Flow equalizing irrigation method based on intelligent control of multi-water-outlet electric ball valve

Abstract

The invention discloses a flow-equalizing irrigation method based on intelligent control of a multi-water-outlet electric ball valve, and relates to the technical field of farmland irrigation, and the method comprises the following steps: collecting farmland irrigation pipeline characteristic data, carrying out farmland irrigation characteristic prediction in combination with farmland prediction environment characteristic data and farmland soil crop characteristic data, and generating a farmland irrigation scheme, and performing multi-water-outlet electric ball valve control search learning, establishing a farmland irrigation ball valve control space, performing multi-target joint optimization in combination with a ball valve control optimization function, generating a farmland irrigation ball valve control scheme meeting ball valve control optimization feature constraints, and performing irrigation control. The technical problems that in the prior art, an electromagnetic valve is prone to being affected by water quality conditions, blockage is prone to occurring when water quality impurities are large, the opening degree cannot be adjusted, and constant-pressure and uniform-flow irrigation cannot be achieved are solved, and the technical effects that the water outlet pressure is intelligently adjusted through the multi-water-outlet electric ball valve, constant-pressure and uniform-flow irrigation is achieved, and the irrigation efficiency is improved are achieved.

Description

Technical Field

[0001] This invention relates to the field of farmland irrigation technology, specifically to a method for equal flow irrigation based on intelligent control of multi-outlet electric ball valves. Background Technology

[0002] Currently, the main bottlenecks hindering the widespread application of automated drip irrigation technology in farmland irrigation are high cost, large initial investment, and high maintenance costs. A prominent technical issue is that the solenoid valves of key automatic control devices are affected by water quality conditions, resulting in significant head loss, easy clogging, and difficulty in cleaning. This leads to intermittent valve malfunctions and the inability to adjust the flow channel opening. In specific areas, due to underground pipe network issues (long and short pipe irrigation) and the fact that the head pump is generally low-head, high-flow, some valves require pressure regulation, but the working principle of solenoid valves prevents them from regulating pipeline pressure. This limits the application of solenoid valves in certain areas. Summary of the Invention

[0003] This application provides a method for equal flow irrigation based on intelligent control of multi-outlet electric ball valves, which solves the technical problems in the prior art where solenoid valves are easily affected by water quality conditions, are prone to clogging when there are many impurities in the water, the flow channel opening is not adjustable, and constant pressure equal flow irrigation cannot be achieved over long distances.

[0004] The first aspect of this application provides a method for equalizing irrigation based on intelligent control of a multi-outlet electric ball valve. The bottom of the valve is the inlet, and the outlet is on the side. A flow meter is installed at the inlet, and a pressure sensor is installed at each outlet. The flow rate is adjusted by adjusting the rotation angle of the ball core through the numerical feedback of the sensors. The method includes: connecting to the automated irrigation control platform, reading soil monitoring parameters and crop monitoring parameters of the farmland, and generating farmland soil and crop characteristic data; activating the environmental prediction module within the automated irrigation control platform, and performing environmental prediction on the farmland in conjunction with a predetermined future time zone window to obtain farmland predicted environmental characteristic data; obtaining farmland irrigation pipeline characteristic data based on irrigation pipeline parameters collected by the automated irrigation control platform, wherein the farmland irrigation pipeline is equipped with multiple electric ball valves, each electric ball valve having multiple outlets; based on the farmland irrigation pipeline characteristic data, performing farmland irrigation characteristic prediction based on the farmland predicted environmental characteristic data and the farmland soil and crop characteristic data to generate a farmland irrigation scheme; performing multi-outlet electric ball valve control search and learning based on the farmland irrigation scheme to establish a farmland irrigation ball valve control space that satisfies predetermined control capacity constraints; performing multi-objective joint optimization on the farmland irrigation ball valve control space based on the ball valve control optimization function to generate a farmland irrigation ball valve control scheme that satisfies the ball valve control optimization characteristic constraints; and the automated irrigation control platform managing irrigation of the farmland based on the farmland irrigation ball valve control scheme.

[0005] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0006] This application provides a method for equal flow irrigation based on intelligent control of multi-outlet electric ball valves, relating to the field of farmland irrigation technology. It collects characteristic data of farmland irrigation pipelines, combines this with predicted environmental and soil crop characteristic data to predict farmland irrigation characteristics, generates farmland irrigation schemes, establishes a control space for farmland irrigation ball valves, and combines a ball valve control optimization function to perform multi-objective joint optimization, generating a control scheme for farmland irrigation ball valves for irrigation management. This method solves the technical problems in existing technologies where solenoid valves are easily affected by water quality conditions, prone to clogging when there are many impurities in the water, have unadjustable flow channel openings, and cannot achieve constant pressure and uniform flow irrigation over long distances. It achieves the technical effect of intelligently adjusting the downstream water pressure through multi-outlet electric ball valves to realize constant pressure and uniform flow irrigation and improve irrigation efficiency. Attached Figure Description

[0007] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0008] Figure 1 A schematic diagram of a flow equalization irrigation method based on intelligent control of a multi-outlet electric ball valve, provided for an embodiment of this application;

[0009] Figure 2 A schematic diagram of the process for generating a farmland irrigation scheme in a flow equalization irrigation method based on intelligent control of multi-outlet electric ball valves provided in an embodiment of this application;

[0010] Figure 3 This is a flowchart illustrating the process of generating a farmland irrigation ball valve control scheme that satisfies the ball valve control optimization feature constraints in a flow equalization irrigation method based on intelligent control of multi-outlet electric ball valves provided in this application embodiment. Detailed Implementation

[0011] This application provides a method for equal flow irrigation based on intelligent control of multi-outlet electric ball valves, which solves the technical problems in the prior art where solenoid valves are easily affected by water quality conditions, are prone to clogging when there are many impurities in the water, the flow channel opening is not adjustable, and constant pressure equal flow irrigation cannot be achieved over long distances.

[0012] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0013] It should be noted that the terms "first," "second," etc., in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or modules not explicitly listed or inherent to such processes, methods, products, or devices.

[0014] Example 1

[0015] like Figure 1 As shown, this application provides a method for equal flow irrigation based on intelligent control of multi-outlet electric ball valves, the method comprising:

[0016] P10: Connect to the automated irrigation control platform, read soil monitoring parameters and crop monitoring parameters of farmland, and generate farmland soil and crop characteristic data;

[0017] Specifically, by connecting to an automated irrigation control platform, soil monitoring parameters and crop monitoring parameters of the target farmland are read. Soil monitoring parameters typically include soil moisture and soil temperature, while crop monitoring parameters include crop growth status, such as plant height, stem diameter, and chlorophyll content. Different crops require different growth indicators, which can be used to determine whether the crop is growing normally and whether it needs additional water and fertilizer support. These soil and crop monitoring parameters, combined as farmland soil-crop characteristic data, can serve as the basis for subsequent irrigation plan development.

[0018] P20: Activate the environmental prediction module in the automated irrigation control platform, and perform environmental prediction on the farmland in combination with a predetermined future time zone window to obtain farmland predicted environmental characteristic data.

[0019] Optionally, the environmental prediction module within the automated irrigation control platform can be activated. This module can predict future environmental conditions based on current environmental information and can utilize historical environmental monitoring data as training data, combined with a neural network model. Through this module, combined with a predetermined future time zone window, environmental predictions are performed on the farmland to obtain predicted environmental characteristic data, including predicted future environmental parameters such as temperature, humidity, rainfall, and wind speed. This data can serve as an important basis for formulating irrigation plans, helping the irrigation system to make adaptive adjustments in advance. The length of the predetermined future time zone window can be set according to actual needs, for example, it can be set to a period of 24 hours, 48 ​​hours, or longer.

[0020] P30: The automated irrigation control platform collects the irrigation pipeline parameters of the farmland to obtain the characteristic data of the farmland irrigation pipeline. The irrigation pipeline of the farmland is equipped with multiple electric ball valves, and each electric ball valve has multiple outlets.

[0021] It should be understood that by retrieving the pipeline basic information of the automated irrigation control platform, the irrigation pipeline parameters of the farmland are collected. The farmland's irrigation pipeline is equipped with multiple electric ball valves, each with multiple outlets. The bottom of each multi-outlet electric ball valve is the inlet, and the outlets are on the side. A flow meter is installed at the inlet, and a pressure sensor is installed at each outlet. Flow regulation is achieved by adjusting the rotation angle of the ball valve based on the sensor feedback. The collected irrigation pipeline parameters, including pipeline length, diameter, material type, pressure rating, flow range, and valve location, serve as characteristic data of the farmland irrigation pipeline. This data reflects the overall layout of the irrigation system, the connectivity of the pipeline network, and the valve configuration, providing important reference for developing irrigation plans and optimizing system operation.

[0022] P40: Based on the farmland irrigation pipeline characteristic data, farmland irrigation characteristics are predicted according to the farmland predicted environmental characteristic data and the farmland soil and crop characteristic data, and a farmland irrigation plan is generated.

[0023] Furthermore, such as Figure 2 As shown, step P40 in this embodiment further includes:

[0024] P41: Based on the automated irrigation control platform, load multiple farmland irrigation record databases;

[0025] P42: Based on the multiple farmland irrigation record databases, train the farmland irrigation feature prediction channel;

[0026] P43: Input the farmland irrigation pipeline characteristic data, the farmland predicted environmental characteristic data, and the farmland soil crop characteristic data into the farmland irrigation characteristic prediction channel, and output multiple predicted farmland irrigation decisions;

[0027] P44: The farmland irrigation scheme is obtained by fusing data from the multiple predicted farmland irrigation decisions.

[0028] Specifically, based on farmland irrigation pipeline characteristic data, farmland predicted environmental characteristic data, and farmland soil and crop characteristic data, farmland irrigation characteristics are predicted. First, based on the automated irrigation control platform, multiple farmland irrigation record databases are loaded. These databases are derived from different farmland samples and include historical irrigation pipeline characteristics, historical predicted environmental characteristics, historical soil and crop characteristics, and corresponding historical irrigation schemes.

[0029] Furthermore, the multiple farmland irrigation record databases are used as training data, and supervised training is performed in conjunction with machine learning algorithms or deep learning models to learn the mapping relationship between irrigation features and irrigation decisions until the output data converges, thereby obtaining multiple farmland irrigation feature prediction models, which together constitute the farmland irrigation feature prediction channel.

[0030] Furthermore, the farmland irrigation pipeline characteristic data, the farmland predicted environmental characteristic data, and the farmland soil crop characteristic data are input into the farmland irrigation characteristic prediction channel, and multiple farmland irrigation characteristic prediction models are used to make predictions to obtain multiple predicted farmland irrigation decisions. These decisions may include different irrigation times, irrigation amounts, and irrigation methods.

[0031] Furthermore, multiple predicted farmland irrigation decisions are fused together. For example, a weighted average method can be used for fusion. The multiple predicted farmland irrigation decisions can be weighted based on the prediction accuracy of the multiple farmland irrigation feature prediction models or based on the crop growth conditions of each farmland sample. The weighted average is then fused according to the weight allocation to obtain a more accurate and reliable irrigation decision as the final farmland irrigation scheme.

[0032] Furthermore, step P42 in this embodiment of the application also includes:

[0033] P42-1: Extract the first farmland irrigation record database from the plurality of farmland irrigation record databases, wherein the first farmland irrigation record database includes any one of the plurality of farmland irrigation record databases;

[0034] P42-2: Clean the data based on the first farmland irrigation record database to obtain the first farmland irrigation record database;

[0035] P42-3: Activate the farmland irrigation feature learning channel, wherein the farmland irrigation feature learning channel includes multiple farmland irrigation feature learners;

[0036] P42-4: Randomly extract the first farmland irrigation feature learner according to the farmland irrigation feature learning channel;

[0037] P42-5: Supervised learning is performed on the first farmland irrigation feature learner based on the first farmland irrigation record database to obtain the irrigation feature prediction bias;

[0038] P42-6: When the number of consecutive predetermined number of irrigation feature prediction deviations is less than the irrigation feature prediction deviation threshold, a first farmland irrigation feature prediction model is generated.

[0039] P42-7: Add the first farmland irrigation feature prediction model to the farmland irrigation feature prediction channel.

[0040] In one possible embodiment of this application, any record library is randomly selected from the plurality of farmland irrigation record libraries as the first farmland irrigation record library, and the first farmland irrigation record library is cleaned to remove erroneous, duplicate, missing or incomplete data to ensure data quality and consistency, thereby obtaining the first farmland irrigation record database.

[0041] Furthermore, the farmland irrigation feature learning channel is activated. This channel consists of multiple farmland irrigation feature learners, which can be different machine learning or deep learning models, such as neural networks or vector machines. A first farmland irrigation feature learner is randomly extracted from the channel as the initial learning model. Supervised learning (supervised training) is then performed on the first farmland irrigation record database. The irrigation feature prediction deviation is calculated based on the difference between the output predicted irrigation decision and the actual irrigation decision. The irrigation feature prediction deviation is continuously evaluated. When the prediction deviation is less than a predetermined threshold for a certain number of consecutive times, the model's accuracy is considered to have met the preset requirements, resulting in the first farmland irrigation feature prediction model. This first farmland irrigation feature prediction model is then added to the farmland irrigation feature prediction channel to work in conjunction with other farmland irrigation feature prediction models.

[0042] P50: Based on the farmland irrigation scheme, perform multi-outlet electric ball valve control search and learning to establish a farmland irrigation ball valve control space that meets the predetermined control capacity constraints;

[0043] Furthermore, step P50 in this embodiment of the application also includes:

[0044] P51: Based on the control constraint information of the multi-outlet electric ball valve corresponding to the farmland collected by the automated irrigation control platform, the wide area of ​​control of the multi-outlet electric ball valve is obtained.

[0045] P52: Based on the farmland irrigation scheme, perform an association search for electric ball valve control and establish an association domain for multi-outlet electric ball valve control.

[0046] P53: Based on the control association domain of the multi-outlet electric ball valve, perform a concentrated control interval analysis of the electric ball valve to generate a concentrated control domain for the multi-outlet electric ball valve.

[0047] P54: Find the intersection of the wide domain of the multi-outlet electric ball valve control and the concentrated domain of the multi-outlet electric ball valve control to generate the analytical constraint domain of the multi-outlet electric ball valve control.

[0048] P55: Perform electric ball valve control scheduling based on the control analysis constraint domain of the multi-outlet electric ball valve to generate multiple farmland irrigation ball valve control decisions that satisfy the predetermined control capacity constraint;

[0049] P56: Add the multiple farmland irrigation ball valve control decisions to the farmland irrigation ball valve control space.

[0050] Specifically, based on the aforementioned farmland irrigation scheme, a control strategy for electric ball valves that satisfies predetermined control capacity constraints is searched and learned, and a corresponding farmland irrigation ball valve control space is constructed. Specifically, the control constraint information of the multi-outlet electric ball valves corresponding to the target farmland is collected through an automated irrigation control platform, such as the opening / closing time of the ball valves, the corresponding outlet opening range, and flow limits, to obtain the wide-area control of the multi-outlet electric ball valves, that is, the set of all possible control parameters and conditions of the electric ball valves.

[0051] Furthermore, based on the aforementioned farmland irrigation scheme, an association search is performed on the electric ball valve control. Considering the direct and indirect relationship between the irrigation scheme and the ball valve control, a multi-outlet electric ball valve control association domain is established, which is the set of all combinations of electric ball valve control parameters that can directly meet the requirements of the irrigation scheme. A concentrated interval analysis is then performed on the multi-outlet electric ball valve control association domain. For example, all data in the multi-outlet electric ball valve control association domain is extracted, including historical farmland irrigation ball valve control records, irrigation schemes, and ball valve control parameters. Statistical analysis is then performed on the ball valve control parameters in these historical control records, such as calculating the mean, standard deviation, maximum value, and minimum value of each parameter. Based on the results of the statistical analysis, effective or common intervals for the ball valve control parameters are identified, generating a concentrated domain for multi-outlet electric ball valve control, which is the set of ball valve control parameter combinations that are more commonly used or more effective in actual operation.

[0052] Furthermore, the intersection operation is performed between the wide-domain control and the condensed domain control of the multi-outlet electric ball valve to generate the analytical constraint domain for multi-outlet electric ball valve control. This domain represents a set of electric ball valve control parameter combinations that satisfy all possible control conditions and are effective and feasible in actual operation. Further, based on this analytical constraint domain, electric ball valve control scheduling is performed to generate multiple farmland irrigation ball valve control decisions that satisfy predetermined control capacity constraints. These multiple farmland irrigation ball valve control decisions are then added to the farmland irrigation ball valve control space as foundational data for subsequent ball valve control decision optimization.

[0053] Furthermore, step P52 in this embodiment of the application also includes:

[0054] P52-1: Retrieve multiple farmland irrigation ball valve control record groups according to the automated irrigation control platform;

[0055] P52-2: Extract the first farmland irrigation ball valve control record group from the multiple farmland irrigation ball valve control record groups, wherein the first farmland irrigation ball valve control record group includes the first sample farmland irrigation scheme and the first sample farmland irrigation ball valve control scheme;

[0056] P52-3: Based on the farmland irrigation scheme and the first sample farmland irrigation scheme, similarity identification is performed to obtain the irrigation similarity coefficient of the first sample;

[0057] P52-4: Determine whether the irrigation similarity coefficient of the first sample is greater than or equal to the irrigation similarity threshold;

[0058] P52-5: If the irrigation similarity coefficient of the first sample is greater than or equal to the irrigation similarity threshold, add the first sample farmland irrigation ball valve control scheme to the multi-outlet electric ball valve control association domain.

[0059] P52-6: Based on the irrigation similarity threshold, the multiple farmland irrigation ball valve control record groups are correlated and filtered to obtain the multi-outlet electric ball valve control correlation domain.

[0060] Optionally, multiple sets of farmland irrigation ball valve control records are retrieved and obtained from the automated irrigation control platform. These record sets may contain irrigation data and ball valve control information from different farmlands, at different times, and under different conditions in the past. A first set of farmland irrigation ball valve control records is randomly extracted from the multiple sets of farmland irrigation ball valve control records, and the first set of farmland irrigation ball valve control records includes a first sample farmland irrigation scheme and a first sample farmland irrigation ball valve control scheme.

[0061] Furthermore, the farmland irrigation plan and the first sample farmland irrigation plan are subjected to similarity identification, that is, the predicted irrigation plan and the sample irrigation plan are compared. A similarity algorithm, such as cosine similarity or Euclidean distance, is used to obtain the first sample irrigation similarity coefficient. It is then determined whether the first sample irrigation similarity coefficient is greater than or equal to an irrigation similarity threshold, which is set by professionals according to irrigation requirements.

[0062] Furthermore, if the irrigation similarity coefficient of the first sample is greater than or equal to the irrigation similarity threshold, then the irrigation ball valve control scheme of the first sample farmland is considered sufficiently similar to the irrigation scheme required by the farmland currently requiring irrigation. Therefore, the irrigation ball valve control scheme of the first sample farmland is added to the multi-outlet electric ball valve control association domain. Similarly, the above similarity calculation and threshold judgment process is repeated, and the irrigation similarity threshold is used to associate and filter the multiple farmland irrigation ball valve control record groups to obtain the multi-outlet electric ball valve control association domain.

[0063] P60: Based on the ball valve control optimization function, perform multi-objective joint optimization on the control space of the farmland irrigation ball valve to generate a farmland irrigation ball valve control scheme that satisfies the ball valve control optimization characteristic constraints;

[0064] The ball valve control optimization function is as follows:

[0065]

[0066] Among them, CBQY represents the irrigation ball valve control optimization index, QXA represents the control irrigation efficiency coefficient, OXA represents the preset expected irrigation efficiency coefficient, QXB represents the control irrigation balance coefficient, OXB represents the preset expected irrigation balance coefficient, QXC represents the control ball valve failure probability coefficient, and OXC represents the preset expected ball valve failure probability coefficient.

[0067] Specifically, a ball valve control optimization function is used to perform multi-objective joint optimization on the control space of the farmland irrigation ball valve, wherein the ball valve control optimization function is: Among them, CBQY represents the irrigation ball valve control optimization index, QXA represents the control irrigation efficiency coefficient, OXA represents the preset expected irrigation efficiency coefficient, QXB represents the control irrigation balance coefficient, OXB represents the preset expected irrigation balance coefficient, QXC represents the control ball valve failure probability coefficient, and OXC represents the preset expected ball valve failure probability coefficient.

[0068] The optimization function of the ball valve control can be used to calculate the optimization index of multiple farmland irrigation ball valve control decisions within the control space of the farmland irrigation ball valve, and screen out farmland irrigation ball valve control schemes that meet the ball valve control optimization characteristic constraints, thereby improving the quality and adaptability of farmland irrigation ball valve control schemes and achieving more efficient and reliable farmland irrigation management.

[0069] Furthermore, such as Figure 3 As shown, step P60 in this embodiment further includes:

[0070] P61: Extract the first farmland irrigation ball valve control decision based on the farmland irrigation ball valve control space;

[0071] P62: Calculate the first irrigation ball valve control optimization index corresponding to the first farmland irrigation ball valve control decision based on the ball valve control optimization function.

[0072] P63: Extract the second farmland irrigation ball valve control decision based on the farmland irrigation ball valve control space;

[0073] P64: Calculate the second irrigation ball valve control optimization index corresponding to the second farmland irrigation ball valve control decision based on the ball valve control optimization function.

[0074] P65: Compare the first irrigation ball valve control optimization index and the second irrigation ball valve control optimization index to determine the current optimal irrigation ball valve control optimization index, and combine the first farmland irrigation ball valve control decision and the second farmland irrigation ball valve control decision to match the current optimal farmland irrigation ball valve control decision;

[0075] P66: Based on the control space of the farmland irrigation ball valve and the optimization index of the current optimal irrigation ball valve control, continue to iterate and optimize the current optimal farmland irrigation ball valve control decision until the number of iterations satisfies the ball valve control optimization characteristic constraint, and then generate the farmland irrigation ball valve control scheme.

[0076] Optionally, a first farmland irrigation ball valve control decision is extracted from the farmland irrigation ball valve control space, and a first irrigation ball valve control optimization index corresponding to the first farmland irrigation ball valve control decision is calculated according to the ball valve control optimization function. Similarly, a second farmland irrigation ball valve control decision is extracted from the farmland irrigation ball valve control space, and a second irrigation ball valve control optimization index corresponding to it is calculated using the ball valve control optimization function. The first irrigation ball valve control optimization index and the second irrigation ball valve control optimization index are compared, and the one with the higher optimization index is selected as the current optimal irrigation ball valve control optimization index. The current optimal farmland irrigation ball valve control decision corresponding to the current optimal irrigation ball valve control optimization index is then selected.

[0077] Following the same logic, referring to the above method, based on the control space of the farmland irrigation ball valve and the current optimal irrigation ball valve control optimization index, iterative optimization continues until the number of iterations meets the ball valve control optimization characteristic constraint. If the maximum number of iterations is reached, the control decision obtained from the last optimization is taken as the farmland irrigation ball valve control scheme.

[0078] Furthermore, step P62 in this embodiment of the application also includes:

[0079] P62-1: Based on the irrigation simulation module in the automated irrigation control platform, a model is generated by modeling the farmland irrigation pipeline characteristic data, the farmland predicted environmental characteristic data, and the farmland soil and crop characteristic data.

[0080] P62-2: Based on the irrigation simulation module, the farmland irrigation characteristic simulation model is fitted with irrigation according to the control decision of the first farmland irrigation ball valve to obtain the first control fitting working condition dataset.

[0081] P62-3: Input the first control fitting working condition dataset into the irrigation evaluation module in the automated irrigation control platform to obtain the first ball valve control evaluation result, wherein the first ball valve control evaluation result includes the first control irrigation efficiency coefficient, the first control irrigation balance coefficient and the first control ball valve failure probability coefficient.

[0082] P62-4: Input the first ball valve control evaluation result into the ball valve control optimization function to generate the first irrigation ball valve control optimization index.

[0083] It should be understood that, based on the irrigation simulation module within the automated irrigation control platform, modeling is performed using farmland irrigation pipeline characteristic data, farmland predicted environmental characteristic data, and farmland soil and crop characteristic data to generate a farmland irrigation characteristic simulation model. This farmland irrigation characteristic simulation model can simulate the actual irrigation process of farmland and reflect the irrigation effects under different control decisions.

[0084] Furthermore, using the irrigation simulation module, the farmland irrigation characteristic simulation model is fitted with irrigation based on the control decision of the first farmland irrigation ball valve to simulate the irrigation situation in actual farmland according to the control decision, and a corresponding first control fitting condition dataset is generated. The first control fitting condition dataset contains various parameters and indicators in the simulated irrigation process.

[0085] Furthermore, the first control fitting condition dataset is input into the irrigation evaluation module within the automated irrigation control platform to evaluate the irrigation effect and obtain the first ball valve control evaluation result. The irrigation evaluation module can be trained based on historical irrigation evaluation data and machine learning principles. The first ball valve control evaluation result includes a first control irrigation efficiency coefficient, a first control irrigation balance coefficient, and a first control ball valve failure probability coefficient, reflecting performance in three aspects: irrigation efficiency, irrigation balance, and ball valve failure probability, respectively.

[0086] Furthermore, the evaluation result of the first ball valve control is input into the ball valve control optimization function. Through function calculation, the first irrigation ball valve control optimization index is obtained, which is the comprehensive evaluation index of the first farmland irrigation ball valve control decision. It can reflect the comprehensive performance of the control decision in terms of irrigation efficiency, irrigation balance and ball valve failure probability.

[0087] P70: The automated irrigation control platform manages irrigation of the farmland based on the farmland irrigation ball valve control scheme.

[0088] Specifically, the automated irrigation control platform, referring to the farmland irrigation ball valve control scheme, manages the irrigation of the target farmland to achieve uniform pressure and flow irrigation control, thereby ensuring optimal irrigation results.

[0089] In summary, the embodiments of this application have at least the following technical effects:

[0090] This application collects characteristic data of farmland irrigation pipelines, combines farmland predicted environmental characteristic data and farmland soil and crop characteristic data to predict farmland irrigation characteristics, generates farmland irrigation schemes, establishes farmland irrigation ball valve control space, and combines ball valve control optimization function to perform multi-objective joint optimization to generate farmland irrigation ball valve control schemes for irrigation management.

[0091] The technology achieves the effect of intelligently adjusting the outlet water pressure through a multi-outlet electric ball valve, thereby realizing constant pressure and uniform flow irrigation and improving irrigation efficiency.

[0092] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, the above description focuses on specific embodiments of this specification. Additionally, the processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are possible or may be advantageous.

[0093] The above description is only a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

[0094] This specification and accompanying drawings are merely illustrative examples of this application and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Clearly, those skilled in the art can make various alterations and modifications to this application without departing from its scope. Therefore, if such modifications and modifications fall within the scope of this application and its equivalents, this application intends to include such modifications and variations.

Claims

1. A method for equalizing irrigation based on intelligent control of multi-outlet electric ball valves, characterized in that, The method is applied to an automated irrigation control platform, and the method includes: Connect to the automated irrigation control platform, read the soil monitoring parameters and crop monitoring parameters of the farmland, and generate farmland soil and crop characteristic data; Activate the environmental prediction module in the automated irrigation control platform, and combine it with a predetermined future time zone window to perform environmental prediction on the farmland and obtain farmland predicted environmental characteristic data. The automated irrigation control platform collects the irrigation pipeline parameters of the farmland to obtain the characteristic data of the farmland irrigation pipeline. The irrigation pipeline of the farmland is equipped with multiple electric ball valves, and each electric ball valve has multiple outlets. Based on the farmland irrigation pipeline characteristic data, farmland irrigation characteristics are predicted according to the farmland predicted environmental characteristic data and the farmland soil and crop characteristic data, and a farmland irrigation plan is generated. Based on the farmland irrigation scheme, a multi-outlet electric ball valve control search and learning process is performed to establish a farmland irrigation ball valve control space that meets the predetermined control capacity constraints. Based on the ball valve control optimization function, a multi-objective joint optimization is performed on the control space of the farmland irrigation ball valve to generate a farmland irrigation ball valve control scheme that satisfies the ball valve control optimization characteristic constraints. The automated irrigation control platform manages irrigation of the farmland based on the farmland irrigation ball valve control scheme.

2. The method as described in claim 1, characterized in that, Based on the farmland irrigation pipeline characteristic data, farmland irrigation characteristics are predicted according to the farmland predicted environmental characteristic data and the farmland soil crop characteristic data, and a farmland irrigation plan is generated, including: According to the automated irrigation control platform, multiple farmland irrigation record databases are loaded; Based on the multiple farmland irrigation record databases, a farmland irrigation feature prediction channel is trained; The farmland irrigation pipeline characteristic data, the farmland predicted environmental characteristic data, and the farmland soil crop characteristic data are input into the farmland irrigation characteristic prediction channel, and multiple predicted farmland irrigation decisions are output. The farmland irrigation scheme is obtained by fusing data from the multiple predicted farmland irrigation decisions.

3. The method as described in claim 2, characterized in that, Based on the aforementioned multiple farmland irrigation record databases, a farmland irrigation feature prediction channel is trained, including: Extract the first farmland irrigation record database from the plurality of farmland irrigation record databases, wherein the first farmland irrigation record database includes any one of the plurality of farmland irrigation record databases; Data cleaning is performed on the first farmland irrigation record database to obtain the first farmland irrigation record database. Activate the farmland irrigation feature learning channel, wherein the farmland irrigation feature learning channel includes multiple farmland irrigation feature learners; The first farmland irrigation feature learner is randomly extracted based on the farmland irrigation feature learning channel; Supervised learning is performed on the first farmland irrigation feature learner based on the first farmland irrigation record database to obtain the irrigation feature prediction bias. When the number of consecutive predetermined number of irrigation feature prediction deviations is less than the irrigation feature prediction deviation threshold, a first farmland irrigation feature prediction model is generated. Add the first farmland irrigation feature prediction model to the farmland irrigation feature prediction channel.

4. The method as described in claim 1, characterized in that, Based on the aforementioned farmland irrigation scheme, a multi-outlet electric ball valve control search and learning process is performed to establish a farmland irrigation ball valve control space that satisfies predetermined control capacity constraints, including: Based on the control constraint information of the multi-outlet electric ball valve corresponding to the farmland collected by the automated irrigation control platform, the wide area of ​​control of the multi-outlet electric ball valve is obtained. Based on the farmland irrigation scheme, an association search for electric ball valve control is performed to establish an association domain for multi-outlet electric ball valve control. Based on the control association domain of the multi-outlet electric ball valve, the control concentration interval of the electric ball valve is analyzed to generate the control concentration domain of the multi-outlet electric ball valve. Find the intersection of the wide domain of the multi-outlet electric ball valve control and the concentrated domain of the multi-outlet electric ball valve control to generate the analytical constraint domain of the multi-outlet electric ball valve control. Based on the control analysis constraint domain of the multi-outlet electric ball valve, the control scheduling of the multi-outlet electric ball valve is performed to generate multiple farmland irrigation ball valve control decisions that satisfy the predetermined control capacity constraint. The multiple farmland irrigation ball valve control decisions are added to the farmland irrigation ball valve control space.

5. The method as described in claim 4, characterized in that, Based on the aforementioned farmland irrigation scheme, an association search for electric ball valve control is performed to establish a multi-outlet electric ball valve control association domain, including: The automated irrigation control platform retrieves multiple farmland irrigation ball valve control record groups. The first farmland irrigation ball valve control record group is extracted from the plurality of farmland irrigation ball valve control record groups, wherein the first farmland irrigation ball valve control record group includes a first sample farmland irrigation scheme and a first sample farmland irrigation ball valve control scheme. Based on the farmland irrigation scheme and the first sample farmland irrigation scheme, similarity identification is performed to obtain the irrigation similarity coefficient of the first sample. Determine whether the irrigation similarity coefficient of the first sample is greater than or equal to the irrigation similarity threshold; If the irrigation similarity coefficient of the first sample is greater than or equal to the irrigation similarity threshold, the first sample farmland irrigation ball valve control scheme will be added to the multi-outlet electric ball valve control association domain. The multiple farmland irrigation ball valve control record groups are correlated and filtered according to the irrigation similarity threshold to obtain the multi-outlet electric ball valve control correlation domain.

6. The method as described in claim 1, characterized in that, Based on the ball valve control optimization function, a multi-objective joint optimization is performed on the control space of the farmland irrigation ball valve to generate a farmland irrigation ball valve control scheme that satisfies the ball valve control optimization characteristic constraints, including: The first farmland irrigation ball valve control decision is extracted based on the farmland irrigation ball valve control space. Based on the ball valve control optimization function, calculate the first irrigation ball valve control optimization index corresponding to the first farmland irrigation ball valve control decision. The second farmland irrigation ball valve control decision is extracted based on the farmland irrigation ball valve control space. Based on the ball valve control optimization function, calculate the second irrigation ball valve control optimization index corresponding to the second farmland irrigation ball valve control decision. By comparing the first irrigation ball valve control optimization index and the second irrigation ball valve control optimization index, the current optimal irrigation ball valve control optimization index is determined, and the current optimal farmland irrigation ball valve control decision is matched by combining the first farmland irrigation ball valve control decision and the second farmland irrigation ball valve control decision. Based on the control space of the farmland irrigation ball valve and the optimization index of the current optimal irrigation ball valve control, the current optimal farmland irrigation ball valve control decision is iteratively optimized until the number of iterations meets the ball valve control optimization characteristic constraint, and then the farmland irrigation ball valve control scheme is generated.

7. The method as described in claim 6, characterized in that, Based on the ball valve control optimization function, calculate the first irrigation ball valve control optimization index corresponding to the first farmland irrigation ball valve control decision, including: Based on the irrigation simulation module within the automated irrigation control platform, a model is generated by modeling the farmland irrigation pipeline characteristic data, the farmland predicted environmental characteristic data, and the farmland soil and crop characteristic data. Based on the irrigation simulation module, the farmland irrigation characteristic simulation model is fitted with irrigation according to the control decision of the first farmland irrigation ball valve to obtain the first control fitting working condition dataset. The first control fitting working condition dataset is input into the irrigation evaluation module in the automated irrigation control platform to obtain the first ball valve control evaluation result, wherein the first ball valve control evaluation result includes the first control irrigation efficiency coefficient, the first control irrigation balance coefficient, and the first control ball valve failure probability coefficient. The first ball valve control evaluation result is input into the ball valve control optimization function to generate the first irrigation ball valve control optimization index.

8. The method as described in claim 1, characterized in that, The ball valve control optimization function is: Among them, CBQY represents the irrigation ball valve control optimization index, QXA represents the control irrigation efficiency coefficient, OXA represents the preset expected irrigation efficiency coefficient, QXB represents the control irrigation balance coefficient, OXB represents the preset expected irrigation balance coefficient, QXC represents the control ball valve failure probability coefficient, and OXC represents the preset expected ball valve failure probability coefficient.

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