CN117598190B

CN117598190B - Reservoir branch canal irrigation control method, device, equipment and storage medium

Info

Publication number: CN117598190B
Application number: CN202410085785.0A
Authority: CN
Inventors: 郭中磊; 侯爽; 贾志军; 吕旺; 魏亮; 徐淑敏; 徐秀强; 金江波; 田广信; 信文鹏
Original assignee: Hebei Water Resource Research And Water Conservancy Technology Test Popularization Center
Current assignee: Hebei Water Resource Research And Water Conservancy Technology Test Popularization Center
Priority date: 2024-01-22
Filing date: 2024-01-22
Publication date: 2024-04-16
Anticipated expiration: 2044-01-22
Also published as: CN117598190A

Abstract

The application is suitable for the technical field of electric digital data processing, and provides a method, a device, equipment and a storage medium for controlling irrigation of a reservoir branch canal, wherein the method comprises the following steps: obtaining channel types and crop types of a target main diversion channel, obtaining historical water discharge amount of the target main diversion channel and historical weather data of a place where the target main diversion channel is located, and obtaining rainfall of the place where the target main diversion channel is located in a future irrigation time period; based on historical weather data, predicting and obtaining the evaporation capacity of downstream crops of the target main dividing channel in a future irrigation time period; predicting to obtain the initial planned water discharge of the target main diversion trench in a future irrigation time period based on the crop types downstream of the target main diversion trench and the historical water discharge of the target main diversion trench; calculating to obtain the actual water discharge amount based on the initial planned water discharge amount, rainfall and evaporation amount; and controlling the opening time of the target main dividing channel based on the actual water discharge amount and the channel type of the target main dividing channel. The application can realize the accurate irrigation of crops at the downstream of the split main channel and improve the water resource utilization rate.

Description

Reservoir branch canal irrigation control method, device, equipment and storage medium

Technical Field

The application belongs to the technical field of electric digital data processing, and particularly relates to a reservoir branch canal irrigation control method, device, equipment and storage medium.

Background

The agricultural water consumption in China is about 80% of the total water consumption, and the agricultural irrigation efficiency is generally low, so that the water utilization rate is only 45%, wherein the irrigation of crops downstream of the branch channels is usually regulated in a manual control mode, namely, the irrigation of the crops downstream is met by controlling the opening time of the branch channels.

The manual control method is wasteful. Because farmers can switch off according to own irrigation experience after considering that irrigation is finished, the problems that whether irrigation crops are saturated or not, the water storage and evaporation capacity of local soil is judged by individuals, water is wasted in the middle period of manually cutting off water supply, and natural factors are not considered after manual irrigation are solved, water resources are wasted, accurate irrigation to crops cannot be achieved due to manual irrigation, and the utilization rate of the water resources cannot be effectively improved are caused.

Disclosure of Invention

The embodiment of the application provides a method, a device, equipment and a storage medium for controlling irrigation of a branch canal of a reservoir, so as to realize accurate irrigation of crops at the downstream of the branch canal, reduce waste of water resources and improve the utilization rate of the water resources.

The application is realized by the following technical scheme:

In a first aspect, an embodiment of the present application provides a method for controlling irrigation in a branch canal of a reservoir, including:

The method comprises the steps of obtaining channel types and crop types of a target main diversion channel, obtaining historical water discharge amount of the target main diversion channel and historical weather data of a place, and obtaining rainfall of the place of the target main diversion channel in a future irrigation time period.

Based on historical weather data, predicting and obtaining the evaporation capacity of downstream crops of the target main dividing channel in a future irrigation time period; based on the crop species downstream of the target main diversion trench and the historical discharge of the target main diversion trench, an initial planned discharge of the target main diversion trench in a future irrigation time period is predicted.

And calculating to obtain the actual water discharge based on the initial planned water discharge, the rainfall and the evaporation capacity.

And controlling the opening time of the target main dividing channel in a future irrigation time period based on the actual water discharge amount and the channel type of the target main dividing channel.

With reference to the first aspect, in some possible implementations, predicting, based on historical weather data, an evaporation amount of the downstream crop of the target branch canal in a future irrigation period includes:

inputting the historical weather data into a weather data prediction model, and predicting to obtain target weather data; the target weather data are weather data in a future irrigation time period; the target weather data includes: target air temperature, target average air pressure, and target wind speed; the weather data prediction model is a long-term and short-term memory neural network model.

And calculating the evaporation quantity of the downstream crops in the future irrigation time period of the target main diversion channel based on the target air temperature, the target average air pressure and the target air speed.

With reference to the first aspect, in some possible implementations, the target air temperature includes a target highest air temperature and a target lowest air temperature; the calculation formula of the evaporation capacity of the downstream crops in the future irrigation time period of the target branch canal is as follows:

Wherein is the evaporation capacity of the downstream crop in the future irrigation time period, wherein/ is the sea level average air pressure, wherein/ is the target average air pressure, wherein/ is the slope at the average air temperature on the saturated water vapor pressure-temperature curve, wherein the average air temperature is the average of the target maximum air temperature and the target minimum air temperature, wherein/ is the hygrometer constant, wherein/ is the actual atmospheric pressure in the air, wherein/ is the saturated water vapor pressure, wherein/ is the wind speed at 2m, wherein/ is obtained by the target wind speed, wherein/ is the wind speed correction coefficient related to the target maximum air temperature and the target minimum air temperature, and wherein/ is the net solar radiation.

With reference to the first aspect, in some possible implementations, the training process of the weather data prediction model includes:

And randomly generating initial values of a group of neuron numbers, learning rates and iteration times, and establishing a current weather data prediction model based on the initial values of the neuron numbers, the learning rates and the iteration times which are randomly generated.

And obtaining a predicted value of the current weather data based on the historical weather data and the current weather data prediction model.

And acquiring a current true value corresponding to the predicted value of the current weather data in the historical weather data.

And taking the error of the predicted value and the current true value of the current weather data as the fitness value of the particle swarm algorithm, and taking the initial values of the number of a group of neurons, the learning rate and the iteration number as one particle in the particle swarm algorithm.

And acquiring a historical individual optimal position, a global optimal position, a position vector and a speed vector of the particles in the particle swarm algorithm.

And obtaining an updated global optimal position vector based on the historical individual optimal position, the global optimal position, the position vector and the speed vector of the particles.

And obtaining values of the number of the updated neurons, the learning rate and the iteration number based on the updated global optimal position vector, and establishing an updated weather data prediction model based on the values of the number of the updated neurons, the learning rate and the iteration number.

And obtaining a predicted value of the updated weather data based on the historical weather data and the updated weather data prediction model.

And acquiring an updated true value corresponding to the predicted value of the updated weather data in the historical weather data.

And obtaining the adaptability value of the current weather data prediction model based on the predicted value and the current true value of the current weather data.

And obtaining the adaptability value of the updated weather data prediction model based on the updated predicted value and the updated actual value of the weather data.

Judging whether the maximum iteration number of the particle swarm algorithm is reached at the moment, if the maximum iteration number of the particle swarm algorithm is not reached, selecting a model with a smaller fitness value from the fitness value of the current weather data prediction model and the fitness value of the updated weather data prediction model as the current weather data prediction model in the next iteration process.

If the maximum iteration number of the particle swarm algorithm is reached, selecting a model with a smaller fitness value from the fitness value of the current weather data prediction model and the fitness value of the updated weather data prediction model as a weather data prediction model.

With reference to the first aspect, in some possible implementations, obtaining an updated global optimal position vector based on the historical individual optimal position, the global optimal position, the position vector of the particle, and the velocity vector includes:

And based on the historical individual optimal position, the global optimal position, the position vector and the speed vector of the particles, and combining a first formula to obtain an updated global optimal position vector.

The first formula is:

Wherein is a global optimum position vector of the/> dimensions of the/> particles when the iteration number is/> , the/> is a global optimum position vector of the/> dimensions of the/> particles when the iteration number is/> , the is a velocity vector of the/> dimensions of the/> particles when the iteration number is/> , the/> is a velocity vector of the/> dimensions of the/> particles when the iteration number is/> , the/> is an individual learning factor, the/> is a group learning factor, the/> and are uniformly distributed pseudo-random numbers valued within [0,1], the/> is an inertia weight coefficient, the/> is an individual optimum position, and the/> is a global optimum position.

With reference to the first aspect, in some possible implementations, a calculation formula of the inertia weight coefficient is:

Wherein is the maximum value of the inertia weight coefficient,/> is the minimum value of the inertia weight coefficient,/> is the maximum iteration number of the particle swarm algorithm, and/> is the current iteration number.

With reference to the first aspect, in some possible implementations, predicting an initial planned discharge of the target main culvert in a future irrigation time period based on the crop species downstream of the target main culvert and the historical discharge of the target main culvert includes:

and acquiring the historical soil moisture content of the target branch canal and the soil moisture content of the target branch canal in a future irrigation time period.

Based on the crop species downstream of the target split canal and the historical discharge of the target split canal, the historical discharge of different stages of a growth cycle of the crop species is obtained.

The stage at which the crop species is now located is obtained.

Calculating the average value of a plurality of historical water discharge amounts corresponding to the stage of the crop type at the moment, and calculating the average value of soil moisture content corresponding to the plurality of historical water discharge amounts.

And calculating the sum of the average value of a plurality of historical water discharge amounts corresponding to the stage of the crop type at the moment and the average value of soil moisture content corresponding to the plurality of historical water discharge amounts to obtain the target water discharge amount.

And calculating the difference value of soil moisture content of the target water discharge amount and the target main dividing channel in the future irrigation time period to obtain the initial planned water discharge amount.

Based on the initial planned discharge amount, the rainfall amount and the evaporation amount, the actual discharge amount is calculated, which comprises the following steps: and subtracting the rainfall from the initial planned water discharge amount and adding the evaporation amount to obtain the actual water discharge amount.

Based on the actual discharge amount and the channel type of the target main diversion channel, controlling the opening time of the target main diversion channel in a future irrigation time period, comprising: and obtaining the water flow in the unit time of the channel based on the channel type of the target main dividing channel. Calculating the quotient of the actual water discharge amount and the water flow in the unit time of the channel, obtaining the opening time of the target main dividing channel in the future irrigation time period, and controlling the opening of the target main dividing channel according to the opening time.

In a second aspect, an embodiment of the present application provides a reservoir split canal irrigation control device, including:

The system comprises an acquisition module, a control module and a control module, wherein the acquisition module is used for acquiring the channel type and the crop type of the target main dividing channel, acquiring the historical water discharge amount of the target main dividing channel and the historical weather data of the place, and acquiring the rainfall of the place of the target main dividing channel in a future irrigation time period.

The prediction module is used for predicting and obtaining the evaporation capacity of the downstream crops of the target branch canal in a future irrigation time period based on historical weather data; based on the crop species downstream of the target main diversion trench and the historical discharge of the target main diversion trench, an initial planned discharge of the target main diversion trench in a future irrigation time period is predicted.

And the calculation module is used for calculating the actual water discharge amount based on the initial planned water discharge amount, the rainfall and the evaporation amount.

The control module is used for controlling the opening time of the target main dividing channel in a future irrigation time period based on the actual water discharge amount and the channel type of the target main dividing channel.

In a third aspect, an embodiment of the present application provides a terminal device, including: a processor and a memory for storing a computer program which when executed by the processor implements the method of controlling split canal irrigation of a reservoir as set out in any of the first aspects.

In a fourth aspect, embodiments of the present application provide a computer readable storage medium storing a computer program which when executed by a processor implements a reservoir sub-canal irrigation control method as set out in any of the first aspects.

It will be appreciated that the advantages of the second to fourth aspects may be found in the relevant description of the first aspect and are not repeated here.

Compared with the prior art, the embodiment of the application has the beneficial effects that:

According to the method, the evaporation capacity of the downstream crops in the future irrigation time period is obtained through the prediction of historical weather data, the initial planned water discharge capacity is obtained according to the crop types and the historical water discharge capacity of the downstream crops of the target main dividing channel, the accurate actual water discharge capacity is obtained through calculation according to the evaporation capacity, the rainfall capacity and the initial planned water discharge capacity, and the opening time is controlled according to the actual water discharge capacity and the channel type. The automatic control of the target main dividing channel gate is realized, unnecessary water resource waste is reduced compared with manual control, in addition, the weather condition in the irrigation time period is considered, crops are irrigated in combination with the weather condition, the accurate control of the irrigation water consumption of the crops is realized, and the utilization rate of the water resource is improved.

It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the embodiments or the description of the prior art will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present application, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.

FIG. 1 is a schematic flow chart of a method for controlling irrigation of a branch canal of a reservoir according to an embodiment of the present application;

FIG. 2 is a schematic flow chart of a method for controlling irrigation of a branch canal of a reservoir according to another embodiment of the present application;

FIG. 3 is a schematic diagram of a reservoir split canal irrigation control device according to an embodiment of the present application;

Fig. 4 is a schematic structural diagram of a terminal device according to an embodiment of the present application.

Detailed Description

In the following description, for purposes of explanation and not limitation, specific details are set forth such as the particular system architecture, techniques, etc., in order to provide a thorough understanding of the embodiments of the present application. It will be apparent, however, to one skilled in the art that the present application may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present application with unnecessary detail.

It should be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.

It should also be understood that the term "and/or" as used in the present specification and the appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes such combinations.

As used in the present description and the appended claims, the term "if" may be interpreted as "when..once" or "in response to a determination" or "in response to detection" depending on the context. Similarly, the phrase "if a determination" or "if a [ described condition or event ] is detected" may be interpreted in the context of meaning "upon determination" or "in response to determination" or "upon detection of a [ described condition or event ]" or "in response to detection of a [ described condition or event ]".

Furthermore, the terms "first," "second," "third," and the like in the description of the present specification and in the appended claims, are used for distinguishing between descriptions and not necessarily for indicating or implying a relative importance.

Reference in the specification to "one embodiment" or "some embodiments" or the like means that a particular feature, structure, or characteristic described in connection with the embodiment is included in one or more embodiments of the application. Thus, appearances of the phrases "in one embodiment," "in some embodiments," "in other embodiments," and the like in the specification are not necessarily all referring to the same embodiment, but mean "one or more but not all embodiments" unless expressly specified otherwise. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless expressly specified otherwise.

According to the reservoir main dividing channel irrigation control method, the accurate actual water discharge amount is calculated, and then the opening time of the target main dividing channel gate is controlled according to the actual water discharge amount, so that automatic control of the target main dividing channel gate is achieved, unnecessary water resource waste is reduced compared with manual control, accurate control of the water consumption for crop irrigation is achieved, and the utilization rate of the water resource is improved.

Fig. 1 is a schematic flow chart of a method for controlling the irrigation of a branch canal of a reservoir according to an embodiment of the present application, and referring to fig. 1, the method for controlling the irrigation of a branch canal of a reservoir is described in detail as follows:

step 101, obtaining channel types and crop types of the target main diversion tunnel, obtaining historical water discharge amount of the target main diversion tunnel and historical weather data of the place, and obtaining rainfall of the place of the target main diversion tunnel in a future irrigation time period.

Step 102, predicting and obtaining the evaporation capacity of the downstream crops of the target main dividing channel in a future irrigation time period based on historical weather data; based on the crop species downstream of the target main diversion trench and the historical discharge of the target main diversion trench, an initial planned discharge of the target main diversion trench in a future irrigation time period is predicted.

Illustratively, predicting the evaporation capacity of the downstream crop of the target branch canal in a future irrigation time period based on historical weather data may include:

Specifically, based on a weather data prediction model (long-short-term memory neural network model), weather data in a future irrigation time period can be predicted more accurately, and the change situation of the weather data in the future time period can be fully considered, so that a more accurate prediction result is obtained. Furthermore, the evaporation capacity of the crops in the future irrigation time period calculated according to the prediction results can be more accurate, the influence of the weather conditions in the future irrigation time period on the evaporation capacity of the crops can be fully considered, accurate irrigation can be guaranteed, and the waste of water resources is reduced.

Illustratively, the target air temperature includes a target highest air temperature and a target lowest air temperature; the calculation formula of the evaporation capacity of the downstream crops in the future irrigation time period of the target branch canal can be as follows:

Specifically, the calculation formula of the slope at the average air temperature on the saturated water vapor pressure-temperature curve may be:

the calculation formula of the saturated water vapor pressure can be as follows:

Wherein T is the average air temperature.

Specifically, the predicted target wind speed is typically the wind speed at the standard detection point of the weather station, and in order to obtain the wind speed at 2m, the predicted target wind speed needs to be multiplied by 0.75 to obtain the wind speed at 2 m.

For example, the training process of the weather data prediction model may include:

Specifically, the weather data prediction model is optimized through the particle swarm optimization, so that the iteration speed of the weather data prediction model can be increased, the optimal weather data prediction model can be obtained more quickly and accurately, and the accuracy of the weather data prediction model prediction result is improved.

Specifically, for better understanding of the training process, an example of training is given below, and in conjunction with fig. 2, the training process is:

And step1, randomly generating a group of initial values of neuron number, learning rate and iteration number, and constructing a current weather data prediction model A. The initial value generated is the super parameter of the long-short-period memory neural network model. And calculating a difference value A1 between the predicted value and the actual value of the current weather data prediction model A.

And 2, taking the values of the number of neurons, the learning rate and the iteration times as one particle in the particle swarm algorithm, acquiring a historical individual optimal position, a global optimal position, a position vector and a speed vector of the particle in the particle swarm algorithm, updating the position vector and the speed vector of the particle according to the historical individual optimal position and the global optimal position to obtain an updated global optimal position, wherein the particle swarm algorithm completes an iteration process, and the updated global optimal position is the values of the number of neurons, the learning rate and the iteration times after updating.

And 3, establishing an updated weather data prediction model B. And calculating a difference value B1 between the predicted value and the actual value of the updated weather data prediction model B.

And step 4, judging whether the iteration times of the particle swarm algorithm reach the maximum iteration times of the particle swarm algorithm. And if the maximum iteration number is reached, selecting a model corresponding to a smaller value from A1 and B1 as a weather data prediction model after training. If the maximum iteration number is not reached, selecting a model corresponding to a smaller value from A1 and B1 as a current weather data prediction model A' in the next iteration process.

In the next iteration process, the difference A1 'between the predicted value and the actual value of the current weather data prediction model A' is calculated. Updating the values of the neuron number, the learning rate and the iteration number to obtain an updated weather data prediction model B ', calculating a difference value B1' between the predicted value and the actual value of the updated weather data prediction model B ', and repeating the process of the step 4.

Illustratively, obtaining an updated global optimal position vector based on the historical individual optimal position, the global optimal position, the position vector and the velocity vector of the particle may include:

The first formula may be:

Specifically, by introducing the inertia weight coefficient and uniformly distributing the pseudo random numbers, the global searching capability of the population during optimization iteration is improved, and the finally obtained global optimal position vector can be more accurate.

The calculation formula of the inertia weight coefficient is as follows:

Specifically, in order to avoid oscillation phenomenon generated near the global optimal solution in the early stage and the later stage of the particle swarm algorithm, an inertial weight coefficient is added with a correction factor .

Illustratively, predicting an initial planned discharge of the target main culvert over a future irrigation period based on the crop species downstream of the target main culvert and the historical discharge of the target main culvert, comprising:

The stage at which the crop species is now located is obtained.

Specifically, in order to obtain the initial planned water discharge more accurately, the historical soil moisture content and different stages in the growth cycle of the crops are comprehensively considered, and the historical soil moisture content is considered when the historical water discharge is discharged, so that the sum of the historical water discharge and the historical soil moisture content is the water required by the growth of the crops in the period of time, the accurate target water discharge is obtained through a mean value obtaining mode, and further the more accurate initial planned water discharge can be calculated according to the soil moisture content in the future irrigation period of time.

And 103, calculating the actual water discharge amount based on the initial planned water discharge amount, the rainfall and the evaporation amount.

Illustratively, step 103 may include: and subtracting the rainfall from the initial planned water discharge amount and adding the evaporation amount to obtain the actual water discharge amount.

Step 104, controlling the opening time of the target main diversion channel in the future irrigation time period based on the actual water discharge amount and the channel type of the target main diversion channel.

Illustratively, step 104 may include: and obtaining the water flow in the unit time of the channel based on the channel type of the target main dividing channel. Calculating the quotient of the actual water discharge amount and the water flow in the unit time of the channel, obtaining the opening time of the target main dividing channel in the future irrigation time period, and controlling the opening of the target main dividing channel according to the opening time.

Specifically, in order to ensure accurate control of irrigation water for crops downstream of the target main diversion channel, the gate opening duration needs to be controlled according to the type of the channel at the gate. When the channel type is fixed, the water flow in unit time is fixed, and then the accurate control of the actual water discharge amount can be realized by accurately controlling the opening time of the gate.

Specifically, when the straight section length of the channel of the target main dividing channel is larger than 30m, the channel section is regular, concrete or masonry lining is adopted, the influence of a downstream water diversion opening or a throttle gate on the water flow at the section is small, and the water flow is stable, the type of the channel is an open channel standard section, the average flow velocity and the effective water depth of the water flow at the flow section can be adopted, and the flow capacity (the open channel standard section flow velocity method) of the channel section is calculated, so that the water flow in unit time is obtained.

If the straight section of the channel is relatively short, the flow section is small, the channel adopting the standard section of the open channel for water measurement is not provided, but the free outflow can be formed after the no-throat water measuring tank is additionally arranged, the jacking is not easy to form at the downstream of the flow measuring section, and when the channel water flow is stable and smooth, the excessive flow of the channel section can be measured through the no-throat water measuring tank (no-throat method).

According to the reservoir branch canal irrigation control method, the evaporation capacity of downstream crops in a future irrigation time period is obtained through the prediction of historical weather data, the initial planned water discharge capacity is obtained according to the crop types and the historical water discharge capacity of the downstream crops of the target branch canal, the accurate actual water discharge capacity is obtained according to the evaporation capacity, the rainfall capacity and the initial planned water discharge capacity, and the opening time is controlled according to the actual water discharge capacity and the channel type. The automatic control of the target main dividing channel gate is realized, unnecessary water resource waste is reduced compared with manual control, in addition, the weather condition in the irrigation time period is considered, crops are irrigated in combination with the weather condition, the accurate control of the irrigation water consumption of the crops is realized, and the utilization rate of the water resource is improved.

It should be understood that the sequence number of each step in the foregoing embodiment does not mean that the execution sequence of each process should be determined by the function and the internal logic, and should not limit the implementation process of the embodiment of the present application.

Corresponding to the method for controlling the irrigation of the branch canal of the reservoir described in the above embodiments, fig. 3 shows a block diagram of the device for controlling the irrigation of the branch canal of the reservoir according to the embodiment of the present application, and for convenience of explanation, only the parts related to the embodiment of the present application are shown.

Referring to fig. 3, the reservoir split canal irrigation control device in the embodiment of the present application may include:

The acquisition module 201 is configured to acquire a channel type and a crop type of the target main diversion channel, acquire historical water discharge amount of the target main diversion channel and historical weather data of a place, and acquire rainfall of the place of the target main diversion channel in a future irrigation time period.

The prediction module 202 is used for predicting and obtaining the evaporation capacity of the downstream crops of the target branch canal in a future irrigation time period based on historical weather data; based on the crop species downstream of the target main diversion trench and the historical discharge of the target main diversion trench, an initial planned discharge of the target main diversion trench in a future irrigation time period is predicted.

The calculating module 203 is configured to calculate an actual water discharge amount based on the initial planned water discharge amount, the rainfall amount, and the evaporation amount.

The control module 204 is used for controlling the opening time of the target main diversion channel in the future irrigation time period based on the actual water discharge amount and the channel type of the target main diversion channel.

Illustratively, the prediction module 202 may be configured to:

For example, the target air temperature may include a target highest air temperature and a target lowest air temperature; the calculation formula of the evaporation capacity of the downstream crops in the future irrigation time period of the target branch canal can be as follows:

The first formula may be:

For example, the calculation formula of the inertia weight coefficient may be:

Wherein is the maximum value of the inertia weight coefficient,/> is the minimum value of the inertia weight coefficient,/> is the maximum iteration number of the particle swarm algorithm, and/> is the current iteration number. /(I)

Illustratively, the prediction module 202 may also be configured to:

And acquiring the historical soil moisture content of the target branch canal and the soil moisture content of the target branch canal in a future irrigation time period. Based on the crop species downstream of the target split canal and the historical discharge of the target split canal, the historical discharge of different stages of a growth cycle of the crop species is obtained. The stage at which the crop species is now located is obtained. Calculating the average value of a plurality of historical water discharge amounts corresponding to the stage of the crop type at the moment, and calculating the average value of soil moisture content corresponding to the plurality of historical water discharge amounts. And calculating the sum of the average value of a plurality of historical water discharge amounts corresponding to the stage of the crop type at the moment and the average value of soil moisture content corresponding to the plurality of historical water discharge amounts to obtain the target water discharge amount. And calculating the difference value of soil moisture content of the target water discharge amount and the target main dividing channel in the future irrigation time period to obtain the initial planned water discharge amount.

The computing module 203 may be configured to: and subtracting the rainfall from the initial planned water discharge amount and adding the evaporation amount to obtain the actual water discharge amount.

The control module 204 may be configured to: and obtaining the water flow in the unit time of the channel based on the channel type of the target main dividing channel. Calculating the quotient of the actual water discharge amount and the water flow in the unit time of the channel, obtaining the opening time of the target main dividing channel in the future irrigation time period, and controlling the opening of the target main dividing channel according to the opening time.

It should be noted that, because the content of information interaction and execution process between the above devices/units is based on the same concept as the method embodiment of the present application, specific functions and technical effects thereof may be referred to in the method embodiment section, and will not be described herein.

It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-described division of the functional units and modules is illustrated, and in practical application, the above-described functional distribution may be performed by different functional units and modules according to needs, i.e. the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-described functions. The functional units and modules in the embodiment may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit, where the integrated units may be implemented in a form of hardware or a form of a software functional unit. In addition, the specific names of the functional units and modules are only for distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working process of the units and modules in the above system may refer to the corresponding process in the foregoing method embodiment, which is not described herein again.

The embodiment of the present application further provides a terminal device, referring to fig. 4, the terminal device 300 may include: at least one processor 310, a memory 320, the memory 320 being configured to store a computer program 321, the processor 310 being configured to invoke and execute the computer program 321 stored in the memory 320 to perform the steps of any of the various method embodiments described above, such as steps 101 to 104 in the embodiment shown in fig. 1. Or the processor 310, when executing the computer program, performs the functions of the modules/units in the above-described apparatus embodiments, for example, the functions of the modules shown in fig. 3.

By way of example, the computer program 321 may be partitioned into one or more modules/units that are stored in the memory 320 and executed by the processor 310 to complete the present application. The one or more modules/units may be a series of computer program segments capable of performing specific functions for describing the execution of the computer program in the terminal device 300.

It will be appreciated by those skilled in the art that fig. 4 is merely an example of a terminal device and is not limiting of the terminal device, and may include more or fewer components than shown, or may combine certain components, or different components, such as input-output devices, network access devices, buses, etc.

The processor 310 may be a central processing unit (Central Processing Unit, CPU), but may also be other general purpose processors, digital signal processors (DIGITAL SIGNAL processor, DSP), application SPECIFIC INTEGRATED Circuit (ASIC), off-the-shelf programmable gate array (field-programmable GATE ARRAY, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.

The memory 320 may be an internal storage unit of the terminal device, or may be an external storage device of the terminal device, such as a plug-in hard disk, a smart memory card (SMART MEDIA CARD, SMC), a Secure Digital (SD) card, a flash memory card (FLASH CARD), or the like. The memory 320 is used for storing the computer program and other programs and data required by the terminal device. The memory 320 may also be used to temporarily store data that has been output or is to be output.

The bus may be an industry standard architecture (Industry Standard Architecture, ISA) bus, an external device interconnect (PERIPHERAL COMPONENT, PCI) bus, or an extended industry standard architecture (Extended Industry Standard Architecture, EISA) bus, among others. The buses may be divided into address buses, data buses, control buses, etc. For ease of illustration, the buses in the drawings of the present application are not limited to only one bus or to one type of bus.

The reservoir branch canal irrigation control method provided by the embodiment of the application can be applied to terminal equipment such as a computer, wearable equipment, vehicle-mounted equipment, a tablet personal computer, a notebook computer, a netbook and the like, and the embodiment of the application does not limit the specific type of the terminal equipment.

The embodiment of the application also provides a computer readable storage medium, wherein the computer readable storage medium stores a computer program, and the computer program realizes the steps in each embodiment of the reservoir branch canal irrigation control method when being executed by a processor.

Embodiments of the present application provide a computer program product that, when run on a mobile terminal, enables the mobile terminal to perform the steps described in the various embodiments of the reservoir sub-canal irrigation control method described above.

The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the present application may implement all or part of the flow of the method of the above embodiments, and may be implemented by a computer program to instruct related hardware, where the computer program may be stored in a computer readable storage medium, and when the computer program is executed by a processor, the computer program may implement the steps of each of the method embodiments described above. Wherein the computer program comprises computer program code which may be in source code form, object code form, executable file or some intermediate form etc. The computer readable medium may include at least: any entity or device capable of carrying computer program code to a photographing device/terminal apparatus, recording medium, computer memory, read-only memory (ROM), random access memory (RAM, random Access Memory), electrical carrier signals, telecommunications signals, and software distribution media. Such as a U-disk, removable hard disk, magnetic or optical disk, etc.

In the foregoing embodiments, the descriptions of the embodiments are emphasized, and in part, not described or illustrated in any particular embodiment, reference is made to the related descriptions of other embodiments.

Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.

In the embodiments provided in the present application, it should be understood that the disclosed apparatus/network device and method may be implemented in other manners. For example, the apparatus/network device embodiments described above are merely illustrative, e.g., the division of the modules or units is merely a logical functional division, and there may be additional divisions in actual implementation, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection via interfaces, devices or units, which may be in electrical, mechanical or other forms.

The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

The above embodiments are only for illustrating the technical solution of the present application, and not for limiting the same; although the application has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present application, and are intended to be included in the scope of the present application.

Claims

1. A method for controlling the irrigation of a branch canal of a reservoir, which is characterized by comprising the following steps:

Obtaining channel types and crop types of a target main diversion channel, obtaining historical water discharge amount of the target main diversion channel and historical weather data of a place, and obtaining rainfall of the place of the target main diversion channel in a future irrigation time period;

Predicting the evaporation capacity of the downstream crops of the target branch canal in the future irrigation time period based on the historical weather data; predicting an initial planned discharge of the target main diversion trench in a future irrigation time period based on the crop species downstream of the target main diversion trench and the historical discharge of the target main diversion trench;

Calculating to obtain the actual water discharge amount based on the initial planned water discharge amount, the rainfall amount and the evaporation amount;

controlling the opening time of the target main dividing channel in the future irrigation time period based on the actual water discharge amount and the channel type of the target main dividing channel;

the predicting, based on the historical weather data, an evaporation amount of the target sub-canal for the downstream crop over the future irrigation period, comprising:

Inputting the historical weather data into a weather data prediction model, and predicting to obtain target weather data; the target weather data are weather data in a future irrigation time period; the target weather data includes: target air temperature, target average air pressure, and target wind speed; the weather data prediction model is a long-term and short-term memory neural network model;

Calculating the evaporation capacity of the target branch canal for the downstream crops in the future irrigation time period based on the target air temperature, the target average air pressure and the target air speed;

the training process of the weather data prediction model comprises the following steps:

Randomly generating initial values of a group of neuron number, learning rate and iteration number, and establishing a current weather data prediction model based on the initial values of the randomly generated neuron number, learning rate and iteration number;

obtaining a predicted value of current weather data based on the historical weather data and the current weather data prediction model;

Acquiring a current true value corresponding to the predicted value of the current weather data in the historical weather data;

Taking the error of the predicted value of the current weather data and the current true value as an adaptability value of a particle swarm algorithm, and taking initial values of the neuron number, the learning rate and the iteration number as one particle in the particle swarm algorithm;

Acquiring a historical individual optimal position, a global optimal position, a position vector and a speed vector of particles in a particle swarm algorithm;

obtaining an updated global optimal position vector based on the historical individual optimal position, the global optimal position, the position vector and the speed vector of the particles;

Obtaining values of the number of the updated neurons, the learning rate and the iteration times based on the updated global optimal position vector, and establishing an updated weather data prediction model based on the values of the number of the updated neurons, the learning rate and the iteration times;

Obtaining a predicted value of the updated weather data based on the historical weather data and the updated weather data prediction model;

Acquiring an updated true value corresponding to the predicted value of the updated weather data in the historical weather data;

Obtaining an adaptability value of the current weather data prediction model based on the predicted value of the current weather data and the current true value;

obtaining an adaptability value of the updated weather data prediction model based on the predicted value of the updated weather data and the updated real value;

Judging whether the maximum iteration number of the particle swarm algorithm is reached at the moment, if the maximum iteration number of the particle swarm algorithm is not reached, selecting a model with a smaller fitness value from the fitness value of the current weather data prediction model and the fitness value of the updated weather data prediction model as the current weather data prediction model in the next iteration process;

If the maximum iteration number of the particle swarm algorithm is reached, selecting a model with a smaller fitness value from the fitness value of the current weather data prediction model and the fitness value of the updated weather data prediction model as the weather data prediction model;

the obtaining an updated global optimal position vector based on the historical individual optimal position, the global optimal position, the position vector and the speed vector of the particles comprises the following steps:

Based on the historical individual optimal position, the global optimal position, the position vector and the speed vector of the particles, and combining a first formula, obtaining an updated global optimal position vector;

the first formula is:

Wherein is a global optimum position vector of the/> dimensions of the/> particles when the iteration number is/> , the/> is a global optimum position vector of the/> dimensions of the/> particles when the iteration number is/> , the/> is a velocity vector of the/> dimensions of the/> particles when the iteration number is/> , the/> is a velocity vector of the/> dimensions of the/> particles when the iteration number is/> , the/> is an individual learning factor, the/> is a group learning factor, the/> and/> are uniformly distributed pseudo-random numbers taking values in [0,1], the/> is an inertia weight coefficient, the/> is an individual optimum position, and the/> is a global optimum position.

The calculation formula of the inertia weight coefficient is as follows:

2. The method of controlling split canal irrigation in a reservoir according to claim 1, wherein the target air temperature comprises a target maximum air temperature and a target minimum air temperature; the calculation formula of the evaporation capacity of the downstream crops in the future irrigation time period of the target main dividing canal is as follows:

Wherein is the evaporation capacity of the target main canal downstream crops in the future irrigation time period,/> is sea level average air pressure,/> is the target average air pressure,/> is the slope of saturated water vapor pressure-temperature curve at average air temperature, the average air temperature is the average of the target maximum air temperature and the target minimum air temperature,/> is hygrometer constant,/> is the actual atmospheric pressure in air,/> is saturated water vapor pressure,/> is the wind speed at 2m,/> is obtained by the target wind speed,/> is the wind speed correction coefficient related to the target maximum air temperature and the target minimum air temperature, and/> is solar net radiation.

3. The reservoir diversion channel irrigation control method as set forth in claim 1, wherein the predicting an initial planned discharge of the target diversion channel over a future irrigation period based on a crop type downstream of the target diversion channel and a historical discharge of the target diversion channel includes:

Acquiring the historical soil moisture content of the target branch canal and the soil moisture content of the target branch canal in a future irrigation time period;

obtaining the historical water discharge amount of different stages in one growth cycle of the crop variety based on the crop variety downstream of the target main diversion trench and the historical water discharge amount of the target main diversion trench;

Acquiring the stage of the crop type at the moment;

Calculating the average value of a plurality of historical water discharge amounts corresponding to the stage of the crop type at the moment, and calculating the average value of soil moisture contents corresponding to the plurality of historical water discharge amounts;

Calculating the sum of the average value of a plurality of historical water discharge amounts corresponding to the stage of the crop type at the moment and the average value of soil moisture contents corresponding to the plurality of historical water discharge amounts to obtain target water discharge amount;

calculating the difference value of soil moisture content of the target water discharge amount and the target main dividing channel in a future irrigation time period to obtain the initial planned water discharge amount;

The calculating to obtain the actual water discharge based on the initial planned water discharge, the rainfall and the evaporation capacity includes:

subtracting the rainfall from the initial planned water discharge amount and adding the evaporation amount to obtain the actual water discharge amount;

The controlling the opening time of the target main diversion canal in the future irrigation time period based on the actual water discharge amount and the canal type of the target main diversion canal comprises the following steps:

obtaining the water flow in the unit time of the channel based on the channel type of the target main dividing channel;

Calculating the quotient of the actual water discharge amount and the water flow in the unit time of the channel to obtain the opening time of the target main dividing channel in the future irrigation time period, and controlling the opening of the target main dividing channel according to the opening time.

4. A reservoir sub-canal irrigation control device, comprising:

The system comprises an acquisition module, a control module and a control module, wherein the acquisition module is used for acquiring the channel type and the crop type of a target main dividing channel, acquiring the historical water discharge amount of the target main dividing channel and the historical weather data of the place, and acquiring the rainfall of the place of the target main dividing channel in a future irrigation time period;

The prediction module is used for predicting and obtaining the evaporation capacity of the downstream crops of the target main dividing channel in the future irrigation time period based on the historical weather data; predicting an initial planned discharge of the target main diversion trench in a future irrigation time period based on the crop species downstream of the target main diversion trench and the historical discharge of the target main diversion trench;

The calculation module is used for calculating the actual water discharge amount based on the initial planned water discharge amount, the rainfall amount and the evaporation amount;

the control module is used for controlling the opening time of the target main diversion channel in the future irrigation time period based on the actual water discharge amount and the channel type of the target main diversion channel;

The prediction module is further configured to:

the first formula is:

The calculation formula of the inertia weight coefficient is as follows:

5. A terminal device, comprising: a processor and a memory, wherein the memory stores a computer program executable on the processor, and wherein the processor implements the reservoir split canal irrigation control method according to any one of claims 1 to 3 when executing the computer program.

6. A computer readable storage medium storing a computer program, wherein the computer program when executed by a processor implements the reservoir split canal irrigation control method according to any one of claims 1 to 3.