CN115933762A

CN115933762A - Method, device and equipment for adjusting opening degree of gate

Info

Publication number: CN115933762A
Application number: CN202211685674.0A
Authority: CN
Inventors: 宋彦斌; 王永惠; 赵元; 王常玲; 蔡庆宇; 蒋小燕
Original assignee: China United Network Communications Group Co Ltd; Unicom Digital Technology Co Ltd
Current assignee: China United Network Communications Group Co Ltd; Unicom Digital Technology Co Ltd
Priority date: 2022-12-27
Filing date: 2022-12-27
Publication date: 2023-04-07

Abstract

The application provides a method, a device and equipment for adjusting the opening of a gate, which are used for acquiring water conservancy data of a target hydropower station; determining a flow value of a target hydropower station according to flow speed data in the water conservancy data; if the difference value between the flow value and the preset flow value is larger than or equal to a first preset threshold value, inputting the flow value into a preset prediction model to obtain a predicted flow value; and adjusting the gate opening of the gate of the target hydropower station according to the predicted flow value. According to the method and the device, the water flow data of the current target hydropower station can be predicted, and the gate opening of the gate of the hydropower station is dynamically adjusted according to the predicted water flow, so that the purpose of intelligent operation of the hydropower station is achieved, and the operation and maintenance difficulty of operation and maintenance personnel is reduced.

Description

Method, device and equipment for adjusting opening degree of gate

Technical Field

The application relates to the technical field of water conservancy operation and maintenance, in particular to a method, a device and equipment for adjusting the opening degree of a gate.

Background

The more the convenient for people hydraulic engineering is made, the bigger the water resource distribution required by people life is met. In order to control the flood discharge or the closure operation of the dam and ensure the safe and reliable operation of the hydropower station, the opening degree of a gate of the hydropower station needs to be adjusted in time.

In the prior art, various parameters and state data of water conservancy are detected through detection equipment, data analysis is carried out, and the opening degree of a gate is adjusted in time according to an analysis result.

However, in the above manner, the water conservancy data changes in real time, so that the opening degree of the gate of the hydropower station cannot be dynamically adjusted, and further, the operation and maintenance difficulty of operation and maintenance personnel cannot be reduced.

Disclosure of Invention

The application provides a method, a device and equipment for adjusting the opening of a gate, which are used for solving the problem that the opening of the gate of a hydropower station cannot be dynamically adjusted due to real-time change of water conservancy data.

In a first aspect, the present application provides a method for adjusting a gate opening, including:

acquiring water conservancy data of a target hydropower station; the water conservancy data are water conservancy data of the target hydropower station within a preset time period;

determining a flow value of the target hydropower station according to flow speed data in the water conservancy data; the flow rate data is the water flow rate of the target hydropower station in a preset time period, and the flow value represents the water flow of the target hydropower station in the preset time period;

if the difference value between the flow value and the preset flow value is larger than or equal to a first preset threshold value, inputting the flow value into a preset prediction model to obtain a predicted flow value; the predicted flow value represents the predicted water flow of the target hydropower station in the current time period;

and adjusting the gate opening of the gate of the target hydropower station according to the predicted flow value.

In one example, after acquiring the water conservancy data of the target hydropower station, the method further includes:

acquiring motor data in the water conservancy data; the motor data is motor data of a gate motor of the target hydropower station within a preset time period;

determining a gate opening value according to the motor data; the gate opening value represents the gate opening of the gate of the target hydropower station in the preset time period;

if the difference value between the gate opening value and the preset gate opening value is determined to be larger than or equal to a second preset threshold value, inputting the gate opening value into the preset prediction model to obtain a predicted gate opening value; wherein the predicted gate opening value represents a gate opening of a gate of the target hydropower station within a predicted current time period;

and adjusting the gate opening degree of the gate of the target hydropower station according to the predicted gate opening degree value.

In one example, before adjusting the gate opening of the gate of the target hydropower station according to the predicted flow rate value, the method further includes: determining a control mode; wherein the control mode is a control mode of a gate of the target hydropower station;

adjusting the gate opening of the gate of the target hydropower station according to the predicted flow value, and the method comprises the following steps:

and adjusting the gate opening of the gate of the target hydropower station according to the control mode and the predicted flow value.

In one example, the control mode is any one of the following: a local electric control mode, a remote electric control mode and a local mechanical hand-operated control mode.

In one example, the method further comprises:

sending the working state information of the target hydropower station; wherein the operating state information characterizes the operating state of the target hydropower station; the working state of the target hydropower station comprises water conservancy data, gate opening and motor running state of the target hydropower station; and the motor running state represents whether a gate motor of the target hydropower station is in a normal state or an abnormal state.

In one example, the hydraulic data includes flow rate data and motor data.

In a second aspect, the present application provides a model training method applied to adjust a gate opening, the method including:

acquiring data to be trained of a target hydropower station; the data to be trained 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 data to be trained is checked to obtain checked data to be trained;

training an initial model according to the checked data to be trained to obtain the preset prediction model; the preset prediction model is used for processing the water conservancy data in the method in the first aspect to obtain predicted water conservancy data and then adjusting the gate opening.

In one example, the step of verifying the data to be trained to obtain verified data to be trained includes:

according to the data to be trained, determining an autocorrelation coefficient and a partial autocorrelation coefficient corresponding to the data to be trained; the autocorrelation coefficients represent the correlation degree between data corresponding to any two different moments in the data to be trained; the partial autocorrelation coefficient is used for representing the correlation degree between data corresponding to any two adjacent moments in the data to be trained;

and checking the data to be trained according to the autocorrelation coefficient and the partial autocorrelation coefficient to obtain the checked data to be trained.

In one example, the verifying the data to be trained according to the autocorrelation coefficient and the partial autocorrelation coefficient to obtain verified data to be trained includes:

if the data to be trained are determined not to pass the test, carrying out differential conversion processing on the data to be trained to obtain converted data to be trained; and determining the converted data to be trained as the verified data to be trained.

In one example, the data to be trained has actual water conservancy data; the actual water conservancy data are water conservancy data of the target hydropower station;

training an initial model according to the inspected data to be trained to obtain the preset prediction model, wherein the training comprises the following steps:

inputting flow data in the checked data to be trained into the initial model to obtain predicted water conservancy data corresponding to the initial model and the trained initial model; the predicted water conservancy data are predicted water conservancy data of the target hydropower station;

determining 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 hydraulic data and the actual hydraulic data;

and optimizing the trained initial model according to the residual data to obtain the preset prediction model.

In a third aspect, the present application provides a gate opening adjusting device, including:

the first acquisition unit is used for acquiring water conservancy data of a target hydropower station; the water conservancy data are water conservancy data of the target hydropower station within a preset time period;

the first determining unit is used for determining the flow value of the target hydropower station according to the flow speed data in the water conservancy data; the flow rate data is the water flow rate of the target hydropower station in a preset time period, and the flow value represents the water flow of the target hydropower station in the 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 the difference value between the flow value and a preset flow value is determined to be larger than or equal to a first preset threshold value; wherein the predicted flow value represents a predicted water flow of the target hydropower station within a current time period;

and the first adjusting unit is used for adjusting the gate opening of the gate of the target hydropower station according to the predicted flow value.

In one example, after the first obtaining unit is used for obtaining the water conservancy data of the target hydropower station, the method further comprises the following steps:

the second acquisition unit is used for acquiring motor data in the water conservancy data; the motor data is motor data of a gate motor of the target hydropower station within a preset time period;

the second determining unit is used for determining a gate opening value according to the motor data; the gate opening value represents the 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 the difference value between the gate opening value and a preset gate opening value is determined to be greater than or equal to a second preset threshold value; the predicted gate opening value represents the gate opening of the gate of the target hydropower station in the predicted current time period;

and the second adjusting unit is used for adjusting the gate opening degree of the gate of the target hydropower station according to the predicted gate opening degree value.

In one example, before the first adjusting unit is configured to adjust a gate opening of a gate of the target hydropower station according to the predicted flow value, the method further includes: a third determination unit configured to determine a control manner; wherein the control mode is a control mode of a gate of the target hydropower station;

the first adjusting unit includes:

and the adjusting module is used for adjusting the gate opening of the gate of the target hydropower station according to the control mode and the predicted flow value.

In one example, the apparatus further comprises:

the transmitting unit is used for transmitting the working state information of the target hydropower station; wherein the operating state information characterizes the operating state of the target hydropower station; the working state of the target hydropower station comprises water conservancy data, gate opening and motor running state of the target hydropower station; and the motor running state represents whether a gate motor of the target hydropower station is in a normal state or an abnormal state.

In one example, the hydraulic data includes flow rate data and motor data.

In a fourth aspect, the present application provides a model training device applied to adjust a gate opening, including:

the acquisition unit is used for acquiring data to be trained of the target hydropower station; the data to be trained 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 checking unit is used for checking the data to be trained to obtain the checked data to be trained;

the training unit is used for training an initial model according to the checked data to be trained to obtain the preset prediction model; the preset prediction model is used for processing the water conservancy data in the device according to the third aspect, and adjusting the gate opening after the predicted water conservancy data is obtained.

In one example, the inspection unit includes:

the first determining module is used for determining an autocorrelation coefficient and a partial autocorrelation coefficient corresponding to the data to be trained according to the data to be trained; the autocorrelation coefficients represent the correlation degree between data corresponding to any two different moments in the data to be trained; the partial autocorrelation coefficient represents the degree of correlation between data corresponding to any two adjacent moments in the data to be trained;

and the checking module is used for checking the data to be trained according to the autocorrelation coefficient and the partial autocorrelation coefficient to obtain the checked data to be trained.

In one example, the verification module includes:

the conversion submodule is used for carrying out differential conversion processing on the data to be trained to obtain converted data to be trained if the data to be trained is determined not to pass the test;

and the determining submodule is used for determining the converted data to be trained as the verified data to be trained.

the training unit comprises:

the generating module is used for inputting the flow data in the checked data to be trained into the initial model to obtain the predicted water conservancy data corresponding to the initial model and the trained initial model; the predicted water conservancy data are predicted water conservancy data of the target hydropower station;

the second determining module is used for determining residual error data according to the predicted water conservancy data and the actual water conservancy data; wherein the residual data comprises a difference between the predicted hydraulic data and the actual hydraulic data;

and the training module is used for optimizing the trained initial model according to the residual error data to obtain the preset prediction model.

In a fifth aspect, the present application provides an electronic device, comprising: a processor, and a memory communicatively coupled to the processor;

the memory stores computer-executable instructions;

the processor executes computer-executable instructions stored by the memory to implement the methods of the first and second aspects.

In a sixth aspect, the present application provides a computer-readable storage medium having stored thereon computer-executable instructions for implementing the method of the first and second aspects when executed by a processor.

In a seventh aspect, the present application provides a computer program product comprising: a computer program, stored in a readable storage medium, from which at least one processor of an electronic device can read the computer program, execution of the computer program by the at least one processor causing the electronic device to perform the method of the first and second aspects.

According to the method, the device and the equipment for adjusting the opening of the gate, water conservancy data of a target hydropower station are obtained; the water conservancy data is water conservancy data of a target hydropower station in a preset time period; determining a flow value of a target hydropower station according to flow speed data in the water conservancy data; the flow rate data is the water flow rate of the target hydropower station in a preset time period, and the flow value represents the water flow of the target hydropower station in the preset time period; if the difference value between the flow value and the preset flow value is larger than or equal to a first preset threshold value, inputting the flow value into a preset prediction model to obtain a predicted flow value; the predicted flow value represents the predicted water flow of the target hydropower station in the current time period; and adjusting the gate opening of the gate of the target hydropower station according to the predicted flow value. The method comprises the steps of predicting the water flow data of the current target hydropower station by collecting water flow velocity data of the target hydropower station in a period of time and through a deep learning model obtained by artificial intelligence technology training, and dynamically adjusting the gate opening of a gate of the hydropower station according to the predicted water flow, so that the aim of intelligently transporting the hydropower station is fulfilled, and the operation and maintenance difficulty of operation and maintenance personnel is reduced.

Drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and together with the description, serve to explain the principles of the application.

Fig. 1 is a schematic flow chart of a method for adjusting a gate opening according to an embodiment of the present disclosure;

fig. 2 is a schematic flow chart of another method for adjusting the opening degree of a gate according to an embodiment of the present disclosure;

fig. 3 is a schematic structural diagram of a water conservancy terminal device according to an embodiment of the present application;

fig. 4 is a schematic structural diagram of a water conservancy terminal control scheme provided in an embodiment of the present application;

fig. 5 is a schematic flowchart of a model training method applied to adjust a gate opening according to an embodiment of the present disclosure;

fig. 6 is a schematic flowchart of another model training method applied to adjust the opening of the gate according to the embodiment of the present disclosure;

fig. 7 is a schematic structural diagram of an adjusting device for a gate opening according to an embodiment of the present disclosure;

fig. 8 is a schematic structural diagram of another device for adjusting a gate opening according to an embodiment of the present disclosure;

fig. 9 is a schematic structural diagram of a model training device applied to adjust the opening of a gate according to an embodiment of the present disclosure;

FIG. 10 is a schematic structural diagram of another model training device applied to adjust the opening of a gate according to an embodiment of the present disclosure;

fig. 11 is a schematic structural diagram of an electronic device according to an embodiment of the present application;

FIG. 12 is a block diagram illustrating an electronic device in accordance with an example embodiment.

With the above figures, there are shown specific embodiments of the present application, which will be described in more detail below. These drawings and written description are not intended to limit the scope of the inventive concepts in any manner, but rather to illustrate the inventive concepts to those skilled in the art by reference to specific embodiments.

Detailed Description

Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. The following description refers to the accompanying drawings in which the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present application, as detailed in the appended claims.

With the improvement of the capital construction level of China, the more and more the civil water conservancy projects are, such as large water conservancy projects of three gorges dam and the like, flood disasters are effectively prevented and controlled, and water resource distribution required by life of people is met. In order to control the flood discharge or the closure operation of the dam and ensure the safe and reliable operation of the hydropower station, the opening degree of a gate of the hydropower station needs to be adjusted in time.

In one example, a telemetering terminal system based on water conservancy intelligent perception is provided, which comprises a plurality of telemetering terminal machines, each telemetering terminal machine is connected with a plurality of servers through wireless, the plurality of servers are arranged in different monitoring centers, and each telemetering terminal machine is further connected with: a plurality of collecting devices for collecting water conservancy data; a surveillance camera for monitoring the condition of the field; an electromagnetic valve for controlling the presence of lubricating water; a limit switch for collecting the open and close state of the gate; a Global Positioning System (GPS) module arranged beside the telemetry terminal; each server is connected with a display; various parameters and state data of water conservancy are detected, safety monitoring is carried out on water level states, foreign matters and the like, analysis is carried out through a system where a terminal is located, and timely switching is carried out on the equipment state of the hydropower station.

However, in the above manner, when the water conservancy data changes in real time, the opening degree of the gate of the hydropower station cannot be dynamically adjusted, so that the operation and maintenance difficulty of operation and maintenance personnel cannot be reduced, and the intelligent operation and maintenance of the hydropower station cannot be realized.

The application provides a method, a device and equipment for adjusting the opening degree of a gate, and aims to solve the technical problems in the prior art.

The following describes the technical solutions of the present application and how to solve the above technical problems with specific embodiments. The following several specific embodiments may be combined with each other, and details of the same or similar concepts or processes may not be repeated in some embodiments. Embodiments of the present application will be described below with reference to the accompanying drawings.

Fig. 1 is a schematic flowchart of a method for adjusting a gate opening according to an embodiment of the present application, and as shown in fig. 1, the method includes:

s101, acquiring water conservancy data of a target hydropower station; the water conservancy data are water conservancy data of a target hydropower station in a preset time period.

For example, the execution subject of the embodiment may be an electronic device, or a server, or a terminal device, or other apparatuses or devices that can execute the embodiment. In this embodiment, an execution subject is taken as an example of an electronic device.

Based on electronic equipment, like water conservancy terminal, can be used for gate control, protection control, water level and water yield monitoring, pressure detection etc. of power station, to the target power station that the user confirmed, can set up the control unit of a water conservancy terminal, obtain the water conservancy data of target power station in the time quantum of predetermineeing.

S102, determining a flow value of a target hydropower station according to flow speed data in the water conservancy data; the flow rate data is the water flow rate of the target hydropower station in a preset time period, and the flow value represents the water flow of the target hydropower station in the preset time period.

Illustratively, water flow velocity data, namely flow velocity data, of the target hydropower station in the preset time period is extracted from the water conservancy data according to the acquired water conservancy data of the target hydropower station in the preset time period, and according to the flow velocity data, water flow quantity, namely a flow value, of the target hydropower station in the preset time period is obtained on the basis of analysis processing and calculation of a data acquisition unit in the water conservancy terminal.

In one example, the water conservancy terminal acquires water conservancy data from a flowmeter or a flow plate, the installation place of the flowmeter or the flow plate is selected at a stable water flow position downstream of a gate, and the water conservancy terminal is based on a flow closed-loop control mode of the water conservancy terminal.

S103, if the difference value between the flow value and the preset flow value is determined to be larger than or equal to a first preset threshold value, inputting the flow value into a preset prediction model to obtain a predicted flow value; and the predicted flow value represents the predicted water flow of the target hydropower station in the current time period.

For example, at the beginning of adjustment of a gate of a hydropower station, a preset flow value, which is a flow rate of a target hydropower station in a preset current time period, and a flow control error value, which is a first preset threshold, are set based on manual experience, a flow value, which is a flow rate, of the target hydropower station in the preset time period is obtained based on calculation, a difference between the flow value and the preset flow value is calculated to obtain a difference between the flow value and the preset flow value, an absolute value of the difference is obtained, the absolute value of the difference is 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 is indicated that the current flow rate of the hydropower station needs to be controlled, in order to adjust the gate opening to control the flow rate more accurately and timely, a preset prediction model, such as a time-series-based neural network model, needs to be retrieved, and the current flow rate, which is a flow rate of the target hydropower station in the current time period, is input into the preset prediction model, and the prediction model analyzes and compares the input data of the input flow rate of the target hydropower station, and outputs the predicted flow rate of the target hydropower station in the current time period.

And S104, adjusting the gate opening of the gate of the target hydropower station according to the predicted flow value.

Illustratively, according to the water flow of the target hydropower station in the predicted current time period, the gate opening value of the gate of the target hydropower station in the current time period is calculated based on the recorded gate position and flow change relation, the gate opening value of the gate of the target hydropower station in the current time period can be manually set based on a touch screen of a water conservancy terminal or can be automatically set by a terminal system in water conservancy, and a gate control module arranged by the water conservancy terminal controls the rising, falling or stopping of the gate of the target hydropower station according to the set gate opening value, so that the gate opening value of the gate of the target hydropower station is adjusted, the purpose of dynamically adjusting the gate opening value is achieved, intelligent operation and maintenance are performed on the hydropower station, and the operation and maintenance difficulty of operation and maintenance personnel is reduced.

In the embodiment, water conservancy data of a target hydropower station is obtained; the water conservancy data is water conservancy data of a target hydropower station in a preset time period; determining a flow value of a target hydropower station according to flow speed data in the water conservancy data; the flow rate data is the water flow rate of the target hydropower station in a preset time period, and the flow value represents the water flow of the target hydropower station in the preset time period; if the difference value between the flow value and the preset flow value is determined to be larger than or equal to a first preset threshold value, inputting the flow value into a preset prediction model to obtain a predicted flow value; the predicted flow value represents the predicted water flow of the target hydropower station in the current time period; and adjusting the gate opening of the gate of the target hydropower station according to the predicted flow value. The method comprises the steps of predicting the water flow data of the current target hydropower station by collecting water flow velocity data of the target hydropower station in a period of time and a deep learning model obtained by artificial intelligence technical training, and dynamically adjusting the gate opening of a gate of the hydropower station according to the predicted water flow, so that the aim of intelligently transporting the hydropower station is fulfilled, and the operation and maintenance difficulty of operation and maintenance personnel is reduced.

Fig. 2 is a schematic flowchart of another method for adjusting a gate opening according to an embodiment of the present application, and as shown in fig. 2, the method includes:

s201, acquiring water conservancy data of a target hydropower station; the water conservancy data are water conservancy data of a target hydropower station in a preset time period.

In one example, the hydraulic data includes flow rate data and motor data.

For example, the execution subject of the embodiment may be an electronic device, or a server, or a terminal device, or other apparatuses or devices that can execute the embodiment. In this embodiment, an execution main body is taken as an example for description.

Based on electronic equipment, like water conservancy terminal, can be used for gate control, protection control, water level and water yield monitoring, pressure detection etc. of power station, to the target power station that the user confirms, can set up the acquisition element at a water conservancy terminal, obtain the water conservancy data of target power station in the predetermined time quantum, wherein, water conservancy data includes the velocity of flow data and the motor data of power station.

In an example, fig. 3 is a schematic structural diagram of a water conservancy terminal device provided in an embodiment of the present application, as shown in fig. 3, based on an electronic device, such as a water conservancy terminal, including a gate control unit, a water level and flow rate acquisition unit, a gate opening detection unit, a solar power unit, a 5G communication unit, a display unit, an alarm unit, and the like, where the gate control unit is mainly responsible for core control of a gate; the water level flow acquisition unit acquires channel flow in real time and uploads the channel flow to the upper computer; the gate opening detection unit acquires the lifting height of a gate in real time; the solar power unit adopts energy-saving and environment-friendly solar power supply and high-performance maintenance-free storage batteries with the temperature of more than 65AH ((-40-70 ℃ of various environmental adaptations), thereby greatly saving the electric power investment cost and providing a stable power supply for the system; the 5G communication unit replaces the original optical fiber communication, so that the investment cost is reduced, the communication cost is extremely low, and the data can be transmitted in a wireless manner in time and reliably; the alarm unit comprises an indicator light and an alarm; the display unit is mainly a display screen; the water conservancy terminal adopts a micro-C/OS-II operating system, deploys a collection task, mainly collects water level gauge information, collection plate information, solar controller information and motor encoder information, and acquires water conservancy data of the target hydropower station in a preset time period based on a water level flow collection unit and a gate opening detection unit of the water conservancy terminal, wherein the water conservancy data comprises flow rate data and motor data of the hydropower station. It is worth to explain that the water conservancy terminal has requirements on safety, adopts solar energy power supply and a high-performance polymer battery to provide a reliable power supply for the gate, and the system can be in a standby state for 24 hours; the early warning device has the early warning functions of over-low and over-high power supplies, and can give an alarm and forbid the operation of the gate during early warning; the system protects the gate safety through upper and lower limit protection, blocking protection, motor torque protection and the like.

After step S201, step S202 or step S206 may be performed.

S202, acquiring motor data in water conservancy data; the motor data is motor data of a gate motor of the target hydropower station in a preset time period.

Illustratively, after step S201, according to the acquired water conservancy data of the target hydropower station in the preset time period, motor data of a gate motor of the target hydropower station in the preset time period, that is, motor data, is extracted from the water conservancy data.

In one example, based on the water conservancy data of the target hydropower station in the preset time period acquired by the gate opening detection unit of the water conservancy terminal, based on the opening closed-loop mode of the water conservancy terminal, motor data, namely motor data, of a gate motor of the target hydropower station in the preset time period is extracted from the water conservancy data, and for example, motor encoder values including magnetic pole positions and servo motor rotation angles and rotation speeds measured by an encoder are collected.

S203, determining a gate opening value according to the motor data; and the gate opening value represents the gate opening of the gate of the target hydropower station in a preset time period.

Illustratively, according to the acquired motor data of the gate motor of the target hydropower station in the preset time period, based on analysis processing and calculation of a data acquisition unit in the water conservancy terminal, a gate opening value, namely a gate opening value, of the motor gate of the target hydropower station in the preset time period is obtained.

In one example, the water conservancy terminal acquires motor data of a gate motor of the target hydropower station within a preset time period based on an opening closed-loop control mode of the water conservancy terminal, and in the mode, a collection task of the water conservancy terminal collects a motor encoder value and calculates a real-time opening value.

S204, if the difference value between the gate opening value and the preset gate opening value is determined to be larger than or equal to a second preset threshold value, inputting the gate opening value into a preset prediction model to obtain a predicted gate opening value; and the predicted gate opening value represents the gate opening of the gate of the target hydropower station in the predicted current time period.

For example, at the beginning of adjustment of a gate of a hydropower station, a gate opening value of a target hydropower station in a preset current time period, that is, a preset gate opening value, and a gate opening control error value, that is, a second preset threshold value, are set based on manual experience, the gate opening value of the target hydropower station in the preset time period is obtained based on calculation, a difference value between the gate opening value and the preset gate opening value is obtained by calculating a difference value, an absolute value of the difference value is obtained, the absolute value of the difference value is compared with the second preset threshold value, if the absolute value of the difference value is determined to be greater than or equal to the second preset threshold value, it is determined that the gate opening value of the current hydropower station needs to be controlled, a preset prediction model, for example, a time-series-based neural network model needs to be called, and the gate opening value of the target hydropower station in the current time period is input 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, so that the gate in the current time period can be predicted.

And S205, adjusting the gate opening degree of the gate of the target hydropower station according to the predicted gate opening degree value.

Illustratively, according to the forecast gate opening value of the target hydropower station in the current time period, the gate opening value of the gate of the next target hydropower station can be manually set based on a touch screen of the water conservancy terminal or automatically set by a terminal system in water conservancy, and a gate control module arranged by the water conservancy terminal controls the rising, falling or stopping of the gate of the target hydropower station according to the set gate opening value, so that the gate opening value of the gate of the target hydropower station is adjusted, the purpose of dynamically adjusting the gate opening value is achieved, intelligent operation and maintenance are performed on the hydropower station, and the operation and maintenance difficulty of operation and maintenance personnel is reduced.

S206, determining a flow value of the target hydropower station according to flow speed data in the water conservancy data; the flow rate data is the water flow rate of the target hydropower station in a preset time period, and the flow value represents the water flow of the target hydropower station in the preset time period.

Illustratively, after step S201, based on the acquired water conservancy data of the target hydropower station in the preset time period, water flow rate data, i.e., flow rate data, of the target hydropower station in the preset time period is extracted from the water conservancy data, and based on analysis, processing and calculation of a data acquisition unit in the water conservancy terminal, water flow rate, i.e., a flow value, of the target hydropower station in the preset time period is obtained according to the flow rate data.

S207, if the difference value between the flow value and the preset flow value is determined to be larger than or equal to a first preset threshold value, inputting the flow value into a preset prediction model to obtain a predicted flow value; and the predicted flow value represents the predicted water flow of the target hydropower station in the current time period.

For example, this step may refer to step S103 described above, and is not described here again.

S208, determining a control mode; wherein the control mode is a control mode of a gate of the target hydropower station.

Illustratively, the opening and closing of the gate of the target hydropower station supports one of a local electric control mode, a remote electric control mode and a local mechanical hand-operated control mode, and the control mode of the gate of the target hydropower station is determined according to the requirements of users.

In an example, fig. 4 is a schematic structural diagram of a water conservancy terminal control scheme provided in an embodiment of the present application, as shown in fig. 4, a terminal is defaulted to remote control after being powered on according to a user demand, and the water conservancy terminal control scheme at this time includes a management and application system and a water conservancy terminal, where the management and application system is a water conservancy terminal control system platform, and is responsible for collecting and processing field data and the like by remotely controlling a gate through the system platform; the water conservancy terminal such as a water core series terminal can carry out operations such as gate control and the like on site through the water conservancy terminal, collects field data and then sends the field data to the system platform.

And S209, adjusting the gate opening of the gate of the target hydropower station according to the control mode and the predicted flow value.

Illustratively, according to the predicted water flow of the target hydropower station in the current time period, the gate opening value of the gate of the target hydropower station in the current time period is calculated based on the recorded gate position and flow change relation, the gate opening value of the gate of the target hydropower station in the current time period can be manually set based on a touch screen of a water conservancy terminal or automatically set by a terminal system in water conservancy, the set gate opening value is sent to a gate control module of the water conservancy terminal based on a determined control mode of the gate of the hydropower station, such as remote control, the rising, falling or stopping of the gate of the target hydropower station is controlled according to a remote control instruction of the module, and then the gate opening value of the gate of the target hydropower station is adjusted, so that the purpose of dynamically adjusting the gate opening value is achieved, intelligent operation and maintenance are carried out on the hydropower station, and the operation and maintenance difficulty of operation and maintenance personnel is reduced.

In one example, according to the predicted water flow of the target hydropower station in the current time period, a target flow is set based on remote control, the gate automatically recognizes that the set regulation target flow is achieved, the gate of the target hydropower station is controlled to ascend, descend or stop, the gate opening value of the gate of the target hydropower station is further adjusted, the purpose of dynamically adjusting the gate opening value is achieved, intelligent operation and maintenance are performed on the hydropower station, and the operation and maintenance difficulty of operation and maintenance personnel is reduced.

S210, sending working state information of the target hydropower station; the working state information represents the working state of the target hydropower station; the working state of the target hydropower station comprises water conservancy data, gate opening and motor running state of the target hydropower station; the motor running state represents whether a gate motor of the target hydropower station is in a normal state or an abnormal state.

Illustratively, based on the water conservancy terminal equipment, in order to monitor the working state of a target hydropower station, an application software part in the equipment can deploy a display task, the display task is applied to a display unit in the water conservancy terminal equipment, and based on the working state information of the target hydropower station acquired by the water conservancy terminal equipment, the working state information includes water conservancy data, gate opening and motor running state of the target hydropower station acquired by a water conservancy terminal equipment acquisition unit, the working state information of the target hydropower station is sent to the display unit in the water conservancy terminal equipment or user application software connected with the water conservancy terminal equipment, wherein the motor running state represents whether a gate motor of the target hydropower station is in a normal state or an abnormal state, and according to the working state information displayed by the display unit, a user can judge whether the water unit works abnormally, and if the water unit works abnormally, an alarm can be sent out based on an alarm unit in the water conservancy terminal equipment.

In the embodiment, on the basis of the embodiment, the water conservancy data of the target hydropower station is obtained; the water conservancy data is water conservancy data of a target hydropower station in a preset time period; acquiring motor data in water conservancy data; 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; the gate opening value represents the gate opening of a gate of a target hydropower station in a preset time period; if the difference value between the gate opening value and the preset gate opening value is determined to be larger than or equal to a second preset threshold value, inputting the gate opening value into a preset prediction model to obtain a predicted gate opening value; the predicted gate opening value represents the gate opening of the gate of the target hydropower station in the predicted current time period; adjusting the gate opening degree of a gate of the target hydropower station according to the predicted gate opening degree value; transmitting the working state information of the target hydropower station; the working state information represents the working state of the target hydropower station; the working state of the target hydropower station comprises water conservancy data, gate opening and motor running state of the target hydropower station; the motor running state represents whether a gate motor of the target hydropower station is in a normal state or an abnormal state. The method comprises the steps of collecting motor data of a gate motor of a target hydropower station in a recent period, analyzing the data based on a preset prediction model, predicting the gate opening degree of the gate of the target hydropower station in the current period, adjusting the gate opening degree value of the gate of the target hydropower station according to a prediction result, achieving the purpose of dynamically adjusting the gate opening degree value, carrying out intelligent operation and maintenance on the hydropower station, and reducing the operation and maintenance difficulty of operation and maintenance personnel.

Fig. 5 is a schematic flowchart of a model training method applied to adjust a gate opening according to an embodiment of the present disclosure, and as shown in fig. 5, the method includes:

s301, acquiring data to be trained of the target hydropower station; the data to be trained are water conservancy data of the target hydropower station in a preset time period, and the water conservancy data comprise flow data and gate opening data.

Based on electronic equipment, network equipment, like water conservancy terminal, can be used for gate control, protection control, water level and water yield monitoring, pressure detection etc. of power station, to the target power station that the user confirmed, can set up the acquisition element of a water conservancy terminal, obtain the water conservancy data of target power station in the preset time quantum, treat training data promptly, wherein, water conservancy data include flow data and gate aperture data.

S302, the data to be trained are checked to obtain the checked data to be trained.

Illustratively, in order to obtain accurate data, according to the collected water conservancy data of the target hydropower station to be trained, white noise detection is carried out on the water conservancy data of the target hydropower station to be trained, and the detected water conservancy data to be trained is obtained and used for model training.

S303, training the initial model according to the checked data to be trained to obtain a preset prediction model; the preset prediction model is used for processing water conservancy data in the method for adjusting the opening of the gate, and the opening of the gate is adjusted after the predicted water conservancy data are obtained.

Illustratively, a preset initial prediction model is called, the water conservancy data to be trained after the test is output to the initial prediction model, the initial prediction model is trained to obtain a preset prediction model, and the preset prediction model is used for processing the water conservancy data in the gate opening adjusting method to obtain the predicted water conservancy data and then adjusting the gate opening.

In the embodiment, data to be trained of a target hydropower station are acquired; the method comprises the following steps that data to be trained are water conservancy data of a target hydropower station in a preset time period, wherein the water conservancy data comprise flow data and gate opening data; the data to be trained are checked to obtain the checked data to be trained; training the initial model according to the checked data to be trained to obtain a preset prediction model; the preset prediction model is used for processing water conservancy data in the method for adjusting the opening of the gate, and the opening of the gate is adjusted after the predicted water conservancy data are obtained. The water conservancy data of the target hydropower station are collected and used for training the model, the water conservancy data of the current target hydropower station are predicted according to the trained prediction model, and then the opening degree of the gate is adjusted according to the prediction result.

Fig. 6 is a schematic flowchart of another model training method applied to adjust the opening degree of a gate according to an embodiment of the present application, and as shown in fig. 6, the method includes:

s401, acquiring data to be trained of a target hydropower station; the data to be trained are water conservancy data of the target hydropower station in a preset time period, and the water conservancy data comprise flow data and gate opening data.

For example, this step may refer to step S301, and is not described again.

S402, according to the data to be trained, determining an autocorrelation coefficient and a partial autocorrelation coefficient corresponding to the data to be trained; the autocorrelation coefficients represent the correlation degree between data corresponding to any two different moments in the data to be trained; the partial autocorrelation coefficient is used for representing the degree of correlation between data corresponding to any two adjacent moments in the data to be trained.

Illustratively, according to the acquired water conservancy data of the target hydropower station to be trained, the autocorrelation coefficient and the partial autocorrelation coefficient of the data are calculated, the autocorrelation graph and the partial autocorrelation graph are drawn, the correlation degree between the data corresponding to any two different moments in the water conservancy data to be trained and the correlation degree between the data corresponding to any two adjacent moments in the water conservancy data to be trained can be obtained, and data inspection is performed.

And S403, checking the data to be trained according to the autocorrelation coefficient and the partial autocorrelation coefficient to obtain the checked data to be trained.

In one example, step S403 includes: if the data to be trained are determined not to pass the test, carrying out differential conversion processing on the data to be trained to obtain converted data to be trained; and determining the converted data to be trained as the verified data to be trained.

Illustratively, according to autocorrelation coefficients and partial autocorrelation coefficients obtained by the water conservancy data to be trained, which are obtained through calculation, an autocorrelation graph and a partial autocorrelation graph are drawn, whether the distribution of a sequence is always floated up and down around a constant is mainly observed through the autocorrelation graph and the partial autocorrelation graph, whether the distribution of the sequence is a stable non-white noise sequence is judged according to the result, if the water conservancy data to be trained is a stable sequence, the water conservancy data to be trained is determined to be checked to be passed, the checked data is obtained, if the data is a non-stable data sequence which is checked to be passed, the data is firstly converted into a stable sequence in a difference mode, and the checked water conservancy data to be trained is obtained and used for further model training.

S404, inputting flow data in the detected data to be trained into the initial model to obtain predicted water conservancy data corresponding to the initial model and the trained initial model; the predicted water conservancy data are predicted water conservancy data of the target hydropower station.

In one example, the data to be trained has actual water conservancy data; and the actual water conservancy data is the actual water conservancy data of the target hydropower station.

Illustratively, according to the collected water conservancy data of the target hydropower station in the preset time period, actual water conservancy data, namely the water conservancy data of the target hydropower station, can be obtained through analysis.

Further, a preset initial network quality prediction model is called, and when the model is selected again, model selection and order determination are performed through an autocorrelation graph and a partial autocorrelation graph, as shown in table 1 below.

TABLE 1 model selection parameter Table

Autocorrelation chart	Partial auto-correlation diagram	Selecting a model
			Tailing	p-order truncation	AR(p)
q-order truncation	Tailing	MA(q)
			Tailing	Tailing	ARMA(p，q)

And outputting the processed characteristic data to be trained to an initial prediction model, such as an Autoregressive moving average model (ARMA), to obtain predicted water conservancy data of the target hydropower station.

S405, determining residual error 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.

Illustratively, in order to obtain a model with accurate prediction, a difference value between the predicted water conservancy data and the actual water conservancy data is calculated according to the obtained predicted water conservancy data and the actual water conservancy data, residual data is obtained, and then model optimization is carried out.

And S406, optimizing the trained initial model according to the residual error data to obtain a preset prediction model.

Illustratively, residual data corresponding to the predicted water conservancy data and the actual water conservancy data are optimized for the initial identification model, parameters of the initial prediction model are optimized, for example, the parameters are determined according to an Akamai Information Criterion (AIC) and a Bayesian Information Criterion (BIC) of the model, subjectivity of an autocorrelation graph and a partial autocorrelation graph order can be made up, and the AIC Criterion is a weighting function of fitting accuracy and the number of the parameters: AIC =2k-2lnl, bic criteria: BIC = lnn k-2lnL; wherein n is the number of data, and k is the number of parameters in the model; n and k are integers greater than or equal to 1; and L is a maximum likelihood function of the model, wherein the smaller the values of the AIC and the BIC, the better the values are, the optimal preset prediction model is obtained and is used for processing the water conservancy data in the method for adjusting the gate opening degree, and the gate opening degree is adjusted after the predicted water conservancy data is obtained.

In this embodiment, on the basis of the above embodiment, according to the data to be trained, the autocorrelation coefficient and the partial autocorrelation coefficient corresponding to the data to be trained are determined; the autocorrelation coefficient represents the correlation degree between data corresponding to any two different moments in the data to be trained; the partial autocorrelation coefficient is used for representing the correlation degree between data corresponding to any two adjacent moments in the data to be trained; according to the autocorrelation coefficient and the partial autocorrelation coefficient, checking the data to be trained to obtain the checked data to be trained; inputting flow data in the detected data to be trained into the initial model to obtain predicted water conservancy data corresponding to the initial model and the trained initial model; the predicted water conservancy data are water conservancy data of a predicted target hydropower station; determining residual error data according to the predicted water conservancy data and the actual water conservancy data; the residual data comprise a difference value between the predicted water conservancy data and the actual water conservancy data; and optimizing the trained initial model according to the residual data to obtain a preset prediction model. The collected water conservancy data to be trained are subjected to inspection conversion processing, so that the data quality of the data to be trained is further improved, and the accuracy of a prediction model is further improved; and training the initial prediction model for multiple times and optimizing the model based on the data to be trained to obtain a preset prediction model so as to predict the water conservancy data of the current target hydropower station, and adjusting the opening of the gate according to the prediction result.

Fig. 7 is a schematic structural diagram of an apparatus for adjusting a gate opening according to an embodiment of the present application, and as shown in fig. 7, the apparatus 500 includes:

the first obtaining unit 501 is configured to obtain water conservancy data of a target hydropower station; the water conservancy data are water conservancy data of a target hydropower station in a preset time period.

The first determining unit 502 is used for determining a flow value of the target hydropower station according to flow speed data in the water conservancy data; the flow rate data is the water flow rate of the target hydropower station in a preset time period, and the flow value represents the water flow of the target hydropower station in the preset time period.

The first prediction unit 503 is configured to, if it is determined that a difference between the flow value and the preset flow value is greater than or equal to a first preset threshold, input the flow value into a preset prediction model to obtain a predicted flow value; and the predicted flow value represents the predicted water flow of the target hydropower station in the current time period.

A first adjusting unit 504, configured to adjust a gate opening of the gate of the target hydropower station according to the predicted flow rate value.

The apparatus of this embodiment may execute the technical solution in the method, and the specific implementation process and the technical principle are the same, which are not described herein again.

Fig. 8 is a schematic structural diagram of another device for adjusting the opening degree of a gate according to an embodiment of the present application, and as shown in fig. 8, the device 600 includes:

the first acquisition unit 601 is used for acquiring water conservancy data of a target hydropower station; the water conservancy data are water conservancy data of a target hydropower station in a preset time period.

The first determining unit 602 is configured to determine a flow value of the target hydropower station according to flow rate data in the water conservancy data; the flow rate data is the water flow rate of the target hydropower station in a preset time period, and the flow value represents the water flow of the target hydropower station in the preset time period.

The first prediction unit 603 is configured to, if it is determined that a difference between the flow value and the preset flow value is greater than or equal to a first preset threshold, input the flow value into a preset prediction model to obtain a predicted flow value; and the predicted flow value represents the predicted water flow of the target hydropower station in the current time period.

A first adjusting unit 604 for adjusting the gate opening of the gate of the target hydropower station according to the predicted flow rate value.

In one example, after the first obtaining unit 601 is used for obtaining the water conservancy data of the target hydropower station, the method further includes:

a second obtaining unit 605, configured to obtain motor data in the water conservancy data; the motor data is motor data of a gate motor of the target hydropower station in a preset time period.

A second determining unit 606, configured to determine a gate opening value according to the motor data; and the gate opening value represents the gate opening of the gate of the target hydropower station in a preset time period.

A second prediction unit 607, configured to, if it is determined that a difference between the gate opening value and the preset gate opening value is greater than or equal to a second preset threshold, input the gate opening value into a preset prediction model to obtain a predicted gate opening value; and the predicted gate opening value represents the gate opening of the gate of the target hydropower station in the predicted current time period.

And a second adjusting unit 608 for adjusting the gate opening degree of the gate of the target hydropower station according to the predicted gate opening degree value.

In one example, before the first adjusting unit 604 is configured to adjust the gate opening of the gate of the target hydropower station according to the predicted flow value, the method further includes: a third determining unit 609 configured to determine a control manner; wherein the control mode is a control mode of a gate of the target hydropower station.

The first adjusting unit 604 includes:

and an adjusting module 6041, configured to adjust a gate opening of a gate of the target hydropower station according to the control manner and the predicted flow value.

In one example, the apparatus 600 further comprises:

a transmitting unit 610 for transmitting the operating state information of the target hydropower station; the working state information represents the working state of the target hydropower station; the working state of the target hydropower station comprises water conservancy data, gate opening and motor running state of the target hydropower station; the motor running state represents whether a gate motor of the target hydropower station is in a normal state or an abnormal state.

In one example, the hydraulic data includes flow rate data and motor data.

Fig. 9 is a schematic structural diagram of a model training apparatus for adjusting a gate opening according to an embodiment of the present application, and as shown in fig. 9, the apparatus 700 includes:

an obtaining unit 701, configured to obtain data to be trained of a target hydropower station; the data to be trained are water conservancy data of the target hydropower station in a preset time period, and the water conservancy data comprise flow data and gate opening data.

The checking unit 702 is configured to check the data to be trained to obtain the checked data to be trained.

The training unit 703 is configured to train the initial model according to the checked data to be trained, so as to obtain a preset prediction model; the preset prediction model is used for processing the water conservancy data in the device in the third aspect, and the gate opening degree is adjusted after the predicted water conservancy data is obtained.

Fig. 10 is a schematic structural diagram of another model training apparatus for adjusting a gate opening according to an embodiment of the present application, and as shown in fig. 10, the apparatus 800 includes:

an obtaining unit 801, configured to obtain data to be trained of a target hydropower station; the data to be trained are water conservancy data of the target hydropower station in a preset time period, and the water conservancy data comprise flow data and gate opening data.

The checking unit 802 is configured to check the data to be trained to obtain the checked data to be trained.

A training unit 803, configured to train the initial model according to the checked data to be trained, so as to obtain a preset prediction model; the preset prediction model is used for processing the water conservancy data in the device of the third aspect, and the gate opening degree is adjusted after the predicted water conservancy data are obtained.

In one example, the verification unit 802 includes:

a first determining module 8021, configured to determine, according to the data to be trained, an autocorrelation coefficient and a partial autocorrelation coefficient corresponding to the data to be trained; the autocorrelation coefficient represents the correlation degree between data corresponding to any two different moments in the data to be trained; the partial autocorrelation coefficient is used for representing the correlation degree between data corresponding to any two adjacent moments in the data to be trained;

the inspection module 8022 is configured to inspect the data to be trained according to the autocorrelation coefficient and the partial autocorrelation coefficient, so as to obtain inspected data to be trained.

In one example, inspection module 8022 includes:

the conversion submodule is used for carrying out differential conversion processing on the data to be trained if the data to be trained is determined not to pass the test, so as to obtain the converted data to be trained;

A training unit 803, comprising:

a generating module 8031, configured to input flow data in the checked data to be trained to the initial model, so as to obtain predicted water conservancy data corresponding to the initial model and the trained initial model; the predicted water conservancy data are predicted water conservancy data of the target hydropower station;

a second determining module 8032, configured to determine residual data according to the predicted water conservancy data and the actual water conservancy data; the residual data comprise a difference value between the predicted water conservancy data and the actual water conservancy data;

the training module 8033 is configured to optimize the trained initial model according to the residual data to obtain a preset prediction model.

The apparatus of this embodiment may execute the technical solution in the method, and the specific implementation process and technical principle are the same, which are not described herein again.

Fig. 11 is a schematic structural diagram of an electronic device according to an embodiment of the present application, and as shown in fig. 11, the electronic device 900 includes: a memory 91, a processor 92; a memory 91 for storing instructions executable by the processor 92.

Wherein the processor 92 is configured to perform the methods provided in the above embodiments.

The terminal device further comprises a receiver 93 and a transmitter 94. The receiver 93 is used for receiving instructions and data transmitted from other devices, and the transmitter 94 is used for transmitting instructions and data to an external device.

FIG. 12 is a block diagram illustrating an electronic device, which may be a mobile phone, a computer, a digital broadcast terminal, a messaging device, a game console, a tablet device, a medical device, an exercise device, a personal digital assistant, and the like, in accordance with an exemplary embodiment.

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

The processing component 1002 generally controls overall operation of the electronic device 1000, such as operations associated with display, telephone calls, data communications, camera operations, and recording operations. The processing components 1002 may include one or more processors 1020 to execute instructions to perform all or a portion of the steps of the methods described above. Further, processing component 1002 may include one or more modules that facilitate interaction between processing component 1002 and other components. For example, processing component 1002 can include a multimedia module to facilitate interaction between multimedia component 10010 and processing component 1002.

The memory 1004 is configured to store various types of data to support operations at the electronic device 1000. Examples of such data include instructions for any application or method operating on the electronic device 1000, contact data, phonebook data, messages, pictures, videos, and so forth. The memory 1004 may be implemented by any type or combination of volatile or non-volatile storage devices, such as static random access memory, electrically erasable programmable read only memory, magnetic memory, flash memory, magnetic or optical disk.

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

The multimedia component 1008 includes a screen that provides an output interface between the 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 touch screen to receive an input signal from a user. The touch panel includes one or more touch sensors to sense touch, slide, and gestures on the touch panel. The touch sensor may not only sense the boundary of a touch or slide action, but also detect the duration and pressure associated with the touch or slide operation. In some embodiments, the multimedia component 10010 comprises a front-facing camera and/or a rear-facing camera. The front camera and/or the rear camera may receive external multimedia data when the electronic device 1000 is in an operation mode, such as a photographing mode or a video mode. Each front camera and rear camera may be a fixed optical lens system or have a focal length and optical zoom capability.

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

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: a home button, a volume button, a start button, and a lock button.

The sensor assembly 1014 includes one or more sensors for providing various aspects of status assessment for the electronic device 1000. For example, the sensor assembly 1014 may detect an open/closed state of the electronic device 1000, a relative positioning of components, such as a display and keypad of the electronic device 1000, a change in position of the electronic device 1000 or a component of the electronic device 1000, the presence or absence of user contact with the electronic device 1000, an orientation or acceleration/deceleration of the electronic device 1000, and a change in temperature of the electronic device 1000. The sensor assembly 1014 may include a proximity sensor configured to detect the presence of a nearby object in the absence of any physical contact. The sensor assembly 1014 may also include a light sensor, such as an image sensor, for use in imaging applications. In some embodiments, the sensor assembly 1014 may also include an acceleration sensor, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.

The communications component 1016 is configured to facilitate communications between the electronic device 1000 and other devices in a wired or wireless manner. The electronic device 1000 may access a wireless network based on a communication standard. In an exemplary embodiment, the communication component 1016 receives a broadcast signal or broadcast related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 1016 further 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 technology, infrared data association technology, ultra wideband technology, bluetooth technology, and other technologies.

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 for performing the above-described methods.

In an exemplary embodiment, a non-transitory computer-readable storage medium comprising instructions, such as the memory 1004 comprising instructions, executable by the processor 1020 of the electronic device 1000 to perform the above-described method is also provided. For example, the non-transitory computer readable storage medium may be a random access memory, a magnetic tape, a floppy disk, an optical data storage device, and the like.

Embodiments of the present application further provide a non-transitory computer-readable storage medium, where instructions in the storage medium, when executed by a processor of an electronic device, enable the electronic device to perform the above method.

An embodiment of the present application further provides a computer program product, where the computer program product includes: a computer program, stored in a readable storage medium, from which at least one processor of the electronic device can read the computer program, and the execution of the computer program by the at least one processor causes the electronic device to perform the solutions provided by any of the above embodiments.

Other embodiments of the present application will be apparent to those skilled in the art from consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the invention following, in general, the principles of the application and including such departures from the present disclosure as come within known or customary practice within the art to which the invention pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the application being indicated by the following claims.

It will be understood that the present application is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the application is limited only by the appended claims.

Claims

1. A method for adjusting the opening degree of a gate, the method comprising:

2. The method of claim 1, after obtaining the hydraulic data of the target hydropower station, further comprising:

3. The method according to claim 1, before adjusting the gate opening of the gate of the target hydroelectric power station based on the predicted flow value, further comprising: determining a control mode; wherein the control mode is a control mode of a gate of the target hydropower station;

adjusting the gate opening of the gate of the target hydropower station according to the predicted flow value, including:

4. The method according to claim 3, wherein the control mode is any one of: a local electric control mode, a remote electric control mode and a local mechanical hand-operated control mode.

5. The method according to any one of claims 1-4, further comprising:

6. The method of any one of claims 1 to 4, wherein the hydraulic data includes flow rate data and motor data.

7. A model training method applied to adjusting the opening degree of a gate is characterized by comprising the following steps:

the data to be trained is checked to obtain checked data to be trained;

training an initial model according to the checked data to be trained to obtain the preset prediction model; the preset prediction model is used for processing the water conservancy data in the method according to any one of claims 1 to 6, and adjusting the gate opening after the predicted water conservancy data is obtained.

8. The method of claim 7, wherein the step of verifying the data to be trained to obtain verified data to be trained comprises:

9. The method according to claim 8, wherein the step of examining the data to be trained according to the autocorrelation coefficient and the partial autocorrelation coefficient to obtain examined data to be trained comprises:

10. The method according to any one of claims 7-9, wherein the data to be trained has actual water conservancy data; the actual water conservancy data are water conservancy data of the target hydropower station;

training an initial model according to the checked data to be trained to obtain the preset prediction model, wherein the training comprises the following steps:

11. An apparatus for adjusting a gate opening degree, the apparatus comprising:

the first determining unit is used for determining the flow value of the target hydropower station according to flow speed data in the water conservancy data; the flow rate data is the water flow rate of the target hydropower station in a preset time period, and the flow value represents the water flow of the target hydropower station in the 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 the difference value between the flow value and a preset flow value is determined to be larger than or equal to a first preset threshold value; the predicted flow value represents the predicted water flow of the target hydropower station in the current time period;

12. The apparatus according to claim 11, wherein after the first obtaining unit is used for obtaining the water conservancy data of the target hydropower station, the apparatus further comprises:

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 the difference value between the gate opening value and a preset gate opening value is determined to be larger than or equal to a second preset threshold value; the predicted gate opening value represents the gate opening of the gate of the target hydropower station in the predicted current time period;

13. The apparatus according to claim 11, before the first adjusting unit is configured to adjust the gate opening of the gate of the target hydroelectric power station based on the predicted flow value, further comprising: a third determination unit configured to determine a control manner; wherein the control mode is a control mode of a gate of the target hydropower station;

the first adjusting unit includes:

14. The apparatus according to claim 13, wherein the control mode is any one of: a local electric control mode, a remote electric control mode and a local mechanical hand-operated control mode.

15. The apparatus according to any one of claims 11-14, further comprising:

16. An apparatus according to any one of claims 11 to 14, wherein the hydraulic data includes flow rate data and motor data.

17. A model training device applied to adjusting the opening degree of a gate is characterized by comprising:

the acquisition unit is used for acquiring data to be trained of the target hydropower station; the data to be trained are water conservancy data of the target hydropower station in a preset time period, and the water conservancy data comprise flow data and gate opening data;

the training unit is used for training an initial model according to the inspected data to be trained to obtain the preset prediction model; the preset prediction model is used for processing water conservancy data in the device according to any one of claims 11-16, and adjusting the opening degree of a gate after the predicted water conservancy data is obtained.

18. The apparatus of claim 17, wherein the inspection unit comprises:

the first determining module is used for determining an autocorrelation coefficient and a partial autocorrelation coefficient corresponding to the data to be trained according to the data to be trained; the autocorrelation coefficient represents the correlation degree between data corresponding to any two different moments in the data to be trained; the partial autocorrelation coefficient is used for representing the correlation degree between data corresponding to any two adjacent moments in the data to be trained;

19. The apparatus of claim 18, wherein the inspection module comprises:

the conversion sub-module is used for carrying out differential conversion processing on the data to be trained if the data to be trained is determined not to pass the test, so as to obtain converted data to be trained;

20. The apparatus according to any one of claims 17-19, wherein the data to be trained has actual hydraulic data; the actual water conservancy data are water conservancy data of the target hydropower station;

the training unit comprises:

the generating module is used for inputting flow data in the inspected data to be trained into the initial model to obtain predicted water conservancy data corresponding to the initial model and the trained initial model; the predicted water conservancy data are predicted water conservancy data of the target hydropower station;

and the training module is used for optimizing the trained initial model according to the residual data to obtain the preset prediction model.

21. An electronic device, comprising: a processor, and a memory communicatively coupled to the processor;

the memory stores computer-executable instructions;

the processor executes computer-executable instructions stored by the memory to implement the method of any of claims 1-10.

22. A computer-readable storage medium having computer-executable instructions stored thereon, which when executed by a processor, perform the method of any one of claims 1-10.