A valve control method and system based on Internet of Things

Abstract

The present invention proposes a valve control method and system based on the Internet of Things. It is characterized in that the method includes: installing a sensor at the pipeline valve, collecting pipeline-related data through the sensor, and transmitting the collected data to the edge device, and pre-processing the relevant data through the edge device; transmitting the relevant data pre-processed by the edge device to the cloud, the cloud stores the relevant data in segments, and analyzes and processes the relevant data in each segment through parallel processing to obtain analysis results; comparing the analysis results with preset thresholds, and formulating corresponding control plans based on the comparison results; and transmitting the control plan to the valve controller through the Internet of Things. By installing sensors at pipeline valves, performing preliminary processing through edge devices, and transmitting data to the cloud for processing through Internet of Things technology, the processing speed can be improved and the computing pressure on the cloud can be reduced.

Description

Technical Field

[0001] The present invention proposes a valve control method and system based on the Internet of Things, belonging to the technical field of intelligent control. Background Art

[0002] In traditional pipeline systems, the regulation of pipeline valves usually relies on manual operation, such as manually adjusting the opening of the valve to change the flow rate of the liquid in the pipeline. This operation method is not only inefficient, but also in some highly dangerous or environmentally demanding scenarios, manual operation may cause errors and even safety accidents.

[0003] With the continuous development of IoT technology, more and more devices are connected to the Internet, making it possible to make devices intelligent and automated. In the field of valve control, there are already some IoT-based solutions. However, these solutions often only focus on how to control valves through the cloud, while ignoring the real-time monitoring and adjustment of pipeline-related data during the valve control process. Summary of the invention

[0004] The present invention provides a valve control method based on the Internet of Things, which is used to solve the problems that the valve control in the prior art mostly relies on manual labor, and the control accuracy and real-time performance are not high enough:

[0005] The present invention proposes a valve control method based on the Internet of Things, the method comprising:

[0006] Install sensors at pipeline valves, collect pipeline-related data through the sensors, and transmit the collected data to edge devices to pre-process the relevant data through the edge devices;

[0007] The relevant data pre-processed by the edge device is transmitted to the cloud, the cloud stores the relevant data in segments, and analyzes and processes the relevant data in each segment through parallel processing to obtain analysis results;

[0008] The analysis result is compared with a preset threshold value, and a corresponding control plan is formulated according to the comparison result; and the control plan is transmitted to the valve controller through the Internet of Things; for example, the valve opening is changed to adjust the flow rate of the liquid in the pipeline.

[0009] After receiving the corresponding control plan, the valve controller adjusts the valve opening, and uses the sensor to monitor the pipeline related data after the valve adjustment in real time, and adjusts the valve opening in real time based on the real-time monitoring results.

[0010] Furthermore, the method includes installing a sensor at the pipeline valve, collecting pipeline-related data through the sensor, and transmitting the collected data to the edge device to pre-process the relevant data through the edge device; including:

[0011] Install sensors at pipeline valves, the sensors include pressure sensors and flow sensors, and collect pipeline-related data through the sensors, the relevant data include pressure and flow in the pipeline;

[0012] The collected relevant data is transmitted to the edge device via wired or wireless transmission. The edge device performs standardized operations on the relevant data and preprocesses the relevant data after the standardized operation. The preprocessing includes data cleaning, data calibration and anomaly detection.

[0013] Furthermore, the relevant data pre-processed by the edge device is transmitted to the cloud, the cloud stores the relevant data in segments, and analyzes and processes the relevant data in each segment through parallel processing to obtain analysis results; including:

[0014] The edge device transmits the pre-processed relevant data to the cloud via wireless transmission;

[0015] The cloud receives the relevant data transmitted by the edge device, segments the relevant data according to the characteristics of the relevant data, divides the segmented relevant data into multiple data blocks, and stores the multiple data blocks in different storage areas respectively;

[0016] Index and identify data in different storage spaces; generate parallel processing tasks to process data blocks in different storage areas, and assign each parallel processing task to a different computing node;

[0017] And obtain the resource consumption of each computing node in real time, and schedule the computing resources of each computing node through the scheduling algorithm;

[0018] After the processing is completed, the analysis results obtained from each parallel task are integrated through a merging algorithm to generate a complete data processing result; and the analysis results are stored in a database.

[0019] Furthermore, the analysis result is compared with a preset threshold, and a corresponding control scheme is formulated according to the comparison result; and the control scheme is transmitted to the valve controller through the Internet of Things; including:

[0020] Extract key data indicators from the analysis results; obtain a preset threshold, compare the key data indicators with the preset threshold, and determine whether the current condition in the pipeline is within a normal threshold range;

[0021] If it is within the normal threshold range, continue to maintain the original status unchanged;

[0022] If it is not within the normal threshold range, formulate corresponding control plans;

[0023] The formulated control scheme is encapsulated into a message or command, and the encapsulated message or command is transmitted to the target valve controller through the Internet of Things.

[0024] Furthermore, after receiving the corresponding control scheme, the valve controller adjusts the valve opening, and monitors the pipeline related data after the valve adjustment in real time through the sensor, and adjusts the valve opening in real time according to the real-time monitoring result; including:

[0025] The valve controller receives a message or command transmitted through the Internet of Things, parses the message or command, and obtains a control instruction or command;

[0026] Based on the received control instructions and parameters, the required valve opening is calculated or set according to the control scheme requirements; and the opening adjustment is performed by adjusting the valve actuator or driver;

[0027] The sensors monitor the pipeline-related data in real time, and the edge devices process and calculate the real-time monitoring results, and transmit the calculation results to the valve controller. The valve controller determines whether the current pipeline status meets the requirements based on the calculation results.

[0028] If the monitoring result is within the target range, the current valve opening is maintained and monitoring is continued;

[0029] If the monitoring result exceeds the upper limit of the target range, the valve opening is reduced;

[0030] If the monitoring result is lower than the lower limit of the target range, increase the valve opening.

[0031] The present invention proposes a valve control system based on the Internet of Things, the system comprising:

[0032] Data acquisition module: Install sensors at pipeline valves to collect pipeline-related data through the sensors, and transmit the collected data to edge devices for preprocessing of the relevant data through edge devices;

[0033] Data processing module: transmits the relevant data pre-processed by the edge device to the cloud, which stores the relevant data in segments and analyzes and processes the relevant data in each segment through parallel processing to obtain analysis results;

[0034] Plan formulation module: compare the analysis results with the preset threshold value, formulate a corresponding control plan according to the comparison result; and transmit the control plan to the valve controller through the Internet of Things; for example, change the valve opening to adjust the flow rate of the liquid in the pipeline.

[0035] Solution execution module: After receiving the corresponding control solution, the valve controller adjusts the valve opening and uses sensors to monitor the pipeline-related data after the valve adjustment in real time. Based on the real-time monitoring results, the valve opening is adjusted in real time.

[0036] Furthermore, the data acquisition module includes:

[0037] Sensor installation module: Install sensors at pipeline valves. The sensors include pressure sensors and flow sensors. The sensors are used to collect pipeline-related data, including pressure and flow in the pipeline.

[0038] Transmission module: transmits the collected relevant data to the edge device via wired or wireless transmission. The edge device performs standardized operations on the relevant data and preprocesses the relevant data after the standardized operation. The preprocessing includes data cleaning, data calibration and anomaly detection.

[0039] Furthermore, the data processing module includes:

[0040] Wireless transmission module: The edge device transmits the pre-processed relevant data to the cloud via wireless transmission;

[0041] Data segmentation module: The cloud receives the relevant data transmitted by the edge device, segments the relevant data according to the characteristics of the relevant data, divides the segmented relevant data into multiple data blocks, and stores the multiple data blocks in different storage areas respectively;

[0042] Index identification module: indexes and identifies data in different storage spaces; generates parallel processing tasks to process data blocks in different storage areas, and assigns each parallel processing task to a different computing node;

[0043] Resource scheduling module: obtains the resource consumption of each computing node in real time, and schedules the computing resources of each computing node through the scheduling algorithm;

[0044] Result merging module: After the processing is completed, the analysis results obtained from each parallel task are integrated through the merging algorithm to generate a complete data processing result; and the analysis results are stored in the database.

[0045] Furthermore, the program formulation module includes:

[0046] Threshold comparison module: extract key data indicators from the analysis results; obtain a preset threshold, compare the key data indicators with the preset threshold, and determine whether the current condition in the pipeline is within the normal threshold range;

[0047] The first judgment module: if it is within the normal threshold range, continue to maintain the original state unchanged;

[0048] The second judgment module: if it is not within the normal threshold range, formulate a corresponding control plan;

[0049] Command transmission module: encapsulates the formulated control plan into messages or commands, and transmits the encapsulated messages or commands to the target valve controller through the Internet of Things.

[0050] Furthermore, the scheme execution module includes:

[0051] Parsing module: The valve controller receives a message or command transmitted through the Internet of Things, parses the message or command, and obtains the control instruction or command;

[0052] Opening calculation module: based on the received control instructions and parameters, according to the control scheme requirements, calculate or set the required valve opening; and perform opening adjustment by adjusting the valve actuator or driver;

[0053] Status judgment module: The sensor monitors pipeline-related data in real time, processes and calculates the real-time monitoring results through edge devices, and transmits the calculation results to the valve controller. The valve controller determines whether the current pipeline status meets the requirements based on the calculation results;

[0054] The third judgment module: if the monitoring result is within the target range, the current valve opening is maintained and monitoring is continued;

[0055] Fourth judgment module: if the monitoring result exceeds the upper limit of the target range, the valve opening is reduced;

[0056] The fifth judgment module: if the monitoring result is lower than the lower limit of the target range, the valve opening is increased.

[0057] The beneficial effects of the present invention are as follows: by installing sensors at the pipeline valves, performing preliminary processing through edge devices, and transmitting data to the cloud for processing through the Internet of Things technology, the processing speed can be improved and the computing pressure on the cloud can be reduced. The real-time monitoring of the pipeline status is realized, and timely regulation is carried out to ensure that the pipeline operates within a suitable working range; the cloud stores the transmitted relevant data in segments, and analyzes and processes the data in each segment through parallel processing to obtain analysis results. In this way, the efficiency and accuracy of data processing can be effectively improved, and a basis can be provided for the formulation of subsequent regulation schemes; according to the comparison between the analysis results and the preset threshold, a corresponding regulation scheme is formulated, and the regulation scheme is transmitted to the valve controller through the Internet of Things. After receiving the regulation scheme, the valve controller automatically adjusts the opening of the valve to achieve the regulation target. In this way, the automatic regulation of the pipeline system can be realized, and the accuracy and efficiency of the regulation can be improved; by processing and calculating the real-time monitoring results, it can be judged whether the pipeline status meets the requirements. If the monitoring result exceeds the upper limit of the target range or is lower than the lower limit of the target range, the valve opening can be reduced or increased according to the situation to maintain the pipeline in a normal working state. At the same time, if an abnormal situation occurs, the system can issue an early warning to help operation and maintenance personnel deal with the fault in a timely manner; through real-time monitoring and precise regulation, the operation status of the pipeline can be optimized, the operation efficiency can be improved, and energy consumption and emissions can be reduced. This helps to reduce operating costs, improve resource utilization, and also meets the requirements of sustainable development. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 A step diagram of a valve control method based on the Internet of Things proposed by the present invention;

[0059] Figure 2 This is a module diagram of a valve control system based on the Internet of Things proposed by the present invention. DETAILED DESCRIPTION

[0060] In order to more clearly understand the above-mentioned purpose, features and advantages of the present invention, the present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0061] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. The embodiments described are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0062] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art of the present invention. The terms used in the specification of the present invention herein are only for the purpose of describing specific embodiments and are not intended to limit the present invention.

[0063] One embodiment of the present invention provides a valve control method based on the Internet of Things, the method comprising:

[0064] S1: Install a sensor at the pipeline valve, collect pipeline-related data through the sensor, and transmit the collected data to the edge device to pre-process the relevant data through the edge device;

[0065] S2: transmitting the relevant data pre-processed by the edge device to the cloud, which stores the relevant data in segments and analyzes the relevant data in each segment through parallel processing to obtain analysis results;

[0066] S3: Compare the analysis result with a preset threshold value, formulate a corresponding control plan according to the comparison result; and transmit the control plan to the valve controller through the Internet of Things; for example, change the valve opening to adjust the flow rate of the liquid in the pipeline.

[0067] S4: After receiving the corresponding control plan, the valve controller adjusts the valve opening, and monitors the pipeline related data after the valve adjustment in real time through the sensor, and adjusts the valve opening in real time according to the real-time monitoring results.

[0068] The working principle of the above technical solution is: various types of sensors are installed at the pipeline valve, such as flow sensors, pressure sensors, temperature sensors, etc. These sensors can monitor various parameters in the pipeline in real time and transmit the data to the edge device; the edge device receives the raw data collected by the sensor and performs preprocessing operations. Preprocessing includes data cleaning, data calibration, outlier detection and repair, etc., to ensure that the data transmitted to the cloud is of high quality and reliability; the data preprocessed by the edge device is transmitted to the cloud through the Internet of Things. The cloud receives the data and stores it in segments, and can also perform data backup and real-time monitoring; the cloud performs parallel processing and analysis on the received data. This includes processing data using big data technology and machine learning algorithms, such as data mining, pattern recognition, anomaly detection, etc. These analysis results can provide important basis for subsequent regulation; the analysis results are compared with the preset threshold. If the analysis result exceeds the preset threshold, the system will formulate a corresponding regulation plan according to the preset regulation strategy and logic, such as increasing or decreasing the valve opening to adjust the flow rate or pressure of the liquid in the pipeline; the formulated regulation plan is transmitted to the valve controller through the Internet of Things. After receiving the control plan, the valve controller adjusts the valve opening accordingly according to the plan requirements; after the valve is adjusted, the sensor monitors the pipeline data after the valve adjustment in real time. According to the real-time monitoring results, the valve opening is adjusted in real time. This process is a closed-loop feedback system that can continuously optimize and adjust the operation status of the pipeline to meet demand and remain within the range of safety and efficiency.

[0069] The effects of the above technical solutions are as follows: by installing sensors and using the Internet of Things technology, pipeline-related data can be monitored in real time, and the valve opening can be adjusted in real time according to the real-time monitoring results to achieve accurate pipeline flow control; by analyzing and processing the collected data, and comparing it with the preset threshold, a corresponding control plan can be formulated to achieve automatic valve adjustment. This can improve pipeline flow efficiency, reduce energy consumption, and save resources; by real-time monitoring and analysis of pipeline-related data, abnormal conditions can be discovered in time and corresponding controls can be made to ensure the safe operation of the pipeline and avoid accidents; through parallel processing and analysis in the cloud, a detailed understanding of the pipeline operation status can be obtained, and data analysis and prediction can be performed with the help of intelligent algorithms to provide a more accurate basis for pipeline control; through the application of Internet of Things technology, automated management of pipelines can be achieved, human resource investment can be reduced, and management costs can be reduced.

[0070] In one embodiment of the present invention, a sensor is installed at a pipeline valve, pipeline-related data is collected by the sensor, and the collected data is transmitted to an edge device, and the edge device is used to pre-process the relevant data, including:

[0071] S11: installing sensors at the pipeline valve, the sensors including pressure sensors and flow sensors, and collecting pipeline-related data through the sensors, the relevant data including pressure and flow in the pipeline;

[0072] S12: The collected relevant data is transmitted to the edge device by wired or wireless transmission, and the edge device performs standardization operation on the relevant data, and preprocesses the relevant data after the standardization operation, and the preprocessing includes data cleaning, data calibration and anomaly detection. Among them, the standardization operation step includes feature processing of the relevant data, generating a conversion feature vector, performing standardization calculation on the conversion feature vector, and generating a data set of the calculated relevant data; the standardization calculation formula is as follows:

[0073]

[0074] in, represents the dataset of related data after normalization calculation, F ab represents the value of the bth feature of the ath related data, P b represents the mean value of the eigenvector, and n represents the number of samples.

[0075] The working principle of the above technical solution is: install pressure sensors and flow sensors at the pipeline valves, which can collect relevant data such as pressure and flow in the pipeline in real time; transmit the relevant data collected by the sensor to the edge device by wired or wireless means. The edge device can be a device connected near the pipeline, responsible for receiving and processing sensor data; the edge device performs standardized operations on the received relevant data. The steps of standardized operation include feature processing and generating conversion feature vectors. Feature processing can include operations such as filtering and noise reduction of data, with the purpose of extracting useful feature information. After generating the conversion feature vector, the edge device calculates the average value of the feature vector and uses the standardized calculation formula to perform standardized calculation on the relevant data; after the standardized operation, the edge device preprocesses the relevant data after the standardized calculation. Preprocessing includes data cleaning, data calibration and anomaly detection. Data cleaning can remove noise, repair lost data, etc. Data calibration can map data to a suitable range to ensure the accuracy and reliability of the data. Anomaly detection can detect and handle abnormal situations in a timely manner by comparing the differences between real-time data and historical data.

[0076] The effect of the above technical solution is: by installing sensors at the pipeline valves, relevant data such as pressure and flow in the pipeline can be monitored and collected in real time, and the operating status of the pipeline system can be obtained in time; by transmitting the collected data to the edge device through wired or wireless transmission, a large amount of data can be quickly transmitted and pre-processed on the edge device. This can improve the efficiency of data transmission and processing and reduce data delay; the edge device performs pre-processing operations such as standardized operations, data cleaning, data calibration, and anomaly detection on the transmitted relevant data, which can improve the quality and accuracy of the data, remove noise and outliers, and ensure the reliability of the data; by analyzing and comparing the pre-processed data, abnormal conditions can be discovered in time and fault warnings can be issued, thereby avoiding possible accidents and losses. In addition, through the analysis and mining of data, the operation and maintenance strategy of the pipeline system can be optimized, and the reliability and efficiency of the system can be improved; through this technical solution, intelligent management of large-scale pipeline systems can be realized, including monitoring, control, prediction, etc., to improve the safety, stability and operation effect of the pipeline system. Through the application of standardized calculation formulas, data standardization, feature processing, data set generation, data cleaning and data calibration can be achieved, the quality and availability of data can be improved, and more accurate and reliable data support can be provided for subsequent data analysis and decision-making. At the same time, by performing standardized calculations on related data, data with different features can be unified into the same scale range, eliminating the dimensional differences between different features and making the data comparable; by performing feature processing on related data, information useful for problem solving can be extracted, and irrelevant or redundant data can be eliminated, thereby reducing the dimension of the data set and simplifying the subsequent data analysis and processing process; through standardized calculations, a data set of calculated related data can be generated, which contains data after feature processing and standardized operations, providing a basis for subsequent data analysis and modeling; data cleaning in preprocessing can detect and correct outliers, missing values ​​and noise in the data, and improve the quality and reliability of the data; in response to possible errors and drift problems of sensors, data calibration can be used to correct the collected data to ensure the accuracy and credibility of the data.

[0077] In one embodiment of the present invention, the relevant data pre-processed by the edge device is transmitted to the cloud, the cloud stores the relevant data in segments, and analyzes and processes the relevant data in each segment through parallel processing to obtain analysis results; including:

[0078] S21: The edge device transmits the pre-processed relevant data to the cloud via wireless transmission;

[0079] S22: The cloud receives the relevant data transmitted by the edge device, segments the relevant data according to the characteristics of the relevant data, divides the segmented relevant data into multiple data blocks, and stores the multiple data blocks in different storage areas respectively;

[0080] S23: indexing and identifying the data in different storage spaces; generating parallel processing tasks to process the data blocks in different storage areas, and assigning each parallel processing task to a different computing node;

[0081] S24: Obtain the resource consumption of each computing node in real time, and schedule the computing resources of each computing node through a scheduling algorithm; the calculation formula of the resource consumption is as follows:

[0082]

[0083] Among them, G α represents the resource consumption of the αth computing node, J expend Indicates the basic resource consumption required by the computing node when processing data, N spend represents the network overhead, ξ α represents the amount of data that the αth computing node needs to process, The total amount of data that all computing nodes need to process, represents the time required for the αth computing node to process data; Represents the total time required by all computing nodes to process data; Indicates the number of CPU cores required for the αth computing node to process data; Indicates the total number of CPU cores required by all computing nodes to process data.

[0084] S25: After the processing is completed, the analysis results obtained from each parallel task are integrated through a merging algorithm to generate a complete data processing result; and the analysis results are stored in a database.

[0085] The working principle of the above technical solution is: the edge device transmits the pre-processed relevant data to the cloud through wireless transmission; the cloud receives the relevant data transmitted by the edge device, and segments it according to the characteristics of the relevant data, and divides the segmented relevant data into multiple data blocks, and stores the multiple data blocks in different storage areas respectively; indexes and identifies the data in different storage spaces; generates parallel processing tasks to process the data blocks in different storage areas, and assigns each parallel processing task to different computing nodes; and obtains the resource consumption of each computing node in real time, and schedules the computing resources of each computing node through a scheduling algorithm; after the processing is completed, the analysis results obtained by each parallel task are integrated through a merging algorithm to generate a complete data processing result; and the analysis results are stored in a database.

[0086] The effect of the above technical solution is: the pre-processed data of the edge device is transmitted to the cloud through wireless transmission. Compared with traditional wired connections, wireless transmission can provide greater flexibility and convenience, making data transmission more efficient; after receiving the data, the cloud segmented the data according to the characteristics of the data, divided the data into multiple data blocks, and stored them in different storage areas. At the same time, indexes and identifiers are established for the data in each storage space for subsequent retrieval and management. This segmented storage and indexing strategy can improve the reading and writing speed and storage efficiency of data; the cloud generates parallel processing tasks, processes the data blocks in different storage areas, and assigns these tasks to different computing nodes for parallel processing. By obtaining the resource consumption of each computing node in real time and dynamically scheduling the computing resources through the scheduling algorithm, the computing resources can be fully utilized to improve the data processing speed and efficiency; after the processing is completed, the analysis results obtained by each parallel task are integrated through the merging algorithm to generate a complete data processing result. These analysis results can be stored in the database for subsequent query, analysis and use. Through the storage and management of the database, users can access and obtain data processing results at any time. The above formula can help evaluate the resource consumption of each computing node in real time, and reasonably allocate and schedule computing resources through scheduling algorithms, thereby improving the efficiency and performance of the entire system. This formula takes into account factors such as the basic resource consumption of computing nodes, network overhead, data volume, processing time, and number of CPU cores, making resource scheduling more intelligent and accurate. Through this formula, the system can dynamically schedule according to the resource consumption of different computing nodes, thereby avoiding resource waste and improving system utilization. This ensures that the system can use computing resources more efficiently when processing large-scale data, reducing processing time and improving the speed and efficiency of data processing. At the same time, this formula also helps to optimize the allocation of parallel tasks in the data processing process, ensure that the load of each computing node is relatively balanced, avoid excessive concentration or idleness of resources, and further improve the overall performance and stability of the system.

[0087] In one embodiment of the present invention, the analysis result is compared with a preset threshold value, a corresponding control scheme is formulated according to the comparison result; and the control scheme is transmitted to the valve controller through the Internet of Things; including:

[0088] S31: extracting key data indicators from the analysis results; obtaining a preset threshold, comparing the key data indicators with the preset threshold, and determining whether the current condition in the pipeline is within a normal threshold range;

[0089] S32: If it is within the normal threshold range, continue to maintain the original state;

[0090] S33: If it is not within the normal threshold range, formulate a corresponding control plan;

[0091] S34: Encapsulate the formulated control scheme into a message or command, and transmit the encapsulated message or command to the target valve controller through the Internet of Things.

[0092] The working principle of the above technical solution is: connect sensor devices through the Internet of Things, collect various parameters, status and other data in the pipeline in real time, and upload these data to the cloud; the cloud processes and analyzes the received data, extracts key data indicators, and compares them with the preset thresholds to determine whether the current condition in the pipeline is within the normal threshold range; if the condition in the pipeline is not within the normal threshold range, formulate a corresponding control plan based on the analysis results and the preset threshold. For example, if the pressure is too high, it is necessary to reduce the water supply or open the pressure relief valve, etc.; encapsulate the formulated control plan into a message or command, and transmit it to the target valve controller through the Internet of Things, and establish an index for accurate transmission. The valve controller automatically adjusts according to the received command to return the condition in the pipeline to the normal threshold range.

[0093] The effect of the above technical solution is: by collecting key data indicators in the pipeline in real time, such as pressure, flow, temperature, etc., the system can promptly detect abnormal conditions in the pipeline, including leakage, excessive or low pressure, etc. Once an abnormality is found, the system can respond immediately to avoid potential safety risks; according to the analysis results and preset thresholds, the system can automatically formulate corresponding control plans, such as adjusting the valve opening, changing the water supply / gas supply, etc. These control plans will be encapsulated into messages or commands and transmitted to the target valve controller through the Internet of Things to achieve automatic adjustment. This automated control greatly reduces the need for human intervention and improves the efficiency and reliability of pipeline operation; the preset thresholds and automated control plans set by the system can help achieve fault warning. Once the conditions in the pipeline exceed the normal range, the system will immediately issue a warning signal and take corresponding preventive measures to reduce the losses caused by pipeline failures; the automated monitoring and control process reduces the demand for human resources and reduces maintenance costs. It reduces the continuous monitoring and intervention of personnel, improves management efficiency, and reduces operating costs; through timely control and maintenance, the system can ensure the stable operation of the pipeline, reduce the possibility of accidents, and improve the safety and reliability of the pipeline.

[0094] In one embodiment of the present invention, after receiving the corresponding control scheme, the valve controller adjusts the valve opening, and monitors the pipeline related data after the valve adjustment in real time through the sensor, and adjusts the valve opening in real time according to the real-time monitoring result; comprising:

[0095] S41: The valve controller receives a message or command transmitted through the Internet of Things, parses the message or command, and obtains a control instruction or command;

[0096] S42: Based on the received control instructions and parameters, the required valve opening is calculated or set according to the control scheme requirements; and the opening is adjusted by adjusting the actuator or driver of the valve;

[0097] S43: Real-time monitoring of pipeline-related data is performed through sensors, and real-time monitoring results are processed and calculated through edge devices, and the calculation results are transmitted to the valve controller, and the valve controller determines whether the current pipeline state meets the requirements according to the calculation results;

[0098] S44: If the monitoring result is within the target range, the current valve opening is maintained and monitoring is continued;

[0099] S45: If the monitoring result exceeds the upper limit of the target range, the valve opening is reduced;

[0100] S46: If the monitoring result is lower than the lower limit of the target range, the valve opening is increased.

[0101] The working principle of the above technical solution is as follows: the valve controller receives the transmitted message or command through the Internet of Things, and parses and obtains the control instruction or command; based on the received control instruction and parameters, the valve controller calculates or sets the required valve opening according to the control scheme requirements. This may involve some algorithms and logic to ensure that the valve opening can meet the requirements of pipeline operation; the valve controller actually performs the adjustment of the valve opening by adjusting the valve actuator or driver. This may involve electric actuators, pneumatic actuators, etc., which are adjusted according to the specific valve type and control requirements; the valve controller monitors the pipeline-related data in real time through sensors. These sensors can detect key indicators such as pressure, flow, and temperature to obtain data on the current state of the pipeline; the valve controller processes and calculates the real-time monitoring results through edge devices. This may include filtering, analyzing, and counting the data to obtain more accurate pipeline status information; based on the calculation results, the valve controller determines whether the current pipeline status meets the requirements. If the monitoring result is within the target range, the valve controller will maintain the current valve opening and continue to monitor. If the monitoring result exceeds the upper limit of the target range, the valve controller will reduce the valve opening. If the monitoring result is lower than the lower limit of the target range, the valve controller will increase the valve opening.

[0102] The effect of the above technical solution is: by real-time monitoring of pipeline-related data and real-time adjustment of the valve opening according to the monitoring results, real-time dynamic regulation of pipeline fluid can be achieved. For example, when the pipeline load suddenly increases, the system can immediately adjust the valve opening to ensure stable fluid flow, thereby effectively responding to emergencies and ensuring the normal operation of the pipeline system; precise valve control can adjust the fluid according to actual needs to avoid unnecessary energy waste. By optimizing the valve opening, the system can achieve optimal fluid control, thereby reducing energy consumption, improving the energy efficiency of the system, and achieving the effect of energy conservation and emission reduction; the technical solution can accurately calculate the required valve opening according to the specific control scheme requirements and monitoring data, and realize accurate valve adjustment through the actuator. This precise control can ensure that the pipeline system can maintain a stable operating state under various working conditions, and improve the reliability and stability of the pipeline system; by real-time monitoring of the pipeline state and adjusting the valve opening according to the monitoring results, abnormal pipeline conditions can be discovered in time and measures can be taken to adjust them. In this way, the system can prevent pipeline accidents, improve the safety and reliability of the pipeline system, and reduce maintenance and repair costs; data analysis and optimization can be performed by processing and calculating the real-time monitoring results through edge devices. The system can analyze historical data, predict pipeline operation status and make corresponding adjustments, providing a more scientific and convenient decision-making basis for pipeline system operation and improving the intelligence level of the pipeline system.

[0103] One embodiment of the present invention is a valve control system based on the Internet of Things, the system comprising:

[0104] Data acquisition module: Install sensors at pipeline valves to collect pipeline-related data through the sensors, and transmit the collected data to edge devices for preprocessing of the relevant data through edge devices;

[0105] Data processing module: transmits the relevant data pre-processed by the edge device to the cloud, which stores the relevant data in segments and analyzes and processes the relevant data in each segment through parallel processing to obtain analysis results;

[0106] Plan formulation module: compare the analysis results with the preset threshold value, formulate a corresponding control plan according to the comparison result; and transmit the control plan to the valve controller through the Internet of Things; for example, change the valve opening to adjust the flow rate of the liquid in the pipeline.

[0107] Solution execution module: After receiving the corresponding control solution, the valve controller adjusts the valve opening and uses sensors to monitor the pipeline-related data after the valve adjustment in real time. Based on the real-time monitoring results, the valve opening is adjusted in real time.

[0108] The working principle of the above technical solution is: various types of sensors are installed at the pipeline valve, such as flow sensors, pressure sensors, temperature sensors, etc. These sensors can monitor various parameters in the pipeline in real time and transmit the data to the edge device; the edge device receives the raw data collected by the sensor and performs preprocessing operations. Preprocessing includes data cleaning, data calibration, outlier detection and repair, etc., to ensure that the data transmitted to the cloud is of high quality and reliability; the data preprocessed by the edge device is transmitted to the cloud through the Internet of Things. The cloud receives the data and stores it in segments, and can also perform data backup and real-time monitoring; the cloud performs parallel processing and analysis on the received data. This includes processing data using big data technology and machine learning algorithms, such as data mining, pattern recognition, anomaly detection, etc. These analysis results can provide important basis for subsequent regulation; the analysis results are compared with the preset threshold. If the analysis result exceeds the preset threshold, the system will formulate a corresponding regulation plan according to the preset regulation strategy and logic, such as increasing or decreasing the valve opening to adjust the flow rate or pressure of the liquid in the pipeline; the formulated regulation plan is transmitted to the valve controller through the Internet of Things. After receiving the control plan, the valve controller adjusts the valve opening accordingly according to the plan requirements; after the valve is adjusted, the sensor monitors the pipeline data after the valve adjustment in real time. According to the real-time monitoring results, the valve opening is adjusted in real time. This process is a closed-loop feedback system that can continuously optimize and adjust the operation status of the pipeline to meet demand and remain within the range of safety and efficiency.

[0109] The effects of the above technical solutions are as follows: by installing sensors and using the Internet of Things technology, pipeline-related data can be monitored in real time, and the valve opening can be adjusted in real time according to the real-time monitoring results to achieve accurate pipeline flow control; by analyzing and processing the collected data, and comparing it with the preset threshold, a corresponding control plan can be formulated to achieve automatic valve adjustment. This can improve pipeline flow efficiency, reduce energy consumption, and save resources; by real-time monitoring and analysis of pipeline-related data, abnormal conditions can be discovered in time and corresponding controls can be made to ensure the safe operation of the pipeline and avoid accidents; through parallel processing and analysis in the cloud, a detailed understanding of the pipeline operation status can be obtained, and data analysis and prediction can be performed with the help of intelligent algorithms to provide a more accurate basis for pipeline control; through the application of Internet of Things technology, automated management of pipelines can be achieved, human resource investment can be reduced, and management costs can be reduced.

[0110] In one embodiment of the present invention, the data acquisition module includes:

[0111] Sensor installation module: Install sensors at pipeline valves. The sensors include pressure sensors and flow sensors. The sensors are used to collect pipeline-related data, including pressure and flow in the pipeline.

[0112] Transmission module: transmits the collected relevant data to the edge device via wired or wireless transmission. The edge device performs standardized operations on the relevant data and preprocesses the relevant data after the standardized operation. The preprocessing includes data cleaning, data calibration and anomaly detection.

[0113] The step of standardization operation includes performing feature processing on the relevant data, generating a conversion feature vector, performing standardization calculation on the conversion feature vector, and generating a data set of the calculated relevant data; the standardization calculation formula is as follows:

[0114]

[0115] in, represents the dataset of related data after normalization calculation, F ab represents the value of the bth feature of the ath related data, P b represents the mean value of the eigenvector, and n represents the number of samples.

[0116] The working principle of the above technical solution is: install pressure sensors and flow sensors at the pipeline valves, which can collect relevant data such as pressure and flow in the pipeline in real time; transmit the relevant data collected by the sensor to the edge device by wired or wireless means. The edge device can be a device connected near the pipeline, responsible for receiving and processing sensor data; the edge device performs standardized operations on the received relevant data. The steps of standardized operation include feature processing and generating conversion feature vectors. Feature processing can include operations such as filtering and noise reduction of data, with the purpose of extracting useful feature information. After generating the conversion feature vector, the edge device calculates the average value of the feature vector and uses the standardized calculation formula to perform standardized calculation on the relevant data; after the standardized operation, the edge device preprocesses the relevant data after the standardized calculation. Preprocessing includes data cleaning, data calibration and anomaly detection. Data cleaning can remove noise, repair lost data, etc. Data calibration can map data to a suitable range to ensure the accuracy and reliability of the data. Anomaly detection can detect and handle abnormal situations in a timely manner by comparing the differences between real-time data and historical data.

[0117] The effect of the above technical solution is: by installing sensors at the pipeline valves, relevant data such as pressure and flow in the pipeline can be monitored and collected in real time, and the operating status of the pipeline system can be obtained in time; by transmitting the collected data to the edge device through wired or wireless transmission, a large amount of data can be quickly transmitted and pre-processed on the edge device. This can improve the efficiency of data transmission and processing and reduce data delay; the edge device performs pre-processing operations such as standardized operations, data cleaning, data calibration, and anomaly detection on the transmitted relevant data, which can improve the quality and accuracy of the data, remove noise and outliers, and ensure the reliability of the data; by analyzing and comparing the pre-processed data, abnormal conditions can be discovered in time and fault warnings can be issued, thereby avoiding possible accidents and losses. In addition, through the analysis and mining of data, the operation and maintenance strategy of the pipeline system can be optimized, and the reliability and efficiency of the system can be improved; through this technical solution, intelligent management of large-scale pipeline systems can be realized, including monitoring, control, prediction, etc., to improve the safety, stability and operation effect of the pipeline system. By applying standardized calculation formulas, data standardization, feature processing, data set generation, data cleaning and data calibration can be achieved, the quality and availability of data can be improved, and more accurate and reliable data support can be provided for subsequent data analysis and decision-making. At the same time, by performing standardized calculations on related data, data with different features can be unified into the same scale range, eliminating the dimensional differences between different features and making the data comparable; by performing feature processing on related data, information useful for problem solving can be extracted, and irrelevant or redundant data can be eliminated, thereby reducing the dimension of the data set and simplifying the subsequent data analysis and processing process; by standardized calculations, a data set of calculated related data can be generated, which contains data after feature processing and standardized operations, providing a basis for subsequent data analysis and modeling; by performing data cleaning in preprocessing, outliers, missing values ​​and noise in the data can be detected and corrected, and the quality and reliability of the data can be improved; in view of possible errors and drift problems of sensors, the collected data can be corrected through data calibration to ensure the accuracy and credibility of the data. In one embodiment of the present invention, the data processing module includes:

[0118] Wireless transmission module: The edge device transmits the pre-processed relevant data to the cloud via wireless transmission;

[0119] Data segmentation module: The cloud receives the relevant data transmitted by the edge device, segments the relevant data according to the characteristics of the relevant data, divides the segmented relevant data into multiple data blocks, and stores the multiple data blocks in different storage areas respectively;

[0120] Index identification module: indexes and identifies data in different storage spaces; generates parallel processing tasks to process data blocks in different storage areas, and assigns each parallel processing task to a different computing node;

[0121] Resource scheduling module: obtains the resource consumption of each computing node in real time, and schedules the computing resources of each computing node through a scheduling algorithm; the calculation formula for the resource consumption is as follows:

[0122]

[0123] Among them, G α represents the resource consumption of the αth computing node, J expend Indicates the basic resource consumption required by the computing node when processing data, N expend represents the network overhead, ξ α represents the amount of data that the αth computing node needs to process, The total amount of data that all computing nodes need to process, represents the time required for the αth computing node to process data; Represents the total time required by all computing nodes to process data; Indicates the number of CPU cores required for the αth computing node to process data; Indicates the total number of CPU cores required by all computing nodes to process data.

[0124] Result merging module: After the processing is completed, the analysis results obtained from each parallel task are integrated through the merging algorithm to generate a complete data processing result; and the analysis results are stored in the database.

[0125] The working principle of the above technical solution is: the edge device transmits the pre-processed relevant data to the cloud through wireless transmission; the cloud receives the relevant data transmitted by the edge device, and segments it according to the characteristics of the relevant data, and divides the segmented relevant data into multiple data blocks, and stores the multiple data blocks in different storage areas respectively; indexes and identifies the data in different storage spaces; generates parallel processing tasks to process the data blocks in different storage areas, and assigns each parallel processing task to different computing nodes; and obtains the resource consumption of each computing node in real time, and schedules the computing resources of each computing node through a scheduling algorithm; after the processing is completed, the analysis results obtained by each parallel task are integrated through a merging algorithm to generate a complete data processing result; and the analysis results are stored in a database.

[0126] The effect of the above technical solution is: the pre-processed data of the edge device is transmitted to the cloud through wireless transmission. Compared with traditional wired connections, wireless transmission can provide greater flexibility and convenience, making data transmission more efficient; after receiving the data, the cloud segmented the data according to the characteristics of the data, divided the data into multiple data blocks, and stored them in different storage areas. At the same time, indexes and identifiers are established for the data in each storage space for subsequent retrieval and management. This segmented storage and indexing strategy can improve the reading and writing speed and storage efficiency of data; the cloud generates parallel processing tasks, processes the data blocks in different storage areas, and assigns these tasks to different computing nodes for parallel processing. By obtaining the resource consumption of each computing node in real time and dynamically scheduling the computing resources through the scheduling algorithm, the computing resources can be fully utilized to improve the data processing speed and efficiency; after the processing is completed, the analysis results obtained by each parallel task are integrated through the merging algorithm to generate a complete data processing result. These analysis results can be stored in the database for subsequent query, analysis and use. Through the storage and management of the database, users can access and obtain data processing results at any time. The above formula can help evaluate the resource consumption of each computing node in real time, and reasonably allocate and schedule computing resources through scheduling algorithms, thereby improving the efficiency and performance of the entire system. This formula takes into account factors such as the basic resource consumption of computing nodes, network overhead, data volume, processing time, and number of CPU cores, making resource scheduling more intelligent and accurate. Through this formula, the system can dynamically schedule according to the resource consumption of different computing nodes, thereby avoiding resource waste and improving system utilization. This ensures that the system can use computing resources more efficiently when processing large-scale data, reducing processing time and improving the speed and efficiency of data processing. At the same time, this formula also helps to optimize the allocation of parallel tasks in the data processing process, ensure that the load of each computing node is relatively balanced, avoid excessive concentration or idleness of resources, and further improve the overall performance and stability of the system.

[0127] In one embodiment of the present invention, the solution formulation module includes:

[0128] Threshold comparison module: extract key data indicators from the analysis results; obtain a preset threshold, compare the key data indicators with the preset threshold, and determine whether the current condition in the pipeline is within the normal threshold range;

[0129] The first judgment module: if it is within the normal threshold range, continue to maintain the original state unchanged;

[0130] The second judgment module: if it is not within the normal threshold range, formulate a corresponding control plan;

[0131] Command transmission module: encapsulates the formulated control plan into messages or commands, and transmits the encapsulated messages or commands to the target valve controller through the Internet of Things.

[0132] The working principle of the above technical solution is: connect sensor devices through the Internet of Things, collect various parameters, status and other data in the pipeline in real time, and upload these data to the cloud; the cloud processes and analyzes the received data, extracts key data indicators, and compares them with the preset thresholds to determine whether the current condition in the pipeline is within the normal threshold range; if the condition in the pipeline is not within the normal threshold range, formulate a corresponding control plan based on the analysis results and the preset threshold. For example, if the pressure is too high, it is necessary to reduce the water supply or open the pressure relief valve, etc.; encapsulate the formulated control plan into a message or command, and transmit it to the target valve controller through the Internet of Things, and establish an index for accurate transmission. The valve controller automatically adjusts according to the received command to return the condition in the pipeline to the normal threshold range.

[0133] The effect of the above technical solution is: by collecting key data indicators in the pipeline in real time, such as pressure, flow, temperature, etc., the system can promptly detect abnormal conditions in the pipeline, including leakage, excessive or low pressure, etc. Once an abnormality is found, the system can respond immediately to avoid potential safety risks; according to the analysis results and preset thresholds, the system can automatically formulate corresponding control plans, such as adjusting the valve opening, changing the water supply / gas supply, etc. These control plans will be encapsulated into messages or commands and transmitted to the target valve controller through the Internet of Things to achieve automatic adjustment. This automated control greatly reduces the need for human intervention and improves the efficiency and reliability of pipeline operation; the preset thresholds and automated control plans set by the system can help achieve fault warning. Once the conditions in the pipeline exceed the normal range, the system will immediately issue a warning signal and take corresponding preventive measures to reduce the losses caused by pipeline failures; the automated monitoring and control process reduces the demand for human resources and reduces maintenance costs. It reduces the continuous monitoring and intervention of personnel, improves management efficiency, and reduces operating costs; through timely control and maintenance, the system can ensure the stable operation of the pipeline, reduce the possibility of accidents, and improve the safety and reliability of the pipeline.

[0134] In one embodiment of the present invention, the solution execution module includes:

[0135] Parsing module: The valve controller receives a message or command transmitted through the Internet of Things, parses the message or command, and obtains the control instruction or command;

[0136] Opening calculation module: based on the received control instructions and parameters, according to the control scheme requirements, calculate or set the required valve opening; and perform opening adjustment by adjusting the valve actuator or driver;

[0137] Status judgment module: The sensor monitors pipeline-related data in real time, processes and calculates the real-time monitoring results through edge devices, and transmits the calculation results to the valve controller. The valve controller determines whether the current pipeline status meets the requirements based on the calculation results;

[0138] The third judgment module: if the monitoring result is within the target range, the current valve opening is maintained and monitoring is continued;

[0139] Fourth judgment module: if the monitoring result exceeds the upper limit of the target range, the valve opening is reduced;

[0140] The fifth judgment module: if the monitoring result is lower than the lower limit of the target range, the valve opening is increased.

[0141] The working principle of the above technical solution is as follows: the valve controller receives the transmitted message or command through the Internet of Things, and parses and obtains the control instruction or command; based on the received control instruction and parameters, the valve controller calculates or sets the required valve opening according to the control scheme requirements. This may involve some algorithms and logic to ensure that the valve opening can meet the requirements of pipeline operation; the valve controller actually performs the adjustment of the valve opening by adjusting the valve actuator or driver. This may involve electric actuators, pneumatic actuators, etc., which are adjusted according to the specific valve type and control requirements; the valve controller monitors the pipeline-related data in real time through sensors. These sensors can detect key indicators such as pressure, flow, and temperature to obtain data on the current state of the pipeline; the valve controller processes and calculates the real-time monitoring results through edge devices. This may include filtering, analyzing, and counting the data to obtain more accurate pipeline status information; based on the calculation results, the valve controller determines whether the current pipeline status meets the requirements. If the monitoring result is within the target range, the valve controller will maintain the current valve opening and continue to monitor. If the monitoring result exceeds the upper limit of the target range, the valve controller will reduce the valve opening. If the monitoring result is lower than the lower limit of the target range, the valve controller will increase the valve opening.

[0142] The effect of the above technical solution is: by real-time monitoring of pipeline-related data and real-time adjustment of the valve opening according to the monitoring results, real-time dynamic regulation of pipeline fluid can be achieved. For example, when the pipeline load suddenly increases, the system can immediately adjust the valve opening to ensure stable fluid flow, thereby effectively responding to emergencies and ensuring the normal operation of the pipeline system; precise valve control can adjust the fluid according to actual needs to avoid unnecessary energy waste. By optimizing the valve opening, the system can achieve optimal fluid control, thereby reducing energy consumption, improving the energy efficiency of the system, and achieving the effect of energy conservation and emission reduction; the technical solution can accurately calculate the required valve opening according to the specific control scheme requirements and monitoring data, and realize accurate valve adjustment through the actuator. This precise control can ensure that the pipeline system can maintain a stable operating state under various working conditions, and improve the reliability and stability of the pipeline system; by real-time monitoring of the pipeline state and adjusting the valve opening according to the monitoring results, abnormal pipeline conditions can be discovered in time and measures can be taken to adjust them. In this way, the system can prevent pipeline accidents, improve the safety and reliability of the pipeline system, and reduce maintenance and repair costs; data analysis and optimization can be performed by processing and calculating the real-time monitoring results through edge devices. The system can analyze historical data, predict pipeline operation status and make corresponding adjustments, providing a more scientific and convenient decision-making basis for pipeline system operation and improving the intelligence level of the pipeline system.

[0143] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A valve control method based on the Internet of Things, It is characterized in that The method comprises: Install sensors at pipeline valves, collect pipeline-related data through the sensors, and transmit the collected data to edge devices to pre-process the relevant data through the edge devices; The relevant data pre-processed by the edge device is transmitted to the cloud, the cloud stores the relevant data in segments, and analyzes and processes the relevant data in each segment through parallel processing to obtain analysis results; Comparing the analysis result with a preset threshold, formulating a corresponding control plan according to the comparison result; and transmitting the control plan to the valve controller through the Internet of Things; After receiving the corresponding control plan, the valve controller adjusts the valve opening, and uses sensors to monitor the pipeline-related data after the valve adjustment in real time, and adjusts the valve opening in real time based on the real-time monitoring results; The method comprises installing a sensor at the pipeline valve, collecting pipeline-related data through the sensor, and transmitting the collected data to the edge device to pre-process the relevant data through the edge device; including: Install sensors at pipeline valves, the sensors include pressure sensors and flow sensors, and collect pipeline-related data through the sensors, the relevant data include pressure and flow in the pipeline; The collected relevant data is transmitted to the edge device by wired or wireless transmission, and the edge device performs standardized operation on the relevant data and preprocesses the relevant data after the standardized operation, and the preprocessing includes data cleaning, data calibration and anomaly detection; The relevant data pre-processed by the edge device is transmitted to the cloud, the cloud stores the relevant data in segments, and analyzes and processes the relevant data in each segment through parallel processing to obtain analysis results; including: The edge device transmits the pre-processed relevant data to the cloud via wireless transmission; The cloud receives the relevant data transmitted by the edge device, segments the relevant data according to the characteristics of the relevant data, divides the segmented relevant data into multiple data blocks, and stores the multiple data blocks in different storage areas respectively; Index and identify data in different storage spaces; generate parallel processing tasks to process data blocks in different storage areas, and assign each parallel processing task to a different computing node; The resource consumption of each computing node is obtained in real time, and the computing resources of each computing node are scheduled through the scheduling algorithm; the calculation formula of the resource consumption is as follows: in, Indicates The resource consumption of computing nodes, Indicates the basic resource consumption required by the computing node when processing data. Indicates the network overhead, Indicates The amount of data that needs to be processed by each computing node. The total amount of data that all computing nodes need to process, Indicates The time required for a computing node to process data; Represents the total time required by all computing nodes to process data; Indicates The number of CPU cores required for each computing node to process data; Indicates the total number of CPU cores required by the computing node to process data; After the processing is completed, the analysis results obtained from each parallel task are integrated through a merging algorithm to generate a complete data processing result; and the analysis results are stored in a database; The collected relevant data is transmitted to the edge device by wired or wireless transmission, the edge device performs standardized operation on the relevant data, and preprocesses the relevant data after the standardized operation, and the preprocessing includes data cleaning, data calibration and anomaly detection; including: The valve controller receives a message or command transmitted through the Internet of Things, parses the message or command, and obtains a control instruction or command; Based on the received control instructions and parameters, the required valve opening is calculated or set according to the control scheme requirements; and the opening adjustment is performed by adjusting the valve actuator or driver; The sensors monitor the pipeline-related data in real time, and the edge devices process and calculate the real-time monitoring results, and transmit the calculation results to the valve controller. The valve controller determines whether the current pipeline status meets the requirements based on the calculation results. If the monitoring result is within the target range, the current valve opening is maintained and monitoring is continued; If the monitoring result exceeds the upper limit of the target range, the valve opening is reduced; If the monitoring result is lower than the lower limit of the target range, increase the valve opening.

2. According to the valve control method based on the Internet of Things as described in claim 1, It is characterized in that The analysis result is compared with a preset threshold value, and a corresponding control plan is formulated according to the comparison result; And transmitting the control scheme to the valve controller through the Internet of Things; including: Extract key data indicators from the analysis results; obtain a preset threshold, compare the key data indicators with the preset threshold, and determine whether the current condition in the pipeline is within a normal threshold range; If it is within the normal threshold range, continue to maintain the original status unchanged; If it is not within the normal threshold range, formulate corresponding control plans; The formulated control scheme is encapsulated into a message or command, and the encapsulated message or command is transmitted to the target valve controller through the Internet of Things.

3. A valve control system based on the Internet of Things, It is characterized in that The system comprises: Data acquisition module: Install sensors at pipeline valves to collect pipeline-related data through the sensors, and transmit the collected data to edge devices for preprocessing of the relevant data through edge devices; Data processing module: transmits the relevant data pre-processed by the edge device to the cloud, which stores the relevant data in segments and analyzes and processes the relevant data in each segment through parallel processing to obtain analysis results; Solution formulation module: compare the analysis result with the preset threshold value, formulate a corresponding control plan according to the comparison result; and transmit the control plan to the valve controller through the Internet of Things; Solution execution module: After receiving the corresponding control solution, the valve controller adjusts the valve opening, and uses sensors to monitor the pipeline-related data after the valve adjustment in real time. According to the real-time monitoring results, the valve opening is adjusted in real time; The data acquisition module comprises: Sensor installation module: Install sensors at pipeline valves. The sensors include pressure sensors and flow sensors. The sensors are used to collect pipeline-related data, including pressure and flow in the pipeline. Transmission module: transmits the collected relevant data to the edge device through wired or wireless transmission, and the edge device performs standardized operation on the relevant data and preprocesses the relevant data after the standardized operation, and the preprocessing includes data cleaning, data calibration and anomaly detection; The data processing module comprises: Wireless transmission module: The edge device transmits the pre-processed relevant data to the cloud via wireless transmission; Data segmentation module: The cloud receives the relevant data transmitted by the edge device, segments the relevant data according to the characteristics of the relevant data, divides the segmented relevant data into multiple data blocks, and stores the multiple data blocks in different storage areas respectively; Index identification module: indexes and identifies data in different storage spaces; generates parallel processing tasks to process data blocks in different storage areas, and assigns each parallel processing task to a different computing node; Resource scheduling module: obtains the resource consumption of each computing node in real time, and schedules the computing resources of each computing node through a scheduling algorithm; the calculation formula for the resource consumption is as follows: in, Indicates The resource consumption of computing nodes, Indicates the basic resource consumption required by the computing node when processing data. Indicates the network overhead, Indicates The amount of data that needs to be processed by each computing node. The total amount of data that all computing nodes need to process, Indicates The time required for a computing node to process data; Represents the total time required by all computing nodes to process data; Indicates The number of CPU cores required for each computing node to process data; Indicates the total number of CPU cores required by the computing node to process data; Result merging module: after the processing is completed, the analysis results obtained by each parallel task are integrated through the merging algorithm to generate a complete data processing result; and the analysis results are stored in the database; The scheme execution module includes: Parsing module: The valve controller receives a message or command transmitted through the Internet of Things, parses the message or command, and obtains the control instruction or command; Opening calculation module: based on the received control instructions and parameters, according to the control scheme requirements, calculate or set the required valve opening; and perform opening adjustment by adjusting the valve actuator or driver; Status judgment module: The sensor monitors pipeline-related data in real time, processes and calculates the real-time monitoring results through edge devices, and transmits the calculation results to the valve controller. The valve controller determines whether the current pipeline status meets the requirements based on the calculation results; The third judgment module: if the monitoring result is within the target range, the current valve opening is maintained and monitoring is continued; Fourth judgment module: if the monitoring result exceeds the upper limit of the target range, the valve opening is reduced; The fifth judgment module: if the monitoring result is lower than the lower limit of the target range, the valve opening is increased.

4. According to the valve control system based on the Internet of Things as claimed in claim 3, It is characterized in that The program formulation module includes: Threshold comparison module: extract key data indicators from the analysis results; obtain a preset threshold, compare the key data indicators with the preset threshold, and determine whether the current condition in the pipeline is within the normal threshold range; The first judgment module: if it is within the normal threshold range, continue to maintain the original state unchanged; The second judgment module: if it is not within the normal threshold range, formulate a corresponding control plan; Command transmission module: encapsulates the formulated control plan into messages or commands, and transmits the encapsulated messages or commands to the target valve controller through the Internet of Things.

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