Big-data-based operation diagnosis and optimization decision-making system for LNG intelligent control valve
Patent Information
- Application Number
- PCT/CN2025/128421
- Authority / Receiving Office
- WO · WO
- Patent Type
- Applications
- Current Assignee / Owner
- Priority Date
- 2025-02-18
- Filing Date
- 2025-10-17
- Publication Date
- 2026-08-27
Smart Images
Figure CN2025128421_27082026_PF_FP_ABST
Abstract
Description
LNG intelligent control valve operation diagnosis and optimization decision-making system based on big data
[0001] This invention belongs to the field of liquefied natural gas technology, and in particular relates to an LNG intelligent control valve operation diagnosis and optimization decision-making system based on big data. Background Technology
[0002] Liquefied natural gas (LNG), as a crucial component of clean energy, requires highly precise control valves during transportation and storage to ensure system safety and stability. However, existing valve control systems generally lack intelligent fault warning and maintenance decision-making capabilities, leading to delayed responses to equipment malfunctions and even serious safety hazards. Therefore, maintenance and inspection methods are needed to monitor the status of control valves.
[0003] Current fault detection methods are usually based on manual or simple automated detection, which have long response times, low efficiency, and cannot guarantee detection accuracy. In order to solve this problem, there is an urgent need for an LNG intelligent control valve operation diagnosis and optimization decision-making system based on big data. Technical issues
[0004] The purpose of this invention is to address the problems of current fault detection methods, which are usually based on manual or simple automated detection, resulting in long response times, low efficiency, and unreliable detection accuracy. The invention proposes a big data-based intelligent control valve operation diagnosis and optimization decision-making system. Technical solutions
[0005] To achieve the above objectives, the present invention adopts the following technical solution: an LNG intelligent control valve operation diagnosis and optimization decision-making system based on big data, comprising a multi-dimensional data acquisition module and a data preprocessing module, wherein the output end of the multi-dimensional data acquisition module is connected to the input end of the data preprocessing module, the output end of the data preprocessing module is connected to the input end of the data mining and analysis module, the output end of the data mining and analysis module is connected to the input end of the fault diagnosis and decision-making module, and the output end of the fault diagnosis and decision-making module is connected to the input end of the intelligent comprehensive evaluation module.
[0006] As a further description of the above technical solution:
[0007] The multidimensional data acquisition module includes a temperature data acquisition module, a vibration data acquisition module, a pressure data acquisition module, and a flow rate data acquisition module. The output terminals of the temperature data acquisition module, vibration data acquisition module, pressure data acquisition module, and flow rate data acquisition module are all connected to the input of the data centralization module.
[0008] As a further description of the above technical solution:
[0009] The data preprocessing module includes a data centralization module, the output of which is connected to the input of the edge computing module, and the output of which is connected to the input of the data cleaning module.
[0010] As a further description of the above technical solution:
[0011] The output of the data cleaning module is connected to the input of the abnormal data marking module, the output of the abnormal data marking module is connected to the input of the abnormal data deletion module, and the output of the abnormal data deletion module is connected to the input of the deleted data re-input module.
[0012] As a further description of the above technical solution:
[0013] The data mining and analysis module includes a historical operation data extraction module and an association rule mining algorithm module. The output of the historical operation data extraction module is connected to the input of the association rule mining algorithm module, the output of the association rule mining algorithm module is connected to the input of the valve fault identification module, and the output of the valve fault identification module is connected to the input of the decision tree algorithm import module.
[0014] As a further description of the above technical solution:
[0015] The output of the decision tree algorithm import module is connected to the input of the fault diagnosis and decision model training module, and the output of the fault diagnosis and decision model training module is connected to the input of the model automatic learning and updating module.
[0016] As a further description of the above technical solution:
[0017] The fault diagnosis and decision-making module includes a fault prevention and control decision model import module and a system anomaly detection module. The output of the fault prevention and control decision model import module is connected to the input of the system anomaly detection module, and the output of the system anomaly detection module is connected to the input of the fault diagnosis module.
[0018] As a further description of the above technical solution:
[0019] The output of the fault diagnosis module is connected to the input of the maintenance suggestion automatic generation module, and the output of the maintenance suggestion automatic generation module is connected to the input of the control strategy optimization module.
[0020] This invention also discloses a method for LNG intelligent control valve operation diagnosis and optimization decision-making based on big data, including the following steps:
[0021] S1. Employ multi-dimensional sensors installed on the LNG control valve to collect the valve's operating status in real time;
[0022] S2. Transfer the valid data to the cloud database;
[0023] S3. In the data center, historical operating data is analyzed using association rule mining algorithms to identify potential correlation factors for valve failures.
[0024] S4. When the system detects an abnormal situation, the diagnostic module will quickly analyze the real-time data, use the constructed fault prevention and control decision model to make an accurate fault diagnosis, and automatically provide maintenance suggestions.
[0025] S5. Intelligent evaluation of the overall system operation data and fault diagnosis results.
[0026] As a further description of the above technical solution:
[0027] In step S1, multi-dimensional sensors installed on the LNG control valve are used to collect the valve's operating status in real time. The collected data is initially processed by an edge computing device to filter out invalid or redundant data. The multi-dimensional sensors include temperature, pressure, flow, and vibration sensors. In step S3, in the data center, historical operating data is analyzed using an association rule mining algorithm to identify potential correlation factors for valve failures. A decision tree algorithm is then used to train a fault diagnosis and decision-making model suitable for this specific valve based on this data. In step S4, when the system detects an anomaly, the diagnostic module performs rapid analysis based on real-time data and uses the constructed fault prevention and control decision-making model to make an accurate fault diagnosis within ≤0.5 seconds and automatically provide maintenance suggestions. The system also optimizes control strategies based on the current environment and operating conditions to ensure the safe and stable operation of the valve. In step S5, through intelligent evaluation of the overall system operating data and fault diagnosis effectiveness, the system provides a comprehensive score to ensure that the system's intelligence level reaches or exceeds 27 points. Beneficial effects
[0028] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are:
[0029] In this invention, the system utilizes association rule mining and decision tree algorithms to construct a fault prevention and control decision model, enabling intelligent diagnosis and maintenance decisions for the operation of special valves. The system response time is reduced to ≤0.5 seconds, and the overall intelligence level reaches over 27 points, significantly improving the operating efficiency and fault prevention capabilities of LNG control valves. This system can greatly enhance the system's response speed to faults and the accuracy of maintenance decisions, thereby improving the overall operating efficiency and safety level of the equipment. Attached Figure Description
[0030] Figure 1 is a schematic diagram of the module structure of the LNG intelligent control valve operation diagnosis and optimization decision-making system based on big data.
[0031] Figure 2 is a schematic diagram of the sub-module structure of the multi-dimensional data acquisition module in the LNG intelligent control valve operation diagnosis and optimization decision-making system based on big data.
[0032] Figure 3 is a schematic diagram of the sub-module structure of the data preprocessing module in the LNG intelligent control valve operation diagnosis and optimization decision-making system based on big data.
[0033] Figure 4 is a schematic diagram of the sub-module structure of the data mining and analysis module in the LNG intelligent control valve operation diagnosis and optimization decision-making system based on big data.
[0034] Figure 5 is a schematic diagram of the sub-module structure of the fault diagnosis and decision-making module in the LNG intelligent control valve operation diagnosis and optimization decision-making system based on big data.
[0035] Figure 6 is a flowchart of the LNG intelligent control valve operation diagnosis and optimization decision-making method based on big data.
[0036] Legend:
[0037] 1. Multidimensional data acquisition module; 2. Data preprocessing module; 3. Data mining and analysis module; 4. Fault diagnosis and decision-making module. The best embodiment of the present invention
[0038] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention. Example
[0039] Please refer to Figures 1-5. This invention provides a technical solution: an LNG intelligent control valve operation diagnosis and optimization decision-making system based on big data, including a multi-dimensional data acquisition module 1 and a data preprocessing module 2. The output end of the multi-dimensional data acquisition module 1 is connected to the input end of the data preprocessing module 2. The output end of the data preprocessing module 2 is connected to the input end of the data mining and analysis module 3. The output end of the data mining and analysis module 3 is connected to the input end of the fault diagnosis and decision-making module 4. The output end of the fault diagnosis and decision-making module 4 is connected to the input end of the intelligent comprehensive evaluation module.
[0040] The multidimensional data acquisition module 1 includes a temperature data acquisition module, a vibration data acquisition module, a pressure data acquisition module, and a flow rate data acquisition module. The output terminals of the temperature data acquisition module, the vibration data acquisition module, the pressure data acquisition module, and the flow rate data acquisition module are all connected to the input of the data centralization module.
[0041] The data preprocessing module 2 includes a data centralization module. The output of the data centralization module is connected to the input of the edge computing module. The output of the edge computing module is connected to the input of the data cleaning module. The output of the data cleaning module is connected to the input of the abnormal data marking module. The output of the abnormal data marking module is connected to the input of the abnormal data deletion module. The output of the abnormal data deletion module is connected to the input of the deleted data re-input module.
[0042] The data mining and analysis module 3 includes a historical operation data extraction module and an association rule mining algorithm module. The output of the historical operation data extraction module is connected to the input of the association rule mining algorithm module. The output of the association rule mining algorithm module is connected to the input of the valve fault identification module. The output of the valve fault identification module is connected to the input of the decision tree algorithm import module. The output of the decision tree algorithm import module is connected to the input of the fault diagnosis and decision model training module. The output of the fault diagnosis and decision model training module is connected to the input of the model automatic learning and updating module.
[0043] The fault diagnosis and decision-making module 4 includes a fault prevention and control decision model import module and a system anomaly detection module. The output of the fault prevention and control decision model import module is connected to the input of the system anomaly detection module. The output of the system anomaly detection module is connected to the input of the fault diagnosis module. The output of the fault diagnosis module is connected to the input of the maintenance suggestion automatic generation module. The output of the maintenance suggestion automatic generation module is connected to the input of the control strategy optimization module.
[0044] In this embodiment, the intelligent control and decision-making function of the aforementioned system can be widely applied to LNG production and storage equipment. In practical applications, the system can combine a large amount of historical data with real-time operating data to quickly respond to sudden faults and provide corresponding maintenance decisions. For example, when abnormal pressure is detected in a pipeline, the system will use a decision model to determine the possible fault type and automatically trigger maintenance procedures to prevent further damage to the equipment. Example
[0045] Please refer to Figures 1-5. This invention provides a technical solution: an LNG intelligent control valve operation diagnosis and optimization decision-making system based on big data, including a multi-dimensional data acquisition module 1 and a data preprocessing module 2. The output end of the multi-dimensional data acquisition module 1 is connected to the input end of the data preprocessing module 2. The output end of the data preprocessing module 2 is connected to the input end of the data mining and analysis module 3. The output end of the data mining and analysis module 3 is connected to the input end of the fault diagnosis and decision-making module 4. The output end of the fault diagnosis and decision-making module 4 is connected to the input end of the intelligent comprehensive evaluation module.
[0046] The multidimensional data acquisition module 1 includes a temperature data acquisition module, a vibration data acquisition module, a pressure data acquisition module, and a flow rate data acquisition module. The output terminals of the temperature data acquisition module, the vibration data acquisition module, the pressure data acquisition module, and the flow rate data acquisition module are all connected to the input of the data centralization module.
[0047] The data preprocessing module 2 includes a data centralization module. The output of the data centralization module is connected to the input of the edge computing module. The output of the edge computing module is connected to the input of the data cleaning module. The output of the data cleaning module is connected to the input of the abnormal data marking module. The output of the abnormal data marking module is connected to the input of the abnormal data deletion module. The output of the abnormal data deletion module is connected to the input of the deleted data re-input module.
[0048] The data mining and analysis module 3 includes a historical operation data extraction module and an association rule mining algorithm module. The output of the historical operation data extraction module is connected to the input of the association rule mining algorithm module. The output of the association rule mining algorithm module is connected to the input of the valve fault identification module. The output of the valve fault identification module is connected to the input of the decision tree algorithm import module. The output of the decision tree algorithm import module is connected to the input of the fault diagnosis and decision model training module. The output of the fault diagnosis and decision model training module is connected to the input of the model automatic learning and updating module.
[0049] The fault diagnosis and decision-making module 4 includes a fault prevention and control decision model import module and a system anomaly detection module. The output of the fault prevention and control decision model import module is connected to the input of the system anomaly detection module. The output of the system anomaly detection module is connected to the input of the fault diagnosis module. The output of the fault diagnosis module is connected to the input of the maintenance suggestion automatic generation module. The output of the maintenance suggestion automatic generation module is connected to the input of the control strategy optimization module.
[0050] Please refer to Figure 6. This invention also discloses a method for LNG intelligent control valve operation diagnosis and optimization decision-making based on big data, including the following steps:
[0051] S1. Multi-dimensional sensors installed on the LNG control valve are used to collect the valve's operating status in real time. The collected data is preliminarily processed by an edge computing device to filter out invalid or redundant data. The multi-dimensional sensors include temperature, pressure, flow and vibration sensors.
[0052] S2. Transfer the valid data to the cloud database;
[0053] S3. In the data center, historical operational data is analyzed using association rule mining algorithms to identify potential correlation factors for valve failures. Decision tree algorithms are then used to train fault diagnosis and decision-making models suitable for specific valves based on this data.
[0054] S4. When the system detects an anomaly, the diagnostic module will quickly analyze real-time data and, using the constructed fault prevention and control decision model, make an accurate fault diagnosis within ≤0.5 seconds, and automatically provide maintenance suggestions. The system will also optimize control strategies based on the current environment and operating conditions to ensure the safe and stable operation of the valves.
[0055] S5. By intelligently evaluating the overall system operation data and fault diagnosis results, the system will give a comprehensive score to ensure that the system's intelligence level exceeds 27 points.
[0056] In this embodiment, the system utilizes association rule mining and decision tree algorithms to construct a fault prevention and control decision model, enabling intelligent diagnosis and maintenance decisions for the operation of special valves. The system response time is reduced to ≤0.5 seconds, and the overall intelligence level reaches over 27 points, significantly improving the operating efficiency and fault prevention capabilities of LNG control valves. This system can greatly enhance the system's response speed to faults and the accuracy of maintenance decisions, thereby improving the overall operating efficiency and safety level of the equipment.
[0057] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.
Claims
1. A big data-based LNG intelligent control valve operation diagnosis and optimization decision system, comprising a multi-dimensional data acquisition module (1) and a data preprocessing module (2), characterized in that: The output of the multidimensional data acquisition module (1) is connected to the input of the data preprocessing module (2), the output of the data preprocessing module (2) is connected to the input of the data mining and analysis module (3), the output of the data mining and analysis module (3) is connected to the input of the fault diagnosis and decision-making module (4), and the output of the fault diagnosis and decision-making module (4) is connected to the input of the intelligent comprehensive evaluation module.
2. The big data based LNG intelligent control valve operation diagnosis and optimization decision system according to claim 1, characterized in that, The multidimensional data acquisition module (1) includes a temperature data acquisition module, a vibration data acquisition module, a pressure data acquisition module and a flow data acquisition module. The output terminals of the temperature data acquisition module, the vibration data acquisition module, the pressure data acquisition module and the flow data acquisition module are all connected to the input of the data centralization module.
3. The big data based LNG intelligent control valve operation diagnosis and optimization decision system according to claim 2, characterized in that, The data preprocessing module (2) includes a data centralization module, the output of which is connected to the input of the edge computing module, and the output of which is connected to the input of the data cleaning module.
4. The big data based LNG intelligent control valve operation diagnosis and optimization decision system according to claim 3, characterized in that, The output of the data cleaning module is connected to the input of the abnormal data marking module, the output of the abnormal data marking module is connected to the input of the abnormal data deletion module, and the output of the abnormal data deletion module is connected to the input of the deleted data re-input module.
5. The big data based LNG intelligent control valve operation diagnosis and optimization decision system according to claim 1, characterized in that, The data mining and analysis module (3) includes a historical operation data extraction module and an association rule mining algorithm module. The output end of the historical operation data extraction module is connected to the input end of the association rule mining algorithm module. The output end of the association rule mining algorithm module is connected to the input end of the valve fault identification module. The output end of the valve fault identification module is connected to the input end of the decision tree algorithm import module.
6. The big data based LNG intelligent control valve operation diagnosis and optimization decision system according to claim 5, characterized in that, The output of the decision tree algorithm import module is connected to the input of the fault diagnosis and decision model training module, and the output of the fault diagnosis and decision model training module is connected to the input of the model automatic learning and updating module.
7. The big data based LNG intelligent control valve operation diagnosis and optimization decision system according to claim 1, characterized in that, The fault diagnosis and decision-making module (4) includes a fault prevention and control decision model import module and a system anomaly detection module. The output end of the fault prevention and control decision model import module is connected to the input end of the system anomaly detection module, and the output end of the system anomaly detection module is connected to the input end of the fault diagnosis module.
8. The big data based LNG intelligent control valve operation diagnosis and optimization decision system according to claim 7, characterized in that, The output of the fault diagnosis module is connected to the input of the maintenance suggestion automatic generation module, and the output of the maintenance suggestion automatic generation module is connected to the input of the control strategy optimization module.
9. A method for LNG intelligent control valve operation diagnosis and optimization decision based on big data, characterized in that, Includes the following steps: S1. Employ multi-dimensional sensors installed on the LNG control valve to collect the valve's operating status in real time; S2. Transfer the valid data to the cloud database; S3. In the data center, historical operating data is analyzed using association rule mining algorithms to identify potential correlation factors for valve failures. S4. When the system detects an abnormal situation, the diagnostic module will quickly analyze the real-time data, use the constructed fault prevention and control decision model to make an accurate fault diagnosis, and automatically provide maintenance suggestions. S5. Intelligent evaluation of the overall system operation data and fault diagnosis results.
10. The big data based LNG intelligent control valve operation diagnosis and optimization decision method according to claim 9, characterized in that, In step S1, multi-dimensional sensors installed on the LNG control valve are used to collect the valve's operating status in real time. The collected data is preliminarily processed by an edge computing device to filter out invalid or redundant data. The multi-dimensional sensors include temperature, pressure, flow, and vibration sensors. In step S3, in the data center, historical operating data is analyzed using an association rule mining algorithm to identify potential correlation factors for valve failures. Based on this data, a decision tree algorithm is trained to develop a fault diagnosis and decision model suitable for special valves. In step S4, when the system detects an abnormal situation, the diagnostic module performs rapid analysis based on real-time data and uses the constructed fault prevention and control decision model to make an accurate fault diagnosis within ≤0.5 seconds and automatically provide maintenance suggestions. The system also optimizes the control strategy based on the current environment and operating conditions to ensure the safe and stable operation of the valve. In step S5, the system performs an intelligent evaluation of the overall operating data and fault diagnosis effect, and the system provides a comprehensive score to ensure that the system's intelligence level reaches or exceeds 27 points.