A valve closing control method and system based on a measurement and control device

Abstract

The application discloses a valve closing control method and system based on a measurement and control device, and belongs to the technical field of valve control. The method comprises the following steps: after receiving irrigation water demand data of a user, the system determines a target irrigation branch pipeline and opens a valve. By acquiring pipeline design parameters and real-time water flow and pipeline state data, the system performs pipeline health analysis and fluid characteristic analysis to obtain water flow state and pipeline health condition data. The data are input into a valve closing prediction model together with irrigation demand to calculate valve closing time and speed. When the closing time is reached, the system adjusts the valve closing according to the closing speed to ensure accurate regulation of water flow, dynamically adjusts the speed in real time until the predetermined irrigation amount is reached, and meets the user demand. The scheme ensures optimized utilization of water resources. By monitoring the pipeline state and water flow characteristics in real time and dynamically adjusting the valve closing speed, excessive irrigation or waste of water resources is effectively avoided, and the accuracy of the irrigation system is improved.

Description

Technical Field

[0001] This application belongs to the field of valve control technology, specifically relating to a valve closing control method and system based on a measurement and control device. Background Technology

[0002] With the acceleration of agricultural modernization, precision irrigation technology has become a core means to ensure the efficient use of water resources. Traditional irrigation systems rely heavily on manual experience or fixed-sequence control, making it difficult to dynamically adjust based on real-time crop water requirements, pipeline dynamics, and system health. This leads to water waste, insufficient irrigation uniformity, and increased equipment wear and tear. Therefore, there is an urgent need for a high-precision irrigation control technology that can integrate multi-source data, analyze pipeline status in real time, and intelligently regulate valves to achieve the dual goals of on-demand irrigation and system reliability.

[0003] Now, after receiving irrigation requests, the system selects the target branch pipe and opens the valve; obtains water flow data from the pipe through preset parameters or simple sensor feedback; estimates the irrigation water volume based on empirical formulas or static models; and closes the valve after a set time has elapsed.

[0004] The fixed-closing strategy of existing technology does not take into account the impact of pipeline health status on water flow resistance, which can easily lead to the actual irrigation volume deviating from the demand, resulting in water waste or crop water shortage; and it lacks dynamic response to fluid characteristics, which may cause water hammer effect during valve closure, accelerating pipeline aging or even damage. Summary of the Invention

[0005] To overcome the above-mentioned shortcomings, this invention is proposed to provide a solution or at least partially solve the technical problems of the fixed shut-off strategy in the prior art, which does not take into account the influence of pipeline health status on water flow resistance, which easily leads to the actual irrigation volume deviating from the demand, resulting in water waste or crop water shortage; and lacks dynamic response to fluid characteristics, which may cause water hammer effect during valve shut-off, accelerating pipeline aging or even damage.

[0006] In a first aspect, the present invention provides a valve closing control method based on a measurement and control device, the method comprising: If irrigation water demand data from a user is received, the target irrigation branch pipeline is determined based on the irrigation water demand data, and the valve control mechanism of the target irrigation branch pipeline is controlled to open the valve; The pipeline design parameters of the target irrigation branch pipeline are obtained, as well as the first real-time water flow data and the first real-time pipeline status data of the target irrigation branch pipeline within a preset time window. Based on the pipeline design parameters, the first real-time water flow data, and the first real-time pipeline status data, pipeline health status analysis and fluid characteristic analysis are performed to obtain the water flow status data and pipeline health status data of the target irrigation branch pipeline. The water flow status data, pipeline health status data, and irrigation water demand data are input into a preset valve closure prediction model to obtain the valve closure time and valve closure speed of the target irrigation branch pipeline. If the valve closing time is reached, the valve control mechanism of the target irrigation branch pipeline closes the valve based on the valve closing speed, and dynamically adjusts the valve closing speed during the valve closing process until the total irrigation water volume meets the user's irrigation water demand.

[0007] In a second aspect, the present invention provides a valve closing control system based on a measurement and control device, the system comprising: The pipeline confirmation module is used to, upon receiving irrigation water demand data from a user, determine the target irrigation branch pipeline based on the irrigation water demand data, and control the valve control mechanism of the target irrigation branch pipeline to open the valve; The analysis module is used to acquire the pipeline design parameters of the target irrigation branch pipeline, and to acquire the first real-time water flow data and the first real-time pipeline status data of the target irrigation branch pipeline within a preset time window. Based on the pipeline design parameters, the first real-time water flow data, and the first real-time pipeline status data, the module performs pipeline health status analysis and fluid characteristic analysis to obtain the water flow status data and pipeline health status data of the target irrigation branch pipeline. The prediction module is used to input the water flow status data, pipeline health status data, and irrigation water demand data into a preset valve closure prediction model to obtain the valve closure time and valve closure speed of the target irrigation branch pipeline; The valve control module is used to control the valve control mechanism of the target irrigation branch pipeline to close the valve when the valve closing time is reached, based on the valve closing speed, and dynamically adjust the valve closing speed during the valve closing process until the total irrigation water volume meets the user's irrigation water demand.

[0008] In a third aspect, an electronic device is provided, comprising a processor, a memory, and a program or instructions stored in the memory and executable on the processor, the program or instructions being loaded and run by the processor to perform the steps of the valve closing control method based on a measurement and control device described above.

[0009] In a fourth aspect, a computer-readable storage medium is provided, wherein a plurality of program codes are stored therein, the program codes being adapted to be loaded and run by a processor to perform the steps of the valve closing control method based on a measurement and control device described above.

[0010] The above-described technical solutions of the present invention have at least one or more of the following beneficial effects: In implementing the technical solution of this invention, by precisely controlling the irrigation water flow rate and shut-off time, water resource utilization is optimized while meeting the user's irrigation water needs. By monitoring the pipeline status and water flow characteristics in real time and dynamically adjusting the valve shut-off speed, over-irrigation or water waste is effectively avoided, thus improving the precision of the irrigation system. Attached Figure Description

[0011] The disclosure of this invention will become more readily understood with reference to the accompanying drawings. It will be readily understood by those skilled in the art that these drawings are for illustrative purposes only and are not intended to limit the scope of protection of this invention. Furthermore, similar numbers in the drawings are used to denote similar components, wherein: Figure 1 This is a schematic flowchart of the first main steps of a valve closing control method based on a measurement and control device according to an embodiment of the present invention; Figure 2 This is a schematic flowchart of the second main steps of a valve closing control method based on a measurement and control device according to an embodiment of the present invention; Figure 3 This is a schematic diagram of the main structure of a valve closing control system based on a measurement and control device according to an embodiment of the present invention; Figure 4 This is a schematic diagram of the structure of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0012] Some embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0013] In the description of this invention, "module" and "processor" can include hardware, software, or a combination of both. A module may include hardware circuitry, various suitable sensors, communication ports, memory, and may also include software components, such as program code, or a combination of software and hardware. A processor may be a central processing unit, microprocessor, image processor, digital signal processor, or any other suitable processor. The processor has data and / or signal processing capabilities. The processor may be implemented in software, in hardware, or a combination of both. Non-transitory computer-readable storage media include any suitable medium capable of storing program code, such as magnetic disks, hard disks, optical disks, flash memory, read-only memory, random access memory, etc. The term "A and / or B" means all possible combinations of A and B, such as only A, only B, or A and B. The terms "at least one A or B" or "at least one of A and B" have a similar meaning to "A and / or B" and may include only A, only B, or A and B. The singular terms "a" or "this" may also include plural forms.

[0014] See appendix Figure 1 , Figure 1 This is a schematic flowchart of the first main steps of a valve closing control method based on a measurement and control device according to an embodiment of the present invention. Figure 1 As shown, a valve closing control method based on a measurement and control device in an embodiment of the present invention mainly includes the following steps S1-S4.

[0015] The monitoring and control device is the core control equipment of the entire irrigation system, responsible for real-time monitoring of various parameters of the irrigation system and automatic adjustment according to user needs. It integrates multiple functional modules to achieve intelligent control and optimization of the irrigation process. The sensor module is responsible for real-time acquisition of key data such as flow rate, velocity, pressure, and temperature of the irrigation branch pipes, and transmits this data to the control module of the monitoring and control device to ensure comprehensive monitoring of the irrigation process. Based on the received irrigation water demand data and real-time monitoring data, the control module calculates the valve opening and closing timing, adjusts the valve closing speed, and performs functions such as pipeline health status analysis and fluid characteristic analysis. By analyzing this data, the control module generates water flow status data and pipeline health status data, and inputs this data into the valve closing prediction model to further calculate the valve closing time and speed. The actuator receives instructions from the control module and is responsible for the actual control of valve opening and closing, precisely adjusting the valve closing speed to ensure accurate allocation of irrigation water and control of pipeline valve status. Simultaneously, the communication module enables data transmission between the monitoring and control device and an external platform, transmitting sensor data, control commands, execution status, and health analysis results to the integrated platform in real time, ensuring that the platform can monitor and manage the irrigation system in real time and adjust control strategies according to needs. The monitoring and control device provides precise fluid regulation schemes for the irrigation process by accurately analyzing pipeline design parameters, real-time water flow data, and pipeline status data, ensuring that irrigation proceeds as needed and meets users' water requirements. Furthermore, the device interacts with the central platform via an integrated communication protocol, supporting remote control and optimization of the irrigation system's operation.

[0016] Step S1: If the user's irrigation water demand data is received, the target irrigation branch pipeline is determined based on the irrigation water demand data, and the valve control mechanism of the target irrigation branch pipeline is controlled to open the valve.

[0017] Irrigation water demand data refers to the specific amount of irrigation water or the required irrigation time for users.

[0018] The target irrigation branch pipeline is the specific branch pipeline that needs to perform the water supply task after reading the irrigation water demand data.

[0019] A valve control mechanism is a specialized device or integrated system used to regulate the on / off state of valves in an irrigation system. This mechanism can achieve valve opening and closing operations through different methods such as electric drive, pneumatic drive, or mechanical control, thereby flexibly adjusting the water flow in the pipeline to adapt to different irrigation needs.

[0020] Valves are devices in irrigation systems that control the flow of water.

[0021] Users can draw water at designated locations within the irrigation system by swiping their cards. For example, if the system includes 20 branch pipes spaced 5 meters apart, users can swipe their cards at the desired branch pipe and specify their water needs by setting the required water volume or irrigation time. The branch pipe number and the user's water requirements are then packaged together and transmitted to the system. Upon receiving the irrigation water requirement data, the system analyzes it to identify the target irrigation branch pipe. The system then controls the valve mechanism of the target branch pipe to open the valve, allowing water to flow through the target branch pipe to the user's irrigation area.

[0022] Step S2: Obtain the pipeline design parameters of the target irrigation branch pipeline, and obtain the first real-time water flow data and the first real-time pipeline status data of the target irrigation branch pipeline within a preset time window. Based on the pipeline design parameters, the first real-time water flow data and the first real-time pipeline status data, perform pipeline health status analysis and fluid characteristic analysis to obtain the water flow status data and pipeline health status data of the target irrigation branch pipeline.

[0023] Pipeline design parameters are the design specifications and core parameters of each pipe in an irrigation system, including pipe inner diameter, material, length, maximum flow capacity, maximum pressure capacity, and pipe layout.

[0024] A preset time window is a continuous time segment defined for data analysis, used to collect real-time irrigation-related data such as water flow, pressure, and temperature. Preset time windows allow for precise monitoring of the pipeline's operational status within a specific time period.

[0025] The first real-time water flow data is the initial real-time water flow information collected from the pipeline during the irrigation process, including instantaneous flow velocity and instantaneous flow rate, reflecting the immediate state of water flow in the pipeline.

[0026] The first real-time pipeline status data is the initial real-time operating status data collected from inside the pipeline, including information such as pressure, temperature, and vibration inside the pipeline, which can be used to assess the working status of the pipeline in real time.

[0027] Flow state data is conclusive data generated through multi-dimensional fusion analysis, describing the current fluid flow characteristics within the pipeline and their coupling relationship with the pipeline's health status. This includes flow mode labels: clearly defining the current flow state, such as laminar, transitional, or turbulent flow; hydraulic performance indicators: real-time flow resistance coefficient, estimated head loss along the pipeline, and water conveyance efficiency score; and comprehensive risk assessment: a flow safety level combined with pipeline health status, such as: "normal flow," "high resistance and low efficiency," "risk of turbulence-induced vibration," and "requires flow restriction operation."

[0028] Pipeline health status data is a grading or scoring system that quantitatively assesses the structural integrity and functional reliability of target irrigation branch pipelines, reflecting the overall health status of the pipeline. It includes a health quantification score of 0-100, fault characteristic labels such as leakage and blockage, and degradation trend levels such as normal, watch out, warning, and critical, providing a clear picture of the pipeline's safe operating status.

[0029] The design parameters of the target irrigation branch pipeline are retrieved from the irrigation system database. These parameters are determined during the pipeline construction phase and simultaneously archived in the system. When analysis of a specific branch pipeline is required, the system automatically identifies the target branch pipeline number and extracts its detailed design parameters by querying the corresponding database or configuration file. After identifying the target irrigation branch pipeline, the system needs to obtain the first real-time water flow data for that pipeline within a preset time window. This data directly reflects the immediate state of water flow within the pipeline, with instantaneous velocity and instantaneous flow rate as its core indicators. Real-time velocity data is collected by a velocity sensor installed inside the pipeline, while real-time flow rate data is calculated by a flow sensor to measure the water flow rate per unit time within the pipeline. The velocity sensor continuously monitors the instantaneous velocity of the water flow within the pipeline; the flow sensor accurately captures changes in water flow rate, ensuring that the system can accurately determine the amount of water flowing through the pipeline within a specific time period.

[0030] The system also collects real-time pipeline status data for the target pipeline, specifically including information on pressure, temperature, and vibration within the pipeline. Pressure data is monitored by pressure sensors, which record pressure fluctuations in real time as the water flow velocity changes. Temperature sensors monitor temperature changes in the water flow. Pipeline vibration data is acquired by vibration sensors. After acquiring these various real-time data, the system combines pipeline design parameters, real-time water flow data, and real-time pipeline status data to conduct a pipeline health status analysis. Once the analysis process is initiated, the collected pressure, temperature, and vibration data are first cleaned to remove noise and outliers. After cleaning, key information such as pressure fluctuation characteristics, temperature change trends, and vibration patterns is further extracted. These characteristics allow for a direct assessment of whether the pipeline exhibits abnormalities such as localized blockages, leakage risks, or excessive vibration.

[0031] After feature extraction, the system standardizes each feature to unify their dimensions. A weighted algorithm is then used to assign weights based on the actual impact of each feature on pipeline health; for example, pressure fluctuations, which have a more significant impact on pipeline health, are given higher weights. Next, anomaly detection checks whether each feature value falls within the normal range. Based on the normalized feature data and their corresponding weights, the system calculates a pipeline health score from 0 to 100 using a weighted summation method. Simultaneously, the system compares the current pipeline health status with design standards to determine the pipeline's degradation trend level, specifically categorizing it into four levels: normal, concerning, warning, and critical.

[0032] The system combines the current health score and degradation trend level to accurately identify potential pipeline faults and generate specific fault feature tags such as leakage, blockage, and corrosion. Once the health score falls below a preset threshold or a specific fault feature tag is detected, the system automatically triggers an early warning mechanism, prompting relevant personnel to promptly inspect or repair the pipeline. The aforementioned health score, degradation trend level, and fault feature tags are integrated to form the final pipeline health status data. Based on the pipeline health status analysis, the system simultaneously initiates fluid characteristic analysis. This analysis calculates the Reynolds number of the water flow based on the pipeline's inner diameter data from the design parameters, as well as real-time collected flow velocity, pressure, and temperature data, thereby accurately determining whether the flow regime is laminar, transitional, or turbulent, and generating corresponding flow mode tags.

[0033] The system combines real-time water flow data with pipeline design parameters to calculate the flow resistance coefficient, a key indicator for measuring the degree of energy loss in the water flow. A high resistance coefficient often indicates potential scaling or blockage within the pipeline. After acquiring data on the flow resistance coefficient, Reynolds number, and pipeline health status, the system conducts a comprehensive, in-depth analysis of the fluid flow state. The system couples the water flow pattern, flow resistance coefficient, and pipeline health status. For example, if a high resistance coefficient is detected and the pipeline health score is low, it is classified as a "high resistance, low efficiency" state; if turbulence is detected accompanied by a risk of severe vibration, it is classified as a "turbulence-induced vibration risk" state. Finally, the system outputs complete water flow state data.

[0034] Step S3: Input the water flow status data, pipeline health status data, and irrigation water demand data into the preset valve closure prediction model to obtain the valve closure time and valve closure speed of the target irrigation branch pipeline.

[0035] The preset valve closing prediction model is a computational model built based on historical operating data and the current system state. Its core purpose is to predict the closing time and speed of valves in irrigation systems. The model comprehensively considers factors such as water flow conditions, pipeline health, and irrigation water demand, combining real-time system operating parameters with actual user irrigation needs to dynamically calculate the optimal timing and speed for valve closing. This model improves irrigation efficiency and system control accuracy, ensuring that when the actual water supply reaches the preset irrigation water demand, the valve can close at an appropriate speed, effectively avoiding over-irrigation or water waste.

[0036] The valve closing time is the specific moment of the valve's start-up and closing procedure calculated by the system.

[0037] Valve closing speed is the rate at which the valve is opened and closed.

[0038] The system integrates water flow status data, pipeline health data, and irrigation water demand data, inputting them into a pre-defined valve closure prediction model. This model, based on historical operational data and real-time pipeline status, comprehensively considers multiple factors such as water flow status, pipeline health, and fluid flow characteristics to accurately calculate the valve closure time and speed. After inputting water flow status data, the model first analyzes the real-time flow velocity and flow rate, calculating flow resistance and pressure loss to determine whether the current flow is laminar, transitional, or turbulent, thus clarifying the specific impact of the flow characteristics and flow pattern during closure on valve control. It also incorporates pipeline health data, analyzing pipeline pressure, temperature, and vibration indicators to assess the current pipeline operating status and its potential impact on valve control. If the pipeline health is poor, such as with leaks, blockages, or corrosion, the valve closure speed and time need to be adjusted accordingly to prevent drastic fluctuations in water flow or additional impact on the pipeline due to excessively rapid closure. Finally, the pre-defined valve closure prediction model calculates the optimal valve closure time based on the user-defined irrigation demand or duration. The determination of this point will fully consider the predicted irrigation water consumption, current water flow status, and pipeline health condition to ensure that the actual water supply when the valve is closed accurately matches the user's needs, preventing excess water from continuously flowing into the irrigation area. The valve closing speed will be dynamically adjusted according to real-time water flow conditions and pipeline health status to ensure a smooth and orderly change in water flow during valve closure, fundamentally avoiding the water hammer effect and preventing the problem of excessive irrigation water.

[0039] The training process for the pre-defined valve closure prediction model is as follows: The system first collects and organizes historical water flow status data, historical pipeline health data, and historical irrigation water demand data, integrating these data as input features for the model. Input features include operational indicators such as pipeline velocity, flow rate, pressure, temperature, and vibration, while also incorporating user-defined irrigation water demands. The system then pairs these input features with corresponding valve closing times and speeds from historical records to construct a complete training dataset. In the data preprocessing stage, the system normalizes input features of different dimensions, such as flow velocity and pressure, and divides them into training, validation, and test sets in an 8:1:1 ratio to ensure the model's generalization ability.

[0040] During the training phase, the system utilizes machine learning algorithms such as Long Short-Term Memory (LSTM) networks or Random Forest Regression to process input data and corresponding labels. Specifically, the system uses training set data to fit the model, enabling it to autonomously learn the intrinsic relationship between input features and valve closing time and speed. Validation set data is used to synchronously validate the model during training. Changes in the validation set loss function are monitored to adjust hyperparameters such as the number of hidden layer neurons and the learning rate, which also serve as a criterion for early stopping to prevent overfitting. To accurately guide parameter optimization, the system defines mean squared error as the loss function, quantifying the Euclidean distance between predicted values ​​and actual labels to measure model performance. Simultaneously, the system employs the Adam optimizer, dynamically adjusting the learning rate to accelerate the convergence process.

[0041] During training, the system continuously optimizes model parameters, constantly reducing the error between predicted values ​​and actual labels. To prevent overfitting, the system introduces Dropout layers or L2 regularization terms into the model architecture and combines early stopping with early stopping to halt training when the validation set loss no longer decreases. After training, the system uses test set data that was not involved in the training process to evaluate the performance of the final model, verifying its prediction accuracy in handling unknown water flow fluctuations. Through repeated iterative optimization, the model gradually masters the core principles and can accurately infer the optimal valve closing time and speed based on real-time water flow status, pipeline health, and irrigation needs. Once training is complete, the model can output accurate valve closing time and speed predictions based on newly accessed real-time data.

[0042] Step S4: If the valve closing time is reached, the valve control mechanism of the target irrigation branch pipeline is controlled to close the valve based on the valve closing speed, and the valve closing speed is dynamically adjusted during the valve closing process until the total irrigation water volume meets the user's irrigation water demand.

[0043] When the system detects that the valve closing time has elapsed, the valve control mechanism immediately initiates the closing procedure, strictly adhering to the pre-calculated valve closing speed. Throughout the valve closing process, the system continuously provides feedback on the valve's real-time position and flow data to determine if the closing action matches the expected pace. If the valve closing speed deviates from the preset standard, the system immediately adjusts the control commands, appropriately accelerating or slowing down the closing speed while ensuring all adjustments remain within a safe operating range. The system collects real-time water flow data from the flow sensor, comprehensively recording the dynamic changes in water flow within the pipeline. Through analysis of this data, the system continuously assesses the gap between the current irrigation water volume and the user's needs. When the system detects that the real-time water flow is about to reach the predetermined irrigation water demand, it promptly adjusts the valve closing strategy, gradually slowing down the closing speed to prevent excessive or insufficient irrigation water. If the user has set irrigation based on water volume, the system will precisely control the flow to ensure the final total water volume exactly reaches the set target; if the user has chosen irrigation based on time, the system will flexibly adjust the closing pace based on the remaining time to ensure the entire irrigation process is completed within the specified time. Once the total irrigation water volume is precisely reached, or the preset irrigation time has ended, the valve control system will immediately and completely close the valve. Throughout the valve closing process, the system continuously adjusts the valve closing speed based on real-time feedback from water flow status, the valve's current position, closing speed, actual irrigation volume, and remaining water usage time. This dynamic adjustment method ensures the accuracy of valve closing and fundamentally avoids water waste or insufficient irrigation.

[0044] Based on steps S1-S4 above, by precisely controlling the irrigation water flow and shut-off time, water resource utilization is optimized while meeting the user's irrigation water needs. By monitoring the pipeline status and water flow characteristics in real time and dynamically adjusting the valve shut-off speed, over-irrigation or water waste is effectively avoided, thus improving the precision of the irrigation system.

[0045] Based on the above technical solution, optionally, the valve closing speed can be dynamically adjusted during the valve closing process, including: During the valve closing process, the second real-time water flow data and the second real-time pipeline status data of the target irrigation branch pipeline are continuously acquired. The second real-time flow rate data and the second real-time flow velocity data are continuously extracted from the second real-time water flow data, and the real-time water flow change rate is dynamically calculated based on the second real-time flow rate data and the second real-time flow velocity data. Continuously acquire cumulative flow data of the target irrigation branch pipeline, and based on the cumulative flow data and irrigation water demand data, determine in real time the difference between the irrigated water volume and the irrigation water demand; Based on the water usage difference value and the real-time water flow change rate, a difference analysis is performed in real time to obtain the real-time water volume deviation value; Based on the real-time water volume deviation value and the preset water flow regulation delay data, dynamic deviation calculation is performed to obtain the real-time water flow regulation parameters; Based on the second real-time pipeline status data, the real-time water flow regulation parameters are dynamically verified for safety and weighted for health, to obtain the real-time pipeline status impact value; The real-time pipeline state influence value is dynamically mapped to a preset safety gain coefficient table to obtain the real-time water flow control factor, and the real-time water flow adjustment parameters are dynamically updated based on the real-time water flow control factor; The valve closing speed is dynamically adjusted based on real-time water flow regulation parameters.

[0046] In this solution, the second real-time water flow data is the real-time water flow data updated by the system each time the valve is closed, including real-time water flow velocity, flow rate, etc., which can help the system track the changes in water flow in the pipeline throughout the process.

[0047] The second real-time pipeline status data is the real-time monitoring data of the pipeline operation status during the valve closing process, specifically including information such as pipeline pressure, temperature, and vibration.

[0048] The second real-time flow data is the amount of water flowing through the pipeline per unit time collected by the flow sensor during the valve closing process, which intuitively reflects the real-time water flow rate during the valve closing phase.

[0049] The second real-time flow rate data is the instantaneous velocity of the water flow in the pipeline monitored in real time by the flow rate sensor during the valve closing process, which can help determine the dynamic changes in the water flow velocity in the pipeline when the valve is closed.

[0050] Real-time flow rate of change is the rate of change of water flow or velocity per unit time. It is calculated as the ratio of the increase or decrease in flow or velocity to the corresponding time. It is mainly used to monitor water flow fluctuations.

[0051] The cumulative flow data is the total amount of water flowing through the pipe during the entire irrigation period, starting from when the valve is closed. The data will continue to accumulate as the irrigation process progresses.

[0052] The water usage difference value is the difference between the real-time irrigated water volume and the preset irrigation water demand, which intuitively reflects the degree of deviation between the actual irrigation water volume and the target water volume.

[0053] The real-time water volume deviation value is derived from the analysis of the real-time water flow change rate and water usage difference value. It reflects the real-time difference between the irrigated water volume and the target water usage volume during the valve closing process and is used to assess whether the current irrigation process meets expectations.

[0054] The preset water flow regulation delay data is a time delay parameter set in combination with the system design scheme and expected performance, used to describe the response lag during the water flow regulation process.

[0055] The real-time water flow regulation parameter is a water flow regulation coefficient calculated by the system based on the real-time water volume deviation value, water usage difference value and preset regulation delay data. It determines the adjustment range of the valve closing speed, thereby realizing the real-time regulation of water flow.

[0056] The real-time pipeline status impact value is a value obtained by combining the second real-time pipeline status data and performing safety verification and health weighting on the water flow regulation parameters. It reflects the impact of the current pipeline status on water flow regulation and ensures the safe operation of the pipeline during the regulation process.

[0057] The preset safety gain coefficient table is a reference table used to match the influence value of pipeline status. The table defines the gain coefficients corresponding to different pipeline health states, and the water flow control strategy can be dynamically adjusted according to the actual health state of the pipeline.

[0058] The real-time water flow control factor is an adjustment factor obtained by mapping the real-time pipeline status influence value to a preset safety gain coefficient table. It reflects the impact of the current pipeline health status on water flow control and is used to precisely adjust the valve closing speed.

[0059] During the valve closing phase, the system continuously collects second-real-time water flow data and second-real-time pipeline status data for the target irrigation branch pipeline. The second-real-time water flow data is directly collected by flow meters and velocity sensors, including two core data types: second-real-time flow rate and second-real-time flow velocity, facilitating real-time monitoring of the water flow dynamics within the pipeline. The second-real-time pipeline status data is obtained through pressure, temperature, and vibration sensors on the pipeline, recording the pipeline's real-time health status throughout the process. Subsequently, the system dynamically calculates the real-time water flow change rate based on the second-real-time flow rate and second-real-time flow velocity data. The water flow change rate is the ratio of change in flow rate or flow velocity per unit time, directly reflecting the speed of water flow increase or decrease. Calculation simply involves comparing the current flow rate and flow velocity data with the previous time, calculating the increment, and then dividing by the time interval.

[0060] The system continuously tracks the cumulative flow data of the target irrigation branch pipes, which is the total irrigation water volume actually flowing through the pipes from the moment the valves are opened and closed. The system compares this cumulative flow data with the preset irrigation water demand data to calculate the difference between the irrigated water volume and the target water volume. This value directly reflects the deviation between the actual water volume and the set value: a positive value indicates that the actual irrigation water volume exceeds the demand; a negative value indicates that the target water volume has not been reached. Subsequently, the system combines the water volume difference value with the real-time water flow change rate to conduct a difference analysis, the core of which is calculating the real-time water volume deviation value. During the analysis, the water volume difference value and the water flow change rate are integrated to obtain a comprehensive deviation. The larger the deviation value, the greater the degree to which the water flow deviates from the target water volume, and the greater the adjustment required.

[0061] After obtaining the real-time water flow deviation value, the system combines it with preset water flow regulation delay data to perform dynamic deviation calculation. The water flow regulation delay data is the time lag between the valve issuing an adjustment command and the actual change in water flow, mainly determined by the pipeline's physical characteristics, fluid dynamics, and the control system's response speed. The system combines the water flow deviation value with the delay data to calculate the corresponding real-time water flow regulation parameters, correcting the lag in water flow regulation, making water flow control more precise, and allowing for early prediction of subsequent water flow changes and timely adjustments. Next, the system performs dynamic safety verification and health weighting on the real-time water flow regulation parameters based on second-real-time pipeline status data. Pipeline pressure, temperature, vibration, and other status data directly reflect the current operating status of the pipeline. The system combines this data with the pipeline's health level for weighted calculation to determine whether adjustment parameters need to be corrected. For example, if the pipeline experiences excessive pressure or temperature, the system will appropriately lower the adjustment parameters to avoid over-adjustment and damage to the pipeline. This weighted process ensures safe water flow regulation and extends the pipeline's stable operating cycle.

[0062] The system maps the calculated real-time pipeline status impact values ​​to a preset safety gain coefficient table. This coefficient table, developed based on historical data and experimental verification, specifically adjusts the control coefficients according to the pipeline's health status: when the pipeline is in good condition, the gain coefficient is higher, allowing the system to moderately increase the water flow adjustment amplitude; when the pipeline exhibits aging or damage, the gain coefficient is lowered, strictly limiting the water flow adjustment intensity to ensure the control strategy adapts to the actual pipeline condition, avoiding both increased pipeline burden and water waste. Finally, the system dynamically updates the real-time water flow adjustment parameters using the mapped real-time water flow control factor. This control factor integrates multiple factors including water usage differences, water flow fluctuations, pipeline health, and safety gain. Based on the updated adjustment parameters, the system precisely optimizes the valve closing speed, ensuring that water flow and stability remain within preset standards throughout the valve closure process, achieving efficient and water-saving precise irrigation control.

[0063] This solution continuously monitors real-time water flow and pipeline status data, dynamically calculates and adjusts water flow and valve closing speed to ensure precise water flow control in the irrigation system, meeting water demand. At the same time, it considers pipeline health and safety, optimizes resource utilization, and improves efficiency and safety.

[0064] See appendix Figure 2 , Figure 2 This is a schematic flowchart of the second main step of a valve closing control method based on a measurement and control device according to an embodiment of the present invention. Figure 2 As shown, a valve closing control method based on a measurement and control device in an embodiment of the present invention mainly includes the following steps S5-S9.

[0065] Step S5: Extract pipeline pressure data, pipeline temperature data, and pipeline vibration data from the first real-time pipeline status data. Based on the pipeline pressure data, pipeline temperature data, and pipeline vibration data, perform data cleaning, feature extraction, and fusion processing to obtain the health status feature data of the target irrigation branch pipeline.

[0066] Step S6: Based on the health status characteristic data and the preset pipeline health standards, a health assessment is performed to obtain the pipeline health status data of the target irrigation branch pipeline.

[0067] Step S7: Calculate the Reynolds number based on the first real-time water flow data and pipeline design parameters, and perform flow mode analysis based on the Reynolds number to obtain the water flow analysis results.

[0068] Step S8: Calculate the flow resistance based on the pipeline design parameters and the first real-time water flow data to obtain the flow resistance coefficient.

[0069] Step S9: Based on the flow resistance coefficient, the first real-time water flow data, the pipeline health status data, and the water flow analysis results, perform fluid flow state analysis to obtain water flow state data.

[0070] In this embodiment, the pipeline pressure data is the water pressure value inside the pipeline collected in real time by the pressure sensor, which reflects the water flow resistance and flow state inside the pipeline.

[0071] Pipeline temperature data is the real-time water flow temperature information collected by temperature sensors, reflecting the thermal state of the water flow inside the pipeline.

[0072] Pipeline vibration data is vibration information collected by vibration sensors from inside and around the pipeline, which can clearly reflect the physical impact and vibration of the pipeline when water flows through it.

[0073] Health status characteristic data is feature data extracted from the first real-time pipeline status data, including key indicators such as pressure fluctuation patterns, temperature change trends, and vibration modes. Based on this data, it is possible to accurately determine whether there are problems such as blockage, leakage, and corrosion in the pipeline, and to assess the real-time operating status of the pipeline.

[0074] Preset pipeline health standards are standards that provide a reference for the system assessment of pipeline health, and clarify the reasonable range of indicators such as pressure, temperature, and vibration when the pipeline is operating normally.

[0075] The Reynolds number is a dimensionless number that characterizes the flow state of fluid in a pipe. The flow regime can be determined based on the value. A Reynolds number less than 2000 indicates laminar flow, where the flow is stable; a Reynolds number greater than 4000 indicates turbulent flow, where the flow is disordered.

[0076] The results of the flow analysis are derived from the analysis of flow patterns using Reynolds number to determine the specific type of flow, namely laminar flow, turbulent flow, or transitional flow.

[0077] The flow resistance coefficient is a core parameter for measuring the energy loss of water flow in a pipe. It is affected by the pipe's geometric characteristics, fluid properties, and flow state, and directly reflects the resistance of water flow through the pipe. A high coefficient usually indicates that there are scaling, blockage, or corrosion problems inside the pipe.

[0078] The system first acquires real-time monitoring data through built-in pressure, temperature, and vibration sensors within the pipeline. Pressure sensors record fluctuations in water pressure; changes in flow rate directly reflect changes in pipeline load. Temperature sensors continuously monitor water temperature; abnormal temperature changes indicate heat loss or alterations in the pipeline's physical properties. Vibration sensors collect pipeline vibration data, detecting structural changes caused by blockages or other anomalies. After acquiring the raw pressure, temperature, and vibration data, the system performs data cleaning to remove noise and outliers from the sensor readings. The system sets thresholds to identify invalid data and sudden abnormal fluctuations, such as extreme temperature changes or short-term large vibration fluctuations. Combining historical data with reasonable ranges, it deletes or smooths out unqualified data points. After cleaning, the system extracts features from the data, focusing on analyzing pressure fluctuation characteristics, temperature change trends, and vibration patterns. Pressure fluctuation characteristics reflect the internal pressure stability of the pipeline; temperature change trends reflect the pipeline's thermal state, determining whether overheating or uneven cooling exists; vibration patterns, through frequency and amplitude analysis, can identify mechanical wear and structural problems. The extracted features help the system determine whether there are localized blockages, leaks, or excessive vibrations in the pipeline. All extracted features are fused to integrate multiple data types, including pressure, temperature, and vibration, into a comprehensive health status dataset that fully reflects the pipeline's health level. The system quantifies the impact of each feature on pipeline health through weighted calculations, assigning weights based on experience and data. Pressure fluctuations are given a higher weight than temperature changes, and vibration data is primarily used for diagnosing minor structural problems. After feature fusion, the final health status feature data represents the pipeline's current health level.

[0079] Based on health status characteristic data, the system conducts pipeline health assessments, comparing the characteristic data with preset pipeline health standards one by one. If the measured data exceeds the standard range, the system determines the pipeline health level through quantitative scoring. When the health index is low, it is marked as requiring attention, maintenance, or replacement. For example, if the pipeline pressure fluctuation reaches ±10%, exceeding the standard range of ±5%, the system assesses a health score of 50 points, determining the pipeline condition to be poor and requiring timely maintenance or replacement. The system combines real-time water flow data with pipeline design parameters to calculate the Reynolds number. The calculation formula involves flow velocity, pipeline inner diameter, fluid density, and dynamic viscosity. The flow velocity is taken from real-time water flow data, the inner diameter is provided by pipeline design parameters, and the fluid density and viscosity are estimated from temperature data. After calculation, the flow regime is classified according to the Reynolds number value: Reynolds number below 2000 is laminar flow, above 4000 is turbulent flow, and flow in between is transitional flow. After the Reynolds number is calculated, the system continues to calculate the flow resistance coefficient. Based on the pipeline design parameters and real-time water flow data, the system performs calculations using a fluid dynamics model, taking into account factors such as the pipeline inner diameter, surface roughness, and flow velocity. If the coefficient is too high, it indicates that there may be problems such as scaling, blockage, or corrosion inside the pipeline. The system then further evaluates the pipeline flow state accordingly.

[0080] The system combines calculated flow resistance coefficients, real-time water flow data, pipeline health data, and water flow analysis results to conduct a comprehensive analysis of fluid flow status. First, it correlates the water flow pattern with the flow resistance coefficient to determine if any resistance anomalies exist. These anomalies are often caused by pipeline wear and scaling. Then, it considers the pipeline health status to investigate potential hazards such as leaks, blockages, and corrosion, which directly affect water flow stability and operational efficiency. During the analysis, the system comprehensively considers the interrelationships of various parameters. Pipeline health directly determines water flow resistance and flow pattern stability; abnormal flow resistance coefficients often indicate internal pipeline faults, leading to poor water flow and reduced irrigation efficiency. After completing the above comprehensive analysis, the system generates water flow status data.

[0081] In this embodiment, the health of the pipeline and the water flow condition can be accurately assessed, potential risks can be identified, the operating efficiency of the irrigation system can be optimized, and the pipeline can be ensured to work efficiently and safely, thereby improving the accuracy of irrigation operations and resource utilization.

[0082] Based on the above technical solution, optionally, data cleaning, feature extraction, and fusion processing can be performed on the pipeline pressure data, pipeline temperature data, and pipeline vibration data to obtain health status feature data of the target irrigation branch pipeline, including: Data cleaning is performed on the pipeline pressure data, pipeline temperature data, and pipeline vibration data to obtain cleaned pipeline pressure data, pipeline temperature data, and pipeline vibration data. Smoothing and trend analysis were performed on the cleaned pipeline pressure data to obtain pipeline pressure fluctuation characteristic data. Time series analysis was performed on the temperature data of the cleaned pipeline to obtain temperature change fluctuation characteristics. Frequency domain analysis was performed on the vibration data of the cleaned pipeline to obtain vibration mode characteristic data; Based on the characteristic data of pipeline pressure fluctuation, temperature change fluctuation, and vibration mode, weighted analysis and feature fusion are performed to obtain the health status characteristic data of the target irrigation branch pipeline.

[0083] In this scheme, pressure fluctuation characteristic data is extracted by performing fluctuation analysis on pipeline pressure data. This data reflects the pressure change pattern and fluctuation amplitude, and mainly includes indicators such as pressure peak value, fluctuation amplitude, frequency, and trend of change, which measure the pressure fluctuation during pipeline operation.

[0084] Temperature fluctuation characteristic data is the characteristic data that is compiled after performing time series analysis on pipeline temperature data. It can reflect the temperature fluctuation and trend, including key information such as temperature fluctuation amplitude, rate of change, and fluctuation frequency.

[0085] Vibration mode characteristic data are characteristic data obtained after frequency domain analysis of pipeline vibration data to describe the vibration law of the pipeline. Common indicators include vibration frequency, amplitude, waveform, etc., which can reflect the actual vibration state of the pipeline during operation.

[0086] After collecting pressure, temperature, and vibration data from the pipeline via sensors, the system immediately initiates a data cleaning process. Data cleaning uses set thresholds to filter out data points that clearly violate physical laws or deviate from historical operating trends. After cleaning, the pipeline pressure, temperature, and vibration data are corrected. For the cleaned pipeline pressure data, the system performs smoothing. Smoothing uses a sliding window method to average or weight data over a certain time range, reducing the interference of instantaneous fluctuations on the final analysis results. After smoothing, the system moves to the pressure trend analysis stage, calculating the pressure change trend and fluctuation amplitude to extract pipeline pressure fluctuation characteristic data. For the cleaned pipeline temperature data, the system uses time series analysis to extract temperature change fluctuation characteristic data. Before analysis, the system preprocesses the data according to time series patterns, including removing seasonal fluctuations and conducting preliminary trend analysis. Then, using time series analysis techniques, it extracts temperature change fluctuation characteristic data such as temperature fluctuation amplitude, periodic change patterns, and temperature rise and fall trends. For pipeline vibration data processing, the system extracts vibration mode characteristic data through frequency domain analysis. Vibration signals themselves contain multiple frequency components, including low-frequency periodic vibrations and high-frequency random vibrations. The system first performs a Fourier transform on the vibration data to convert the time-domain data into frequency-domain data, and then extracts key features such as vibration frequency and amplitude to form vibration mode feature data. This data is used to analyze whether the pipeline exhibits abnormal vibration modes, such as excessive vibration caused by pipeline aging or uneven water flow.

[0087] After extracting the three types of feature data mentioned above, the system performs a weighted analysis on the pipeline pressure fluctuation feature data, temperature change fluctuation feature data, and vibration mode feature data. The system assigns corresponding values ​​based on the weight of each feature's impact on pipeline health; for example, pressure fluctuations, which have a more significant impact on pipeline health, are given a higher weight. The system then merges these feature data according to their weights, integrating them into a comprehensive health status feature data set.

[0088] In this solution, by cleaning and analyzing pipeline pressure, temperature, and vibration data, key features are extracted and weighted and fused to accurately assess pipeline health, promptly identify potential problems, effectively prevent malfunctions, and improve the operating efficiency and reliability of the irrigation system.

[0089] Based on the above technical solution, optionally, the Reynolds number is calculated based on the first real-time water flow data and pipeline design parameters, and flow mode analysis is performed based on the Reynolds number to obtain water flow analysis results, including: Based on the pipeline temperature data in the pipeline design parameters, the current water density and current water dynamic viscosity are obtained by mapping the data in a preset physical property mapping table. Extract the pipe inner diameter data from the pipe design parameters, and extract the first real-time flow velocity data from the first real-time water flow data. Then, unify the dimensions of the current water density, current water dynamic viscosity, pipe inner diameter data, and first real-time flow velocity data. Based on the dimensionally unified current water density, current water dynamic viscosity, pipe inner diameter data, first real-time flow velocity data, and the preset Reynolds number calculation formula, the Reynolds number is calculated; wherein, the preset Reynolds number calculation formula is: ; in, It is the Reynolds number; The current water density; This is the first real-time flow rate data; This refers to the pipe's inner diameter data. The current dynamic viscosity of the water body; If the Reynolds number is less than the preset laminar flow critical value, the flow analysis results are obtained as laminar flow. If the Reynolds number is greater than the preset critical value for turbulence, the flow analysis results are obtained as turbulent. If the Reynolds number is within the preset transition range, the flow analysis results are obtained as transition flow type.

[0090] In this scheme, the preset physical property mapping table is a reference table that matches the physical properties of water such as density and dynamic viscosity based on water temperature and related parameters. It will mark the corresponding water physical property parameters according to different water temperatures and operating conditions.

[0091] Current water density refers to the mass of a unit volume of water under actual temperature and pressure conditions on site. It is affected by both temperature and pressure, and the general rule is that as water temperature increases, the density decreases.

[0092] The dynamic viscosity of water is the core physical quantity for measuring the viscosity of water flow. It reflects the magnitude of internal friction in water flow, and its value gradually decreases as water temperature rises, directly determining the magnitude of flow resistance in pipes.

[0093] The pipe inner diameter data is the straight-line distance between the two sides of the inner wall of the pipe.

[0094] The first real-time flow velocity data is the instantaneous flow velocity of water inside the pipe, which is collected in real time by a flow velocity sensor built into the pipe.

[0095] The preset laminar flow critical value is the Reynolds number critical threshold set in advance by the system. The standard is 2000. When the Reynolds number of the water flow is lower than this value, it can be determined to be a laminar flow state.

[0096] Flow type refers to the actual flow state of water inside a pipe, and is mainly divided into three categories: laminar flow, turbulent flow, and transitional flow. The specific type is determined based on the Reynolds number calculation results.

[0097] The preset turbulence threshold is another Reynolds number threshold preset by the system. The standard is 4000. When the Reynolds number of the water flow is higher than this value, it can be determined to be a turbulent state.

[0098] The preset transition range is the intermediate Reynolds number range defined by the system, which is usually between 2000 and 4000. The water flow state is unstable in this range and does not belong to standard laminar or turbulent flow.

[0099] Laminar flow is a stable flow state of water in a pipe, in which fluid molecules flow in layers along parallel trajectories without interfering with each other, and the corresponding Reynolds number is generally less than 2000.

[0100] Turbulent flow is a disordered flow state of water in a pipe, in which the fluid molecules move in irregular trajectories and collide violently with each other, and the corresponding Reynolds number is generally greater than 4000.

[0101] Transitional flow is an intermediate flow state between laminar and turbulent flow, corresponding to a Reynolds number in the range of 2000 to 4000. Its flow characteristics combine the stability of laminar flow with the instability of turbulent flow.

[0102] The system matches parameters based on pipe temperature data from the pipe design parameters in a pre-defined physical property mapping table. This table clearly indicates the correspondence between temperature and water density and dynamic viscosity. By looking up the table, the system can directly retrieve the corresponding current water density and dynamic viscosity based on the current water temperature. This is because these physical properties of water change significantly under different temperature conditions, and the accuracy of their values ​​directly affects the analysis results. The system extracts the pipe inner diameter data from the pipe design parameters. Simultaneously, the system extracts the first real-time flow velocity data from the first real-time water flow data. This type of data accurately reflects the instantaneous velocity of water flow within the pipe. Then, the system performs dimensional unification processing on all relevant parameters. For example, water density is commonly measured in kilograms per cubic meter, and flow velocity is commonly measured in meters per second. The system adjusts the measurement standards of various parameters to be consistent, ensuring that all input data are on the same computational dimension.

[0103] After data preparation, the system inputs the dimensionlessly standardized parameters into the preset Reynolds number calculation formula to perform calculations. The Reynolds number is a dimensionless parameter specifically used to describe fluid flow characteristics. A low Reynolds number indicates laminar flow, while a high Reynolds number indicates turbulent flow. Based on the calculated Reynolds number, the system compares it to preset laminar flow critical values, preset turbulent flow critical values, and preset transition ranges. If the Reynolds number is below the preset laminar flow critical value, the system classifies the flow as laminar, characterized by stable and smooth flow with low intermolecular friction. If the Reynolds number is above the preset turbulent flow critical value, the system classifies it as turbulent, characterized by strong instability and disorder, with significant energy loss. If the Reynolds number falls within the preset transition range, the system considers the flow to be in a transitional flow type, where the flow is neither fully laminar nor fully turbulent, exhibiting some uncertainty in its characteristics. Finally, all the above analysis results are integrated to generate a flow analysis report, clearly indicating the specific flow type.

[0104] In this solution, by accurately calculating parameters such as water density, dynamic viscosity, and flow velocity, and combining them with Reynolds number to assess the flow state, the type of water flow can be accurately determined, the operating efficiency of the irrigation system can be optimized, the water resource utilization rate can be improved, the risk of pipeline failure can be reduced, and the irrigation process can be ensured to be stable and reliable.

[0105] Based on the above technical solution, optionally, the flow resistance can be calculated based on the pipeline design parameters and the first real-time water flow data to obtain the flow resistance coefficient, including: Based on pipeline design parameters, pipeline geometric feature data is determined. Then, the pipeline geometric feature data is subjected to unit conversion and standardization to obtain standardized pipeline geometric feature data. The first real-time flow rate data and the first real-time flow velocity data are extracted from the first real-time water flow data. Based on the standardized pipe geometric feature data, the first real-time flow rate data, and the first real-time flow velocity data, the flow resistance coefficient is obtained by real-time calculation using a fluid dynamics resistance model.

[0106] In this scheme, pipeline geometric feature data are key parameters used to describe the shape, size, and spatial location of the pipeline, including the pipeline's inner diameter, length, bending radius, and surface roughness. These parameters directly affect the flow characteristics of the fluid within the pipeline and also influence the magnitude of the resistance encountered during fluid flow.

[0107] The first real-time flow data is the volume or mass flow rate of water collected in real time by flow sensors during the operation of the irrigation system. It directly reflects the amount of water flowing through the pipe within a specific time period, and the commonly used units of measurement are cubic meters per second or liters per second.

[0108] Fluid dynamics resistance models are mathematical models used to calculate the resistance experienced by fluids flowing in pipes. Based on the principles of fluid dynamics, these models comprehensively incorporate multiple influencing factors such as pipe geometry, water flow velocity, and fluid viscosity. In practical applications, formulas such as the Darcy-Weisbach equation are often used to estimate the flow resistance coefficient.

[0109] The system extracts geometric feature data from pipeline design parameters, specifically including pipeline inner diameter, length, surface roughness, and bending radius. These parameters are determined during the pipeline design phase and stored uniformly in the system database. When calculating flow resistance later, the system only needs to input the target pipeline number to retrieve the corresponding geometric feature data from the database.

[0110] To ensure consistency of these data across different systems and analysis scenarios, the next step is to perform unit conversion and standardization on the pipe geometric feature data. For example, if the pipe's inner diameter is in inches, the system will convert it to standard units such as meters or millimeters; other data such as length and surface roughness will also be processed according to the same principle, ultimately ensuring that the units of all feature data are consistent and the data range is fully compatible with subsequent calculation and analysis work.

[0111] During the standardization process, the system employs algorithms such as min-max normalization or Z-score standardization to transform all data to a uniform scale. The core purpose of this is to eliminate the influence of different data units, ensuring that all parameters are on the same comparison benchmark in subsequent calculations, and preventing some features from having an unreasonable dominant effect on the final calculation results due to excessive differences in units.

[0112] After completing the above processing, the system extracts the first real-time flow rate data and the first real-time velocity data from the first real-time water flow data. These two types of data are monitored and recorded in real time by flow sensors and velocity sensors, respectively. The flow rate data reflects the amount of water flowing through the pipe per unit time, while the velocity data reflects the instantaneous speed of the water flow within the pipe. Both are key data for evaluating the water flow state within the pipe and provide necessary basic input data for subsequent fluid dynamics analysis.

[0113] After acquiring standardized pipe geometric feature data, as well as the first real-time flow rate and velocity data from the first real-time water flow data, the system calls the fluid dynamics resistance model for real-time calculation, ultimately obtaining the flow resistance coefficient. Specifically, in actual use, the system first extracts the standardized pipe geometric feature data and obtains the first real-time flow rate and velocity data. All these data undergo standardization to ensure consistent data scale, avoiding errors introduced by inconsistent data dimensions. The fluid dynamics resistance model can determine and select an appropriate flow resistance calculation method based on the standardized pipe geometric feature data, first real-time flow rate data, and first real-time velocity data. If the relationship between pipe geometry and velocity conforms to the characteristics of general flow, the fluid flow is relatively stable, and the pipe surface is relatively smooth, the model will choose the Darcy-Weisbach equation to calculate the friction factor. In general flow, the velocity is relatively uniformly distributed along the cross-section of the pipe, with the highest velocity in the center and lower near the pipe wall. This situation is usually suitable for pipe surfaces with smooth surfaces and low roughness; therefore, the velocity is relatively low, the flow rate is stable, and the flow resistance coefficient is small. If the water flow velocity or flow rate is high, or if the inner surface of the pipe is rough, the fluid flow will enter a turbulent state. In this case, the model uses the Colebrook-White equation to calculate the friction factor. The rough pipe surface increases the frictional force of the flow, causing the flow to enter a turbulent state, thus increasing the flow resistance. Once the friction factor is calculated using the above equation, the model will convert the friction factor into a flow resistance coefficient through a preset mapping relationship. This mapping relationship is derived from previous experimental data or CFD simulations and optimized through regression analysis or machine learning models to make the relationship between the friction factor and the flow resistance coefficient more accurate.

[0114] The training process for the fluid dynamics resistance model is as follows: When training the fluid dynamics resistance model, the system collects a large amount of historical pipeline data, including historical pipeline geometric features and historical water flow data, including flow rate and velocity. This historical data provides crucial input information for the model, helping to establish the relationship between the flow resistance coefficient and pipeline characteristics and fluid state. In the data preprocessing stage, the system cleans, standardizes, and normalizes the raw data to ensure that data from different sources have a consistent scale, avoiding errors introduced due to inconsistent data dimensions.

[0115] After data preprocessing, the system inputs standardized historical pipeline geometric data and historical flow data, including flow rate and velocity, into the fluid dynamics resistance model. The model calculates the friction factor based on established flow resistance formulas, such as the Darcy-Weisbach equation, which is applicable to general flow resistance calculations and considers both pipeline geometry and flow characteristics. Additionally, the system can employ other methods, such as the Colebrook-White equation, to calculate the friction factor in rough pipelines under turbulent conditions. The mapping relationship between the friction factor and the flow resistance coefficient can be derived from simulated data. First, data on the friction factor and flow resistance coefficient under different pipeline geometry, flow velocity, and flow rate conditions are obtained through computational fluid dynamics (CFD) simulations or experimental data collection. Next, regression analysis or machine learning methods are used to process this data and establish the mathematical relationship between the friction factor and the flow resistance coefficient. The model is optimized and validated to ensure its accuracy before being applied to practical calculations. Finally, the model can calculate the flow resistance coefficient using real-time acquired pipeline geometric data, flow velocity, and flow rate data, based on the derived mapping relationship. During training, the system compares the flow resistance coefficients calculated by the model with actual observation data, adjusts model parameters, and gradually optimizes the model to ensure that the error between the calculated results and the actual data is minimized. Through this process, the model can gradually master the resistance characteristics under different pipe features and flow states. After training, the system can gradually utilize flow rate and velocity data extracted from real-time data, as well as standardized pipe geometric feature data, to perform real-time calculations based on the trained fluid dynamics resistance model to obtain real-time flow resistance coefficients.

[0116] In this scheme, by using standardized pipeline geometric feature data and real-time water flow data, and combining them with a fluid dynamics resistance model for calculation, the flow resistance within the pipeline can be accurately assessed, water flow control can be optimized, the operating efficiency of the irrigation system can be improved, the rational allocation of water resources can be ensured, and unnecessary energy loss can be avoided.

[0117] Based on the above technical solution, optionally, fluid flow state analysis is performed based on the flow resistance coefficient, the first real-time water flow data, the pipeline health status data, and the water flow analysis results to obtain water flow state data, including: Based on the flow analysis results, the target solution strategy is determined. Based on the target solution strategy, multi-dimensional coupling calculations are performed on the flow resistance coefficient, the first real-time flow data, and the pipeline health status data to obtain the flow state data.

[0118] In this scheme, the target solution strategy is to select the corresponding solution method and calculation formula for laminar flow, turbulent flow and transitional flow after determining the flow type. The appropriate physical model, mathematical formula and numerical algorithm are matched according to the actual characteristics of each flow.

[0119] Once the flow analysis results are obtained and the flow type (laminar, turbulent, or transitional) is determined, the system will match the target solution strategy accordingly. In laminar flow, the flow is stable and regular, with flow resistance primarily arising from friction between the pipe wall and the fluid. The solution uses a laminar flow-specific flow resistance coefficient as the core, combined with real-time flow data to assess flow stability. Pipe health data is also incorporated, taking into account increased resistance due to pipe aging and damage, as well as their impact on flow efficiency and stability.

[0120] In turbulent flow, the water flow is no longer stable, and the flow resistance increases significantly. The system adjusts its calculation method according to the characteristics of turbulence, primarily relying on data such as the flow resistance coefficient, velocity, and flow rate, with a focus on the impact of pipe surface roughness on turbulence. Pipe health data reflects the presence of foreign objects, internal corrosion, or pipe cracks, all of which significantly increase turbulent resistance. The system adjusts the resistance coefficient based on these actual conditions to ensure accurate flow results. The transition flow calculation strategy dynamically adjusts according to the real-time Reynolds number, accurately simulating the transition from laminar to turbulent flow. Transition flow resistance calculation requires detailed analysis of the flow state. The system integrates the flow resistance coefficient, velocity, flow rate, and pipe health data, balancing the influence of various parameters to ensure the calculation results closely match the actual flow conditions within the pipe. After determining the calculation strategy, the system integrates the flow resistance coefficient, real-time flow data, and pipe health data for multi-dimensional coupled calculation. The flow resistance coefficient directly determines the energy loss of water flow. The system adjusts this coefficient based on the flow type and pipeline health. When the pipeline is aging or damaged, the resistance coefficient will be appropriately increased to reflect the impact of deteriorating pipeline health. The flow velocity and flow rate in the first real-time water flow data are used to further determine changes in the water flow state, ensure that the water flow operates according to preset conditions, and also provide real-time feedback to the irrigation control system.

[0121] Pipeline health data is crucial for corrections. Data such as pipeline pressure, temperature, and vibration can promptly detect operational anomalies, most of which alter flow resistance. The system monitors this health data in real time, and once issues such as blockages, aging, or cracks are detected, it dynamically adjusts the flow resistance coefficient to correct the calculated flow state. By combining the flow resistance coefficient, real-time flow data, and pipeline health data, the system has completed multi-dimensional coupled calculations, yielding accurate flow state data.

[0122] This solution utilizes multi-dimensional coupling calculations to combine and analyze flow resistance coefficients, real-time water flow data, and pipeline health data, enabling accurate assessment of water flow conditions. This helps optimize water flow control in irrigation systems, improve efficiency, reduce energy loss, and ensure the safe and stable operation of the system.

[0123] It should be noted that although the steps in the above embodiments are described in a specific order, those skilled in the art will understand that, in order to achieve the effects of the present invention, different steps do not necessarily have to be executed in such an order. They can be executed simultaneously (in parallel) or in other orders, and these variations are all within the scope of protection of the present invention.

[0124] Furthermore, the present invention also provides a valve closing control system based on a measurement and control device.

[0125] See appendix Figure 3 , Figure 3 This is a main structural block diagram of a valve closing control system based on a measurement and control device according to an embodiment of the present invention. Figure 3 As shown, it specifically includes: The pipeline confirmation module 301 is used to, upon receiving irrigation water demand data from a user, determine the target irrigation branch pipeline based on the irrigation water demand data, and control the valve control mechanism of the target irrigation branch pipeline to open the valve; The analysis module 302 is used to acquire the pipeline design parameters of the target irrigation branch pipeline, and to acquire the first real-time water flow data and the first real-time pipeline status data of the target irrigation branch pipeline within a preset time window. Based on the pipeline design parameters, the first real-time water flow data, and the first real-time pipeline status data, the module performs pipeline health status analysis and fluid characteristic analysis to obtain the water flow status data and pipeline health status data of the target irrigation branch pipeline. The prediction module 303 is used to input the water flow state data, pipeline health status data, and irrigation water demand data into a preset valve closure prediction model to obtain the valve closure time and valve closure speed of the target irrigation branch pipeline; The valve control module 304 is used to control the valve control mechanism of the target irrigation branch pipeline to close the valve based on the valve closing speed when the valve closing time is reached, and to dynamically adjust the valve closing speed during the valve closing process until the total irrigation water volume meets the user's irrigation water demand.

[0126] The valve closing control system based on a measurement and control device provided in this application embodiment can achieve… Figure 1 The various processes implemented in the method implementation examples will not be described again here to avoid repetition.

[0127] Those skilled in the art will understand that all or part of the processes in the method of the above embodiment of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable file, or some intermediate form. The computer-readable storage medium can include any entity or device capable of carrying the computer program code, a medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory, a random access memory, an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc. It should be noted that the content included in the computer-readable storage medium can be appropriately added or removed according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable storage medium does not include electrical carrier signals and telecommunication signals.

[0128] Furthermore, the present invention also provides an electronic device 400, including a processor 401, a memory 402, and a program or instructions stored in the memory 402 and executable on the processor 401. When the program or instructions are executed by the processor 401, they implement the various processes of the above-described embodiment of a valve closing control method based on a measurement and control device, and can achieve the same technical effect. To avoid repetition, they will not be described again here.

[0129] It should be noted that the electronic devices in the embodiments of this application include the mobile electronic devices and non-mobile electronic devices described above.

[0130] Furthermore, the present invention also provides a computer-readable storage medium. In one embodiment of the computer-readable storage medium according to the present invention, the computer-readable storage medium can be configured to store a program for executing a valve closing control method based on a measurement and control device according to the above-described method embodiments. This program can be loaded and run by a processor to implement the above-described valve closing control method based on a measurement and control device. For ease of explanation, only the parts related to the embodiments of the present invention are shown; for specific technical details not disclosed, please refer to the method section of the embodiments of the present invention. The computer-readable storage medium can be a storage device device comprising various electronic devices. Optionally, in the embodiments of the present invention, the computer-readable storage medium is a non-transitory computer-readable storage medium.

[0131] Furthermore, it should be understood that since the various modules are only provided to illustrate the functional units of the device of the present invention, the physical devices corresponding to these modules may be the processor itself, or a part of the processor's software, hardware, or a combination of software and hardware. Therefore, the number of modules shown in the figures is merely illustrative.

[0132] Those skilled in the art will understand that the various modules in the device can be adaptively split or combined. Such splitting or combining of specific modules will not cause the technical solution to deviate from the principle of the present invention; therefore, the technical solution after splitting or combining will fall within the protection scope of the present invention.

[0133] The technical solution of the present invention has been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

Claims

1. A valve closing control method based on a measurement and control device, characterized in that... The method is executed by a measurement and control device, and the method includes: If irrigation water demand data from a user is received, the target irrigation branch pipeline is determined based on the irrigation water demand data, and the valve control mechanism of the target irrigation branch pipeline is controlled to open the valve; The pipeline design parameters of the target irrigation branch pipeline are obtained, as well as the first real-time water flow data and the first real-time pipeline status data of the target irrigation branch pipeline within a preset time window. Based on the pipeline design parameters, the first real-time water flow data, and the first real-time pipeline status data, pipeline health status analysis and fluid characteristic analysis are performed to obtain the water flow status data and pipeline health status data of the target irrigation branch pipeline. The water flow status data, pipeline health status data, and irrigation water demand data are input into a preset valve closure prediction model to obtain the valve closure time and valve closure speed of the target irrigation branch pipeline. If the valve closing time is reached, the valve control mechanism of the target irrigation branch pipeline closes the valve based on the valve closing speed, and dynamically adjusts the valve closing speed during the valve closing process until the total irrigation water volume meets the user's irrigation water demand.

2. The valve closing control method based on a measurement and control device according to claim 1, characterized in that... Based on the pipeline design parameters, the first real-time water flow data, and the first real-time pipeline status data, pipeline health status analysis and fluid characteristic analysis are performed to obtain the water flow status data and pipeline health status data of the target irrigation branch pipeline, including: Pipeline pressure data, pipeline temperature data, and pipeline vibration data are extracted from the first real-time pipeline status data. Based on the pipeline pressure data, pipeline temperature data, and pipeline vibration data, data cleaning, feature extraction, and fusion processing are performed to obtain the health status feature data of the target irrigation branch pipeline. A health assessment is performed based on the health status characteristic data and the preset pipeline health standards to obtain the pipeline health status data of the target irrigation branch pipeline; The Reynolds number is calculated based on the first real-time water flow data and pipeline design parameters, and flow pattern analysis is performed based on the Reynolds number to obtain the water flow analysis results; The flow resistance coefficient is obtained by calculating the flow resistance based on the pipeline design parameters and the first real-time water flow data; Based on the flow resistance coefficient, the first real-time water flow data, the pipeline health status data, and the water flow analysis results, fluid flow state analysis is performed to obtain water flow state data.

3. The valve closing control method based on a measurement and control device according to claim 2, characterized in that... The process involves data cleaning, feature extraction, and fusion based on the pipeline pressure data, pipeline temperature data, and pipeline vibration data to obtain health status feature data of the target irrigation branch pipeline, including: Data cleaning is performed on the pipeline pressure data, pipeline temperature data, and pipeline vibration data to obtain cleaned pipeline pressure data, pipeline temperature data, and pipeline vibration data. Smoothing and trend analysis were performed on the cleaned pipeline pressure data to obtain pipeline pressure fluctuation characteristic data. Time series analysis was performed on the temperature data of the cleaned pipeline to obtain temperature change fluctuation characteristics. Frequency domain analysis was performed on the vibration data of the cleaned pipeline to obtain vibration mode characteristic data; Based on the characteristic data of pipeline pressure fluctuation, temperature change fluctuation, and vibration mode, weighted analysis and feature fusion are performed to obtain the health status characteristic data of the target irrigation branch pipeline.

4. A valve closing control method based on a measurement and control device according to claim 2, characterized in that... The process involves calculating the Reynolds number based on first real-time water flow data and pipeline design parameters, and then performing flow pattern analysis based on the Reynolds number to obtain the water flow analysis results, including: Based on the pipeline temperature data in the pipeline design parameters, the current water density and current water dynamic viscosity are obtained by mapping the data in a preset physical property mapping table. Extract the pipe inner diameter data from the pipe design parameters, and extract the first real-time flow velocity data from the first real-time water flow data. Then, unify the dimensions of the current water density, current water dynamic viscosity, pipe inner diameter data, and first real-time flow velocity data. Based on the dimensionally unified current water density, current water dynamic viscosity, pipe inner diameter data, first real-time flow velocity data, and the preset Reynolds number calculation formula, the Reynolds number is calculated; wherein, the preset Reynolds number calculation formula is: ; in, It is the Reynolds number; The current water density; This is the first real-time flow rate data; This refers to the pipe's inner diameter data. The current dynamic viscosity of the water body; If the Reynolds number is less than the preset laminar flow critical value, the flow analysis results are obtained as laminar flow. If the Reynolds number is greater than the preset critical value for turbulence, the flow analysis results are obtained as turbulent. If the Reynolds number is within the preset transition range, the flow analysis results are obtained as transition flow type.

5. A valve closing control method based on a measurement and control device according to claim 2, characterized in that... Among them, the flow resistance is calculated based on the pipeline design parameters and the first real-time water flow data to obtain the flow resistance coefficient, including: Based on pipeline design parameters, pipeline geometric feature data is determined. Then, the pipeline geometric feature data is subjected to unit conversion and standardization to obtain standardized pipeline geometric feature data. The first real-time flow rate data and the first real-time flow velocity data are extracted from the first real-time water flow data. Based on the standardized pipe geometric feature data, the first real-time flow rate data, and the first real-time flow velocity data, the flow resistance coefficient is obtained by real-time calculation using a fluid dynamics resistance model.

6. A valve closing control method based on a measurement and control device according to claim 4, characterized in that... Based on the flow resistance coefficient, the first real-time water flow data, the pipeline health status data, and the water flow analysis results, fluid flow state analysis is performed to obtain water flow state data, including: Based on the flow analysis results, the target solution strategy is determined. Based on the target solution strategy, multi-dimensional coupling calculations are performed on the flow resistance coefficient, the first real-time flow data, and the pipeline health status data to obtain the flow state data.

7. A valve closing control method based on a measurement and control device according to claim 1, characterized in that... Among them, dynamically adjusting the valve closing speed during the valve closing process includes: During the valve closing process, the second real-time water flow data and the second real-time pipeline status data of the target irrigation branch pipeline are continuously acquired. The second real-time flow rate data and the second real-time flow velocity data are continuously extracted from the second real-time water flow data, and the real-time water flow change rate is dynamically calculated based on the second real-time flow rate data and the second real-time flow velocity data. Continuously acquire cumulative flow data of the target irrigation branch pipeline, and based on the cumulative flow data and irrigation water demand data, determine in real time the difference between the irrigated water volume and the irrigation water demand; Based on the water usage difference value and the real-time water flow change rate, a difference analysis is performed in real time to obtain the real-time water volume deviation value; Based on the real-time water volume deviation value and the preset water flow regulation delay data, dynamic deviation calculation is performed to obtain the real-time water flow regulation parameters; Based on the second real-time pipeline status data, the real-time water flow regulation parameters are dynamically verified for safety and weighted for health, to obtain the real-time pipeline status impact value; The real-time pipeline state influence value is dynamically mapped to a preset safety gain coefficient table to obtain the real-time water flow control factor, and the real-time water flow adjustment parameters are dynamically updated based on the real-time water flow control factor; The valve closing speed is dynamically adjusted based on real-time water flow regulation parameters.

8. A valve closing control system based on a measurement and control device, characterized in that... The system is configured in the measurement and control device, and the system includes: The pipeline confirmation module is used to, upon receiving irrigation water demand data from a user, determine the target irrigation branch pipeline based on the irrigation water demand data, and control the valve control mechanism of the target irrigation branch pipeline to open the valve; The analysis module is used to acquire the pipeline design parameters of the target irrigation branch pipeline, and to acquire the first real-time water flow data and the first real-time pipeline status data of the target irrigation branch pipeline within a preset time window. Based on the pipeline design parameters, the first real-time water flow data, and the first real-time pipeline status data, the module performs pipeline health status analysis and fluid characteristic analysis to obtain the water flow status data and pipeline health status data of the target irrigation branch pipeline. The prediction module is used to input the water flow status data, pipeline health status data, and irrigation water demand data into a preset valve closure prediction model to obtain the valve closure time and valve closure speed of the target irrigation branch pipeline; The valve control module is used to control the valve control mechanism of the target irrigation branch pipeline to close the valve when the valve closing time is reached, based on the valve closing speed, and dynamically adjust the valve closing speed during the valve closing process until the total irrigation water volume meets the user's irrigation water demand.

9. An electronic device, comprising a processor, a memory, and a program or instructions stored in the memory and executable on the processor, characterized in that... The program or instructions are applicable to being loaded and run by the processor to perform a valve closing control method based on a measurement and control device according to any one of claims 1 to 7.

10. A computer-readable storage medium storing a plurality of program codes, characterized in that... The program code is applicable to being loaded and run by a processor to execute a valve closing control method based on a monitoring and control device as described in any one of claims 1 to 7.

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