Pipeline circumferential weld failure data management system

Abstract

The invention discloses a pipeline circumferential weld failure data management system, relates to the technical field of pipeline transportation, and aims at providing the pipeline circumferential weld failure data management system so as to solve the technical problems that a pipeline failure data system in the related technology is single in function, and effective management of failure data is difficult to achieve. The pipeline circumferential weld failure data management system comprises a data acquisition module, a data analysis module and a visualization module, the data acquisition module is used for acquiring pipeline circumferential weld failure data from various data sources; the data analysis module is used for analyzing the pipeline circumferential weld failure data, determining pipeline circumferential weld failure reasons and determining prevention measures based on the pipeline circumferential weld failure reasons; the visualization module is used for marking the failure condition of the circumferential weld of the pipeline in the pipeline network map and displaying the marked pipeline network map; wherein the failure condition of the pipeline circumferential weld comprises the position, the failure type and the severity of the pipeline circumferential weld failure event.

Description

Technical Field

[0001] The present application relates to the field of pipeline transportation technology, and in particular to a pipeline girth weld failure data management system. Background Art

[0002] Pipelines are critical transportation infrastructure for industries like oil and gas transportation and the chemical industry. Failure can easily lead to energy leaks, environmental pollution, and safety incidents. Therefore, effectively managing and analyzing pipeline failure data is crucial for preventing failures and developing appropriate maintenance plans.

[0003] Currently, some companies and research institutions have built systems that cover pipeline failure data. However, these existing systems have limited functionality, making it difficult to effectively manage failure data and for users to quickly understand the overall status of pipeline failures. Summary of the Invention

[0004] The purpose of this application is to provide a pipeline girth weld failure data management system to solve the technical problem in the related art that the pipeline failure data system has a single function and is difficult to achieve effective management of failure data.

[0005] To achieve the above objectives, this application adopts the following technical solutions:

[0006] The present application provides a pipeline girth weld failure data management system, comprising: a data acquisition module, a data analysis module, and a visualization module; the data acquisition module is used to collect pipeline girth weld failure data from multiple data sources; the data analysis module is used to analyze the pipeline girth weld failure data, determine the cause of the pipeline girth weld failure, and determine preventive measures based on the cause of the pipeline girth weld failure; the visualization module is used to mark the failure status of the pipeline girth weld on a pipeline network map and display the marked pipeline network map; wherein the failure status of the pipeline girth weld includes the location, failure type, and severity of the pipeline girth weld failure event.

[0007] The pipeline girth weld failure data management system provided by this application can collect pipeline girth weld failure data from multiple data sources and analyze it, determine the cause of pipeline girth weld failure, and then determine preventive measures, which can achieve effective management of failure data; and the visualization module provided by this application can intuitively reflect the pipeline girth weld failure status of the existing pipeline network. In summary, the pipeline girth weld failure data management system provided by this application is more comprehensive and complete in function, can meet the needs of actual applications, achieve effective management of failure data, and enable users to quickly grasp the overall status of pipeline failure.

[0008] In one possible implementation, pipeline girth weld failure data includes at least one of the following: pipeline basic attribute data, pipeline setting environment data, macroscopic morphology measurement data, microscopic morphology measurement data, nondestructive testing data, performance test data, and cross-sectional analysis data.

[0009] In another possible implementation, the data acquisition module is further used to clean and preprocess the collected pipeline girth weld failure data; the data acquisition module is further used to encrypt and store the collected pipeline girth weld failure data using a symmetric encryption algorithm.

[0010] In another possible implementation, the data analysis module is specifically configured to analyze pipeline girth weld failure data using a data mining algorithm and / or a machine learning algorithm to determine the cause of the pipeline girth weld failure.

[0011] In another possible implementation, the data analysis module is further configured to analyze pipeline girth weld failure trends based on pipeline girth weld failure data, and perform pipeline girth weld failure risk prediction based on the pipeline girth weld failure trends.

[0012] In another possible implementation, the visualization module is also used to obtain pipeline operating parameters, determine whether an abnormality occurs in the pipeline based on the pipeline operating parameters, and when an abnormality is determined to occur in the pipeline, mark the location of the abnormal point in the pipeline network map and issue an abnormal prompt message.

[0013] In another possible implementation, the pipeline girth weld failure data management system also includes a database module; the database module is used to receive information on pipeline girth weld failure events entered by a user, wherein the information on pipeline girth weld failure events includes at least one of the following: the time of event occurrence, the location of event occurrence, pipeline type, pipeline use, failure type, severity, and event handling process; the database module is also used to classify and store the information on pipeline girth weld failure events from multiple dimensions; wherein the multiple dimensions include at least one of the following: the time of event occurrence, the location of event occurrence, pipeline type, pipeline use, failure type, severity, and event handling process.

[0014] In another possible implementation, the database module is further configured to analyze the correlation between different pipeline girth weld failure events using an association rule mining algorithm to determine the correlation relationship between different pipeline girth weld failure events.

[0015] In another possible implementation, the pipeline girth weld failure data management system also includes a knowledge sharing platform; the knowledge sharing platform is used to store pipeline failure-related knowledge; wherein the pipeline failure-related knowledge includes at least one of the following: academic research results, industry standards and specifications, actual case analysis, and expert experience.

[0016] In another possible implementation, the knowledge sharing platform is also used to obtain user access behavior data, determine user needs based on the user access behavior data, and recommend knowledge based on the user needs; wherein the user access behavior data includes at least one of the following: browsing history, search history, and areas of interest. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0018] Figure 1 A schematic diagram of the architecture of a pipeline girth weld failure data management system provided in this application;

[0019] Figure 2 A schematic diagram of the architecture of another pipeline girth weld failure data management system provided in this application;

[0020] Figure 3 This is a schematic diagram of the architecture of another pipeline girth weld failure data management system provided by this application;

[0021] Figure 4 A schematic diagram of the functional structure of a pipeline girth weld failure data management system provided in this application;

[0022] Figure 5 A schematic diagram of the interaction between various modules of a pipeline girth weld failure data management system provided in this application;

[0023] Figure 6 A schematic diagram of the composition of failure data acquired by a data acquisition module provided in this application;

[0024] Figure 7 A schematic diagram of the composition of an electronic device provided in this application. DETAILED DESCRIPTION

[0025] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0026] In the embodiments of the present application, the terms "comprises," "comprising," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, article, or device comprising a series of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, article, or device. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not preclude the presence of other identical elements in the process, article, or device comprising the element.

[0027] In the embodiments of this application, words such as "exemplary" or "for example" are used to indicate examples, illustrations, or descriptions. Any embodiment or design described as "exemplary" or "for example" in the embodiments of this application should not be interpreted as being preferred or advantageous over other embodiments or designs. Rather, the use of words such as "exemplary" or "for example" is intended to present the relevant concepts in a concrete manner.

[0028] In the description of this specification, specific features, structures, materials or characteristics may be combined in an appropriate manner in any one or more embodiments or examples.

[0029] Pipelines, as essential transportation facilities, play a vital role in the transportation of energy sources like oil and natural gas, as well as in industries like the chemical industry. Pipeline failures can lead to serious consequences such as energy leaks, environmental pollution, and safety accidents. Therefore, effective management and analysis of pipeline failure data is crucial for preventing failures and developing appropriate maintenance plans.

[0030] Although some companies and research institutions have established pipeline failure data management systems, these systems often suffer from limited functionality, a lack of comprehensive data analysis and visualization, and insufficient knowledge accumulation and application. For example, some management systems simply record basic information about failure events, failing to delve into the causes and analyze correlations. Some systems lack intuitive visualization interfaces, making it difficult for users to quickly understand the overall situation of pipeline failures. Furthermore, knowledge management struggles to transform failure data into valuable insights.

[0031] Based on this, the present application provides a pipeline girth weld failure database system that can collect pipeline girth weld failure data from multiple data sources and analyze it, determine the cause of pipeline girth weld failure, and then determine preventive measures, thereby achieving effective management of failure data; and the visualization module provided by the present application can intuitively reflect the pipeline girth weld failure status of the existing pipeline network. In summary, the pipeline girth weld failure data management system provided by the present application is more comprehensive and complete, and can meet the needs of pipeline failure data management, analysis, and knowledge utilization in practical applications, providing strong support for safe operation and maintenance decisions of pipelines.

[0032] Figure 1 This is an architecture diagram of a pipeline girth weld failure data management system provided in this application. The system includes: a data acquisition module 101, a data analysis module 102, and a visualization module 103.

[0033] The data collection module 101 is used to collect pipeline girth weld failure data from various data sources.

[0034] The data analysis module 102 is used to analyze the pipeline girth weld failure data, determine the pipeline girth weld failure cause, and determine preventive measures based on the pipeline girth weld failure cause.

[0035] The visualization module 103 is used to mark the failure status of the pipeline girth weld in the pipeline network map and display the marked pipeline network map; wherein the failure status of the pipeline girth weld includes the location, failure type and severity of the pipeline girth weld failure event.

[0036] In some embodiments, the pipeline girth weld failure data includes at least one of the following: pipeline basic property data, pipeline setting environment data, macroscopic morphology measurement data, microscopic morphology measurement data, non-destructive testing data, performance test data, and cross-sectional analysis data.

[0037] For example, the data acquisition module supports interface connections with multiple data sources, including pipeline monitoring equipment, inspection reporting systems, maintenance record systems, third-party data providers, etc., and realizes seamless docking and real-time transmission of data between different systems through standardized data interface protocols.

[0038] In some embodiments, the data acquisition module 101 is further used to perform data cleaning and data preprocessing on the collected pipeline girth weld failure data.

[0039] Exemplarily, data cleaning and data preprocessing include: filling missing values using interpolation; identifying and correcting outliers to ensure data quality.

[0040] In some embodiments, the data acquisition module 101 is further configured to encrypt and store the collected pipeline girth weld failure data using a symmetric encryption algorithm.

[0041] For example, encryption technology is used to regularly back up data to prevent data loss or damage, and a secure communication protocol is used during data transmission to ensure that data is not stolen or tampered with.

[0042] For example, a unique electronic file is created for each girth weld by identifying the girth weld number for easy storage.

[0043] For example, the electronic file includes basic information of each pipeline girth weld, such as weld number, pipeline location, welding process, welding date, welder information, etc.

[0044] The pipeline girth weld failure data management system provided in the embodiment of the present application has efficient failure data collection capabilities. The data collection module supports multiple data source access, data cleaning preprocessing and data security backup, ensuring the quality and security of the data and providing a reliable data basis for accurate analysis and decision-making of the system.

[0045] In some embodiments, the data analysis module 102 is specifically used to analyze pipeline girth weld failure data using data mining algorithms and / or machine learning algorithms to determine the causes of pipeline girth weld failure; and determine preventive measures based on the causes of pipeline girth weld failure.

[0046] Exemplarily, the data mining algorithm may be a K-Means algorithm, a principal component analysis method, or the like.

[0047] Exemplarily, the machine learning algorithm may be a decision tree algorithm, a neural network algorithm, or the like.

[0048] For example, preventive measures include: optimizing welding technology, strengthening welding quality inspection, adjusting pipeline installation methods, etc.

[0049] It can be understood that the pipeline girth weld failure data management system provided by this application can conduct in-depth mining and correlation analysis of the causes of pipeline failure. The data analysis module uses data mining and machine learning algorithms to analyze the causes of failure, find out the failure patterns and mechanisms, and provide a scientific basis for pipeline maintenance and preventive measures.

[0050] In some embodiments, the data analysis module 102 is further configured to analyze pipeline girth weld failure trends based on pipeline girth weld failure data, and perform pipeline girth weld failure risk prediction based on the pipeline girth weld failure trends.

[0051] In some embodiments, the visualization module 103 is also used to obtain pipeline operating parameters, determine whether an abnormality occurs in the pipeline based on the pipeline operating parameters, and when it is determined that an abnormality occurs in the pipeline, mark the location of the abnormal point in the pipeline network map and issue an abnormal prompt message.

[0052] Exemplarily, pipeline operating parameters include: pressure, temperature, flow rate, etc.

[0053] Exemplarily, the locations of abnormal points and abnormal pipeline operating parameters are marked in the pipeline network map.

[0054] In some embodiments, the visualization module 103 is further used to display a statistical analysis report on failure conditions of pipeline girth welds, and the visualization module 103 allows the user to customize the dimensions and parameters of the report.

[0055] For example, the report includes: the number of failure events in each time period, the proportion of failure types, high-incidence areas of failure, etc.

[0056] It is understandable that the visualization module provided in this application can monitor the operating status of the pipeline, count failure data, and display the distribution of failure trends, so that users can intuitively and comprehensively understand the pipeline failure situation and provide support for timely decision-making.

[0057] In some embodiments, as Figure 2 As shown, the pipeline girth weld failure data management system also includes a database module 104; the database module is used to receive information about pipeline girth weld failure events entered by a user, wherein the information about pipeline girth weld failure events includes at least one of the following: time of event occurrence, location of event occurrence, pipeline type, pipeline use, failure type, severity, and event handling process.

[0058] In some embodiments, the database module is also used to classify and store information on pipeline girth weld failure events from multiple dimensions; wherein the multiple dimensions include at least one of the following: time of event occurrence, location of event occurrence, pipeline type, pipeline usage, failure type, severity, and event handling process.

[0059] In some embodiments, the database module is further configured to analyze the correlation between different pipeline girth weld failure events using an association rule mining algorithm to determine the correlation relationship between different pipeline girth weld failure events.

[0060] Exemplarily, the association rule mining algorithm may be an Apriori algorithm or an FP-Growth algorithm.

[0061] In some embodiments, as Figure 3 As shown, the pipeline girth weld failure data management system further includes a knowledge sharing platform 105 .

[0062] In some embodiments, the knowledge sharing platform is used to store pipeline failure related knowledge; wherein the pipeline failure related knowledge includes at least one of the following: academic research results, industry standards and specifications, actual case analysis, and expert experience.

[0063] Exemplarily, the stored pipeline failure related knowledge is classified and organized to obtain multiple knowledge topics; and the pipeline failure related knowledge under the multiple knowledge topics is displayed on the knowledge sharing platform.

[0064] In some embodiments, the knowledge sharing platform is also used to obtain user access behavior data, determine user needs based on the user access behavior data, and recommend knowledge based on the user's needs; wherein the user's access behavior data includes at least one of the following: browsing history, search history, and areas of interest.

[0065] In some embodiments, users can publish their own experiences and insights on the knowledge sharing platform, and can also browse and search for the experiences and insights of other users; the pipeline girth weld failure database system evaluates the knowledge contribution based on the user's published content; and rewards users based on the evaluation results.

[0066] The pipeline girth weld failure data management system provided in the embodiment of the present application realizes the collection, organization and sharing of pipeline failure-related knowledge. The knowledge sharing platform collects and organizes various types of failure knowledge, provides knowledge sharing and intelligent recommendation functions, promotes knowledge exchange and inheritance within the industry, and helps users better learn and master relevant knowledge.

[0067] Figure 4 The functional structure diagram of a pipeline girth weld failure data management system provided in this application is as follows: Figure 4 As shown, it includes: basic function module, data acquisition module, visualization module, database module, knowledge sharing platform, foreign failure data module and data analysis module.

[0068] Figure 5 This is a schematic diagram of the interaction between the various modules of a pipeline girth weld failure data management system provided by this application, such as Figure 5 As shown, the following steps are included:

[0069] A1. The basic data, failure data and detection analysis data collected by the data acquisition module are stored in the basic database.

[0070] A2. Enter pipeline failure-related knowledge, including failure mechanism and evolution, welding technical standards, quality management, defect detection and evaluation, and defect repair measures.

[0071] A3. The data analysis module extracts basic data from the basic database for routine statistical analysis, and conducts big data analysis and application based on the evaluation of defects, materials, and loads based on stress and strain. It also conducts failure simulation based on the interaction between defects and the stress concentration coupling effect.

[0072] A4. Extract basic data from the basic database and integrate knowledge related to pipeline failure to build a knowledge sharing platform.

[0073] Exemplarily, the knowledge sharing platform includes: a knowledge warehouse, a search engine, and AI technology.

[0074] A5. The visualization module uses the analysis results of the data analysis module and the relevant knowledge of the knowledge sharing platform, and utilizes three-dimensional services and geographic information system (GIS) service technology to perform three-dimensional visualization of the failure data of the pipeline girth weld.

[0075] The following is an introduction to the data acquisition module provided by this application based on a specific embodiment. The data acquisition module supports access to multiple data sources, data cleaning and pre-processing, and data security backup. Specifically, it includes:

[0076] 1) Support interface connections with multiple data sources, including pipeline monitoring equipment (such as flow meters, pressure sensors, corrosion monitors, etc.), inspection reporting systems, maintenance record systems, third-party data providers, etc., through standardized data interface protocols (such as XML (Extensible Markup Language) and JSON (JavaScript Object Notation), etc.), to achieve seamless data connection and real-time transmission between different systems.

[0077] 2) Use data cleaning and preprocessing algorithms to clean and transform the collected data, such as using mean interpolation or multiple regression interpolation to fill missing values, and using distance-based methods (such as box plot method) or model-based methods (such as Gaussian mixture model) to identify and correct outliers to ensure data quality.

[0078] 3) Collected data is encrypted and stored using a symmetric encryption algorithm (e.g., AES-256). Full data backups are performed weekly and stored in a secure storage area protected by a firewall to prevent data loss or corruption. During data transmission, secure communication protocols (e.g., HTTPS (HyperText Transfer Protocol Secure) and SSH (Secure Shell)) are used to prevent data theft or tampering.

[0079] Figure 6 A schematic diagram of the failure data composition obtained by a data acquisition module provided in this application, such as Figure 6 As shown, the failure data may be the test or analysis results of basic data, morphology measurement, non-destructive testing of girth weld defects, performance testing, cross-section analysis, and crack cause analysis.

[0080] Exemplarily, the basic data includes: basic attributes and service environment.

[0081] Among them, the basic attributes include: steel grade, pressure, pipe diameter, wall thickness, pipe length, steel pipe parameters; welding process, weld type (joint weld, variable wall thickness weld and repair weld, etc.); construction year, construction season, construction unit; inspection unit, inspection results; supervision unit; service environment including: terrain, penetration, burial depth, elevation; internal inspection report on weld abnormalities; wall thickness difference, distance from special welds such as joint welds, distance from elbows, and pipe misalignment after cutting; weld coordinates.

[0082] Exemplarily, topography measurement includes macroscopic topography measurement and microscopic topography measurement.

[0083] Among them, macroscopic morphology measurement includes: width, excess height, misalignment, pipe wall thickness, and whether there is weld bead; microscopic morphology measurement includes the number of weld passes and the number of weld layers.

[0084] For example, non-destructive testing of girth weld defects includes comparing the construction period film with the latest re-shooting film to obtain the qualitative and location of defects such as cracks and the period when the defects were formed.

[0085] Exemplarily, the performance tests include: base material properties, weld properties and crack defects.

[0086] Among them, weld properties include: mechanical properties, notch hammer fracture and bending properties; crack defects include: formation period, location, and correlation between crack ring weld and weld formation conditions.

[0087] For example, cross-sectional analysis includes: optical microscope observation, scanning electron microscope observation, metallographic analysis, energy spectrum analysis, microscope hardness analysis

[0088] Exemplarily, the crack cause analysis includes: failure load analysis, main causes: improper welding construction during the construction period and missed assessment and misjudgment of crack defects; secondary causes: root weld stress concentration.

[0089] The following is an introduction to the data analysis module provided by this application based on a specific embodiment. The data analysis module manages girth weld information, analyzes failure causes, and proposes preventive measures. Specifically, it includes:

[0090] 1) Record the basic information of each pipeline girth weld, including weld number, pipeline location (latitude and longitude coordinates), welding process (manual arc welding / automatic welding), welding date, welder name and number, etc., and establish a unique electronic file for each girth weld to facilitate subsequent query and traceability.

[0091] 2) Collect relevant data on girth weld failure, such as macroscopic inspection results (visual inspection, liquid penetrant testing, magnetic particle testing, etc.), microstructural analysis (metallographic analysis, electron microscopy, etc.), and non-destructive testing data (radiographic testing, ultrasonic testing, etc.), and use data mining algorithms (decision tree, cluster analysis, etc.) and machine learning algorithms (support vector machine, neural network, etc.) to analyze these data to identify the main causes of girth weld failure, such as weld slag inclusions, defects, and excessive hardness in the heat-affected zone.

[0092] 3) Based on the failure cause analysis results, the system automatically provides preventive measures for girth weld failure, such as optimizing welding process parameters, strengthening welding quality inspection, adjusting pipeline installation methods (installing expansion joints to reduce stress), etc. These suggestions are associated with the relevant girth weld information and stored for reference in subsequent pipeline construction and maintenance.

[0093] The following is an introduction to the visualization module provided by this application based on a specific embodiment. The visualization module implements real-time monitoring of pipeline failure data, failure status display, and statistical analysis report generation. Specifically, it includes:

[0094] 1) Real-time acquisition of pipeline operating parameters, such as pressure, temperature, and flow. Once an anomaly is detected, the system automatically flags it and issues an alert. The location of the anomaly and related parameter information are displayed on the cockpit interface as a flashing red icon.

[0095] 2) Using Geographic Information System (GIS) technology, the pipeline network is displayed in the form of a map, and the locations of failures are marked on the map. Red dots are used to indicate severe failures, yellow triangles are used to indicate moderate failures, and green squares are used to indicate minor failures. Icons of different colors and shapes are used to distinguish different degrees of failure events.

[0096] 3) Automatically generate various statistical analysis reports, including the number of failure events in different time periods, the proportion of failure types, and high-incidence areas of failure. The reports can be intuitively displayed in the form of bar charts, pie charts, line charts, etc. Users can also customize the time range of the report to one week, one month, three months, or one year, and filter conditions include failure type, pipeline type, geographic location, etc.

[0097] The following describes a specific embodiment of the database module provided by this application. The database module stores and manages failure event information and supports event classification retrieval and correlation analysis. Specifically, it includes:

[0098] 1) Provide a convenient interface that allows users to enter detailed information about pipeline failure events, including the time of occurrence (year, month, day, hour, minute, and second), location (latitude and longitude coordinates), pipeline type (oil pipeline / gas pipeline / chemical pipeline), failure phenomenon description (leakage / burst / deformation, etc.), treatment process (emergency repair / replacement / reinforcement, etc.) and treatment results. The entered information needs to undergo an audit process (reviewed by an expert panel) to ensure the accuracy and completeness of the data.

[0099] 2) Failure events are classified according to multiple dimensions, including failure type (corrosion failure, mechanical failure, stress failure, etc.), pipeline purpose (oil transportation, gas transportation, chemical industry), and failure severity (minor, moderate, or severe). Users can quickly find the required failure event information by searching for various criteria, such as time range, failure type, location, and pipeline type.

[0100] 3) Analyze the correlation between different failure events, for example, whether multiple failure events are caused by the same cause (such as corrosion), or whether a failure event (such as burst) triggers other related failure events (such as leakage). Establish an event correlation model through association rule mining algorithms (such as Apriori algorithm) to help users gain a deeper understanding of the laws and mechanisms of pipeline failure.

[0101] The following describes a specific embodiment of the knowledge sharing platform provided by this application. The knowledge sharing platform collects and organizes invalid knowledge, supports knowledge sharing and intelligent recommendation, and specifically includes:

[0102] 1) Collect pipeline failure-related knowledge from various channels, including academic research results (journal papers, conference papers, etc.), industry standards and specifications (pipeline design specifications, welding specifications, etc.), actual case analysis reports, expert experience summaries, etc., and classify and organize the collected knowledge to form different knowledge themes, such as failure mechanism, detection method, repair technology, etc.

[0103] 2) Provide a knowledge sharing platform where users can publish their experiences and insights, participate in knowledge discussions and exchanges, and the system evaluates (scored by an expert review panel) and rewards (in the form of points) users’ knowledge contributions.

[0104] 3) Based on the user's browsing history, search history, and areas of interest, the system uses intelligent recommendation algorithms such as collaborative filtering recommendation algorithms and content similarity algorithms to recommend relevant failure knowledge and cases to users. Users can also consult the system about pipeline failure-related issues through a natural language interactive interface. The system provides users with accurate answers and solutions through knowledge graphs and natural language processing technology.

[0105] The data acquisition module or data analysis module provided in the embodiments of the present application may be an electronic device.

[0106] For example, the electronic device may be a server, for example, a single server, or a server cluster composed of multiple servers. In some implementations, the server cluster may also be a distributed cluster.

[0107] For example, the electronic device may be a terminal device, such as a mobile phone, a tablet computer, a desktop computer, a laptop computer, a handheld computer, a notebook computer, an ultra-mobile personal computer (UMPC), a netbook, a cellular phone, a personal digital assistant (PDA), an augmented reality (AR) or virtual reality (VR) device, etc. The embodiments of the present application do not impose any particular limitation on the specific form of the terminal device.

[0108] The present disclosure provides a possible structure of the electronic device involved in the above embodiments. Figure 7 As shown, the electronic device 500 includes: a processor 502 and a bus 504. Optionally, the electronic device 500 may further include a memory 501; and optionally, the electronic device 500 may further include a communication interface 503.

[0109] Processor 502 may implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the embodiments of this application. Processor 502 may be a central processing unit, a general-purpose processor, a digital signal processor, an application-specific integrated circuit, a field-programmable gate array, or other programmable logic device, a transistor logic device, a hardware component, or any combination thereof. It may implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the embodiments of this disclosure. Processor 502 may also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, and the like.

[0110] The communication interface 503 is used to connect to other devices via a communication network, which may be Ethernet, wireless access network, wireless local area network (WLAN), etc.

[0111] The memory 501 may be a read-only memory (ROM) or other type of static storage device that can store static information and instructions, a random access memory (RAM) or other type of dynamic storage device that can store information and instructions, or an electrically erasable programmable read-only memory (EEPROM), a disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto.

[0112] As one possible implementation, memory 501 can exist independently of processor 502. Memory 501 can be connected to processor 502 via bus 504 and used to store instructions or program code. When processor 502 calls and executes the instructions or program code stored in memory 501, it can implement the functions of the pipeline girth weld failure data management system provided in the embodiments of this application. In another possible implementation, memory 501 can also be integrated with processor 502.

[0113] The bus 504 may be an extended industry standard architecture (EISA) bus or the like. The bus 504 may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 7 Only one thick line is used in the diagram, but this does not mean that there is only one bus or one type of bus.

[0114] The above are only specific embodiments of the present application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. A pipeline girth weld failure data management system, characterized in that: include: Data acquisition module, data analysis module, and visualization module; The data acquisition module is used to collect pipeline girth weld failure data from multiple data sources; The data analysis module is used to analyze the pipeline girth weld failure data, determine the cause of the pipeline girth weld failure, and determine preventive measures based on the cause of the pipeline girth weld failure; The visualization module is used to mark the failure conditions of pipeline girth welds in the pipeline network map and display the marked pipeline network map; wherein the failure conditions of the pipeline girth welds include the location, failure type and severity of the pipeline girth weld failure event.

2. The pipeline girth weld failure data management system according to claim 1, characterized in that: The pipeline girth weld failure data includes at least one of the following: Pipeline basic property data, pipeline setting environment data, macroscopic morphology measurement data, microscopic morphology measurement data, non-destructive testing data, performance test data, and cross-sectional analysis data.

3. The pipeline girth weld failure data management system according to claim 1, characterized in that: The data acquisition module is also used to perform data cleaning and data preprocessing on the collected pipeline girth weld failure data; The data acquisition module is also used to encrypt and store the collected pipeline girth weld failure data using a symmetric encryption algorithm.

4. The pipeline girth weld failure data management system according to claim 1, characterized in that: The data analysis module is specifically used to analyze the pipeline girth weld failure data using a data mining algorithm and / or a machine learning algorithm to determine the cause of the pipeline girth weld failure.

5. The pipeline girth weld failure data management system according to claim 1, characterized in that: The data analysis module is further used to analyze pipeline girth weld failure trends based on pipeline girth weld failure data, and to predict pipeline girth weld failure risks based on the pipeline girth weld failure trends.

6. The pipeline girth weld failure data management system according to claim 1, characterized in that: The visualization module is also used to obtain pipeline operating parameters, determine whether the pipeline has an abnormal situation based on the pipeline operating parameters, and when it is determined that the pipeline has an abnormal situation, mark the location of the abnormal point in the pipeline network map and issue an abnormal prompt message.

7. The pipeline girth weld failure data management system according to claim 1, characterized in that: The pipeline girth weld failure data management system also includes a database module; The database module is used to receive information on pipeline girth weld failure events entered by a user, wherein the information on pipeline girth weld failure events includes at least one of the following: time of event occurrence, location of event occurrence, pipeline type, pipeline use, failure type, severity, and event handling process; The database module is also used to classify and store information on the pipeline girth weld failure event from multiple dimensions; wherein the multiple dimensions include at least one of the following: time of event occurrence, location of event occurrence, pipeline type, pipeline usage, failure type, severity, and event handling process.

8. The pipeline girth weld failure data management system according to claim 7, characterized in that: The database module is further used to analyze the correlation between different pipeline girth weld failure events using an association rule mining algorithm to determine the correlation relationship between different pipeline girth weld failure events.

9. The pipeline girth weld failure data management system according to claim 1, characterized in that: The pipeline girth weld failure data management system also includes a knowledge sharing platform; The knowledge sharing platform is used to store pipeline failure related knowledge; wherein, pipeline failure related knowledge includes at least one of the following: academic research results, industry standards and specifications, actual case analysis, and expert experience.

10. The pipeline girth weld failure data management system according to claim 9, characterized in that: The knowledge sharing platform is also used to obtain user access behavior data, determine the user's needs based on the user's access behavior data, and recommend knowledge based on the user's needs; wherein the user's access behavior data includes at least one of the following: browsing history, search history, and areas of interest.

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