A method and system for intelligent remote control of an irrigation electric ball valve

Abstract

This invention relates to the field of electric ball valve control, and more specifically, to an intelligent remote control method and system for irrigation electric ball valves. The method includes: acquiring water pressure sequences and water pressure tags for any preset sampling period in history; using a number of water pressure sequences as a water pressure database, where each water pressure sequence corresponds to one water pressure tag; clustering the water pressure sequences in the water pressure database to obtain the optimal cluster and acquiring the water pressure range of the cluster; obtaining the cluster to which the real-time water pressure sequence belongs based on the real-time water pressure tag, calculating the anomaly degree of the real-time water pressure sequence, comparing the anomaly degree with a preset anomaly level, and completing the intelligent control of the electric ball valve. The technical solution of this invention can improve the accuracy of electric ball valve control results.

Description

Technical Field

[0001] This invention relates to the field of electric ball valve control. More specifically, this invention relates to an intelligent remote control method and system for an irrigation electric ball valve. Background Technology

[0002] Electric ball valves for irrigation, as core control devices in modern agricultural irrigation systems, achieve precise water flow regulation through remote operation. However, they face numerous challenges in practical applications. First, the valve's transmission rod is prone to corrosion in high-humidity environments, leading to slower valve opening speeds and smaller opening amplitudes, failing to meet preset precise control requirements and thus affecting the efficiency and effectiveness of the entire irrigation system. Second, the instability of water pressure within the pipeline, especially under high water pressure, can cause water hammer when the valve suddenly opens. This sudden water flow impact not only causes physical damage to the valve but may also accelerate its wear and aging, further shortening the equipment's lifespan.

[0003] Current technology typically employs a linear control method, gradually increasing the valve opening from 10% to 100% over a fixed time period to reduce the negative impact of water flow impact. However, as equipment age and corrosion intensifies, the effectiveness of this method gradually diminishes, manifesting as a progressively smaller opening degree and a slower opening speed. This makes it difficult to meet the rapid response requirements of actual irrigation processes and to effectively address the changes brought about by long-term use. Summary of the Invention

[0004] To address the problems arising from the control of electric ball valves in the prior art, the present invention provides solutions in the following aspects.

[0005] In a first aspect, the present invention discloses an intelligent remote control method for an irrigation electric ball valve, comprising: acquiring a water pressure sequence and a water pressure tag for any preset sampling period in history; using a number of water pressure sequences as a water pressure database, wherein one water pressure sequence corresponds to one water pressure tag; clustering the water pressure sequences in the water pressure database to obtain the optimal cluster and acquiring the water pressure range of the cluster; obtaining the cluster to which the real-time water pressure sequence belongs based on the real-time water pressure tag, calculating the anomaly degree of the real-time water pressure sequence, comparing the anomaly degree with a preset anomaly level, and completing the intelligent control of the electric ball valve; wherein, calculating the anomaly degree includes: for the cluster to which the sequence belongs, taking any sampling time as the target time, constructing a control sequence by taking the water pressure values ​​of all water pressure sequences in the cluster at the target time, taking the average of the maximum and minimum water pressure values ​​in the control sequence as the standard water pressure at the target time, calculating the water pressure difference between the water pressure value of the real-time water pressure sequence at the target time and the standard water pressure, and calculating the ratio of the water pressure difference to the standard water pressure, and accumulating the ratio at each sampling time as the anomaly degree.

[0006] Preferably, obtaining the water pressure tag includes: for any water pressure sequence, recording the instantaneous water pressure value when the irrigation electric ball valve opens after the central control system receives the valve opening command, and using it as the water pressure tag.

[0007] Preferably, obtaining the optimal cluster includes: sorting all water pressure sequences in the water pressure database according to the size of the water pressure label, obtaining several water pressure sequences corresponding to the minimum value of the water pressure label, randomly selecting a first sequence as the first center sequence of the first cluster, calculating the distance between any water pressure sequence and the first center sequence, and if the distance is not greater than the radius response value, then any water pressure sequence is assigned to the first cluster; traversing each water pressure sequence until there is no water pressure sequence belonging to the first cluster, removing the water pressure sequences in the first cluster from the water pressure database, and traversing to obtain the first cluster, clustering the remaining water pressure sequences to obtain the optimal cluster.

[0008] Preferably, obtaining the distance includes: for any water pressure sequence, calculating the similarity between any water pressure sequence and the first center sequence, and calculating the absolute difference between 1 and the similarity; calculating the difference sequence between any water pressure sequence and the first center sequence, and obtaining the mapping value by the mean of the difference sequence through negative correlation mapping; and taking the product of the absolute difference and the mapping value as the distance between any water pressure sequence and the first center sequence.

[0009] Preferably, the similarity is the Pearson correlation coefficient.

[0010] Preferably, the water pressure range includes: obtaining all water pressure tags within any optimal cluster, and using the value range of the water pressure tags as the water pressure range of any optimal cluster.

[0011] Preferably, obtaining the cluster to which the real-time water pressure sequence belongs based on the real-time water pressure tag includes: for any optimal cluster, in response to the value of the real-time water pressure tag being within the water pressure range of any optimal cluster, then any cluster is the cluster to which the real-time water pressure sequence belongs.

[0012] Secondly, the present invention discloses an intelligent remote control system for an irrigation electric ball valve, comprising: a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, an intelligent remote control method for an irrigation electric ball valve as described in any one of the present invention is implemented.

[0013] The beneficial effects of this invention are:

[0014] This invention enables precise and intelligent control of electric ball valves by clustering historical water pressure data and combining real-time water pressure tags with anomaly detection in the water pressure sequence. By dividing the water pressure data into different clusters and determining the water pressure range for each cluster, water pressure fluctuations can be monitored in real time, and anomalies can be identified promptly. The cumulative ratio of the real-time water pressure sequence to the standard water pressure is used as a measure of the degree of anomaly, thereby intelligently adjusting the electric ball valve to ensure the stable operation of the irrigation system and prevent equipment damage or reduced irrigation efficiency caused by abnormal water pressure. Attached Figure Description

[0015] Figure 1 This is a flowchart of an intelligent remote control method for an irrigation electric ball valve according to an embodiment of the present invention. Detailed Implementation

[0016] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are some embodiments of the present invention, but not all embodiments.

[0017] The specific embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0018] Reference Figure 1 A method for intelligent remote control of an irrigation electric ball valve includes steps S1-S2, which are described in detail below.

[0019] S1: Obtain the water pressure sequence and water pressure label for any preset sampling period in history, and use a number of water pressure sequences as a water pressure database, where one water pressure sequence corresponds to one water pressure label; cluster the water pressure sequences in the water pressure database to obtain the optimal cluster, and obtain the water pressure range of the cluster.

[0020] It should be noted that using irrigation electric ball valves in high humidity environments can cause the transmission rod to corrode, affecting its normal operation. Corrosion of the transmission system can lead to instability in opening speed and opening range, resulting in inaccurate execution of remote control commands and impacting irrigation effectiveness. When the irrigation electric ball valve is closed, the water pressure inside the pipeline creates an impact force when the valve opens. Instantaneous pressure changes can cause water hammer damage to the electric ball valve. When the electric ball valve is damaged under this pressure impact and cannot close completely, leakage will occur. Especially with higher pipeline water pressure, the water hammer effect is stronger when the valve is opened, significantly shortening its lifespan. To reduce the impact of water hammer on the electric ball valve, it is recommended to gradually increase the opening range during opening. That is, within a set time, the valve opening degree should gradually increase from 10% to 100%, and this process should follow a linear change to avoid sudden pressure changes. As the irrigation electric ball valve is used for a longer period, the transmission rod may corrode. In this case, gradient control will cause the valve opening degree to gradually decrease, and the opening speed will also slow down.

[0021] In one embodiment, the opening amplitude of the irrigation electric ball valve is collected in real time by a control unit, while water pressure changes in the pipeline are monitored by a water pressure sensor inside the pipeline. When the central control system receives a valve opening command, it records the instantaneous water pressure value at that moment and labels it as a water pressure tag. To build an accurate water pressure database, water pressure sequences and corresponding water pressure tags are obtained for any preset sampling period in history. Each water pressure sequence corresponds to a specific water pressure tag, thus constructing a complete water pressure database.

[0022] The irrigation electric ball valve opens linearly during a preset sampling period, continuously recording water pressure changes to form a complete water pressure sequence. Each valve opening operation generates a water pressure tag and a corresponding water pressure sequence, which are recorded in historical data. By accumulating multiple sets of historical water pressure tags and sequences, a water pressure database is ultimately formed.

[0023] Sort all water pressure sequences in the water pressure database according to the size of their corresponding water pressure labels. Based on the sorting results, select several water pressure sequences corresponding to the minimum water pressure label value, and randomly select one of them as the first center sequence of the first cluster.

[0024] Calculate the distance between each water pressure sequence and the first central sequence. If the distance between a water pressure sequence and the first central sequence is not greater than a set radius response value, then the water pressure sequence is assigned to the first cluster. For example, the radius response value is set to 0.2, but the specific value can be set by those skilled in the art. This process continues to traverse all water pressure sequences until no more sequences meet the conditions for joining the first cluster. After the construction of the first cluster is completed, the water pressure sequences already assigned to this cluster are removed from the database. Then, the same clustering process is continued for the remaining water pressure sequences until all water pressure sequences are assigned to their respective clusters. Ultimately, multiple optimal clusters can be obtained through this clustering method.

[0025] The distance calculation process includes: for any water pressure sequence, calculating the similarity between any water pressure sequence and the first center sequence, and calculating the absolute difference between 1 and the similarity; calculating the difference sequence between any water pressure sequence and the first center sequence, and obtaining the mapping value by the mean of the difference sequence through negative correlation mapping; and taking the product of the absolute difference and the mapping value as the distance between any water pressure sequence and the first center sequence.

[0026] Distance satisfies the following relationship: , Indicates distance, Indicates the first central sequence and the hydraulic sequence The difference sequence, Indicates the first central sequence and the hydraulic sequence Pearson correlation coefficient, This represents the mean. This represents an exponential function used to negatively correlate the mean of a difference sequence.

[0027] Clustering water pressure sequences can identify and distinguish water pressure change patterns under different water pressure labels. For some water pressure labels, the changes in water pressure sequences are not significant, showing similar or stable trends. These water pressure sequences can be classified into the same category to reduce data complexity and facilitate subsequent analysis. Through clustering, the system can better understand the patterns of normal water pressure sequences, and thus quickly identify and distinguish changes that differ from normal patterns when encountering abnormal water pressure sequences. Clustering not only improves data processing efficiency but also provides a clearer benchmark for anomaly detection, thereby enabling rapid detection and response to water pressure sequence anomalies.

[0028] S2: Obtain the cluster to which the real-time water pressure sequence belongs based on the real-time water pressure tag, calculate the degree of anomaly of the real-time water pressure sequence, compare the degree of anomaly with the preset anomaly level, and complete the intelligent control of the electric ball valve.

[0029] In one embodiment, obtaining the water pressure range of the optimal cluster based on the optimal cluster obtained in step S1 includes: acquiring all water pressure tags within any optimal cluster; analyzing these water pressure tags to determine the value range of the water pressure tags within the optimal cluster, which represents the water pressure variation characteristics of the optimal cluster. Within this water pressure range, the water pressure sequences exhibit similar variation patterns and trends, and therefore can be considered as the water pressure range of the optimal cluster. This water pressure range not only helps in understanding the water pressure characteristics of the optimal cluster but also provides a benchmark for subsequent classification and anomaly detection of real-time water pressure sequences.

[0030] For any optimal cluster, if the real-time collected water pressure tag falls within the water pressure range of that cluster, then that cluster can be identified as the cluster to which the real-time water pressure sequence belongs. This is because the water pressure tag within the range represents the typical water pressure change pattern within that cluster. Therefore, when the value of the real-time water pressure tag falls within this range, it means that the change pattern of the current water pressure sequence matches the pattern of that cluster. Based on this judgment, the system will classify the real-time water pressure sequence into the corresponding cluster for further monitoring and analysis. The key to this process is to dynamically identify the water pressure change trend by comparing the real-time water pressure tag with the existing cluster water pressure range, thereby achieving real-time classification and monitoring of the water pressure status.

[0031] The calculation of the anomaly degree of the real-time water pressure sequence includes: for the corresponding cluster, taking any sampling time as the target time, constructing a control sequence by taking the water pressure values ​​of all water pressure sequences in the corresponding cluster at the target time, taking the average of the maximum and minimum water pressure values ​​in the control sequence as the standard water pressure at the target time, calculating the water pressure difference between the real-time water pressure sequence at the target time and the standard water pressure, and calculating the ratio of the water pressure difference to the standard water pressure. The sum of the ratios at each sampling time is taken as the anomaly degree.

[0032] The degree of anomaly satisfies the following relation: , Indicates the degree of abnormality. This indicates the real-time water pressure sequence at the target time. water pressure value, Indicates the target time Standard water pressure This indicates the number of sampling times within the preset sampling period.

[0033] By comparing the degree of abnormality with the preset abnormality level, intelligent control of the electric ball valve is achieved.

[0034] Specifically, in the intelligent control of electric ball valves, the first step is to detect anomalies in the real-time water pressure sequence and calculate the degree of anomaly. Then, this degree of anomaly is compared with a preset fault level to determine the current operating status of the electric ball valve. For example, the preset fault level divides the degree of anomaly into three ranges: minor fault... Medium-level faults are The serious fault is Based on the severity of the anomaly detected in real time, the system will automatically adjust the fault level of the electric ball valve to the corresponding level. If the anomaly falls within the minor fault range, the system may choose to slightly adjust the ball valve's on / off state or provide simple maintenance prompts; if the anomaly is in the moderate fault range, more complex control strategies may be required, such as reducing the ball valve's operating frequency or sending maintenance alarms; if the anomaly reaches the severe fault range, the system will immediately take emergency measures, shutting down the ball valve or performing a more comprehensive system check to prevent further damage.

[0035] The system includes a processor and a memory, the memory storing computer program instructions, which, when executed by the processor, implement an intelligent remote control method for an irrigation electric ball valve according to the first aspect of the present invention.

[0036] The system also includes other components well known to those skilled in the art, such as communication buses and communication interfaces, the settings and functions of which are known in the art and will not be described in detail here.

[0037] It should be noted that those skilled in the art can make various modifications and improvements without departing from the inventive concept, and these all fall within the scope of protection of this invention. Therefore, the scope of protection of this patent should be determined by the appended claims.

Claims

1. A method for intelligent remote control of an irrigation electric ball valve, characterized in that, include: Obtain water pressure sequences and labels for any preset sampling period in history, and use a number of water pressure sequences as a water pressure database, where each water pressure sequence corresponds to a water pressure label; cluster the water pressure sequences in the water pressure database to obtain the optimal cluster, and obtain the water pressure range of the cluster; The cluster to which the real-time water pressure sequence belongs is obtained based on the real-time water pressure tag, and the degree of anomaly of the real-time water pressure sequence is calculated. The degree of anomaly is compared with the preset anomaly level to complete the intelligent control of the electric ball valve. The calculation of the degree of abnormality includes: For each cluster, any sampling time is taken as the target time. The water pressure values ​​of all water pressure sequences in the cluster at the target time are used to construct a control sequence. The average of the maximum and minimum water pressure values ​​in the control sequence is taken as the standard water pressure at the target time. The water pressure difference between the real-time water pressure sequence at the target time and the standard water pressure is calculated. The ratio of the water pressure difference to the standard water pressure is calculated. The sum of the ratios at each sampling time is used as the degree of anomaly. The process of obtaining the optimal cluster includes: Sort all water pressure sequences in the water pressure database according to the size of the water pressure label, obtain several water pressure sequences corresponding to the minimum value of the water pressure label, select one first sequence as the first center sequence of the first cluster, calculate the distance between any water pressure sequence and the first center sequence, and if the distance is not greater than the radius response value, then any water pressure sequence is assigned to the first cluster. Traverse each water pressure sequence until there is no water pressure sequence belonging to the first cluster. Remove the water pressure sequences in the first cluster from the water pressure database. Repeat the process of traversing to obtain the first cluster. Then, cluster the remaining water pressure sequences to obtain the optimal cluster. Obtaining the distance includes: For any water pressure sequence, calculate the similarity between any water pressure sequence and the first center sequence, and calculate the absolute difference between 1 and the similarity. Calculate the difference sequence between any water pressure sequence and the first center sequence, and use an exponential function to negatively correlate the mean of the difference sequence to obtain the mapped value; The product of the absolute difference and the mapping value is used as the distance between any water pressure sequence and the first center sequence.

2. The intelligent remote control method for an irrigation electric ball valve according to claim 1, characterized in that, Obtaining the water pressure tag includes: For any water pressure sequence, record the instantaneous water pressure value when the irrigation electric ball valve opens after the central control system receives the valve opening command, and use it as a water pressure tag.

3. The intelligent remote control method for an irrigation electric ball valve according to claim 1, characterized in that, The similarity is the Pearson correlation coefficient.

4. The intelligent remote control method for an irrigation electric ball valve according to claim 1, characterized in that, The water pressure range includes: Obtain all water pressure labels within any optimal cluster, and use the range of values ​​for the water pressure labels as the water pressure range for any optimal cluster.

5. The intelligent remote control method for an irrigation electric ball valve according to claim 1, characterized in that, The clusters to which the real-time water pressure sequence belongs, obtained based on the real-time water pressure tag, include: For any optimal cluster, if the value of the real-time water pressure label falls within the water pressure range of any optimal cluster, then any cluster is the cluster to which the real-time water pressure sequence belongs.

6. An intelligent remote control system for an irrigation electric ball valve, characterized in that, include: A processor and a memory, the memory storing computer program instructions that, when executed by the processor, implement an intelligent remote control method for an irrigation electric ball valve according to any one of claims 1-5.

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