A gas pipeline pressure monitoring and analysis system and method
By setting up collection points and collection time points in gas pipelines, real-time data can be obtained and operational influencing factors can be analyzed to identify anomalies and provide maintenance suggestions. This solves the problem of real-time monitoring and analysis that is difficult in existing technologies, and improves the safety and stability of gas pipeline systems.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- ZHEJIANG YU TAI ALUMINUM IND CO LTD
- Filing Date
- 2024-03-01
- Publication Date
- 2026-05-26
AI Technical Summary
Existing gas pipeline monitoring systems struggle to acquire real-time data in a timely manner, lack systematic analysis, and fail to detect anomalies promptly, increasing safety risks and maintenance costs and affecting the stable operation of the system.
The system employs a real-time data acquisition module, an operational impact factor acquisition module, a real-time data analysis module, an operational status judgment module, an operational anomaly data acquisition module, an operational anomaly data analysis module, and an early warning terminal. By calculating the real-time data and operational impact factors at each collection point, it can identify operational anomalies and provide maintenance suggestions.
It enables real-time monitoring and early warning of anomalies in gas pipelines, improving system safety and stability, reducing downtime and failures, and lowering maintenance costs and risks.
Smart Images
Figure CN117989472B_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of gas pipeline technology, specifically to a gas pipeline pressure monitoring and analysis system and method. Background Technology
[0002] Gas pipelines play a vital role in energy transmission. Natural gas supply systems in large cities require regular monitoring and maintenance to ensure the safety and stability of gas supply for residents. This necessitates real-time monitoring and maintenance of pipeline systems to ensure the normal operation of production equipment. Consequently, a gas pipeline pressure monitoring and analysis system and method have emerged.
[0003] Existing technologies often only provide simple data collection and display, lacking in-depth analysis of real-time data and support for maintenance decisions. Obviously, this monitoring and analysis method has at least the following problems: 1. Existing technologies are difficult to obtain real-time data from various points in the pipeline in a timely manner, making it difficult to accurately monitor the pipeline operation. They lack systematic data analysis, making it difficult for operators to detect abnormal pipeline operation in a timely manner, thus making it difficult to take timely action and maintenance. They also lack analysis and summarization of operational data, making it impossible to optimize pipeline operation and affecting the overall operating efficiency of the pipeline system.
[0004] 2. Existing technology lacks a reliable early warning system for anomalies, which may not be detected in time when pipeline anomalies occur, increasing the safety risks of the pipeline system. The lack of systematic data analysis may increase the difficulty of maintenance and management, increase the uncertainty and management costs of maintenance work, and increase safety risks and maintenance costs, thus failing to fully guarantee the stable operation of the gas pipeline system. Summary of the Invention
[0005] To address the aforementioned technical shortcomings, the purpose of this invention is to provide a gas pipeline pressure monitoring and analysis system and method.
[0006] To solve the above-mentioned technical problems, the present invention adopts the following technical solution: In the first aspect, the present invention provides a gas pipeline pressure monitoring and analysis system, comprising: a real-time data acquisition module, used to set a number of acquisition points and acquisition time points in a target pipeline, thereby acquiring real-time data corresponding to each acquisition point in the target pipeline at each acquisition time point, the real-time data including pressure fluctuation frequency, pressure gradient and pressure peak value, thereby obtaining the real-time data corresponding to each acquisition point in the target pipeline at each acquisition time point.
[0007] The operation impact factor acquisition module is used to acquire the operation data corresponding to each collection point in the target pipeline at each collection time point. The operation data includes temperature, vibration frequency, combustion efficiency and liquid level height. The operation impact factors corresponding to each collection point in the target pipeline at each collection time point are analyzed.
[0008] The real-time data analysis module is used to analyze and obtain the real-time operation evaluation coefficients corresponding to each acquisition point in the target pipeline at each acquisition time point based on the real-time data and operation influencing factors corresponding to each acquisition point at each acquisition time point.
[0009] The operation status judgment module is used to judge the operation status of each acquisition point in the target pipeline at each acquisition time point based on the real-time operation evaluation coefficient corresponding to each acquisition point in the target pipeline at each acquisition time point, and to record each acquisition point in the target pipeline at each acquisition time point with abnormal operation as an acquisition point to be maintained.
[0010] The abnormal operation data acquisition module is used to acquire the abnormal operation data corresponding to each collection point to be maintained. The abnormal operation data includes pressure change rate, duration of pressure abnormality, and pressure drop rate.
[0011] The abnormal data analysis module is used to analyze the abnormal data of each data collection point to be maintained and obtain the abnormal evaluation coefficient of each data collection point to be maintained.
[0012] The maintenance mode and sequence analysis module is used to analyze the maintenance mode and sequence of each data collection point based on the abnormal operation data corresponding to each data collection point to be maintained, and then perform maintenance on each data collection point according to the corresponding maintenance mode and sequence.
[0013] The early warning terminal is used to issue an early warning when a certain collection point in the target pipeline is malfunctioning at a certain collection time.
[0014] Preferably, the analysis obtains the operational influencing factors corresponding to each sampling point in the target pipeline at each sampling time point. The specific analysis process is as follows: the temperature, vibration frequency, combustion efficiency, and liquid level height corresponding to each sampling point in the target pipeline at each sampling time point are respectively denoted as... , , and ,in, This indicates the number corresponding to each data collection time point. , This indicates the number corresponding to each collection point. Let n be any integer greater than 2, and u be any integer greater than 2. Substitute them into the calculation formula. In this process, the operational impact factors corresponding to each collection point in the target pipeline at each collection time point are obtained. ,in, , , , These are the standard temperature, standard vibration frequency, standard combustion efficiency, and standard liquid level corresponding to the sampling points in the set target pipeline. , , , These are the weighting factors corresponding to the temperature at the sampling point in the target pipeline, the vibration frequency, the combustion efficiency, and the liquid level.
[0015] Preferably, the analysis obtains the real-time operation evaluation coefficients corresponding to each sampling point in the target pipeline at each sampling time point. The specific analysis process is as follows: the pressure fluctuation frequency, pressure gradient, and pressure peak value corresponding to each sampling point in the target pipeline at each sampling time point are respectively denoted as... , and Substitute into the calculation formula In this process, the real-time operation evaluation coefficients corresponding to each collection point in the target pipeline at each collection time point are obtained. ,in, , , These represent the standard pressure fluctuation frequency, standard pressure gradient, and standard pressure peak value corresponding to the sampling points in the set target pipeline. , , These are the weighting factors corresponding to the pressure fluctuation frequency, pressure gradient, and pressure peak value at the sampling points in the target pipeline, respectively.
[0016] Preferably, the specific judgment process for determining the operating status of each sampling point in the target pipeline at each sampling time point is as follows: The real-time operating evaluation coefficient of each sampling point in the target pipeline at each sampling time point is compared with the real-time operating evaluation coefficient of the sampling point in the set standard target pipeline. If the real-time operating evaluation coefficient of a sampling point in the target pipeline at a certain sampling time point is less than the real-time operating evaluation coefficient of the sampling point in the set standard target pipeline, then the operation of that sampling point in the target pipeline at that sampling time point is determined to be abnormal. If the real-time operating evaluation coefficient of a sampling point in the target pipeline at a certain sampling time point is greater than or equal to the real-time operating evaluation coefficient of the sampling point in the set standard target pipeline, then the operation of that sampling point in the target pipeline at that sampling time point is determined to be normal. This method is used to determine the operating status of each sampling point in the target pipeline at each sampling time point.
[0017] Preferably, the analysis obtains the operational anomaly evaluation coefficients corresponding to each data collection point to be maintained. The specific analysis process is as follows: the pressure change rate, pressure anomaly duration, and pressure drop rate corresponding to each data collection point to be maintained are respectively denoted as... , and ,in, This indicates the corresponding number of each data collection point to be maintained. m is any integer greater than 2. Substitute it into the calculation formula. In this process, the operational anomaly evaluation coefficients corresponding to each data collection point to be maintained are obtained. ,in, , , These are the standard pressure change rate, standard pressure anomaly duration, and standard pressure decrease rate corresponding to the set data collection points to be maintained. , , These are the weighting factors corresponding to the pressure change rate of the data collection points to be maintained, the weighting factor corresponding to the duration of pressure anomalies, and the weighting factor corresponding to the pressure drop rate.
[0018] Preferably, the analysis of the maintenance mode corresponding to each data collection point to be maintained is carried out in the following specific process: the operation anomaly evaluation coefficient corresponding to each data collection point to be maintained is compared with the set standard operation anomaly evaluation coefficient corresponding to the data collection point to be maintained. If the operation anomaly evaluation coefficient corresponding to a data collection point to be maintained is less than the set standard operation anomaly evaluation coefficient corresponding to the data collection point to be maintained, then the data collection point to be maintained needs online maintenance. If the operation anomaly evaluation coefficient corresponding to a data collection point to be maintained is greater than or equal to the set standard operation anomaly evaluation coefficient corresponding to the data collection point to be maintained, then the data collection point to be maintained needs to be shut down for maintenance. The maintenance mode corresponding to each data collection point to be maintained is analyzed in this way.
[0019] Preferably, the analysis of the maintenance order corresponding to each data collection point to be maintained is carried out in the following specific process: the operation anomaly evaluation coefficients corresponding to each data collection point to be maintained are arranged in descending order, and then the data collection points to be maintained are maintained in the order of their operation anomaly evaluation coefficients.
[0020] The present invention provides a method for monitoring and analyzing the pressure of a gas pipeline in a second aspect, comprising: Step 1, acquiring real-time data: setting up several collection points and collection time points in the target pipeline, thereby collecting real-time data corresponding to each collection point in the target pipeline at each collection time point, the real-time data including pressure fluctuation frequency, pressure gradient and pressure peak value, thereby obtaining the real-time data corresponding to each collection point in the target pipeline at each collection time point.
[0021] Step 2: Obtaining operational influencing factors: Obtain operational data corresponding to each sampling point in the target pipeline at each sampling time point. The operational data includes temperature, vibration frequency, combustion efficiency, and liquid level. Analyze the data to obtain the operational influencing factors corresponding to each sampling point in the target pipeline at each sampling time point.
[0022] Step 3: Analysis of real-time data: Based on the real-time data and operational influencing factors corresponding to each acquisition point in the target pipeline at each acquisition time point, the real-time operational evaluation coefficient corresponding to each acquisition point in the target pipeline at each acquisition time point is obtained.
[0023] Step 4: Determine the operational status: Based on the real-time operational evaluation coefficients corresponding to each acquisition point in the target pipeline at each acquisition time point, determine the operational status of each acquisition point in the target pipeline at each acquisition time point, and record each acquisition point in the target pipeline at each acquisition time point that is in abnormal operation as an acquisition point to be maintained.
[0024] Step 5: Acquisition of abnormal operation data: Acquire the abnormal operation data corresponding to each data collection point to be maintained. The abnormal operation data includes the pressure change rate, the duration of the pressure anomaly, and the pressure drop rate.
[0025] Step Six: Analysis of Operational Anomaly Data: Based on the operational anomaly data corresponding to each data collection point to be maintained, analyze and obtain the operational anomaly evaluation coefficient corresponding to each data collection point to be maintained.
[0026] Step 7: Analysis of Maintenance Mode and Sequence: Based on the abnormal operation data corresponding to each data collection point to be maintained, analyze the maintenance mode and sequence corresponding to each data collection point to be maintained, and perform maintenance on each data collection point to be maintained according to the corresponding maintenance mode and sequence.
[0027] The beneficial effects of this invention are as follows: 1. This invention provides a gas pipeline pressure monitoring and analysis system and method, which can monitor pipeline operation data in real time and evaluate pipeline operation through operation influencing factor analysis. This helps to ensure the safe operation and maintenance of the gas pipeline system, thereby improving the stability and safety of gas supply. Simultaneously, it can identify operational anomalies and mark data collection points requiring maintenance, provide maintenance suggestions through operational anomaly data analysis and maintenance mode sequence analysis, and the early warning function can promptly alert to operational anomalies, improving pipeline safety and reliability and reducing maintenance costs and risks.
[0028] 2. In this embodiment of the invention, analyzing abnormal pipeline operation data helps in making maintenance decisions and rationally arranging maintenance sequences. Real-time monitoring, anomaly warning, and maintenance management of gas pipelines help improve the safety and stability of the pipeline system, reduce accident risks, increase operational efficiency, and promptly maintain the data collection points to be maintained, thus fixing potential problems and enhancing system stability. This reduces system failures and downtime, improving system reliability and availability.
[0029] 3. In this embodiment of the invention, by analyzing the maintenance mode and sequence of the data collection points to be maintained based on the abnormal operation data, those data collection points with more operational anomalies can be prioritized for processing, thereby improving maintenance efficiency. This can prevent more serious problems from occurring at important data collection points due to maintenance delays, reduce unnecessary maintenance work, and lower maintenance costs. Attached Figure Description
[0030] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0031] Figure 1 This is a schematic diagram of the system module connections of the present invention.
[0032] Figure 2 This is a flowchart illustrating the implementation steps of the method of the present invention. Detailed Implementation
[0033] 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.
[0034] Examples of embodiments of the present invention Figure 1 As shown, a gas pipeline pressure monitoring and analysis system includes: a real-time data acquisition module, an operation influencing factor acquisition module, a real-time data analysis module, an operation status judgment module, an operation anomaly data acquisition module, an operation anomaly data analysis module, a maintenance mode and sequence analysis module, an early warning terminal, and a database.
[0035] The operation impact factor acquisition module is connected to the real-time data acquisition module and the real-time data analysis module, respectively. The operation status judgment module is connected to the real-time data analysis module and the operation anomaly data acquisition module, respectively. The operation anomaly data analysis module is connected to the operation anomaly data acquisition module and the maintenance mode and sequence analysis module, respectively. The early warning terminal is connected to the maintenance mode and sequence analysis module and the operation status judgment module, respectively. The database is connected to the maintenance mode and sequence analysis module.
[0036] The real-time data acquisition module is used to set several acquisition points and acquisition time points in the target pipeline, so as to collect real-time data corresponding to each acquisition point in the target pipeline at each acquisition time point. The real-time data includes pressure fluctuation frequency, pressure gradient and pressure peak value, thereby obtaining the real-time data corresponding to each acquisition point in the target pipeline at each acquisition time point.
[0037] It should be noted that corresponding pressure sensors are installed at each sampling point in the target pipeline. By using the pressure sensors installed at each sampling point in the target pipeline, the pressure fluctuation frequency, pressure gradient, and pressure peak value of each sampling point in the target pipeline at each sampling time point can be obtained.
[0038] The operation impact factor acquisition module is used to acquire the operation data corresponding to each collection point in the target pipeline at each collection time point. The operation data includes temperature, vibration frequency, combustion efficiency and liquid level height. The operation impact factors corresponding to each collection point in the target pipeline at each collection time point are analyzed.
[0039] It should be noted that temperature sensors, vibration sensors, combustion product analyzers, and liquid level sensors are installed at each sampling point in the target pipeline. The temperature sensors at each sampling point in the target pipeline are used to obtain the temperature at each sampling point at each sampling time. The vibration sensors at each sampling point in the target pipeline are used to obtain the vibration frequency at each sampling point at each sampling time. The combustion product analyzers at each sampling point in the target pipeline are used to obtain the combustion efficiency at each sampling point at each sampling time. The liquid level sensors at each sampling point in the target pipeline are used to obtain the liquid level height at each sampling point at each sampling time.
[0040] In a specific embodiment, the analysis obtains the operational influencing factors corresponding to each sampling point in the target pipeline at each sampling time point. The specific analysis process is as follows: the temperature, vibration frequency, combustion efficiency, and liquid level height corresponding to each sampling point in the target pipeline at each sampling time point are respectively denoted as... , , and ,in, This indicates the number corresponding to each data collection time point. , This indicates the number corresponding to each collection point. Let n be any integer greater than 2, and u be any integer greater than 2. Substitute them into the calculation formula. In this process, the operational impact factors corresponding to each collection point in the target pipeline at each collection time point are obtained. ,in, , , , These are the standard temperature, standard vibration frequency, standard combustion efficiency, and standard liquid level corresponding to the sampling points in the set target pipeline. , , , These are the weighting factors corresponding to the temperature at the sampling point in the target pipeline, the vibration frequency, the combustion efficiency, and the liquid level.
[0041] It should be noted that, , , , All are greater than 0 and less than 1.
[0042] The real-time data analysis module is used to analyze and obtain the real-time operation evaluation coefficients corresponding to each acquisition point in the target pipeline at each acquisition time point based on the real-time data and operation influencing factors corresponding to each acquisition point at each acquisition time point.
[0043] In a specific embodiment, the analysis obtains the real-time operation evaluation coefficients corresponding to each sampling point in the target pipeline at each sampling time point. The specific analysis process is as follows: the pressure fluctuation frequency, pressure gradient, and pressure peak value corresponding to each sampling point in the target pipeline at each sampling time point are respectively denoted as... , and Substitute into the calculation formula In this process, the real-time operation evaluation coefficients corresponding to each collection point in the target pipeline at each collection time point are obtained. ,in, , , These represent the standard pressure fluctuation frequency, standard pressure gradient, and standard pressure peak value corresponding to the sampling points in the set target pipeline. , , These are the weighting factors corresponding to the pressure fluctuation frequency, pressure gradient, and pressure peak value at the sampling points in the target pipeline, respectively.
[0044] It should be noted that, , , All are greater than 0 and less than 1.
[0045] The operation status judgment module is used to judge the operation status of each acquisition point in the target pipeline at each acquisition time point based on the real-time operation evaluation coefficient corresponding to each acquisition point in the target pipeline at each acquisition time point, and to record each acquisition point in the target pipeline at each acquisition time point with abnormal operation as an acquisition point to be maintained.
[0046] In a specific embodiment, the determination of the operating status of each acquisition point in the target pipeline at each acquisition time point is carried out as follows: The real-time operating evaluation coefficient of each acquisition point in the target pipeline at each acquisition time point is compared with the real-time operating evaluation coefficient of the acquisition point in the set standard target pipeline. If the real-time operating evaluation coefficient of a certain acquisition point in the target pipeline at a certain acquisition time point is less than the real-time operating evaluation coefficient of the acquisition point in the set standard target pipeline, then the operation of that acquisition point in the target pipeline at that acquisition time point is determined to be abnormal. If the real-time operating evaluation coefficient of a certain acquisition point in the target pipeline at a certain acquisition time point is greater than or equal to the real-time operating evaluation coefficient of the acquisition point in the set standard target pipeline, then the operation of that acquisition point in the target pipeline at that acquisition time point is determined to be normal. The operating status of each acquisition point in the target pipeline at each acquisition time point is determined in this way.
[0047] The invention's embodiments, by analyzing abnormal pipeline operation data, facilitate maintenance decision-making and the rational arrangement of maintenance sequences. Real-time monitoring, anomaly warning, and maintenance management of gas pipelines help improve the safety and stability of the pipeline system, reduce accident risks, increase operational efficiency, and promptly perform maintenance on data collection points to address potential problems, thereby enhancing system stability. This reduces system failures and downtime, improving system reliability and availability.
[0048] The abnormal operation data acquisition module is used to acquire the abnormal operation data corresponding to each collection point to be maintained. The abnormal operation data includes pressure change rate, duration of pressure abnormality, and pressure drop rate.
[0049] It should be noted that corresponding pressure sensors are installed at various collection points along the target pipeline. The pressure change rate, obtained from these sensors, refers to the amount of pressure change per unit time. This is calculated by dividing the pressure difference between adjacent time points by the time interval: Pressure change rate = (Current pressure value - Previous pressure value) / Time interval. By monitoring pressure data in real time, when pressure exceeds the normal range or abnormal pressure fluctuations are detected, the time point at which the abnormality begins is recorded, and monitoring continues until the pressure returns to normal. This allows us to determine the duration of the pressure anomaly. The pressure drop rate, on the other hand, refers to the amount of pressure reduction per unit time. This is calculated by dividing the pressure difference between adjacent time points by the time interval: Pressure drop rate = (Current pressure value - Previous pressure value) / Time interval.
[0050] The abnormal data analysis module is used to analyze the abnormal data of each data collection point to be maintained and obtain the abnormal evaluation coefficient of each data collection point to be maintained.
[0051] In a specific embodiment, the analysis yields the operational anomaly evaluation coefficients corresponding to each data acquisition point to be maintained. The specific analysis process is as follows: the pressure change rate, pressure anomaly duration, and pressure drop rate corresponding to each data acquisition point to be maintained are respectively denoted as... , and ,in, This indicates the corresponding number of each data collection point to be maintained. m is any integer greater than 2. Substitute it into the calculation formula. In this process, the operational anomaly evaluation coefficients corresponding to each data collection point to be maintained are obtained. ,in, , , These are the standard pressure change rate, standard pressure anomaly duration, and standard pressure decrease rate corresponding to the set data collection points to be maintained. , , These are the weighting factors corresponding to the pressure change rate of the data collection points to be maintained, the weighting factor corresponding to the duration of pressure anomalies, and the weighting factor corresponding to the pressure drop rate.
[0052] It should be noted that, , , All are greater than 0 and less than 1.
[0053] The maintenance mode and sequence analysis module is used to analyze the maintenance mode and sequence of each data collection point based on the abnormal operation data corresponding to each data collection point to be maintained, and then perform maintenance on each data collection point according to the corresponding maintenance mode and sequence.
[0054] In a specific embodiment, the analysis of the maintenance mode corresponding to each data collection point to be maintained is carried out as follows: the operation anomaly evaluation coefficient corresponding to each data collection point to be maintained is compared with the set standard operation anomaly evaluation coefficient corresponding to the data collection point to be maintained. If the operation anomaly evaluation coefficient corresponding to a data collection point to be maintained is less than the set standard operation anomaly evaluation coefficient corresponding to the data collection point to be maintained, then the data collection point to be maintained needs online maintenance. If the operation anomaly evaluation coefficient corresponding to a data collection point to be maintained is greater than or equal to the set standard operation anomaly evaluation coefficient corresponding to the data collection point to be maintained, then the data collection point to be maintained needs to be shut down for maintenance. The maintenance mode corresponding to each data collection point to be maintained is analyzed in this way.
[0055] In another specific embodiment, the analysis of the maintenance order corresponding to each data collection point to be maintained is carried out as follows: the operation anomaly evaluation coefficients corresponding to each data collection point to be maintained are arranged in descending order, and then the data collection points to be maintained are maintained in the order of their operation anomaly evaluation coefficients.
[0056] The early warning terminal is used to issue an early warning when a certain collection point in the target pipeline is malfunctioning at a certain collection time.
[0057] Examples of embodiments of the present invention Figure 2 As shown, a gas pipeline pressure monitoring and analysis method includes: Step 1, real-time data acquisition: Several collection points and collection time points are set in the target pipeline, so as to collect the real-time data corresponding to each collection point in the target pipeline at each collection time point. The real-time data includes pressure fluctuation frequency, pressure gradient and pressure peak value, thereby obtaining the real-time data corresponding to each collection point in the target pipeline at each collection time point.
[0058] Step 2: Obtaining operational influencing factors: Obtain operational data corresponding to each sampling point in the target pipeline at each sampling time point. The operational data includes temperature, vibration frequency, combustion efficiency, and liquid level. Analyze the data to obtain the operational influencing factors corresponding to each sampling point in the target pipeline at each sampling time point.
[0059] Step 3: Analysis of real-time data: Based on the real-time data and operational influencing factors corresponding to each acquisition point in the target pipeline at each acquisition time point, the real-time operational evaluation coefficient corresponding to each acquisition point in the target pipeline at each acquisition time point is obtained.
[0060] Step 4: Determine the operational status: Based on the real-time operational evaluation coefficients corresponding to each acquisition point in the target pipeline at each acquisition time point, determine the operational status of each acquisition point in the target pipeline at each acquisition time point, and record each acquisition point in the target pipeline at each acquisition time point that is in abnormal operation as an acquisition point to be maintained.
[0061] Step 5: Acquisition of abnormal operation data: Acquire the abnormal operation data corresponding to each data collection point to be maintained. The abnormal operation data includes the pressure change rate, the duration of the pressure anomaly, and the pressure drop rate.
[0062] Step Six: Analysis of Operational Anomaly Data: Based on the operational anomaly data corresponding to each data collection point to be maintained, analyze and obtain the operational anomaly evaluation coefficient corresponding to each data collection point to be maintained.
[0063] Step 7: Analysis of Maintenance Mode and Sequence: Based on the abnormal operation data corresponding to each data collection point to be maintained, analyze the maintenance mode and sequence corresponding to each data collection point to be maintained, and perform maintenance on each data collection point to be maintained according to the corresponding maintenance mode and sequence.
[0064] This invention, through analysis of maintenance patterns and sequences of data collection points based on operational anomaly data, prioritizes the handling of collection points experiencing frequent operational anomalies, thereby improving maintenance efficiency. This prevents critical collection points from suffering more serious problems due to maintenance delays, reduces unnecessary maintenance work, and lowers maintenance costs.
[0065] This invention provides a gas pipeline pressure monitoring and analysis system and method, which can monitor pipeline operation data in real time and evaluate pipeline operation through operation influencing factor analysis. This helps ensure the safe operation and maintenance of gas pipeline systems, thereby improving the stability and safety of gas supply. Simultaneously, it can identify operational anomalies and mark data collection points requiring maintenance, provide maintenance suggestions through operational anomaly data analysis and maintenance pattern sequence analysis, and its early warning function can promptly alert to operational anomalies, improving pipeline safety and reliability while reducing maintenance costs and risks.
[0066] The above description is merely an example and illustration of the concept of the present invention. Those skilled in the art can make various modifications or additions to the specific embodiments described or use similar methods to replace them, as long as they do not deviate from the concept of the invention or exceed the scope defined in this specification, they should all fall within the protection scope of the present invention.
Claims
1. A gas pipeline pressure monitoring and analysis system, characterized by, include: The real-time data acquisition module is used to set several collection points and collection time points in the target pipeline, so as to collect real-time data corresponding to each collection point in the target pipeline at each collection time point. The real-time data includes pressure fluctuation frequency, pressure gradient and pressure peak value, thereby obtaining the real-time data corresponding to each collection point in the target pipeline at each collection time point. The operation impact factor acquisition module is used to acquire the operation data corresponding to each collection point in the target pipeline at each collection time point. The operation data includes temperature, vibration frequency, combustion efficiency and liquid level height. The operation impact factors corresponding to each collection point in the target pipeline at each collection time point are analyzed and obtained. The analysis yielded the operational influencing factors for each sampling point in the target pipeline at each sampling time point. The specific analysis process is as follows: The temperature, vibration frequency, combustion efficiency, and liquid level at each sampling point in the target pipeline at each sampling time point are respectively denoted as follows: , , and ,in, This indicates the number corresponding to each data collection time point. , This indicates the number corresponding to each collection point. Let n be any integer greater than 2, and u be any integer greater than 2. Substitute them into the calculation formula. In this process, the operational impact factors corresponding to each collection point in the target pipeline at each collection time point are obtained. ,in, , , , These are the standard temperature, standard vibration frequency, standard combustion efficiency, and standard liquid level corresponding to the sampling points in the set target pipeline. , , , These are the weighting factors corresponding to the temperature at the sampling point in the target pipeline, the weighting factor corresponding to the vibration frequency, the weighting factor corresponding to the combustion efficiency, and the weighting factor corresponding to the liquid level height. The real-time data analysis module is used to analyze and obtain the real-time operation evaluation coefficients corresponding to each collection point in the target pipeline at each collection time point based on the real-time data and operation influencing factors corresponding to each collection point at each collection time point. The analysis yields the real-time operational evaluation coefficients for each sampling point in the target pipeline at each sampling time point. The specific analysis process is as follows: The pressure fluctuation frequency, pressure gradient, and pressure peak value at each sampling point in the target pipeline at each sampling time point are respectively denoted as: , and Substitute into the calculation formula In this process, the real-time operation evaluation coefficients corresponding to each collection point in the target pipeline at each collection time point are obtained. ,in, , , These represent the standard pressure fluctuation frequency, standard pressure gradient, and standard pressure peak value corresponding to the sampling points in the set target pipeline. , , These are the weighting factors corresponding to the pressure fluctuation frequency, pressure gradient, and pressure peak value at the sampling points in the target pipeline, respectively. The operation status judgment module is used to judge the operation status of each collection point in the target pipeline at each collection time point based on the real-time operation evaluation coefficient corresponding to each collection point in the target pipeline at each collection time point, and to record each collection point in the target pipeline at each collection time point with abnormal operation as a collection point to be maintained. The abnormal operation data acquisition module is used to acquire the abnormal operation data corresponding to each collection point to be maintained. The abnormal operation data includes pressure change rate, pressure abnormality duration and pressure drop rate. The abnormal data analysis module is used to analyze the abnormal data of each collection point to be maintained and obtain the abnormal evaluation coefficient of each collection point to be maintained. The maintenance mode and sequence analysis module is used to analyze the maintenance mode and sequence of each data collection point to be maintained based on the abnormal operation data corresponding to each data collection point to be maintained, and then maintain each data collection point to be maintained according to the corresponding maintenance mode and sequence. The early warning terminal is used to issue an early warning when a certain collection point in the target pipeline is malfunctioning at a certain collection time.
2. The gas pipeline pressure monitoring and analysis system as described in claim 1, characterized in that, The specific process for determining the operational status of each acquisition point in the target pipeline at each acquisition time point is as follows: The real-time operation evaluation coefficients corresponding to each acquisition point in the target pipeline at each acquisition time point are compared with the real-time operation evaluation coefficients corresponding to the acquisition points in the set standard target pipeline. If the real-time operation evaluation coefficient of a certain acquisition point in the target pipeline at a certain acquisition time point is less than the real-time operation evaluation coefficient of the acquisition point in the set standard target pipeline, then the operation of that acquisition point in the target pipeline at that acquisition time point is determined to be abnormal. If the real-time operation evaluation coefficient of a certain acquisition point in the target pipeline at a certain acquisition time point is greater than or equal to the real-time operation evaluation coefficient of the acquisition point in the set standard target pipeline, then the operation of that acquisition point in the target pipeline at that acquisition time point is determined to be normal. The operation status of each acquisition point in the target pipeline at each acquisition time point is determined in this way.
3. The gas pipeline pressure monitoring and analysis system as described in claim 1, characterized in that, The analysis yielded the operational anomaly evaluation coefficients for each data collection point to be maintained. The specific analysis process is as follows: The pressure change rate, duration of pressure anomaly, and pressure decrease rate corresponding to each data collection point to be maintained are respectively denoted as: , and ,in, This indicates the number corresponding to each data collection point that needs maintenance. m is any integer greater than 2. Substitute it into the calculation formula. In this process, the operational anomaly evaluation coefficients corresponding to each data collection point to be maintained are obtained. ,in, , , These are the standard pressure change rate, standard pressure anomaly duration, and standard pressure decrease rate corresponding to the set data collection points to be maintained. , , These are the weighting factors corresponding to the pressure change rate of the data collection points to be maintained, the weighting factor corresponding to the duration of pressure anomalies, and the weighting factor corresponding to the pressure drop rate.
4. The gas pipeline pressure monitoring and analysis system as described in claim 3, characterized in that, The analysis of the maintenance mode corresponding to each data collection point to be maintained is performed as follows: The operational anomaly assessment coefficients corresponding to each data collection point to be maintained are compared with the set standard operational anomaly assessment coefficients corresponding to the data collection points to be maintained. If the operational anomaly assessment coefficient of a data collection point to be maintained is less than the set standard operational anomaly assessment coefficient, then the data collection point to be maintained needs online maintenance. If the operational anomaly assessment coefficient of a data collection point to be maintained is greater than or equal to the set standard operational anomaly assessment coefficient, then the data collection point to be maintained needs to be shut down for maintenance. The maintenance mode corresponding to each data collection point to be maintained is analyzed in this way.
5. A gas pipeline pressure monitoring and analysis system as described in claim 4, characterized in that, The maintenance sequence for each data collection point to be maintained is analyzed, and the specific analysis process is as follows: Arrange the operational anomaly assessment coefficients corresponding to each data collection point to be maintained in descending order, and then maintain each data collection point to be maintained in the order of their operational anomaly assessment coefficients.
6. A method for monitoring and analyzing gas pipeline pressure using the gas pipeline pressure monitoring and analysis system according to any one of claims 1-5, characterized in that, include: Step 1: Real-time data acquisition: Set up several collection points and collection time points in the target pipeline, and collect real-time data corresponding to each collection point in the target pipeline at each collection time point. The real-time data includes pressure fluctuation frequency, pressure gradient and pressure peak value, and thus obtain the real-time data corresponding to each collection point in the target pipeline at each collection time point. Step 2: Obtaining operational influencing factors: Obtain operational data corresponding to each sampling point in the target pipeline at each sampling time point. The operational data includes temperature, vibration frequency, combustion efficiency, and liquid level. Analyze the data to obtain the operational influencing factors corresponding to each sampling point in the target pipeline at each sampling time point. Step 3: Analysis of real-time data: Based on the real-time data and operational influencing factors corresponding to each acquisition point in the target pipeline at each acquisition time point, analyze and obtain the real-time operational evaluation coefficient corresponding to each acquisition point in the target pipeline at each acquisition time point; Step 4: Judging the operational status: Based on the real-time operational evaluation coefficients corresponding to each acquisition point in the target pipeline at each acquisition time point, the operational status of each acquisition point in the target pipeline at each acquisition time point is judged, and each acquisition point in the target pipeline at each acquisition time point with abnormal operation is recorded as an acquisition point to be maintained. Step 5: Acquisition of abnormal operation data: Acquire the abnormal operation data corresponding to each data collection point to be maintained. The abnormal operation data includes the pressure change rate, the duration of the pressure anomaly, and the pressure drop rate. Step Six: Analysis of Operational Anomaly Data: Based on the operational anomaly data corresponding to each data collection point to be maintained, analyze and obtain the operational anomaly evaluation coefficient corresponding to each data collection point to be maintained; Step 7: Analysis of Maintenance Mode and Sequence: Based on the abnormal operation data corresponding to each data collection point to be maintained, analyze the maintenance mode and sequence corresponding to each data collection point to be maintained, and perform maintenance on each data collection point to be maintained according to the corresponding maintenance mode and sequence.