A gas pipeline data management system and method based on artificial intelligence

Abstract

The present invention discloses a gas pipeline data management system and method based on artificial intelligence, which relates to the field of data management technology. The system of the present invention includes: a data acquisition and processing module, an abnormal point identification and path construction module, a dominant point analysis and deviation calculation module, a real-time data analysis and abnormality detection module, and a historical data comparison and maintenance suggestion module; the data acquisition and processing module is responsible for acquiring sensor gas pipeline data, processing it into data units and extracting features; the abnormal point identification and path construction module analyzes data features, identifies abnormal points, and constructs abnormal point paths; the dominant point analysis and deviation calculation module analyzes abnormal point path features, determines abnormal dominant points, and calculates deviation indexes; the real-time data analysis and abnormality detection module acquires and analyzes real-time data, identifies and constructs abnormal point paths; the historical data comparison and maintenance suggestion module compares real-time and historical abnormal point path data and outputs notification information.

Description

Technical Field

[0001] The present invention relates to the field of data management technology, and in particular to an artificial intelligence-based gas pipeline data management system and method. Background Art

[0002] With the acceleration of urbanization and the widespread use of gas, gas pipeline systems, as a vital component of urban infrastructure, have become increasingly important. Their safety and stability are directly linked to the smooth operation of residents' lives and industrial production. However, gas pipeline systems are complex, extensive, and widely distributed. They are often affected by various factors, including environmental factors, material aging, and improper construction. These systems are prone to failures such as leaks, blockages, and abnormal pressure. If these failures are not detected and addressed promptly, they can not only cause economic losses but can also lead to serious safety incidents.

[0003] While existing gas pipeline data management systems can meet these needs to a certain extent, they still have shortcomings. Existing gas pipeline data management methods rely heavily on manual inspections and periodic testing, which are subject to low efficiency, high costs, and slow response times. This makes it difficult to achieve effective, 24 / 7, real-time monitoring and management of gas pipeline systems. In particular, when addressing gas pipeline anomalies, analysis is often limited to single-point data, ignoring the potential correlations between anomalies at different locations. This impacts the accuracy of fault location and the efficiency of troubleshooting. Summary of the Invention

[0004] The purpose of the present invention is to provide a gas pipeline data management system and method based on artificial intelligence to solve the problems raised in the above background technology.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0006] A gas pipeline data management method based on artificial intelligence, the method comprising the following steps:

[0007] Step S100: Obtain gas pipeline data from all sensor devices within the monitoring area, record the location data of the sensor devices, and associate the gas pipeline data with the location data of the sensor devices to form a plurality of data units; analyze the gas pipeline data of each data unit to obtain corresponding data features;

[0008] Step S200. Analyze the time-varying trend of the data features according to the data features of the data units, thereby identifying abnormal points in the gas pipeline; obtain the location data of all abnormal points in the gas pipeline, and construct an abnormal point path based on the location data of the abnormal points in the gas pipeline;

[0009] Step S300: Based on the abnormal point path, analyze the correlation between the data characteristics and location data of different gas pipeline abnormal points, and obtain the abnormal dominant point of the abnormal point path based on the correlation; analyze the deviation relationship between the abnormal dominant point and other gas pipeline abnormal points on the abnormal point path, and thus obtain the corresponding deviation index;

[0010] Step S400. Acquire real-time gas pipeline data in the monitoring area, analyze the real-time gas pipeline data at selected time intervals to obtain corresponding real-time data features, and identify real-time gas pipeline abnormal points; construct a real-time abnormal point path based on the real-time gas pipeline abnormal points; calculate the deviation index between the real-time gas pipeline abnormal points on the real-time abnormal point path, and output corresponding prompt information based on the deviation index.

[0011] Furthermore, step S100 includes:

[0012] S101. Obtain a gas pipeline distribution plan within the monitoring area, mark the sensor device location on the gas pipeline distribution plan and number the sensor device, and each sensor device number is unique; record the location data of the sensor device according to the gas pipeline distribution plan, and obtain the corresponding gas pipeline data according to the sensor device number;

[0013] S102. Obtain the gas pipeline data for the selected time period within the monitoring area in chronological order, and match the gas pipeline data of each sensor device with the corresponding location data to form several data units, where each data unit includes the location data of a sensor device and the corresponding gas pipeline data; perform time series analysis on each data unit to obtain the corresponding data characteristics.

[0014] Furthermore, step S200 includes:

[0015] S201. Obtain data features of the data unit and preprocess the features of the data unit; for each data unit, construct a feature vector V based on the preprocessed data features, and the feature vector V = [v1, v2, ..., vn], where v1 represents the first feature data of the data unit, v2 represents the second feature data of the data unit, and so on, vn represents the nth feature data of the data unit, and n represents the data feature number of the data unit; perform principal component analysis on the feature vector V of each data unit to obtain a data feature representation Vr after dimensionality reduction;

[0016] S202. Based on the data characteristic representation Vr of each data unit, a trend curve L of each characteristic data corresponding to the data characteristic representation Vr over time is plotted in chronological order. Based on the set standard characteristic data trend curve range LQ, the trend anomaly point of each data unit is identified. Based on the position data of the sensor device corresponding to the data unit, the location of the gas pipeline anomaly point is obtained;

[0017] S203. Obtain the location data of all abnormal points in the gas pipelines, mark the abnormal points in the gas pipelines on the gas pipeline distribution plan, and connect the abnormal points in the gas pipelines with adjacent location data to form several abnormal point paths.

[0018] Furthermore, principal component analysis is performed on the eigenvector V of each data unit. The specific analysis process is as follows:

[0019] Standardize the eigenvectors V of all data units so that the mean of each feature is 0 and the variance is 1, and record the standardized eigenvector as X; calculate the covariance matrix C of the standardized data features, and C = [1 / (m-1)]X T X, where m represents the number of data units; perform eigenvalue decomposition on the covariance matrix C to obtain the eigenvalue λ and the corresponding eigenvector W, and C = WλW T ; Get all eigenvalues and arrange them in descending order, and record the corresponding eigenvectors; select the first k eigenvalues and their corresponding eigenvectors, combine these k eigenvectors to form a principal component matrix Wk, and Wk = [w1, w2, ..., wk], where w1 represents the eigenvector corresponding to the first eigenvalue, w2 represents the eigenvector corresponding to the second eigenvalue, and so on, wk represents the eigenvector corresponding to the kth eigenvalue; use the principal component matrix Wk to perform a linear transformation on the standardized eigenvector X to obtain the reduced-dimensional data feature representation Vr, and Vr = XWk.

[0020] Furthermore, step S300 includes:

[0021] S301. For each abnormal point path, obtain the corresponding gas pipeline abnormal point data feature representation Vr and the corresponding gas pipeline abnormal point location data; obtain the corresponding trend curve L based on the gas pipeline abnormal point data feature representation Vr, calculate the area S enclosed by the trend curve L and the coordinate axis, and associate the area S with the location coordinates of the gas pipeline abnormal point to form an associated data pair;

[0022] S302. Based on the associated data pairs, obtain the areas S corresponding to different gas pipeline anomalies in the current anomaly path. Compare the areas S and select the gas pipeline anomaly point with the largest area S as the dominant anomaly point in the current anomaly path. Based on the position coordinates of other gas pipeline anomalies in the current anomaly path, calculate the distances between the dominant anomaly point and other gas pipeline anomalies in the path. The area enclosed by the dominant anomaly point in the current anomaly path as the center and the maximum distance between the dominant anomaly point and other gas pipeline anomalies in the path as the radius is the radiation area of the dominant anomaly point.

[0023] S303. Obtain the associated data pairs of the abnormal dominant point and other gas pipeline abnormal points on the path of the abnormal point. Based on the associated data pairs, calculate the deviation index P between the abnormal dominant point and other gas pipeline abnormal points on the path of the abnormal point. The specific calculation formula is: P = [(1 / H)Σ h∈[1,H] (S1-Sh)] / S0, where S1 represents the area enclosed by the trend curve L of the dominant abnormal point and the coordinate axis, Sh represents the area enclosed by the trend curve L of the hth gas pipeline abnormal point in the current abnormal point path and the coordinate axis, and S0 represents the difference between the maximum and minimum areas enclosed by the trend curve L and the coordinate axis corresponding to the set standard characteristic data trend curve range LQ; the deviation index P of each abnormal point path is associated with the radiation area Sf of the dominant abnormal point to form a historical abnormal point path data pair;

[0024] S304. Obtain historical abnormal point path data pairs of different abnormal point paths, combine them with historical gas pipeline maintenance records, obtain the time period with maintenance records, and match the historical abnormal point path data pairs of the corresponding time period with the corresponding maintenance records.

[0025] Furthermore, step S400 includes:

[0026] S401. Acquire real-time gas pipeline data in the monitoring area, analyze the real-time gas pipeline data at selected time intervals, and obtain corresponding real-time data features; analyze the real-time data features in the same manner as step S200 to identify real-time gas pipeline anomalies, and construct a real-time anomaly path based on the real-time gas pipeline anomalies;

[0027] S402. For each real-time outlier path, the corresponding real-time outlier dominant point and the area Sf0 of the radiation region of the real-time outlier dominant point are obtained according to the same analysis method as step S300; based on the real-time outlier dominant point and the radiation region of the real-time outlier dominant point, the real-time deviation index P0 of the current outlier path is calculated, thereby obtaining a real-time outlier path data pair;

[0028] S403. Compare the real-time abnormal point path data pair with the historical abnormal point path data pair. If the similarity between the real-time abnormal point path data pair and the historical abnormal point path data pair is greater than or equal to the similarity threshold, the real-time abnormal point path data pair, the real-time abnormal dominant point and the corresponding historical maintenance record are output to the relevant personnel; if the similarity between the real-time abnormal point path data pair and the historical abnormal point path data pair is less than the similarity threshold, only the real-time abnormal point path data pair and the real-time abnormal dominant point are output, and the relevant personnel perform the corresponding maintenance processing.

[0029] An artificial intelligence-based gas pipeline data management system, comprising: a data acquisition and processing module, an anomaly identification and path building module, a dominant point analysis and deviation calculation module, a real-time data analysis and anomaly detection module, and a historical data comparison and maintenance suggestion module;

[0030] The data acquisition and processing module obtains the gas pipeline data and location data of all sensor devices in the monitoring area, records and connects the data to form data units; performs time series analysis on the data units and extracts data features; the anomaly point identification and path construction module analyzes the trend changes of data features and identifies gas pipeline anomalies; constructs anomaly point paths based on the location data of gas pipeline anomalies and marks all gas pipeline anomalies; the dominant point analysis and deviation calculation module analyzes the correlation between the data features and location data on the anomaly point path to obtain the anomaly dominant point; calculates the deviation index between the anomaly dominant point and other gas pipeline anomalies to form the anomaly dominant point radiation area; the real-time data analysis and anomaly detection module obtains and analyzes real-time gas pipeline data, identifies real-time anomalies and constructs real-time anomaly point paths, calculates the deviation index of the real-time anomaly point paths, and compares them with historical data; the historical data comparison and maintenance suggestion module compares the real-time anomaly point path data pairs with the historical anomaly point path data pairs, and outputs the corresponding notification information based on the similarity threshold.

[0031] Furthermore, the data acquisition and processing module includes a data acquisition unit, a data recording unit, and a data timing analysis unit;

[0032] The data acquisition unit obtains gas pipeline data and sensor location data from the sensor equipment in the monitoring area, marks the sensor equipment location and its number according to the gas pipeline distribution plan, and records the location data; the data recording unit records the location data of each sensor device according to the sensor number and the gas pipeline distribution plan, and corresponds the gas pipeline data with the sensor location data to form a data unit; the data timing analysis unit performs timing analysis on each data unit, extracts and records data features.

[0033] Furthermore, the outlier identification and path construction module includes a data feature analysis unit and an outlier annotation and path construction unit;

[0034] The data feature analysis unit performs principal component analysis on the data features to obtain the data feature representation after dimensionality reduction; draws a trend curve of the feature data over time and identifies trend anomalies; the anomaly marking and path construction unit marks the gas pipeline anomalies based on the identified anomaly location data and connects the adjacent location data to form an anomaly path;

[0035] The dominant point analysis and deviation calculation module includes an abnormal point data feature extraction unit, a dominant point determination unit, and a deviation calculation unit;

[0036] The abnormal point data feature extraction unit obtains the data feature representation of each gas pipeline abnormal point in the abnormal point path and calculates the area enclosed by its trend curve and the coordinate axis; the dominant point determination unit compares the areas of different gas pipeline abnormal points to determine the abnormal dominant point; calculates the deviation index between the abnormal dominant point and other gas pipeline abnormal points and generates the abnormal dominant point radiation area; the deviation calculation unit calculates the deviation index between the abnormal dominant point and other gas pipeline abnormal points and associates it with the historical abnormal point path data.

[0037] Furthermore, the real-time data analysis and anomaly detection module includes a real-time data acquisition and feature analysis unit, a real-time anomaly path construction unit, and a real-time dominant point and deviation calculation unit;

[0038] The real-time data acquisition and feature analysis unit acquires real-time gas pipeline data, analyzes it at selected time intervals, extracts real-time data features, and identifies abnormal points in the real-time gas pipeline. The real-time abnormal point path construction unit constructs a real-time abnormal point path based on the real-time gas pipeline abnormal points. The real-time dominant point and deviation calculation unit analyzes the real-time abnormal dominant point, calculates the deviation index of the real-time abnormal dominant point, and generates real-time abnormal point path data pairs.

[0039] The historical data comparison and maintenance suggestion module includes a historical data comparison unit and a maintenance record association and suggestion unit;

[0040] The historical data comparison unit compares the similarity between the real-time abnormal point path data pair and the historical abnormal point path data pair; based on the similarity threshold, it generates a comparison result; the maintenance record association and suggestion unit outputs the real-time abnormal point path data pair and the historical maintenance record according to the similarity result.

[0041] Compared with the prior art, the present invention has the following beneficial effects:

[0042] Artificial intelligence algorithms are used to analyze gas pipeline data in real time, enabling 24 / 7 real-time monitoring. This avoids the inefficiency, high cost, and slow response associated with traditional methods that rely on manual inspections and periodic testing, significantly improving system response speed and management efficiency. By analyzing the temporal characteristics of data units and principal component analysis, it comprehensively considers the changing trends of multi-point data. This approach not only identifies single-point anomalies but also reveals potential correlations between anomalies at different locations, improving the accuracy and comprehensiveness of anomaly detection. Constructing anomaly paths based on anomaly location data effectively visualizes the relationships between anomalies, helping to identify anomaly patterns and potential system vulnerabilities. This path analysis method is more comprehensive than traditional single-point anomaly detection and helps quickly locate the source of the problem. By calculating the deviation index of the leading anomaly point, the severity and impact of the anomaly point can be quantified. This method accurately assesses the relative importance of anomalies and helps prioritize leading anomaly points for treatment, improving the accuracy of fault location and repair. Comparing real-time anomaly path data with historical anomaly path data effectively identifies changing trends in anomaly patterns. This comparative analysis not only helps understand the relationship between current anomalies and historical anomalies but also optimizes maintenance strategies based on historical maintenance records. Outputting prompt information based on the deviation index and similarity threshold helps to respond to and handle potential issues in a timely manner. The system can automatically feedback important information to relevant personnel, thereby improving the timeliness and accuracy of problem handling and reducing the need for human intervention. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0044] Figure 1 This is a schematic diagram of a gas pipeline data management system module based on artificial intelligence in the present invention. DETAILED DESCRIPTION

[0045] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0046] See also Figure 1 , the present invention provides a technical solution:

[0047] An artificial intelligence-based gas pipeline data management system, comprising: a data acquisition and processing module, an anomaly identification and path building module, a dominant point analysis and deviation calculation module, a real-time data analysis and anomaly detection module, and a historical data comparison and maintenance suggestion module;

[0048] The data acquisition and processing module obtains the gas pipeline data and location data of all sensor devices in the monitoring area, records and connects the data to form data units; performs time series analysis on the data units and extracts data features; the anomaly point identification and path construction module analyzes the trend changes of data features and identifies gas pipeline anomalies; constructs anomaly point paths based on the location data of gas pipeline anomalies and marks all gas pipeline anomalies; the dominant point analysis and deviation calculation module analyzes the correlation between the data features and location data on the anomaly point path to obtain the anomaly dominant point; calculates the deviation index between the anomaly dominant point and other gas pipeline anomalies to form the anomaly dominant point radiation area; the real-time data analysis and anomaly detection module obtains and analyzes real-time gas pipeline data, identifies real-time anomalies and constructs real-time anomaly point paths, calculates the deviation index of the real-time anomaly point paths, and compares them with historical data; the historical data comparison and maintenance suggestion module compares the real-time anomaly point path data pairs with the historical anomaly point path data pairs, and outputs the corresponding notification information based on the similarity threshold.

[0049] The data acquisition and processing module includes a data acquisition unit, a data recording unit and a data timing analysis unit;

[0050] The data acquisition unit obtains gas pipeline data and sensor location data from the sensor equipment in the monitoring area, marks the sensor equipment location and its number according to the gas pipeline distribution plan, and records the location data; the data recording unit records the location data of each sensor device according to the sensor number and the gas pipeline distribution plan, and corresponds the gas pipeline data with the sensor location data to form a data unit; the data timing analysis unit performs timing analysis on each data unit, extracts and records data features.

[0051] The outlier identification and path construction module includes a data feature analysis unit and an outlier annotation and path construction unit;

[0052] The data feature analysis unit performs principal component analysis on the data features to obtain the data feature representation after dimensionality reduction; draws a trend curve of the feature data over time and identifies trend anomalies; the anomaly marking and path construction unit marks the gas pipeline anomalies based on the identified anomaly location data and connects the adjacent location data to form an anomaly path;

[0053] The dominant point analysis and deviation calculation module includes an abnormal point data feature extraction unit, a dominant point determination unit, and a deviation calculation unit;

[0054] The abnormal point data feature extraction unit obtains the data feature representation of each gas pipeline abnormal point in the abnormal point path and calculates the area enclosed by its trend curve and the coordinate axis; the dominant point determination unit compares the areas of different gas pipeline abnormal points to determine the abnormal dominant point; calculates the deviation index between the abnormal dominant point and other gas pipeline abnormal points and generates the abnormal dominant point radiation area; the deviation calculation unit calculates the deviation index between the abnormal dominant point and other gas pipeline abnormal points and associates it with the historical abnormal point path data.

[0055] The real-time data analysis and anomaly detection module includes a real-time data acquisition and feature analysis unit, a real-time anomaly path construction unit, and a real-time dominant point and deviation calculation unit;

[0056] The real-time data acquisition and feature analysis unit acquires real-time gas pipeline data, analyzes it at selected time intervals, extracts real-time data features, and identifies abnormal points in the real-time gas pipeline. The real-time abnormal point path construction unit constructs a real-time abnormal point path based on the real-time gas pipeline abnormal points. The real-time dominant point and deviation calculation unit analyzes the real-time abnormal dominant point, calculates the deviation index of the real-time abnormal dominant point, and generates real-time abnormal point path data pairs.

[0057] The historical data comparison and maintenance suggestion module includes a historical data comparison unit and a maintenance record association and suggestion unit;

[0058] The historical data comparison unit compares the similarity between the real-time abnormal point path data pair and the historical abnormal point path data pair; based on the similarity threshold, it generates a comparison result; the maintenance record association and suggestion unit outputs the real-time abnormal point path data pair and the historical maintenance record according to the similarity result.

[0059] A gas pipeline data management method based on artificial intelligence, the method comprising the following steps:

[0060] Step S100: Obtain gas pipeline data from all sensor devices within the monitoring area, record the location data of the sensor devices, and associate the gas pipeline data with the location data of the sensor devices to form a plurality of data units; analyze the gas pipeline data of each data unit to obtain corresponding data features;

[0061] Step S200. Analyze the time-varying trend of the data features according to the data features of the data units, thereby identifying abnormal points in the gas pipeline; obtain the location data of all abnormal points in the gas pipeline, and construct an abnormal point path based on the location data of the abnormal points in the gas pipeline;

[0062] Step S300: Based on the abnormal point path, analyze the correlation between the data characteristics and location data of different gas pipeline abnormal points, and obtain the abnormal dominant point of the abnormal point path based on the correlation; analyze the deviation relationship between the abnormal dominant point and other gas pipeline abnormal points on the abnormal point path, and thus obtain the corresponding deviation index;

[0063] Step S400. Acquire real-time gas pipeline data in the monitoring area, analyze the real-time gas pipeline data at selected time intervals to obtain corresponding real-time data features, and identify real-time gas pipeline abnormal points; construct a real-time abnormal point path based on the real-time gas pipeline abnormal points; calculate the deviation index between the real-time gas pipeline abnormal points on the real-time abnormal point path, and output corresponding prompt information based on the deviation index.

[0064] Step S100 includes:

[0065] S101. Obtain a gas pipeline distribution plan within the monitoring area, mark the sensor device location on the gas pipeline distribution plan and number the sensor device, and each sensor device number is unique; record the location data of the sensor device according to the gas pipeline distribution plan, and obtain the corresponding gas pipeline data according to the sensor device number;

[0066] S102. Obtain the gas pipeline data for the selected time period within the monitoring area in chronological order, and match the gas pipeline data of each sensor device with the corresponding location data to form several data units, where each data unit includes the location data of a sensor device and the corresponding gas pipeline data; perform time series analysis on each data unit to obtain the corresponding data characteristics.

[0067] In this embodiment, the process of obtaining the data characteristics of the data unit is as follows:

[0068] The data units are organized into a time series format to obtain the corresponding statistical features, including basic statistics such as mean, standard deviation, maximum and minimum values. The trend line of the data unit is fitted by calculating the difference and slope of consecutive time points to obtain the trend coefficient and intercept as the trend characteristics of the data unit. The amplitude, phase and frequency of the periodic component are extracted as periodic characteristics using Fourier transform or seasonal decomposition methods (such as STL). However, for gas pipeline data in practical applications, people are often more concerned with low-frequency seasonal changes or long-term trend changes rather than high-frequency small fluctuations. Therefore, when processing such data, the complete Fourier transform results may not be needed. Instead, some simplified methods are used to estimate the periodic characteristics. The simplified method for extracting periodic features is as follows:

[0069] Seasonal indicators: If there is a clear seasonal pattern in the data (such as annual, quarterly, or monthly changes), calculate seasonal indicators. For example, for annual data, you can calculate the mean or median for each season (such as spring, summer, autumn, winter) and use these seasonal means as features.

[0070] Cyclical Rate of Change: Calculates the rate of change of data at the same point in time within adjacent periods, which can help identify cyclical growth or decline trends.

[0071] Seasonal decomposition: Use seasonal decomposition methods (such as STL decomposition) to decompose time series data into trend, seasonal and random components. The seasonal component can be directly used as a periodic feature, while the trend component can be used to analyze long-term changes.

[0072] Step S200 includes:

[0073] S201. Obtain data features of the data unit and preprocess the features of the data unit; for each data unit, construct a feature vector V based on the preprocessed data features, and the feature vector V = [v1, v2, ..., vn], where v1 represents the first feature data of the data unit, v2 represents the second feature data of the data unit, and so on, vn represents the nth feature data of the data unit, and n represents the data feature number of the data unit; perform principal component analysis on the feature vector V of each data unit to obtain a data feature representation Vr after dimensionality reduction;

[0074] S202. Based on the data characteristic representation Vr of each data unit, a trend curve L of each characteristic data corresponding to the data characteristic representation Vr over time is plotted in chronological order. Based on the set standard characteristic data trend curve range LQ, the trend anomaly point of each data unit is identified. Based on the position data of the sensor device corresponding to the data unit, the location of the gas pipeline anomaly point is obtained;

[0075] S203. Obtain the location data of all abnormal points in the gas pipelines, mark the abnormal points in the gas pipelines on the gas pipeline distribution plan, and connect the abnormal points in the gas pipelines with adjacent location data to form several abnormal point paths.

[0076] For each data unit’s eigenvector V, principal component analysis is performed. The specific analysis process is as follows:

[0077] Standardize the eigenvectors V of all data units so that the mean of each feature is 0 and the variance is 1, and record the standardized eigenvector as X; calculate the covariance matrix C of the standardized data features, and C = [1 / (m-1)]X TX, where m represents the number of data units; perform eigenvalue decomposition on the covariance matrix C to obtain the eigenvalue λ and the corresponding eigenvector W, and C = WλW T ; Get all eigenvalues and arrange them in descending order, and record the corresponding eigenvectors; select the first k eigenvalues and their corresponding eigenvectors, combine these k eigenvectors to form a principal component matrix Wk, and Wk = [w1, w2, ..., wk], where w1 represents the eigenvector corresponding to the first eigenvalue, w2 represents the eigenvector corresponding to the second eigenvalue, and so on, wk represents the eigenvector corresponding to the kth eigenvalue; use the principal component matrix Wk to perform a linear transformation on the standardized eigenvector X to obtain the reduced-dimensional data feature representation Vr, and Vr = XWk.

[0078] Step S300 includes:

[0079] S301. For each abnormal point path, obtain the corresponding gas pipeline abnormal point data feature representation Vr and the corresponding gas pipeline abnormal point location data; obtain the corresponding trend curve L based on the gas pipeline abnormal point data feature representation Vr, calculate the area S enclosed by the trend curve L and the coordinate axis, and associate the area S with the location coordinates of the gas pipeline abnormal point to form an associated data pair;

[0080] S302. Based on the associated data pairs, obtain the areas S corresponding to different gas pipeline anomalies in the current anomaly path. Compare the areas S and select the gas pipeline anomaly point with the largest area S as the dominant anomaly point in the current anomaly path. Based on the position coordinates of other gas pipeline anomalies in the current anomaly path, calculate the distances between the dominant anomaly point and other gas pipeline anomalies in the path. The area enclosed by the dominant anomaly point in the current anomaly path as the center and the maximum distance between the dominant anomaly point and other gas pipeline anomalies in the path as the radius is the radiation area of the dominant anomaly point.

[0081] S303. Obtain the associated data pairs of the abnormal dominant point and other gas pipeline abnormal points on the path of the abnormal point. Based on the associated data pairs, calculate the deviation index P between the abnormal dominant point and other gas pipeline abnormal points on the path of the abnormal point. The specific calculation formula is: P = [(1 / H)Σ h∈[1,H](S1-Sh)] / S0, where S1 represents the area enclosed by the trend curve L of the dominant abnormal point and the coordinate axis, Sh represents the area enclosed by the trend curve L of the hth gas pipeline abnormal point in the current abnormal point path and the coordinate axis, and S0 represents the difference between the maximum and minimum areas enclosed by the trend curve L and the coordinate axis corresponding to the set standard characteristic data trend curve range LQ; the deviation index P of each abnormal point path is associated with the radiation area Sf of the dominant abnormal point to form a historical abnormal point path data pair;

[0082] S304. Obtain historical abnormal point path data pairs of different abnormal point paths, combine them with historical gas pipeline maintenance records, obtain the time period with maintenance records, and match the historical abnormal point path data pairs of the corresponding time period with the corresponding maintenance records.

[0083] Step S400 includes:

[0084] S401. Acquire real-time gas pipeline data in the monitoring area, analyze the real-time gas pipeline data at selected time intervals, and obtain corresponding real-time data features; analyze the real-time data features in the same manner as step S200 to identify real-time gas pipeline anomalies, and construct a real-time anomaly path based on the real-time gas pipeline anomalies;

[0085] S402. For each real-time outlier path, the corresponding real-time outlier dominant point and the area Sf0 of the radiation region of the real-time outlier dominant point are obtained according to the same analysis method as step S300; based on the real-time outlier dominant point and the radiation region of the real-time outlier dominant point, the real-time deviation index P0 of the current outlier path is calculated, thereby obtaining a real-time outlier path data pair;

[0086] S403. Compare the real-time abnormal point path data pair with the historical abnormal point path data pair. If the similarity between the real-time abnormal point path data pair and the historical abnormal point path data pair is greater than or equal to the similarity threshold, the real-time abnormal point path data pair, the real-time abnormal dominant point and the corresponding historical maintenance record are output to the relevant personnel; if the similarity between the real-time abnormal point path data pair and the historical abnormal point path data pair is less than the similarity threshold, only the real-time abnormal point path data pair and the real-time abnormal dominant point are output, and the relevant personnel perform the corresponding maintenance processing.

[0087] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0088] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art will be able to modify the technical solutions described in the aforementioned embodiments or substitute equivalents for some of the technical features. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention shall be included within the scope of protection of the present invention.

Claims

1. A gas pipeline data management method based on artificial intelligence, characterized by: The method comprises the following steps: Step S100: Obtain gas pipeline data from all sensor devices within the monitoring area, record the location data of the sensor devices, and associate the gas pipeline data with the location data of the sensor devices to form a plurality of data units; analyze the gas pipeline data of each data unit to obtain corresponding data features; Step S200. Analyze the time-varying trend of the data features according to the data features of the data units, thereby identifying abnormal points in the gas pipeline; obtain the location data of all abnormal points in the gas pipeline, and construct an abnormal point path based on the location data of the abnormal points in the gas pipeline; Step S300: Based on the abnormal point path, analyze the correlation between the data characteristics and location data of different gas pipeline abnormal points, and obtain the abnormal dominant point of the abnormal point path based on the correlation; analyze the deviation relationship between the abnormal dominant point and other gas pipeline abnormal points on the abnormal point path, and thus obtain the corresponding deviation index; Step S400: Acquire real-time gas pipeline data from the monitoring area. Analyze the real-time gas pipeline data at selected time intervals to obtain corresponding real-time data features and identify real-time gas pipeline anomalies. Construct a real-time anomaly path based on the real-time gas pipeline anomalies. For each real-time anomaly path, perform the same analysis as in step S300 to obtain the corresponding real-time anomaly dominant point and the area of the area radiating from the real-time anomaly dominant point. The area of the radiation zone of the real-time anomaly dominant point is defined as: the area enclosed by the real-time anomaly dominant point of the real-time anomaly point path as the center and the maximum distance between the real-time anomaly dominant point and other gas pipeline anomaly points on the real-time anomaly point path as the radius; Based on the real-time anomaly dominant point and the radiation area of the real-time anomaly dominant point, the real-time deviation index of the current anomaly point path is calculated, and the corresponding prompt information is output according to the deviation index.

2. The artificial intelligence-based gas pipeline data management method according to claim 1, characterized in that: The step S100 includes: S101. Obtain a gas pipeline distribution plan within the monitoring area, mark the sensor device location on the gas pipeline distribution plan and number the sensor device, and each sensor device number is unique; record the location data of the sensor device according to the gas pipeline distribution plan, and obtain the corresponding gas pipeline data according to the sensor device number; S102. Obtain the gas pipeline data for the selected time period within the monitoring area in chronological order, and match the gas pipeline data of each sensor device with the corresponding location data to form several data units, where each data unit includes the location data of a sensor device and the corresponding gas pipeline data; perform time series analysis on each data unit to obtain the corresponding data characteristics.

3. The artificial intelligence-based gas pipeline data management method according to claim 1, characterized in that: The step S200 includes: S201. Obtain data features of the data unit and preprocess the features of the data unit; for each data unit, construct a feature vector V based on the preprocessed data features, and the feature vector V = [v1, v2, ..., vn], where v1 represents the first feature data of the data unit, v2 represents the second feature data of the data unit, and so on, vn represents the nth feature data of the data unit, and n represents the data feature number of the data unit; perform principal component analysis on the feature vector V of each data unit to obtain a data feature representation Vr after dimensionality reduction; S202. Based on the data characteristic representation Vr of each data unit, a trend curve L of each characteristic data corresponding to the data characteristic representation Vr over time is plotted in chronological order. Based on the set standard characteristic data trend curve range LQ, the trend anomaly point of each data unit is identified. Based on the position data of the sensor device corresponding to the data unit, the location of the gas pipeline anomaly point is obtained; S203. Obtain the location data of all abnormal points in the gas pipelines, mark the abnormal points in the gas pipelines on the gas pipeline distribution plan, and connect the abnormal points in the gas pipelines with adjacent location data to form several abnormal point paths.

4. The artificial intelligence-based gas pipeline data management method according to claim 3, characterized in that: The principal component analysis is performed on the eigenvector V of each data unit. The specific analysis process is as follows: Standardize the eigenvectors V of all data units so that the mean of each feature is 0 and the variance is 1, and record the standardized eigenvector as X; calculate the covariance matrix C of the standardized data features, and C = [1 / (m-1)]X T X, where m represents the number of data units; perform eigenvalue decomposition on the covariance matrix C to obtain the eigenvalue λ and the corresponding eigenvector W, and C = WλW T ; Get all eigenvalues and arrange them in descending order, and record the corresponding eigenvectors; select the first k eigenvalues and their corresponding eigenvectors, combine these k eigenvectors to form a principal component matrix Wk, and Wk = [w1, w2, ..., wk], where w1 represents the eigenvector corresponding to the first eigenvalue, w2 represents the eigenvector corresponding to the second eigenvalue, and so on, wk represents the eigenvector corresponding to the kth eigenvalue; use the principal component matrix Wk to perform a linear transformation on the standardized eigenvector X to obtain the reduced-dimensional data feature representation Vr, and Vr = XWk.

5. The artificial intelligence-based gas pipeline data management method according to claim 3, characterized in that: The step S300 includes: S301. For each abnormal point path, obtain the corresponding gas pipeline abnormal point data feature representation Vr and the corresponding gas pipeline abnormal point location data; obtain the corresponding trend curve L based on the gas pipeline abnormal point data feature representation Vr, calculate the area S enclosed by the trend curve L and the coordinate axis, and associate the area S with the location coordinates of the gas pipeline abnormal point to form an associated data pair; S302. Based on the associated data pairs, obtain the areas S corresponding to different gas pipeline anomalies in the current anomaly path. Compare the areas S and select the gas pipeline anomaly point with the largest area S as the dominant anomaly point in the current anomaly path. Based on the position coordinates of other gas pipeline anomalies in the current anomaly path, calculate the distances between the dominant anomaly point and other gas pipeline anomalies in the path. The area enclosed by the dominant anomaly point in the current anomaly path as the center and the maximum distance between the dominant anomaly point and other gas pipeline anomalies in the path as the radius is the radiation area of the dominant anomaly point. S303. Obtain the associated data pairs of the abnormal dominant point and other gas pipeline abnormal points on the path of the abnormal point. Based on the associated data pairs, calculate the deviation index P between the abnormal dominant point and other gas pipeline abnormal points on the path of the abnormal point. The specific calculation formula is: P = [(1 / H)Σ h∈[1,H] (S1-Sh)] / S0, where S1 represents the area enclosed by the trend curve L of the dominant abnormal point and the coordinate axis, Sh represents the area enclosed by the trend curve L of the hth gas pipeline abnormal point in the current abnormal point path and the coordinate axis, and S0 represents the difference between the maximum and minimum areas enclosed by the trend curve L and the coordinate axis corresponding to the set standard characteristic data trend curve range LQ; the deviation index P of each abnormal point path is associated with the radiation area Sf of the dominant abnormal point to form a historical abnormal point path data pair; S304. Obtain historical abnormal point path data pairs of different abnormal point paths, combine them with historical gas pipeline maintenance records, obtain the time period with maintenance records, and match the historical abnormal point path data pairs of the corresponding time period with the corresponding maintenance records.

6. The artificial intelligence-based gas pipeline data management method according to claim 1, characterized in that: The step S400 includes: S401. Acquire real-time gas pipeline data in the monitoring area, analyze the real-time gas pipeline data at selected time intervals, and obtain corresponding real-time data features; analyze the real-time data features in the same manner as step S200 to identify real-time gas pipeline anomalies, and construct a real-time anomaly path based on the real-time gas pipeline anomalies; S402. For each real-time outlier path, the corresponding real-time outlier dominant point and the area Sf0 of the radiation region of the real-time outlier dominant point are obtained according to the same analysis method as step S300; based on the real-time outlier dominant point and the radiation region of the real-time outlier dominant point, the real-time deviation index P0 of the current outlier path is calculated, thereby obtaining a real-time outlier path data pair; S403. Compare the real-time abnormal point path data pair with the historical abnormal point path data pair. If the similarity between the real-time abnormal point path data pair and the historical abnormal point path data pair is greater than or equal to the similarity threshold, the real-time abnormal point path data pair, the real-time abnormal dominant point and the corresponding historical maintenance record are output to the relevant personnel; if the similarity between the real-time abnormal point path data pair and the historical abnormal point path data pair is less than the similarity threshold, only the real-time abnormal point path data pair and the real-time abnormal dominant point are output, and the relevant personnel perform the corresponding maintenance processing.

7. An artificial intelligence-based gas pipeline data management system, applied to the artificial intelligence-based gas pipeline data management method according to any one of claims 1 to 6, characterized in that: The system includes: a data acquisition and processing module, an abnormal point identification and path construction module, a dominant point analysis and deviation calculation module, a real-time data analysis and abnormality detection module, and a historical data comparison and maintenance suggestion module; The data acquisition and processing module acquires the gas pipeline data and location data of all sensor devices in the monitoring area, records and connects the data to form data units; performs time series analysis on the data units to extract data features; the anomaly point identification and path construction module analyzes the trend changes of data features and identifies gas pipeline anomalies; constructs anomaly point paths based on the location data of gas pipeline anomalies and marks all gas pipeline anomalies; the dominant point analysis and deviation calculation module analyzes the correlation between the data features and location data on the anomaly point path to obtain the anomaly dominant point; calculates the deviation index between the anomaly dominant point and other gas pipeline anomalies to form the anomaly dominant point radiation area; the real-time data analysis and anomaly detection module acquires and analyzes real-time gas pipeline data, identifies real-time anomalies and constructs real-time anomaly point paths, calculates the deviation index of the real-time anomaly point paths, and compares them with historical data; the historical data comparison and maintenance suggestion module compares the real-time anomaly point path data pairs with the historical anomaly point path data pairs and outputs corresponding notification information based on the similarity threshold.

8. The artificial intelligence-based gas pipeline data management system according to claim 7, characterized in that: The data acquisition and processing module includes a data acquisition unit, a data recording unit and a data timing analysis unit; The data acquisition unit obtains gas pipeline data and sensor location data from sensor devices in the monitoring area, marks the sensor device location and its number according to the gas pipeline distribution plan, and records the location data; the data recording unit records the location data of each sensor device according to the sensor number and the gas pipeline distribution plan, and corresponds the gas pipeline data with the sensor location data to form a data unit; the data timing analysis unit performs timing analysis on each data unit, extracts and records data features.

9. The artificial intelligence-based gas pipeline data management system according to claim 7, characterized in that: The outlier identification and path construction module includes a data feature analysis unit and an outlier marking and path construction unit; The data feature analysis unit performs principal component analysis on the data features to obtain a data feature representation after dimensionality reduction; Draw a trend curve of the characteristic data changing over time and identify trend abnormal points; the abnormal point marking and path construction unit marks the abnormal points of the gas pipeline according to the identified abnormal point location data, and connects the adjacent location data to form an abnormal point path; The dominant point analysis and deviation calculation module includes an outlier data feature extraction unit, a dominant point determination unit, and a deviation calculation unit; The abnormal point data feature extraction unit obtains the data feature representation of each gas pipeline abnormal point in the abnormal point path and calculates the area enclosed by its trend curve and the coordinate axis; the dominant point determination unit compares the areas of abnormal points of different gas pipelines to determine the abnormal dominant point; Calculate the deviation index between the abnormal dominant point and other abnormal points in the gas pipeline, and generate the radiation area of the abnormal dominant point; The deviation calculation unit calculates the deviation index between the abnormal dominant point and other gas pipeline abnormal points, and associates the deviation index with the historical abnormal point path data.

10. The artificial intelligence-based gas pipeline data management system according to claim 7, characterized in that: The real-time data analysis and anomaly detection module includes a real-time data acquisition and feature analysis unit, a real-time anomaly point path construction unit, and a real-time dominant point and deviation calculation unit; The real-time data acquisition and feature analysis unit acquires real-time gas pipeline data, analyzes it at selected time intervals, extracts real-time data features, and identifies abnormal points in the real-time gas pipeline. The real-time abnormal point path construction unit constructs a real-time abnormal point path based on the abnormal points in the real-time gas pipeline. The real-time dominant point and deviation calculation unit performs real-time abnormal dominant point analysis, calculates the deviation index of the real-time abnormal dominant point, and generates real-time abnormal point path data pairs. The historical data comparison and maintenance suggestion module includes a historical data comparison unit and a maintenance record association and suggestion unit; The historical data comparison unit compares the similarity between the real-time abnormal point path data pair and the historical abnormal point path data pair; based on the similarity threshold, generates a comparison result; the maintenance record association and suggestion unit outputs the real-time abnormal point path data pair and the historical maintenance record according to the similarity result.

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