Intelligent flow measurement and control system of Internet of Things

Abstract

The invention discloses an intelligent flow measurement and control system of the Internet of Things. The system comprises a PHM platform, a data monitoring module, a control module, a management module, an analysis optimization module and a data storage module. The PHM platform is responsible for performing unified management on other modules; the data monitoring module is responsible for monitoring and collecting various operation data of flow in the pipeline in real time; the control module is responsible for receiving and executing an instruction of the PHM platform and adaptively adjusting the flow state of the pipeline by applying a dynamic control method; the management module can realize integrity management of the pipeline and provide a quick and effective operation and maintenance management planning scheme for a user; the analysis optimization module is responsible for optimizing the flow measurement and control system and can reduce the operation cost and energy consumption of the measurement and control system; the storage module is responsible for recording and storing operation data in the measurement and control system. Unified management of the measurement and control system is realized through the PHM platform, lean management of the flow measurement and control system can be realized, and harm caused by emergency accidents is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of flow measurement and control, and in particular to an intelligent flow measurement and control system of the Internet of Things. Background Art

[0002] Pipelines are the most important and safest way to transport dangerous substances such as oil and natural gas. Oil and gas pipelines occupy an extremely important strategic position in the national economy and are known as the national energy artery. In recent years, with the rapid development of the economy and society, the development of the Internet of Things in the field of pipeline transportation has achieved remarkable results, and information-based operation and management have been deeply rooted in the hearts of the people. However, informatization and digitalization are mainly used as auxiliary tools, but have not played a core role in the measurement and control of pipeline flow.

[0003] During the long transportation process of pipeline fluid, it is not only necessary to measure and control the pipeline flow, but also to manage the pipeline integrity. While ensuring the safety of the pipeline, it is necessary to be fully prepared for the handling of emergencies to avoid catastrophic pipeline accidents such as leakage, explosion, casualties, environmental pollution, etc. caused by third-party construction, drilling and oil and gas theft, pipeline corrosion, geological disasters, etc. Therefore, it is very important to combine the Internet of Things with pipeline flow measurement and control to realize the construction of intelligent control systems.

[0004] Chinese invention patent CN112597441A discloses a flow detection system with data analysis and Internet data sharing functions, including a measurement host, which is used to send measurement data to a mobile terminal or receive mobile terminal operation instructions through short-range wireless communication technology; a cloud server, which is used to store the measurement data uploaded by the mobile terminal; and a mobile terminal, which is used to upload the measurement data to the cloud server, to view and download the data stored in the cloud server from the cloud server, to analyze the measured flow data, and to determine whether the current flow value is normal. Although this invention can collect and analyze flow data through real-time monitoring, it cannot achieve complete management of the pipeline. Summary of the invention

[0005] The present invention provides an intelligent flow measurement and control system for the Internet of Things to solve the technical problem that the existing technology cannot achieve lean management of lighting facilities in smart cities.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] The present invention provides an intelligent flow measurement and control system for the Internet of Things, comprising:

[0008] PHM platform, data monitoring module, control module, management module, analysis and optimization module, data storage module;

[0009] The PHM platform is responsible for unified management of other modules, and realizes the mutual coordination and efficient operation of the measurement and control system by analyzing the received data and converting it into instructions to transmit to other modules;

[0010] The data monitoring module is responsible for real-time monitoring and collecting various operating data of the flow in the pipeline, and provides a reliable basis for pipeline data transmission by transmitting the data to the PHM platform;

[0011] The control module integrates intelligent perception, intelligent judgment, and intelligent management and control, receives and executes instructions from the PHM platform, and uses dynamic control methods to adaptively adjust the pipeline flow state;

[0012] The management module can realize pipeline integrity management and provide users with a fast and effective operation and maintenance management planning solution by timely feedback of management data;

[0013] The analysis and optimization module is responsible for optimizing the flow measurement and control system. By analyzing and predicting the operation data of the measurement and control system, the operation cost and energy consumption of the measurement and control system can be reduced.

[0014] The storage module is responsible for recording and storing the operating data in the measurement and control system, and by presenting the data to the user in a visual form, it is convenient for the user to efficiently manage the measurement and control system to ensure its stable operation.

[0015] The beneficial effects brought about by the technical solution provided by the present invention include at least:

[0016] 1. The present invention can realize real-time monitoring of pipeline flow data and pipeline status data by mounting the data monitoring module on the pipeline. When the data monitoring module transmits data to the PHM platform, it can also optimize the data and reduce the delay of data transmission through edge computing, thereby reducing the load of the PHM platform and improving the accuracy of data monitoring.

[0017] 2. The present invention can authenticate the identity of the user through the Web client, and grant the user different operating permissions through login information, so that users can cooperate and collaborate efficiently, and users who have passed the token authentication can directly issue instructions to the PHM platform and other modules, which is convenient for users to make adaptive adjustments between modules according to their own needs.

[0018] 3. The present invention can assign operation and maintenance tasks to users through the management module, and can provide a complete set of emergency plans when encountering emergency accidents through the emergency management system, helping users to analyze accidents and reduce the impact of accidents, while providing users with a complete intelligent management solution.

[0019] 4. The present invention can analyze and optimize the running data through the analysis module, predict the data results through the ARMA model combined with the BP neural network, analyze the law of data operation, and help users to make adaptive adjustments to the flow, pressure and flow rate of the pipeline. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0021] Figure 1 It is a schematic diagram of the system module architecture of the intelligent flow measurement and control system of the Internet of Things provided by an embodiment of the present invention;

[0022] Figure 2 A schematic diagram of an emergency plan management architecture provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0023] In order to make the objectives, technical solutions and advantages of the present invention more clear, the embodiments of the present invention will be further described in detail below with reference to the accompanying drawings.

[0024] Example

[0025] This embodiment provides an intelligent flow measurement and control system for the Internet of Things, including:

[0026] PHM platform, data monitoring module, control module, management module, analysis and optimization module, data storage module;

[0027] The PHM platform is responsible for unified management of other modules, and realizes the mutual coordination and efficient operation of the measurement and control system by analyzing the received data and converting it into instructions to transmit to other modules;

[0028] The data monitoring module is responsible for real-time monitoring and collecting various operating data of the flow in the pipeline, and provides a reliable basis for pipeline data transmission by transmitting the data to the PHM platform;

[0029] The control module integrates intelligent perception, intelligent judgment, and intelligent management and control, receives and executes instructions from the PHM platform, and uses dynamic control methods to adaptively adjust the pipeline flow state;

[0030] The management module can realize pipeline integrity management and provide users with a fast and effective operation and maintenance management planning solution by timely feedback of management data;

[0031] The analysis and optimization module is responsible for optimizing the flow measurement and control system. By analyzing and predicting the operation data of the measurement and control system, the operation cost and energy consumption of the measurement and control system can be reduced.

[0032] The storage module is responsible for recording and storing the operating data in the measurement and control system, and by presenting the data to the user in a visual form, it is convenient for the user to efficiently manage the measurement and control system to ensure its stable operation.

[0033] Please refer to Figure 1-2 It is a schematic diagram of the system structure provided by an embodiment of the present invention.

[0034] For the intelligent flow measurement and control system of the Internet of Things proposed in this embodiment, its component deployment should include a PHM platform, a data monitoring module, a control module, a management module, an analysis and optimization module, and a data storage module;

[0035] The data monitoring module and control module are mounted on the pipeline, and the PHM platform, data monitoring module, control module, management module, analysis and optimization module, and data storage module are deployed on the server. The PHM platform and other modules share and collaborate data through a built-in API data interface and a wireless IP network.

[0036] The PHM platform serves as the core cluster of the measurement and control system, and the data monitoring module, control module, management module, analysis and optimization module, and data storage module serve as auxiliary clusters. The core cluster and the auxiliary cluster together constitute a complete intelligent flow measurement and control system for the Internet of Things.

[0037] 1. PHM platform:

[0038] The PHM platform is composed of a business application system, a data processing system, and a Web client. The business application system is responsible for the operation and management of the infrastructure. The data processing system receives and analyzes data through a database and manages it in a unified manner. The Web client is responsible for user authentication and granting operation permissions to manage the PHM platform and other modules.

[0039] The business application system is responsible for the basic application management of the PHM platform. The basic applications include data management, real-time monitoring, intelligent analysis, system management, and instruction delivery applications. The instruction management application implements automatic management and allocation of business by issuing instructions to other modules.

[0040] It should be noted that the above-mentioned applications are only basic applications of the PHM platform. In the actual system, pipeline network display, three-dimensional assistance, and assessment and scoring applications can be added to meet the different needs of users.

[0041] The database of the data processing system includes a metadata database, a dynamic monitoring database, a pipeline information database, a business management database, and a data processing database. The databases are efficiently managed and allocated through API interfaces.

[0042] It should be noted that the MongoDB database is selected as the service platform here, which can efficiently manage and store various data and facilitate the convenient processing of data between databases.

[0043] The Web client authenticates the user's identity through a Token token, which is issued by the server. The server verifies the user's identity and access rights based on the received Token. The access rights include management rights and read rights. The management rights can grant the user the rights to correct, modify, and delete data, and the read rights can be used by pipeline maintenance personnel to view real-time data of the pipeline.

[0044] 2. Data monitoring module:

[0045] The operation data monitored and collected by the data monitoring module include pipeline flow data and pipeline status data, which are monitored in real time by the SCADA monitoring system and transmitted to the PHM platform for processing;

[0046] The PHM platform uses edge computing nodes to obtain the sum of the nodes uploading data within a certain period of time to reduce the delay of real-time monitoring data transmission, as shown in the following formula:

[0047] F=∑p j (1<=j<=L)

[0048] In the formula, p j It represents the data processing function of the jth node, j represents the number of function nodes, which is determined by the number of sensors; L represents the length of the pipeline.

[0049] The pipeline flow data includes pressure, flow rate, and temperature, which are measured by pressure sensors, flow meters, and thermometers respectively. The distribution of the sensors should be determined according to the type of fluid transported in the pipeline. The dangerousness level of the fluid is positively correlated with the number and density of sensors.

[0050] It should be noted that pipeline flow data can also summarize the type, density, hazard level factors of the fluid, wall thickness of the pipeline, and seasonal factors into the database, so as to specially handle the flow data of special fluids containing hazards.

[0051] The pipeline status data includes pipeline corrosion, leakage, and deformation conditions. The pipeline corrosion and deformation conditions are collected by drones, robots, or manual regular line inspections. The pipeline leakage condition determines the monitoring frequency based on the corrosion and leakage conditions.

[0052] It should be noted that pipeline status data can also add factors such as the age, material, and length of the pipeline into the collected data. By further considering the pipeline data, the occurrence of pipeline leakage can be reduced.

[0053] The pipeline corrosion detection is carried out by carrying an ultrasonic detector to calculate the average leakage thickness of the pipeline inner wall and conduct corrosion assessment on the pipeline inner wall;

[0054] The deformation of the pipeline includes twisting, contraction and expansion, and the deformation of the pipeline can be evaluated by emitting infrared rays through an infrared sensor;

[0055] The monitoring frequency of pipeline leakage is divided into primary, secondary and tertiary standards, corresponding to patrol monitoring, intermittent monitoring and continuous monitoring respectively. The patrol monitoring period is the cycle of regular line inspections, the intermittent monitoring period is hours, minutes and seconds, and the continuous monitoring period is real-time monitoring.

[0056] It should be noted that the first-level standard corresponds to no deformation of the pipeline and no obvious internal corrosion, the second-level standard corresponds to slight deformation of the pipeline or slight internal corrosion, and the third-level standard corresponds to obvious deformation of the pipeline or obvious corrosion on the inner wall of the pipeline.

[0057] The intermittent monitoring period is determined by the inspection personnel, who choose hours, minutes, and seconds as the monitoring frequency based on the situation of the pipeline itself. When the deformation and corrosion of the pipeline still increase when the detection frequency is lowered, the third-level standard should be used directly for detection and the emergency plan management should be started immediately through the management module.

[0058] 3. Control module:

[0059] The control module includes an automatic control system and a manual control system. The automatic control system is controlled by the business application system of the PHM platform. The business application system controls the flow of the solenoid valve according to the data collected by the data monitoring module to ensure the balance of the flow. The manual control system can directly issue control instructions to the solenoid valve after the token is verified by the Web client of the PHM platform.

[0060] It should be noted that the control instructions of the manual control system include the switching of the solenoid valve, the adjustment of the fluid flow rate and pressure, and the monitoring frequency of the sensor.

[0061] The automatic control system uses a PID controller to achieve precise control of the solenoid valve, and the PID controller is mounted on the solenoid valve.

[0062] The automatic control system converts the control signal into an output signal and issues a control instruction to the PID controller, as shown in the following formula:

[0063]

[0064] Where U(t) represents the output of the controller; K P represents the proportional coefficient; err(t) represents the deviation; T D represents the differential time constant; T I represents the differential time constant.

[0065] The PID controller outputs the transfer function after Laplace transformation, which is as follows:

[0066]

[0067] In the formula, G c (s) represents the transfer function of the controller, and s represents the complex frequency variable in Laplace transform.

[0068] It should be noted that the system in the PID controller is divided into two parts, namely the fuzzy controller and the PID controller. The fuzzy controller is responsible for the fuzzy control parameter mapping of the system, and the PID controller realizes conventional system control.

[0069] 4. Management module:

[0070] The management module is divided into an operation and maintenance management system and an emergency management system. The management module authenticates the user's identity through the Web client, and the Web client grants the user the operation authority of the corresponding system according to the user's token information;

[0071] The operation and maintenance management system is responsible for the daily maintenance and overhaul of the pipeline measurement and control system. The operation and maintenance management system distributes operation and maintenance tasks to the Web client through the business application system of the PHM platform. The operation and maintenance tasks include inspection information, fault alarms, and abnormal reminders.

[0072] It should be noted that the operation and maintenance management system can help users quickly find abnormal areas by importing pipeline network data in advance, making it convenient for users to manage abnormal areas in advance and avoid the expansion of problem areas.

[0073] The emergency management system is responsible for the management of emergency plans. The emergency plan response process includes plan activation, information prompts, problem analysis, accident classification, personnel deployment, emergency recovery, summary evaluation, and plan completion. Intelligent management is used to respond to emergency accidents and reduce the impact of accidents.

[0074] It should be noted that the response conditions of the emergency plan include automatic response and manual start. The automatic response conditions are based on the data transmitted by the data monitoring module, and the manual start conditions are based on inspection information and emergency events. Emergency events include emergency allocation of fluids, maintenance and repair of pipelines, and transportation of special fluids.

[0075] 5. Analysis and optimization module:

[0076] The analysis and optimization module is connected to the data processing system of the PHM platform, and can extract data from the database and predict the results of the data by establishing a mathematical model;

[0077] The analysis and optimization module establishes an ARMA model and combines the BP neural network to predict the data results, dividing the time series of the data into two components: linear and nonlinear, as shown in the following formula:

[0078] y t =N t +L t

[0079]

[0080] Where, L t Represents the linear part; N t represents the nonlinear part; Represents the predicted value of the neural network model.

[0081] The analysis and optimization module uses the ARMA algorithm model to simulate the residual and utilizes the linear relationship to improve the accuracy of the model prediction, as shown in the following formula:

[0082] e t =f(e t-1 ,e t-2 ,...e t-n )+ε t

[0083]

[0084] In the formula, is a random error, f is a linear function, is the residual prediction value.

[0085] It should be noted that by using the ARMA model and combining it with the BP neural network to predict the data results, it is possible to analyze the laws of data operation and help users make adaptive adjustments to the flow, pressure and flow rate of the pipeline.

[0086] 6. Storage module:

[0087] The storage module can store and record the operation log information of the measurement and control system. By regularly backing up and storing the logs, the integrity of the log information can be ensured. The storage module can also summarize and manage the log information, so that the log information can be presented to the user in a visual manner.

[0088] It should be noted that log visualization refers to aggregating log data into charts and presenting them to users in the form of charts for easy management.

[0089] In addition, it should be noted that the present invention can be provided as a method, an apparatus or a computer program product. Therefore, the embodiments of the present invention can take the form of a complete hardware embodiment, a complete software embodiment or an embodiment combining software and hardware. Moreover, the embodiments of the present invention can take the form of a computer program product implemented on one or more computer-usable storage media containing computer-usable program code.

[0090] The embodiments of the present invention are described with reference to the flowcharts and / or block diagrams of the methods, terminal devices (systems), and computer program products according to the embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of the processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, an embedded processor, or other programmable data processing terminal device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing terminal device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0091] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing terminal device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured product including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 These computer program instructions can also be loaded onto a computer or other programmable data processing terminal device, so that a series of operation steps are executed on the computer or other programmable terminal device to produce a computer-implemented process, so that the instructions executed on the computer or other programmable terminal device provide for implementing the process in the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0092] It should also be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. The terms "include", "comprises" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or terminal device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or terminal device. In the absence of further restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, article or terminal device including the elements.

[0093] Finally, it should be noted that the above is a preferred embodiment of the present invention. It should be pointed out that although the preferred embodiment of the present invention has been described, for those skilled in the art, once the basic creative concept of the present invention is known, several improvements and modifications can be made without departing from the principles of the present invention, and these improvements and modifications should also be regarded as the protection scope of the present invention. Therefore, the attached claims are intended to be interpreted as including the preferred embodiment and all changes and modifications that fall within the scope of the embodiments of the present invention.

Claims

1. The intelligent flow measurement and control system of the Internet of Things is characterized by: include: PHM platform, data monitoring module, control module, management module, analysis and optimization module, data storage module; The PHM platform is responsible for unified management of other modules, and realizes the mutual coordination and efficient operation of the measurement and control system by analyzing the received data and converting it into instructions to transmit to other modules; The data monitoring module is responsible for real-time monitoring and collecting various operating data of the flow in the pipeline, and provides a reliable basis for pipeline data transmission by transmitting the data to the PHM platform; The control module integrates intelligent perception, intelligent judgment, and intelligent management and control, receives and executes instructions from the PHM platform, and uses dynamic control methods to adaptively adjust the pipeline flow state; The management module can realize the integrity management of pipelines and provide users with fast and effective operation and maintenance management planning solutions by timely feedback of management data; The analysis and optimization module is responsible for optimizing the flow measurement and control system. By analyzing and predicting the operation data of the measurement and control system, the operation cost and energy consumption of the measurement and control system can be reduced. The storage module is responsible for recording and storing the operating data in the measurement and control system, and by presenting the data to the user in a visual form, it is convenient for the user to efficiently manage the measurement and control system to ensure its stable operation.

2. The intelligent flow measurement and control system of the Internet of Things as claimed in claim 1 is characterized in that: The PHM platform, data monitoring module, control module, management module, analysis and optimization module, and data storage module, wherein: The data monitoring module and control module are mounted on the pipeline, and the PHM platform, data monitoring module, control module, management module, analysis and optimization module, and data storage module are deployed on the server. The PHM platform and other modules share and collaborate data through a built-in API data interface and a wireless IP network. The PHM platform serves as the core cluster of the measurement and control system, and the data monitoring module, control module, management module, analysis and optimization module, and data storage module serve as auxiliary clusters. The core cluster and the auxiliary cluster together constitute a complete intelligent flow measurement and control system for the Internet of Things.

3. The intelligent flow measurement and control system of the Internet of Things as claimed in claim 1 is characterized in that: The PHM platform is responsible for unified management of other modules, and realizes the coordination and efficient operation of the measurement and control system by analyzing the received data and converting it into instructions to transmit to other modules, including: The PHM platform is composed of a business application system, a data processing system, and a Web client. The business application system is responsible for the operation and management of the infrastructure. The data processing system receives and analyzes data through a database and manages it in a unified manner. The Web client is responsible for user authentication and granting operation permissions to manage the PHM platform and other modules. The business application system is responsible for the basic application management of the PHM platform. The basic applications include data management, real-time monitoring, intelligent analysis, system management, and instruction delivery applications. The instruction management application implements automatic management and allocation of business by issuing instructions to other modules. The database of the data processing system includes a metadata database, a dynamic monitoring database, a pipeline information database, a business management database, and a data processing database. The databases are efficiently managed and allocated through API interfaces. The Web client authenticates the user's identity through a Token token, which is issued by the server. The server verifies the user's identity and access rights based on the received Token. The access rights include management rights and read rights. The management rights can grant the user the rights to correct, modify, and delete data, and the read rights can be used by pipeline maintenance personnel to view real-time data of the pipeline.

4. The intelligent flow measurement and control system of the Internet of Things as claimed in claim 1 is characterized in that: The data monitoring module is responsible for real-time monitoring and collection of various operating data of the flow in the pipeline, and provides a reliable basis for pipeline data transmission by transmitting the data to the PHM platform, including: The operation data monitored and collected by the data monitoring module include pipeline flow data and pipeline status data, which are monitored in real time by the SCADA monitoring system and transmitted to the PHM platform for processing; The PHM platform uses edge computing nodes to obtain the sum of the nodes uploading data within a certain period of time to reduce the delay of real-time monitoring data transmission, as shown in the following formula: F=∑p j (1<=j<=L) In the formula, p j It represents the data processing function of the jth node, j represents the number of function nodes, which is determined by the number of sensors; L represents the length of the pipeline. The pipeline flow data includes pressure, flow rate, and temperature, which are measured by pressure sensors, flow meters, and thermometers respectively. The distribution of the sensors should be determined according to the type of fluid transported in the pipeline. The dangerousness level of the fluid is positively correlated with the number and density of sensors. The pipeline status data includes pipeline corrosion, leakage, and deformation conditions. The pipeline corrosion and deformation conditions are collected by drones, robots, or manual regular line inspections. The pipeline leakage condition determines the monitoring frequency based on the corrosion and leakage conditions.

5. The pipeline status data according to claim 4 includes corrosion, leakage, and deformation of the pipeline, wherein the corrosion and deformation of the pipeline are collected by drones, robots, or manual regular line inspections, and the pipeline leakage is monitored in real time on the outer wall of the pipeline by an optical fiber sensor, wherein: The pipeline corrosion detection is carried out by carrying an ultrasonic detector to calculate the average leakage thickness of the pipeline inner wall and conduct corrosion assessment on the pipeline inner wall; The deformation of the pipeline includes twisting, contraction and expansion, and the deformation of the pipeline can be evaluated by emitting infrared rays through an infrared sensor; The monitoring frequency of pipeline leakage is divided into primary, secondary and tertiary levels, corresponding to patrol monitoring, intermittent monitoring and continuous monitoring respectively. The patrol monitoring period is the period of regular line patrol, the intermittent monitoring period is hours, minutes and seconds, and the continuous monitoring period is real-time monitoring.

6. The intelligent flow measurement and control system of the Internet of Things as claimed in claim 1, characterized in that: The control module integrates intelligent perception, intelligent judgment, and intelligent management and control, receives and executes instructions from the PHM platform, and uses dynamic control methods to adaptively adjust the pipeline flow state, including: The control module includes an automatic control system and a manual control system. The automatic control system is controlled by the business application system of the PHM platform. The business application system controls the flow of the solenoid valve according to the data collected by the data monitoring module to ensure the balance of the flow. The manual control system can directly issue control instructions to the solenoid valve after the token is verified by the Web client of the PHM platform. The automatic control system uses a PID controller to achieve precise control of the solenoid valve, and the PID controller is mounted on the solenoid valve.

7. The automatic control system according to claim 6 uses a PID controller to achieve precise control of the solenoid valve, wherein the PID controller is mounted on the solenoid valve, wherein: The automatic control system converts the control signal into an output signal and issues a control instruction to the PID controller, as shown in the following formula: Where U(t) represents the output of the controller; K P represents the proportional coefficient; err(t) represents the deviation; T D represents the differential time constant; T I represents the differential time constant. The PID controller outputs the transfer function after Laplace transformation, which is as follows: In the formula, G c (s) represents the transfer function of the controller, and s represents the complex frequency variable in Laplace transform.

8. The intelligent flow measurement and control system of the Internet of Things as claimed in claim 1, characterized in that: The management module can realize pipeline integrity management and provide users with a fast and effective operation and maintenance management planning solution by timely feedback of management data, including: The management module is divided into an operation and maintenance management system and an emergency management system. The management module authenticates the user's identity through the Web client, and the Web client grants the user the operation authority of the corresponding system according to the user's token information; The operation and maintenance management system is responsible for the daily maintenance and overhaul of the pipeline measurement and control system. The operation and maintenance management system distributes operation and maintenance tasks to the Web client through the business application system of the PHM platform. The operation and maintenance tasks include inspection information, fault alarms, and abnormal reminders. The emergency management system is responsible for the management of emergency plans. The emergency plan response process includes plan activation, information prompts, problem analysis, accident classification, personnel deployment, emergency recovery, summary evaluation, and plan completion. Intelligent management is used to respond to emergency accidents and reduce the impact of accidents.

9. The intelligent flow measurement and control system of the Internet of Things as claimed in claim 1, characterized in that: The analysis and optimization module is responsible for optimizing the flow measurement and control system. By analyzing and predicting the operation data of the measurement and control system, the operation cost and energy consumption of the measurement and control system can be reduced. The analysis and optimization module is connected to the data processing system of the PHM platform, and can extract data from the database and predict the results of the data by establishing a mathematical model; The analysis and optimization module establishes an ARMA model and combines the BP neural network to predict the data results, dividing the time series of the data into two components: linear and nonlinear, as shown in the following formula: y t =N t +L t Where, L t Represents the linear part; N t represents the nonlinear part; Represents the predicted value of the neural network model. The analysis and optimization module uses the ARMA algorithm model to simulate the residual and utilizes the linear relationship to improve the accuracy of the model prediction, as shown in the following formula: And t =f(e t-1 ,And t-2 ,...And t-n )+ε t In the formula, is a random error, f is a linear function, is the residual prediction value.

10. The intelligent flow measurement and control system of the Internet of Things as claimed in claim 1, characterized in that: The storage module is responsible for recording and storing the operating data in the measurement and control system. By presenting the data to the user in a visual form, it is convenient for the user to efficiently manage the measurement and control system to ensure its stable operation, wherein: The storage module can store and record the operation log information of the measurement and control system. By regularly backing up and storing the logs, the integrity of the log information can be ensured. The storage module can also summarize and manage the log information, so that the log information can be presented to the user in a visual manner.

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