. The method according to claim 1 or 7, characterized in that, The training data contains actual water conservancy data; wherein, the actual water conservancy data is the actual water conservancy data of the target hydropower station; According to the tested training data, training an initial model to obtain the preset prediction model, includes: Inputting the flow data in the tested training data into the initial model to obtain predicted water conservancy data corresponding to the initial model and the trained initial model; wherein, the predicted water conservancy data is the predicted water conservancy data of the target hydropower station; According to the predicted water conservancy data and the actual water conservancy data, determining residual error data; wherein, the residual error data includes the difference between the predicted water conservancy data and the actual water conservancy data; According to the residual error data, optimizing the trained initial model to obtain the preset prediction model.

9. A gate opening adjusting device characterized by comprising: The device comprises: The first acquisition unit is used for acquiring water conservancy data of a target hydropower station; wherein the water conservancy data is water conservancy data of the target hydropower station in a preset time period; The first determination unit is used for determining a flow value of the target hydropower station according to flow rate data in the water conservancy data; wherein the flow rate data is a water flow rate of the target hydropower station in a preset time period, and the flow value represents a water flow of the target hydropower station in a preset time period; The first prediction unit is used for inputting the flow value into a preset prediction model to obtain a predicted flow value if it is determined that an absolute value of a difference between the flow value and a preset flow value is greater than or equal to a first preset threshold value; wherein the predicted flow value represents a predicted water flow of the target hydropower station in a current time period, and the preset flow value is a preset water flow of the target hydropower station in a current time period; The first adjustment unit is used for adjusting a gate opening of a gate of the target hydropower station according to the predicted flow value; The acquisition unit is used for acquiring training data of a target hydropower station; wherein the training data is water conservancy data of the target hydropower station in a preset time period, and the water conservancy data comprises flow data and gate opening data; The first determination module is further used for determining an autocorrelation coefficient and a partial autocorrelation coefficient corresponding to the training data according to the training data; wherein the autocorrelation coefficient represents a correlation degree between data corresponding to any two different time points in the training data; and the partial autocorrelation coefficient represents a correlation degree between data corresponding to any two adjacent time points in the training data; The inspection unit is used for drawing an autocorrelation graph and a partial autocorrelation graph according to the autocorrelation coefficient and the partial autocorrelation coefficient; determining whether the training data is a stationary non-white noise sequence according to the autocorrelation graph and the partial autocorrelation graph; and determining that the training data passes inspection to obtain inspected training data if the training data is the stationary non-white noise sequence; The training unit is used for training an initial model according to the inspected training data to obtain the preset prediction model.

10. A gate opening adjustment device characterized by comprising: The device comprises: The second acquisition unit is used for acquiring motor data in the water conservancy data; wherein the motor data is gate motor data of the target hydropower station in a preset time period; The second determination unit is used for determining a gate opening value according to the motor data; wherein the gate opening value represents a gate opening of the gate of the target hydropower station in the preset time period; The second prediction unit is used for inputting the gate opening value into the preset prediction model to obtain a predicted gate opening value if it is determined that an absolute value of a difference between the gate opening value and a preset gate opening value is greater than or equal to a second preset threshold value; wherein the predicted gate opening value represents a predicted gate opening of the gate of the target hydropower station in a current time period, and the preset gate opening value is a preset gate opening value of the target hydropower station in a current time period. The second adjusting unit is configured to adjust the gate opening degree of the gate of the target hydropower station according to the predicted gate opening degree value. The obtaining unit is configured to obtain training data of a target hydropower station; wherein the training data is water conservancy data of the target hydropower station in a preset time period, and the water conservancy data includes flow data and gate opening degree data. The first determining module is further configured to determine, according to the training data, an autocorrelation coefficient and a partial autocorrelation coefficient corresponding to the training data; wherein the autocorrelation coefficient represents a correlation degree between data corresponding to any two different time points in the training data; and the partial autocorrelation coefficient represents a correlation degree between data corresponding to any two adjacent time points in the training data. The testing unit is configured to draw an autocorrelation graph and a partial autocorrelation graph according to the autocorrelation coefficient and the partial autocorrelation coefficient; determine whether the training data is a stationary non-white noise sequence according to the autocorrelation graph and the partial autocorrelation graph; and if the training data is the stationary non-white noise sequence, determine that the training data passes the test to obtain tested training data. The training unit is configured to train an initial model according to the tested training data to obtain the preset prediction model.

11. The apparatus of claim 9, wherein, Before the first adjusting unit is configured to adjust the gate opening degree of the gate of the target hydropower station according to the predicted flow value, the device further comprises a third determining unit configured to determine a control mode; wherein the control mode is a control mode of the gate of the target hydropower station. The first adjusting unit comprises: An adjusting module configured to adjust the gate opening degree of the gate of the target hydropower station according to the control mode and the predicted flow value.

12. The apparatus of claim 11, wherein, The control mode is any one of the following: a local electric control mode, a remote electric control mode, and a local mechanical manual control mode.

13. The apparatus of any one of claims 9-12, wherein, The device further comprises: A sending unit configured to send working state information of the target hydropower station; wherein the working state information represents a working state of the target hydropower station; the working state of the target hydropower station includes water conservancy data, gate opening degree, and motor running state of the target hydropower station; and the motor running state represents that the gate motor of the target hydropower station is in a normal state or an abnormal state.

14. The apparatus of any one of claims 9-12, wherein, The water conservancy data includes flow rate data and motor data.

15. The apparatus of claim 9, wherein, The device further comprises: A conversion submodule configured to, if it is determined that the training data does not pass the test, perform difference conversion processing on the training data to obtain converted training data; A determination submodule configured to determine the converted training data as the tested training data.

16. The apparatus of claim 9 or 15, wherein, The training data contains actual water conservancy data; wherein the actual water conservancy data is actual water conservancy data of the target hydropower station. The training unit comprises: A generation module configured to input flow data in the tested training data into the initial model to obtain predicted water conservancy data corresponding to the initial model and a trained initial model; wherein the predicted water conservancy data is predicted water conservancy data of the target hydropower station. A second determining module is configured to determine residual data according to the predicted water conservancy data and the actual water conservancy data, wherein the residual data comprises a difference between the predicted water conservancy data and the actual water conservancy data. A training module is configured to optimize the trained initial model according to the residual data to obtain the preset prediction model.

17. An electronic device, comprising: Comprise: A processor and a memory connected with the processor in communication; The memory stores computer execution instructions; The processor executes the computer execution instructions stored in the memory to implement the method in any one of claims 1-8.

18. A computer-readable storage medium, characterized in that, The computer readable storage medium stores computer execution instructions, and the computer execution instructions are executed by the processor to implement the method in any one of claims 1-8.

19. A computer program product, characterised in that, The computer program product comprises a computer program, and the computer program is executed by the processor to implement the method in any one of claims 1-8.

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