Intelligent alarm management method based on relationship tree

Abstract

This invention discloses an intelligent alarm management method based on a relational tree. This method comprises six steps: constructing a relational tree model of an offshore oil and gas production system, alarm information collection and preprocessing, alarm identification and classification, real-time alarm status tracking, differentiated alarm management, and alarm handling suggestion generation. The intelligent alarm management method based on the relational tree provided by this invention can not only quickly identify real and false alarms, but also manage alarms in a hierarchical manner, prioritizing them according to their severity to prevent further failures, thereby reducing shutdowns, improving system stability and reliability, lowering maintenance costs, and increasing production efficiency.

Description

Technical Field

[0001] This invention belongs to the field of intelligent alarm management technology for offshore oil and gas production systems, and particularly relates to an intelligent alarm management method based on a relation tree. Background Technology

[0002] In offshore oil and gas production systems, due to their large size and complexity, involving numerous devices and subsystems, a large number of alarm messages are generated during operation. Traditional alarm management systems mainly rely on manual investigation and handling of alarms one by one, which has the following problems: (1) Complex alarm information: When a large number of alarm information appears at the same time, it is difficult for humans to quickly and accurately identify real alarms and false alarms, which can easily lead to delays in handling important alarms. For example, on a large offshore oil and gas platform, dozens or even hundreds of alarm information may pop up at the same time, and it is difficult for operators to determine which alarms really need to be dealt with urgently in a short period of time.

[0003] (2) Lack of effective classification: Alarm information is not effectively classified, and different types of alarms are mixed together, which increases the difficulty and time cost of processing. For example, if emergency alarms involving system security are mixed with some routine equipment status abnormality alarms, operators need to spend a lot of time filtering important alarms.

[0004] (3) Inability to track alarm status in real time: It is difficult to track the alarm status in real time, and it is impossible to grasp the development and changes of the alarm in a timely manner, which is not conducive to taking targeted measures. For example, after the temperature alarm of a certain device is triggered, the temperature may continue to rise, but because it is impossible to track in real time, the operator cannot take cooling measures in time, resulting in damage to the equipment.

[0005] (4) Low processing efficiency: Manual alarm processing is inefficient and cannot meet the real-time and accuracy requirements of offshore oil and gas production systems. It may lead to further deterioration of system failures and affect production efficiency and safety. For example, some potential failures may gradually develop into major accidents that seriously affect production due to failure to handle them in time.

[0006] Therefore, there is an urgent need to design an intelligent alarm management method based on a relation tree to solve the problems mentioned above in the alarm management of offshore oil and gas production systems, such as complex alarm information, lack of effective classification, inability to track in real time, and low processing efficiency. Summary of the Invention

[0007] The purpose of this invention is to provide an intelligent alarm management method based on a relation tree. By intelligently analyzing and processing alarm information from offshore oil and gas production systems, it can achieve automatic alarm identification, classification, real-time tracking, and differentiated management, thereby improving the accuracy and efficiency of alarm processing and reducing system failures and production losses caused by untimely or inaccurate alarm processing.

[0008] To achieve the above objectives, the specific technical solution of the intelligent alarm management method based on a relation tree of the present invention is as follows: A smart alarm management method based on a relation tree mainly includes the following steps: S1. Construct a relationship tree model of the offshore oil and gas production system; S2. Alarm information collection and preprocessing; S3. Alarm identification and classification; S4. Real-time tracking of alarm status; S5, Alarm Differentiation Management; S6. Alarm handling suggestions are generated.

[0009] Furthermore, in S1, when determining nodes, the offshore oil and gas production system is divided into levels, with large subsystems as first-level nodes, equipment under the subsystems as second-level nodes, and key parameters of the equipment as third-level nodes.

[0010] Furthermore, in S2, the filtering algorithm employs at least one of the following: moving average filtering algorithm, median filtering algorithm, or Kalman filtering algorithm.

[0011] Furthermore, S3 includes: S31, Alarm recognition; S32, Alarm Classification.

[0012] Furthermore, in S31, based on the technical specifications of each node device, historical operating data statistics, and industry safety standards, upper and lower alarm thresholds are precisely set for each monitoring node in the industrial system; high-precision and high-stability data acquisition devices acquire the operating data of each node in real time, and when the acquired data exceeds the preset threshold range of the corresponding node, an alarm signal is immediately triggered.

[0013] Furthermore, in S32, a comprehensive, detailed, and quantitative alarm classification standard is established based on the degree of impact of alarm events on industrial system safety, the degree of interference with production processes, and the severity of potential consequences. According to this standard, alarm events are classified into three categories: emergency alarms, important alarms, and general alarms. Among them, emergency alarms involve the core safety of industrial systems and may cause major safety accidents or casualties, such as fire alarms and combustible gas leak alarms. Once triggered, the highest level of emergency response will be activated immediately. Important alarms affect production efficiency but will not cause system shutdown in a short period of time, such as alarms for excessively high temperature in critical parts of equipment or alarms for abnormal vibration of major transmission components. These alarms require timely arrangement of professional personnel for troubleshooting and handling. General alarms are alarms for abnormal status of routine equipment and have little impact on the production process, such as alarms for a faulty indicator light or alarms for slight fluctuations in local sensor data. These can be handled according to the routine maintenance plan.

[0014] Furthermore, in S4, the trend analysis technique employs at least one of linear regression analysis, exponential smoothing analysis, or moving average analysis.

[0015] Furthermore, in S4, the neural network technology employs at least one of the following: backpropagation neural network, radial basis function neural network, or long short-term memory network to construct an alarm state prediction model.

[0016] Furthermore, in S5, when determining whether an alarm is genuine or false, if the alarm data exceeds the threshold range, but trend analysis shows that the data will soon return to normal and the neural network predicts that it will not affect the operation of subsequent equipment, then it is determined to be a false alarm; otherwise, it is determined to be a genuine alarm.

[0017] Furthermore, in S6, the expert knowledge base is built by collecting historical fault data, maintenance records, and industry expert experience from offshore oil and gas production systems, and is regularly updated and improved.

[0018] The intelligent alarm management method based on relation trees of the present invention has the following advantages: (1) This invention improves the accuracy of alarm identification: Through the correlation analysis of the relation tree model and the prediction of neural network technology, it can more accurately identify real alarms and false alarms, reduce the occurrence of false alarms and missed alarms, and improve the accuracy of alarm processing.

[0019] (2) This invention realizes alarm classification management: alarms are classified and prioritized, enabling operators to quickly focus on important alarms, arrange the processing order reasonably, and improve alarm processing efficiency.

[0020] (3) The present invention tracks alarm status in real time: by using trend analysis and neural network prediction technology, it can grasp the development trend of alarm status in real time, take countermeasures in advance, and avoid further deterioration of the fault.

[0021] (4) This invention provides professional processing suggestions: Based on the expert knowledge base, it generates detailed alarm processing suggestions, provides professional guidance for operators, reduces the dependence on the operator's experience, and improves the quality and speed of fault handling.

[0022] (5) This invention improves the stability and reliability of the system: Through effective alarm management, system faults can be detected and dealt with in a timely manner, reducing system downtime, improving the stability and reliability of offshore oil and gas production systems, and ensuring the smooth progress of production. Attached Figure Description

[0023] Figure 1 This is a flowchart of the intelligent alarm management method based on a relation tree according to the present invention. Detailed Implementation

[0024] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, 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, 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.

[0025] Those skilled in the art will understand that although some embodiments herein include certain features included in other embodiments but not others, combinations of features from different embodiments are intended to be within the scope of the invention and form different embodiments. For example, in the claims, any of the claimed embodiments can be used in any combination.

[0026] The following is a reference to the appendix. Figure 1 This invention describes an intelligent alarm management method based on a relation tree.

[0027] like Figure 1 As shown, the intelligent alarm management method based on relational trees in this invention mainly includes the following steps: S1. Construct a relationship tree model for offshore oil and gas production systems: Identify alarm nodes: Divide the offshore oil and gas production system into levels, with large subsystems as primary nodes, such as oil-water separation systems and compressor systems; equipment under subsystems as secondary nodes, such as separators in oil-water separation systems and compressors in compressor systems; and key parameters of the equipment as tertiary nodes, such as oil phase level in separators and exhaust pressure in compressors.

[0028] Establishing Association Edges: Based on the operational logic and interrelationships between parameters of the equipment, establish association edges between nodes. For example, an abnormal oil phase level in an oil-water separation system may affect the operation of subsequent processing equipment. Therefore, establish association edges between the oil phase level node of the oil-water separation system and the nodes of subsequent related equipment to form a complete relationship tree model of the offshore oil and gas production system. This model can intuitively reflect the interrelationships between the various parts of the system.

[0029] S2. Alarm Information Acquisition and Preprocessing: Data Acquisition: Through sensors and data acquisition systems, real-time operational data of each node in the offshore oil and gas production system is collected and transmitted to the intelligent alarm management system.

[0030] Data preprocessing: Data cleaning: Cleaning the collected raw data to remove noise and outliers. For example, data that deviates significantly from the normal range due to sensor malfunction or external interference can be filtered and removed by setting reasonable threshold ranges.

[0031] Filtering: Filtering algorithms are used to process the data and reduce short-term fluctuations. Commonly used filtering algorithms include moving average filtering, median filtering, and Kalman filtering. Taking the moving average filtering algorithm as an example, the average value of data over a period of time is taken to smooth data fluctuations and more accurately reflect the true trend of data changes.

[0032] Data normalization: Normalizing data to a specified interval, such as [0, 1], converting it into a format suitable for algorithm processing. The normalization formula is: in, The original data, and These are the minimum and maximum values ​​of the data, respectively. This is the normalized data.

[0033] S3. Alarm Identification and Classification: Threshold judgment: Set corresponding alarm thresholds for each node. When the collected data exceeds the threshold range, an alarm is triggered. For example, set upper and lower limit thresholds for the oil phase level in an oil-water separation system. When the level is higher than the upper limit or lower than the lower limit, an oil phase level abnormality alarm is generated.

[0034] Association analysis based on relationship trees: When a node triggers an alarm, the relationship tree model is used to analyze the relationship between the triggering node and other nodes to determine whether there is a possibility of cascading alarms. For example, when the oil phase level alarm is triggered in an oil-water separation system, the relationship tree model is used to analyze whether the alarm will cause abnormal parameters in subsequent processing equipment, thereby identifying potential cascading alarms in advance.

[0035] Alarm Classification: Based on the severity and scope of impact, alarms are classified into different types, such as emergency alarms, important alarms, and general alarms. Emergency alarms involve system safety, such as fire alarms and combustible gas leak alarms, and require immediate handling; important alarms affect production efficiency but will not immediately cause system shutdown, such as equipment overheating alarms; general alarms are some routine equipment status abnormality alarms, such as indicator light malfunction alarms.

[0036] S4. Real-time tracking of alarm status: Trend Analysis: Trend analysis techniques are used to monitor alarm node data in real time, plotting data trend curves to observe the development trend of alarm status. Commonly used trend analysis methods include linear regression analysis, exponential smoothing analysis, and moving average analysis. Taking linear regression analysis as an example, it predicts the trend of data changes by fitting a straight line to the data points, helping operators understand the development direction of the alarm status.

[0037] Neural Network Prediction: This method utilizes neural network technology to establish alarm status prediction models. Based on historical and real-time alarm data, it predicts the future development trend of alarm status. Commonly used neural network techniques include Backpropagation Neural Network (BPNN), Radial Basis Function Neural Network (RBFNN), and Long Short-Term Memory Network (LSTM). For example, using an LSTM network can process data with time-series characteristics, better capture the changing patterns of alarm status over time, and improve prediction accuracy.

[0038] S5. Alarm Differentiation Management: Real vs. False Alarm Identification: This system combines trend analysis and neural network prediction results to determine the authenticity of alarms. If alarm data exceeds a threshold, but trend analysis shows the data will quickly return to normal and neural network prediction indicates no impact on subsequent equipment operation, it is determined to be a false alarm; otherwise, it is determined to be a real alarm. For example, if a pressure alarm is triggered on a device, and trend analysis shows the pressure briefly rises before starting to fall, and neural network prediction indicates no impact on the system, then it is determined to be a false alarm.

[0039] Prioritization: Alarms are prioritized based on their type, severity, and impact. Emergency alarms have the highest priority and require immediate attention; important alarms are next and should be handled within a shorter timeframe; general alarms can be scheduled for handling at an appropriate time. For example, emergency alarms involving system security are prioritized to ensure operators can handle them immediately, guaranteeing the safe operation of the system.

[0040] S6. Alarm handling suggestions generated: Expert Knowledge Base Construction: Establish an expert knowledge base for offshore oil and gas production systems, storing handling solutions and experiences for various alarms. The expert knowledge base includes information such as equipment failure symptoms, possible causes, and corresponding handling measures. It is constructed by collecting historical failure data, maintenance records, and industry expert experience from offshore oil and gas production systems, and is regularly updated and improved.

[0041] Handling suggestion generation: When an alarm is triggered, based on the alarm type and correlation analysis results, the system retrieves relevant handling suggestions from the expert knowledge base and generates a detailed alarm handling report. For example, when a compressor discharge pressure alarm is triggered, the system analyzes possible causes based on the alarm information and relationship tree model, such as a clogged intake filter or a faulty exhaust valve. Then, it extracts corresponding handling measures from the expert knowledge base, such as cleaning the intake filter or repairing the exhaust valve, and generates a handling report for operators' reference.

[0042] As for the intelligent alarm management method based on relation trees, its implementation environment is as follows: This method is implemented in an intelligent alarm management system for offshore oil and gas production systems. The system hardware includes servers, data acquisition devices, and sensors. The software environment employs an operating system and programming language suitable for algorithm execution, such as Linux and Python. The server possesses sufficient computing power and storage capacity to process large amounts of real-time data and run complex algorithm models. The data acquisition devices can accurately and stably collect data from various devices and parameters within the offshore oil and gas production system. The sensors have high precision and reliability, ensuring that the collected data accurately reflects the actual operating status of the equipment.

[0043] The implementation steps are as follows: First, initialize the system: Install and configure the intelligent alarm management system software on the server to ensure that the software can operate normally; Construct a relationship tree model for the offshore oil and gas production system. Based on the actual structure and equipment parameters of the system, determine the nodes and establish the associated edges according to the method described above, and import the relationship tree model into the system. Importing the expert knowledge base into the system allows it to be built and updated through manual input, importing external database files, and other methods.

[0044] Then, data collection and transmission are performed: The system collects real-time operating data of various equipment and parameters, such as temperature, pressure, and liquid level, through sensors installed in the offshore oil and gas production system. The data acquisition equipment performs preliminary processing and packaging of the collected data, and then transmits the data to the intelligent alarm management system server via the network. Encryption technology is used during transmission to ensure data security and integrity. Then, alarm identification and classification processing are performed: After receiving the data, the system first preprocesses the data, following the data cleaning, filtering, and normalization methods described above. The preprocessed data is subjected to a threshold judgment, and an alarm is triggered when the data exceeds the set threshold range; When an alarm is triggered, a relationship tree model is used to perform correlation analysis on the nodes that triggered the alarm to determine whether there is a possibility of a chain alarm. Based on the severity and scope of the alarm, alarms are classified into different types: emergency alarms, important alarms, and general alarms.

[0045] Then, the alarm status is tracked in real time: Trend analysis technology is used to monitor the data of alarm nodes in real time, draw data trend curves, and display them on the system interface for easy observation by operators; An alarm status prediction model established using neural network technology predicts the future development trend of alarm status and displays the prediction results along with real-time data on the system interface, providing operators with a basis for decision-making.

[0046] Then, implement differentiated alarm management: By combining trend analysis and neural network prediction results, the authenticity and false alarms are determined, and real alarms and false alarms are distinguished by different colors or symbols on the system interface; Based on the type, severity, and scope of impact of alarms, real alarms are prioritized and displayed on the system interface in order of priority.

[0047] Then, alarm handling suggestions are generated and displayed: When an alarm is triggered, the system searches for corresponding handling suggestions from the expert knowledge base based on the alarm type and correlation analysis results. Generate a detailed alarm handling report, including alarm cause analysis, suggested handling measures, and possible impacts, and display it to the operator on the system interface.

[0048] Then, the operators handle and provide feedback: Operators handle faults based on alarm handling reports generated by the system, and can record the handling status and results in real time during the process; After processing is completed, the operator feeds the results back to the system, which records and statistically analyzes the results for subsequent algorithm optimization.

[0049] Finally, system updates and optimizations are performed: Regularly update and optimize the system, and adjust the alarm thresholds according to the actual operating conditions to make the threshold settings more reasonable and accurate; Continuously improve the relationship tree model and adjust nodes and related edges in a timely manner according to the updates and modifications of system equipment; Continuously update the expert knowledge base, collect new failure cases and handling experience, and improve the system's intelligence and processing capabilities.

[0050] As a preferred embodiment, the specific implementation process of this method is as follows: Relationship tree model construction: The oil-water separation system is taken as the first-level node, and its parameters such as oil phase level, water phase level, and temperature are taken as second-level nodes. Relationship edges are established with subsequent wastewater treatment equipment and other related nodes to construct the relationship tree model of the oil-water separation system. Alarm information acquisition and preprocessing: Data such as oil phase level, water phase level, and temperature are collected in real time by sensors installed on the oil-water separation system, and then cleaned, filtered, and normalized. Alarm identification and classification: Upper and lower thresholds for oil phase levels are set. When the collected oil phase level data exceeds these thresholds, an alarm is triggered. A relationship tree model is used to analyze whether the alarm will affect the operation of subsequent wastewater treatment equipment, classifying the alarm as a critical alarm. Real-time alarm status tracking: Trend analysis technology is used to plot oil phase level data trend curves and observe level change trends. Simultaneously, neural network technology is used to establish an oil phase level prediction model to predict future level trends. Alarm Differentiation Management: Based on trend analysis and forecast results, the alarm is determined to be a real alarm. Because this alarm is important, it is given a higher priority. Alarm handling suggestion generation: The system retrieves handling suggestions for abnormal oil phase levels from the expert knowledge base, such as checking for blockages in the inlet valve or adjusting the internal structure of the separator. A handling report is then generated and presented to the operators. Based on the report, the operators troubleshoot and resolve the issue, successfully mitigating the abnormal oil phase level and ensuring the normal operation of the oil-water separation system.

[0051] As can be seen from the above implementation examples, the intelligent alarm management method based on relational tree of the present invention can effectively manage the alarms of offshore oil and gas production systems, improve the stability and reliability of the system, and has practical application value.

[0052] Based on the intelligent alarm management method of relation tree, this invention can not only quickly identify real alarms and false alarms, but also manage alarms in a hierarchical manner, prioritizing them according to their severity to prevent further failures and reduce the number of shutdowns.

[0053] Obviously, the above embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not intended to limit the implementation of the present invention. Those skilled in the art can make other variations or modifications based on the above description. It is neither necessary nor possible to exhaustively describe all embodiments here. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the scope of protection of the claims of the present invention.

Claims

1. A method for intelligent alarm management based on a relationship tree, characterized in that, The main steps include: S1. Construct a relationship tree model of the offshore oil and gas production system; S2. Alarm information collection and preprocessing; S3. Alarm identification and classification; S4. Real-time tracking of alarm status; S5, Alarm Differentiation Management; S6. Alarm handling suggestions are generated.

2. The intelligent alarm management method based on a relation tree according to claim 1, characterized in that, In S1, when determining nodes, the offshore oil and gas production system is divided into levels, with large subsystems as first-level nodes, equipment under the subsystems as second-level nodes, and key parameters of the equipment as third-level nodes.

3. The intelligent alarm management method based on a relation tree according to claim 1, characterized in that, In S2, the filtering algorithm employs at least one of the following: moving average filtering algorithm, median filtering algorithm, or Kalman filtering algorithm.

4. The intelligent alarm management method based on a relation tree according to claim 1, characterized in that, S3 includes: S31, Alarm recognition; S32, Alarm Classification.

5. The intelligent alarm management method based on a relation tree according to claim 4, characterized in that, In S31, based on the technical specifications of each node device, historical operating data statistics, and industry safety standards, the upper limit alarm threshold and lower limit alarm threshold are precisely set for each monitoring node in the industrial system. The system acquires real-time operational data from each node using a high-precision, high-stability data acquisition device. When the acquired data exceeds the preset threshold range of the corresponding node, an alarm signal is immediately triggered.

6. The intelligent alarm management method based on a relation tree according to claim 4, characterized in that, In S32, a comprehensive, detailed, and quantitative alarm classification standard is established based on the degree of impact of alarm events on industrial system safety, the degree of interference with production processes, and the severity of potential consequences. According to this standard, alarm events are classified into three categories: emergency alarms, important alarms, and general alarms. Among them, emergency alarms involve the core safety of industrial systems and may cause major safety accidents or casualties, such as fire alarms and combustible gas leak alarms. Once triggered, the highest level of emergency response will be activated immediately. Important alarms affect production efficiency but will not cause system shutdown in a short period of time, such as alarms for excessively high temperature in critical parts of equipment or alarms for abnormal vibration of major transmission components. These alarms require timely arrangement of professional personnel for troubleshooting and handling. General alarms are alarms for abnormal status of routine equipment and have little impact on the production process, such as alarms for a faulty indicator light or alarms for slight fluctuations in local sensor data. These can be handled according to the routine maintenance plan.

7. The intelligent alarm management method based on a relation tree according to claim 1, characterized in that, In S4, the trend analysis technique employs at least one of linear regression analysis, exponential smoothing analysis, or moving average analysis.

8. The intelligent alarm management method based on a relation tree according to claim 1, characterized in that, In S4, neural network technology employs at least one of backpropagation neural networks, radial basis function neural networks, or long short-term memory networks to construct an alarm state prediction model.

9. The intelligent alarm management method based on a relation tree according to claim 1, characterized in that, In S5, when determining whether an alarm is genuine or false, if the alarm data exceeds the threshold range, but trend analysis shows that the data will soon return to normal and the neural network predicts that it will not affect the operation of subsequent equipment, it is determined to be a false alarm; otherwise, it is determined to be a genuine alarm.

10. The intelligent alarm management method based on a relation tree according to claim 1, characterized in that, In S6, the expert knowledge base is built by collecting historical fault data, maintenance records, and industry expert experience from offshore oil and gas production systems, and is regularly updated and improved.

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