Informatization management system and management method for gas pipe network

Abstract

The invention provides an informatization management system and management method for a gas pipe network, and relates to the technical field of informatization management, and the method comprises the steps: carrying out the key node analysis of the distribution characteristic information of the gas pipe network through a key node analysis module, and obtaining a key node set; the Internet of Things endpoint deployment module carries out Internet of Things endpoint deployment on the key node set; a fault identification module obtains the multi-dimensional operation data flow and carries out fault identification to obtain a node fault feature set; the risk parameter analysis module transmits the node fault feature set to an information management cloud center for integrated analysis to obtain fault risk parameters; and the operation and maintenance strategy analysis module performs operation and maintenance strategy analysis on the fault risk parameters to obtain target fault operation and maintenance strategy parameters. The technical problems of low troubleshooting efficiency and insufficient emergency response capability caused by lack of precise monitoring means for the gas pipe network in the prior art are solved, the real-time performance and accuracy of pipe network fault detection are improved, and the operation safety and reliability of the pipe network are enhanced.

Description

Technical Field

[0001] This application relates to the field of informatization management technology, and particularly to an informatization management system and method for gas pipe networks. Background Art

[0002] Gas pipe networks are an important part of urban infrastructure, undertaking the important tasks of natural gas transportation and distribution, and providing energy guarantee for residents' lives and industrial production. Due to the wide distribution range and complex structure of gas pipe networks, multiple risks such as pipeline aging, leakage, and external damage are faced during the operation of the pipe networks. The traditional management methods of gas pipe networks mainly rely on manual inspections and experience judgments, supplemented by basic detection means such as flow judgment method, acoustic wave detection method, and pressure point analysis method. However, the efficiency of manual inspections is low, it is difficult to detect hidden faults in a timely manner, and as the scale of the pipe network expands, the workload and difficulty of inspections are also increasing continuously.

[0003] With the development of Internet of Things technology, sensors have been widely used in the monitoring and management of gas pipe networks, improving the data acquisition ability. However, the existing management methods usually adopt the method of comprehensively laying sensors for monitoring. Although the coverage is wide, the deployment and maintenance costs are too high, and due to the problem of data redundancy, it is difficult to focus on the key nodes of the pipe network for in-depth analysis, which results in the monitoring data being unable to fully support accurate fault detection and risk assessment. At the same time, these methods generally lack the intelligent analysis ability of data, cannot provide optimized operation and maintenance strategies in a timely manner, resulting in insufficient emergency response ability and increasing the safety risks of the operation of gas pipe networks. Summary of the Invention

[0004] This application provides an informatization management system and method for gas pipe networks, which solves the technical problems of low fault troubleshooting efficiency and insufficient emergency response ability in the prior art due to the lack of precise monitoring and data analysis means for the key nodes of gas pipe networks, and achieves the technical effect of improving the real-time performance and accuracy of fault detection of gas pipe networks, thereby enhancing the safety and reliability of the operation of the pipe network.

[0005] In view of the above problems, on the one hand, the present application provides an information management system for a gas pipeline network, and the system includes: a key node analysis module, configured to obtain the distribution characteristic information of the gas pipeline network, perform key node analysis on the distribution characteristic information of the gas pipeline network, and obtain M key node sets of the gas pipeline network; an Internet of Things endpoint deployment module, configured to sequentially perform Internet of Things endpoint deployment on the M key node sets of the gas pipeline network to obtain M key node Internet of Things endpoint sets, and each endpoint in the M key node Internet of Things endpoint sets includes a sensor acquisition unit, a data communication unit, and a data analysis unit; a fault identification module, configured to obtain M multi-dimensional operation data streams of the gas pipeline network through the sensor acquisition unit, and perform fault identification on the M multi-dimensional operation data streams of the gas pipeline network respectively based on the data analysis unit to obtain K node fault feature sets; a risk parameter analysis module, configured to wirelessly transmit the K node fault feature sets to an information management cloud center through the data communication unit for integrated analysis to obtain gas pipeline network fault risk parameters; an operation and maintenance strategy analysis module, configured to perform operation and maintenance strategy analysis on the gas pipeline network fault risk parameters to obtain target fault operation and maintenance strategy parameters, and perform gas pipeline network operation and maintenance emergency management based on the target fault operation and maintenance strategy parameters.

[0006] On the other hand, the present application also provides an information management method for a gas pipeline network, and the method includes: obtaining the distribution characteristic information of the gas pipeline network, performing key node analysis on the distribution characteristic information of the gas pipeline network, and obtaining M key node sets of the gas pipeline network; sequentially performing Internet of Things endpoint deployment on the M key node sets of the gas pipeline network to obtain M key node Internet of Things endpoint sets, and each endpoint in the M key node Internet of Things endpoint sets includes a sensor acquisition unit, a data communication unit, and a data analysis unit; obtaining M multi-dimensional operation data streams of the gas pipeline network through the sensor acquisition unit, and performing fault identification on the M multi-dimensional operation data streams of the gas pipeline network respectively based on the data analysis unit to obtain K node fault feature sets; wirelessly transmitting the K node fault feature sets to an information management cloud center through the data communication unit for integrated analysis to obtain gas pipeline network fault risk parameters; performing operation and maintenance strategy analysis on the gas pipeline network fault risk parameters to obtain target fault operation and maintenance strategy parameters, and performing gas pipeline network operation and maintenance emergency management based on the target fault operation and maintenance strategy parameters.

[0007] One or more technical solutions provided in the present application have at least the following technical effects or advantages:

[0008] The distribution characteristic information of the gas pipeline network is obtained through the key node analysis module for key node analysis, and M key node sets of the gas pipeline network are screened out to determine the most important and most failure-prone nodes in the pipeline network. Through the Internet of Things endpoint deployment module, the M key node sets of the gas pipeline network are sequentially deployed with Internet of Things endpoints to obtain M key node Internet of Things endpoint sets, avoiding indiscriminate full deployment of sensors, significantly reducing the deployment cost, and ensuring that subsequent monitoring focuses on key areas, improving the resource utilization efficiency. Each endpoint in the M key node Internet of Things endpoint sets includes a sensor acquisition unit, a data communication unit, and a data analysis unit, ensuring real-time acquisition of multi-dimensional pipeline network operation data and being able to perform preliminary analysis on the data, enhancing the real-time and accuracy of pipeline network monitoring. Through the fault identification module, the M multi-dimensional operation data streams of the gas pipeline network are obtained by using the sensor acquisition unit, and based on the data analysis unit, fault identification is respectively performed on the M multi-dimensional operation data streams of the gas pipeline network to obtain K node fault feature sets. Through intelligent analysis, potential faults in the pipeline network are automatically identified, improving the timeliness and accuracy of fault detection and being able to give early warnings at the initial stage of fault occurrence. Through the risk parameter analysis module, the K node fault feature sets are wirelessly transmitted to the information management cloud center through the data communication unit for integrated analysis, realizing centralized processing of information and global assessment of risks. Through risk analysis, potential pipeline network problems can be further accurately predicted, and gas pipeline network fault risk parameters are obtained, improving the accuracy of risk assessment. Through the operation and maintenance strategy analysis module, operation and maintenance strategy analysis is performed on the gas pipeline network fault risk parameters to obtain target fault operation and maintenance strategy parameters, and based on the target fault operation and maintenance strategy parameters, operation and maintenance emergency management of the gas pipeline network is carried out, enhancing the emergency response ability of the pipeline network and ensuring that measures can be taken quickly and effectively for handling in case of a fault.

[0009] In summary, through key node analysis and precise deployment of Internet of Things endpoints, this application realizes intelligent monitoring and management of the gas pipeline network, not only improving the timeliness and accuracy of fault detection, but also enhancing the emergency response ability of the gas pipeline network, effectively reducing the time and cost of fault troubleshooting. At the same time, by formulating operation and maintenance strategies through in-depth analysis of data, potential risks can be warned in advance to prevent accidents from occurring, thus significantly improving the operation safety and reliability of the gas pipeline network and providing a strong guarantee for the stable urban energy supply.

[0010] The above description is only an overview of the technical solution of this application. In order to be able to understand the technical means of this application more clearly, it can be implemented according to the content of the specification. And in order to make the above and other purposes, features and advantages of this application more obvious and understandable, the specific embodiments of this application are specifically given below. Brief Description of the Drawings

[0011] Figure 1Schematic structural diagram of an information management system for a gas pipeline network provided by an embodiment of the present application.

[0012] Figure 2 Flow schematic diagram for obtaining M key node sets of a gas pipeline network in an information management system for a gas pipeline network provided by an embodiment of the present application.

[0013] Figure 3 Flow schematic diagram for obtaining K node fault feature sets in an information management system for a gas pipeline network provided by an embodiment of the present application.

[0014] Figure 4 Flow schematic diagram of an information management method for a gas pipeline network provided by an embodiment of the present application.

[0015] Explanation of reference numerals: Key node analysis module 10, Internet of Things endpoint deployment module 20, fault identification module 30, risk parameter analysis module 40, operation and maintenance strategy analysis module 50. Detailed implementation manners

[0016] By providing an information management system and method for a gas pipeline network in an embodiment of the present application, the technical problems in the prior art, such as low fault troubleshooting efficiency and insufficient emergency response ability due to the lack of precise monitoring and data analysis means for key nodes of the gas pipeline network, are solved, and the technical effects of improving the real-time performance and accuracy of fault detection of the gas pipeline network, thereby enhancing the safety and reliability of the pipeline network operation, are achieved.

[0017] Embodiment 1, as Figure 1 shown, an embodiment of the present application provides an information management system for a gas pipeline network, and the system includes:

[0018] A key node analysis module 10, configured to obtain gas pipeline network distribution characteristic information, perform key node analysis on the gas pipeline network distribution characteristic information, and obtain M key node sets of the gas pipeline network.

[0019] Specifically, the gas pipeline network distribution characteristic information refers to the physical and operation characteristics of the gas pipeline network, including data such as the layout of the pipeline network, the length, diameter, material, burial depth, pressure, and flow rate of the pipeline. Key nodes are points that have a significant impact on the stability and safety of the gas pipeline network, such as intersection points, bifurcation points, important valves, pressure regulating stations, etc.

[0020] Collect various basic information of the gas pipeline network, such as the pipeline layout diagram, pipe diameter data, node connection relationship, etc. in the Geographic Information System (GIS), as well as the operation parameter information of the pipeline network, such as pressure, flow rate, etc. Use data mining and machine learning algorithms to analyze the obtained distribution characteristic information. For example, use the clustering analysis algorithm to identify the nodes with large flow rate and frequent pressure changes as key nodes according to parameters such as the flow rate and pressure of the nodes; or use the decision tree algorithm to find the nodes with high failure occurrence frequency as key nodes according to historical failure data. By analyzing the distribution characteristic information of the gas pipeline network, determine M key nodes to form a key node set. Among them, M is a positive integer, used to represent the total number of key nodes. The key node set of the gas pipeline network provides a precise target area for the subsequent deployment of IoT endpoints, ensuring the reasonable allocation of sensor resources, avoiding resource waste at non-key nodes, and improving resource utilization efficiency.

[0021] The IoT endpoint deployment module 20 is used to sequentially deploy IoT endpoints to the M key node sets of the gas pipeline network to obtain M key node IoT endpoint sets, and each endpoint in the M key node IoT endpoint sets includes a sensor acquisition unit, a data communication unit, and a data analysis unit.

[0022] Specifically, the IoT endpoint is an IoT device installed at the key node, which can collect, transmit, and analyze data in real time, including a sensor acquisition unit, a data communication unit, and a data analysis unit. Among them, the sensor acquisition unit is responsible for collecting various operation parameters of the key node in real time, such as pressure, temperature, flow rate, etc. The data communication unit is responsible for transmitting the collected data to the information management cloud center through the wireless network. The data analysis unit is used to perform preliminary analysis on the collected data to identify abnormal situations. Sequentially deploy IoT endpoints to the M key node sets of the gas pipeline network, install IoT endpoint devices at each key node, and each device includes a sensor acquisition unit, a data communication unit, and a data analysis unit. The IoT endpoint devices of each key node form an endpoint set, and a total of M key node IoT endpoint sets are obtained.

[0023] By deploying IoT endpoints at key nodes, comprehensive perception and data collection of key parts of the gas pipeline network are realized, and an IoT monitoring network of the gas pipeline network is established, providing a real-time data source for fault identification and risk assessment.

[0024] The fault identification module 30 is used to obtain M multi-dimensional operation data streams of the gas pipeline network through the sensor acquisition unit, and perform fault identification on the M multi-dimensional operation data streams of the gas pipeline network respectively based on the data analysis unit to obtain K node fault feature sets.

[0025] Specifically, the multi-dimensional operation data stream refers to the operation data of the gas pipeline network containing multiple dimensions (such as pressure, temperature, flow rate, etc.) obtained from the sensor acquisition unit. The node fault feature set is a set of fault nodes and their feature data identified by the data analysis unit.

[0026] Call the sensor acquisition units in the IoT endpoints of M key nodes to collect various operation parameters of the key nodes in real time, forming a multi-dimensional operation data stream. These data streams contain various data types, such as the changes of pressure, flow rate, temperature, etc. over time. For example, call the pressure sensor, temperature sensor, and flow sensor installed at the key node to collect pressure data, temperature data, and flow data respectively, forming a data stream containing three dimensions of pressure, temperature, and flow rate. Input the multi-dimensional operation data streams collected by the sensor acquisition units of each endpoint into the data analysis unit of that endpoint for data analysis to identify abnormal situations. The data analysis unit analyzes the input data stream according to historical data and predefined fault modes, determines whether there are fault features, determines K fault nodes and their feature data sets, and obtains K node fault feature sets. Among them, K is a positive integer representing the number of fault nodes identified by the data analysis unit, and K is less than or equal to M. For example, by analyzing the multi-dimensional operation data streams of 5 key nodes, 3 fault nodes are determined, namely pipeline leakage, valve damage, and regulator station failure, forming a data set containing 3 node fault features.

[0027] Through fault identification, it is possible to timely and accurately identify potential fault features from the complex operation data stream of the gas pipeline network, improve the efficiency and accuracy of fault detection, and provide a basis for subsequent risk assessment and operation and maintenance strategy formulation.

[0028] The risk parameter analysis module 40 is used to wirelessly transmit the K node fault feature sets to the information management cloud center through the data communication unit for integrated analysis to obtain the gas pipeline network fault risk parameters.

[0029] Specifically, the fault risk parameter is obtained by integrating and analyzing the node fault feature set and is used to evaluate the gas pipeline network fault risk.

[0030] After obtaining the K node fault feature sets, call the data communication units of the M endpoints to wirelessly transmit the corresponding node fault feature sets (such as wireless communication technologies such as Wi-Fi, 4G, or 5G) to the information management cloud center. In the information management cloud center, use big data analysis and machine learning algorithms to perform integrated analysis on these fault feature sets. For example, adopt a neural network algorithm, comprehensively consider the feature data and historical data of each fault node, calculate the risk value of each node, and perform risk ranking and grading to obtain the gas pipeline network fault risk parameters.

[0031] By obtaining the failure risk parameters of the gas pipeline network, the failure risk of the gas pipeline network is quantified, providing accurate risk assessment results for the analysis of operation and maintenance strategies, making the operation and maintenance decisions more scientific and reasonable, and improving the risk prevention and control ability.

[0032] The operation and maintenance strategy analysis module 50 is used to analyze the operation and maintenance strategies for the failure risk parameters of the gas pipeline network, obtain the target failure operation and maintenance strategy parameters, and perform operation and maintenance emergency management for the gas pipeline network based on the target failure operation and maintenance strategy parameters.

[0033] Specifically, the target failure operation and maintenance strategy parameters are the operation and maintenance strategy parameters for specific failure situations, used to guide the operation and maintenance emergency management of the gas pipeline network. According to the failure risk parameters of the gas pipeline network obtained by the risk parameter analysis module 40, combined with the pre-established operation and maintenance strategy knowledge base, through rule matching or intelligent algorithms (such as decision tree algorithms, etc.), the operation and maintenance strategies are analyzed to determine the target failure operation and maintenance strategy parameters of the failure nodes. Based on these target failure operation and maintenance strategy parameters, the operation and maintenance emergency management of the gas pipeline network is carried out to deal with sudden failures. For example, if the risk parameters indicate that the failure risk of a certain node reaches a certain threshold, the corresponding strategy in the operation and maintenance strategy knowledge base may be to dispatch maintenance personnel for on-site inspection and repair, and allocate corresponding resources according to the severity of the failure.

[0034] Through the analysis of operation and maintenance strategies, the target failure operation and maintenance strategy parameters are determined, improving the emergency response ability of the gas pipeline network, reducing the probability of accidents, and ensuring the safe and stable operation of the gas pipeline network.

[0035] Furthermore, as Figure 2 shown, the key node analysis module 10 of the embodiment of the present application is further used to perform the following steps:

[0036] Step P11: Use GIS to perform entity extraction and topological modeling on the distribution characteristic information of the gas pipeline network to obtain a visual model of the gas pipeline network.

[0037] Step P12: According to the information management requirements of the gas pipeline network, determine the preset grid size, and perform grid division on the visual model of the gas pipeline network according to the preset grid size to obtain an initial grid node set.

[0038] Step P13: Perform dynamic simulation on the basis of the visual model of the gas pipeline network to obtain pipeline flow simulation information, pressure change simulation information, and pipeline leakage simulation information.

[0039] Step P14: Based on the pipeline flow simulation information, pressure change simulation information, and pipeline leakage simulation information, perform criticality evaluation and screening on the initial grid node set to obtain the M gas pipeline network key node sets.

[0040] Specifically, first, the distribution characteristic information of the gas pipeline network (including data such as pipeline geographical coordinates, pipe diameter, node positions, etc.) is input into the GIS. The GIS uses its data processing and analysis functions to extract entities from this information and identify entities such as pipelines and nodes. Then, according to the connection relationships between entities, etc., topological modeling is carried out to construct the topological structure of the gas pipeline network, and finally a visual model of the gas pipeline network is obtained. This visual model of the gas pipeline network can intuitively display the layout and structural relationships of the gas pipeline network. For example, through GIS software, the topological structure of the pipeline network is represented by lines of different colors and thicknesses on the map to intuitively display the distribution of the gas pipeline network.

[0041] The preset grid size is the grid size preset according to the management requirements of the gas pipeline network information for dividing the visual model of the gas pipeline network. The initial grid node set is the set containing all grid nodes obtained after grid division. These nodes are the basis for subsequent criticality assessment and screening. According to the management requirements of the gas pipeline network, such as the monitoring accuracy requirements for different regions of the pipeline network and the granularity of data analysis, etc., a suitable preset grid size is determined. For example, for the gas pipeline network in the urban center area, a smaller grid size, such as a 10m×10m grid cell, needs to be set to improve the management fineness; while for the pipeline network in the suburbs, a larger grid size, such as a 100m×100m grid cell, can be set. According to the preset grid size, the visual model of the gas pipeline network is meshed, and the pipeline network is divided into multiple grid cells. Each grid cell corresponds to a node, which can be the vertex or the center position of the grid cell, and all grid nodes are aggregated to form the initial grid node set. By dividing the visual model of the gas pipeline network into grids, the complex gas pipeline network can be divided into smaller management units, providing an ordered and discretized set of analysis objects for subsequent criticality assessment and screening, which helps to analyze the importance of each part of the gas pipeline network more finely.

[0042] The pipeline network flow simulation information is the simulation data of the natural gas flow in the gas pipeline network under different conditions (such as different time periods, different node usage conditions, etc.) obtained through dynamic simulation; the pressure change simulation information is the simulation data of the pressure in the pipeline changing with various factors; the pipeline network leakage simulation information is the relevant information when the pipeline network leaks, such as the leakage location, leakage volume, and the impact on the pipeline network pressure and flow. Using computer simulation technology, the dynamic changes of the flow, pressure, and leakage of the gas pipeline network under different operating conditions are simulated. Exemplarily, the visual model of the gas pipeline network and related operating parameters (such as initial flow rate, pressure setting value, pipeline material information, etc.) can be input into fluid simulation software such as ANSYS Fluent and OpenFOAM. The software conducts dynamic simulation of the gas pipeline network according to the physical model and mathematical algorithm, calculates the flow rate and pressure change conditions at each position of the gas pipeline network under different working states, and simultaneously simulates the pipeline network leakage situation and records the relevant information, so as to obtain the pipeline network flow simulation information, pressure change simulation information, and pipeline network leakage simulation information. These simulation information provide important data support for the subsequent criticality assessment and screening, and can more accurately evaluate the importance of each node.

[0043] Based on the pipeline network flow simulation information, pressure change simulation information, and pipeline network leakage simulation information, the criticality of the nodes in the initial grid node set is evaluated, and the critical nodes are screened out. For example, according to the flow simulation information, the nodes with larger flow rates are identified; according to the pressure change simulation information, the nodes with frequent or large pressure fluctuations are identified; according to the pipeline network leakage simulation information, the nodes with higher leakage risks are identified. After evaluation and screening, M critical nodes are determined to form the critical node set of the gas pipeline network.

[0044] By constructing a visual model of the gas pipeline network and conducting dynamic simulation of the pipeline network, the critical nodes in the gas pipeline network can be accurately identified. These critical nodes are the key parts affecting the operation safety and stability of the gas pipeline network. Focusing on their monitoring and management can improve the overall safety and reliability of the gas pipeline network.

[0045] Furthermore, step P11 includes:

[0046] Step P111: Construct an entity label library for the gas pipeline network, and perform entity extraction on the distribution characteristic information of the gas pipeline network based on the entity label library of the gas pipeline network to obtain a gas pipeline network entity set.

[0047] Step P112: Obtain the entity attribute information of the gas pipeline network entity, where the entity attribute information of the gas pipeline network entity includes entity type, structural size, spatial position, and distribution trend.

[0048] Step P113: Perform attribute marking on the gas pipeline network entity set in sequence according to the gas pipeline network entity attribute information to obtain a gas pipeline network entity attribute parameter set.

[0049] Step P114: Create an entity element symbol library, and use GIS to perform labeled topological modeling on the gas pipeline network entity attribute parameter set based on the entity element symbol library to obtain the gas pipeline network visualization model.

[0050] Specifically, the gas pipeline network entity tag library is a predefined database that contains tag information for identifying different entities (such as pipelines, valves, nodes, etc.) in the gas pipeline network. These tags are a classification identifier for various entities in the gas pipeline network. First, construct an entity tag library to define the tags and attributes of various gas pipeline network entities (such as pipelines, valves, pipeline access points, pressure regulating stations, etc.). Based on the constructed gas pipeline network entity tag library, use GIS software to identify various entities from the gas pipeline network distribution characteristic information, thereby obtaining the gas pipeline network entity set.

[0051] The gas pipeline network entity attribute information is the relevant characteristic description information about the gas pipeline network entity, including entity type (whether it is a pipeline or a valve, etc.), structural dimensions (such as the diameter and length of the pipeline), spatial location (coordinate position in the geographical space), and distribution trend (whether the pipeline trend is straight or curved, and the starting and ending points of the pipeline, etc.). Its attribute information is obtained by querying relevant design documents, databases, or on-site measurements, etc. For example, for a pipeline entity, its structural dimensions (diameter, wall thickness, etc.) can be obtained from the design drawings, its spatial location (coordinate information) can be obtained through GIS, and its distribution trend can be determined according to the overall layout, etc. These information provide detailed data support for subsequent attribute marking and modeling.

[0052] Use the attribute marking tool in the GIS software to perform attribute marking on each entity in the gas pipeline network entity set according to the obtained gas pipeline network entity attribute information. For example, for a certain pipeline entity, mark its type as "pipeline", diameter as 200 mm, length as 500 m, starting point coordinates as (x1, y1), ending point coordinates as (x2, y2), etc. After attribute marking, a set containing all entities and their attribute information is obtained, that is, the gas pipeline network entity attribute parameter set.

[0053] Create an entity element symbol library and define symbols corresponding to different gas pipeline network entities (such as pipelines, valves, etc.). Then, input the gas pipeline network entity attribute parameter set and the entity element symbol library into GIS. GIS annotates the entities in the gas pipeline network entity attribute parameter set according to the symbols in the entity element symbol library, and at the same time performs topological modeling according to the connection relationships between entities. For example, according to the information in the entity attribute parameter set, entities such as pipelines, valves, and pressure regulating stations are marked with corresponding symbols on the GIS map, and the connection relationships between them are established to form a complete pipeline network topological structure, obtaining a visual model of the gas pipeline network.

[0054] Through the construction of the entity label library, attribute marking, and topological modeling, the visual display of the gas pipeline network is realized, providing an important basis and reference for subsequent steps such as grid segmentation, dynamic simulation, and key node analysis, making the analysis and management of the pipeline network more intuitive and efficient.

[0055] Furthermore, as Figure 3 shown, the fault identification module 30 of the embodiment of the present application is further used to perform the following steps:

[0056] Step P31: Respectively extract the associated features of the M multi-dimensional operation data streams of the gas pipeline network to obtain M associated operation feature sets of the gas pipeline network.

[0057] Step P32: Perform correlation analysis and feature selection on the M associated operation feature sets of the gas pipeline network to determine M key operation feature sets of the gas pipeline network.

[0058] Step P33: Obtain the M key node fault analysis trees of the M key node Internet of Things endpoint sets through the data analysis unit.

[0059] Step P34: Based on the M key node fault analysis trees, perform fault identification integration on the M key operation feature sets of the gas pipeline network to obtain the K node fault feature sets.

[0060] Specifically, for each multi-dimensional operation data stream of the gas pipeline network, data mining algorithms (such as association rule mining algorithms like the Apriori algorithm) are used to extract association features by analyzing the frequent patterns among different variables in the data stream. For example, the operation data stream containing data such as pressure, flow rate, and temperature is input into the Apriori algorithm, and the algorithm will find the internal relationships between data in different dimensions, such as the proportional relationship between pressure changes and flow rate changes. Association feature extraction is performed on the multi-dimensional operation data streams of M gas pipeline networks respectively to obtain the association operation feature sets of the M gas pipeline networks. Each association operation feature set corresponds to the operation data stream of a gas pipeline network, and the features in these sets reflect the association relationships among variables during the operation of the gas pipeline network, providing rich feature data support for subsequent fault identification.

[0061] The key operation feature set of the gas pipeline network is a set obtained after correlation analysis and feature selection, which contains the operation features that are most critical for fault identification of the gas pipeline network. For the M association operation feature sets of the gas pipeline network, statistical analysis software is used for correlation analysis, and correlation coefficients (such as Pearson correlation coefficient, Spearman rank correlation coefficient, etc.) between features are calculated to determine the correlation between features. Then, according to the correlation results, feature selection is carried out to select the key features that are most useful for fault identification, and the key operation feature sets of the M gas pipeline networks are determined. For example, if two features are highly correlated (the correlation coefficient is close to 1 or -1), and one of the features makes less contribution to fault identification, then the more representative feature is selected and retained, thus determining the key operation feature sets of the M gas pipeline networks. Through correlation analysis and feature selection, redundant or unimportant features are removed, reducing the complexity of data processing, thereby improving the efficiency and accuracy of fault identification.

[0062] The key node fault analysis tree is a hierarchical fault analysis model used to represent different fault modes and their possible causes in the pipeline network. Using the data analysis unit in the Internet of Things endpoints, the key node fault analysis tree is constructed through predefined algorithms and models. These algorithms and models can be established based on historical fault data, expert experience, etc. For example, according to the past gas pipeline network fault records, the relationships between factors such as abnormal pressure and abnormal flow rate and node faults are summarized, and a tree structure is constructed to obtain M key node fault analysis trees. Constructing the key node fault analysis tree provides a structured analysis framework for fault identification, helping to systematically analyze the possible fault situations of key nodes.

[0063] Taking the M key operation feature sets of the gas pipeline network and the fault analysis trees of M key nodes as inputs, analyzing the key operation feature sets according to the logical structure of the fault analysis trees, and obtaining K node fault feature sets. Based on the fault analysis trees of the key nodes, fault identification and integration of the key operation feature sets can accurately identify the nodes that may have faults and their fault features, providing key input information for subsequent risk parameter analysis and operation and maintenance strategy analysis.

[0064] Further, step P33 includes:

[0065] Step P331: Acquire M key node fault operation feature sets of the M key node Internet of Things endpoint sets through the data analysis unit.

[0066] Step P332: Extract top events from the M key node fault operation feature sets respectively to determine M key node fault top events.

[0067] Step P333: Logically decompose the M key node fault operation data sets step by step based on the M key node fault top events to determine M key node fault event logic gates.

[0068] Step P334: Cascade and optimize the M key node fault operation feature sets based on the M key node fault event logic gates to obtain M key node fault analysis trees and store them in the data analysis unit.

[0069] Specifically, the key node fault operation feature set is a set of features related to the operation of the fault node, containing various information related to faults during the operation of the key node, such as the pressure fluctuation range, flow rate change rate, temperature anomaly, etc. at the key node. The data analysis unit connects to the sensor acquisition unit in the Internet of Things endpoint to obtain the data collected by the sensor about the M key node Internet of Things endpoint sets. These data include various information related to fault operation, such as the changes in pressure, flow rate, temperature, etc. over time. Then, the data analysis unit sorts out and extracts features from these data, such as calculating the fluctuation amplitude of pressure, the change trend of flow rate, etc., so as to obtain M key node fault operation feature sets.

[0070] The top event of a critical node failure is the most critical event in a critical node failure and is also the starting point for constructing a fault analysis tree. For each critical node failure operation feature set, the top event is extracted by analyzing the importance of each feature in the feature set and its correlation with the failure, and using expert experience or data analysis algorithms (such as rule-based algorithms). For example, if a feature indicates that the pressure at a node suddenly drops to zero, and this situation is one of the most serious situations in previous failure cases, then "node pressure drops to zero" is extracted as the top event. Through the above top event extraction operation, the top event of each node is determined node by node, thereby obtaining M top events of critical node failures.

[0071] The key node fault event logic gate refers to the logical relationship between each fault event. For example, the "AND gate" means that the output event occurs only when all input events occur, and the "OR gate" means that the output event occurs as long as one input event occurs. According to the determined M key node fault top events, the corresponding key node fault operation data set is logically decomposed step by step, that is, based on the top event, the top event is analyzed step by step according to the causal chain of the fault occurrence, and the top event is decomposed into multiple sub-events, and then the sub-event is further decomposed into the next level of sub-events, and so on, so as to clarify the causal relationship and logical relationship between each event. This process can be based on the basic principles of fault tree analysis (FTA). By analyzing historical fault data, expert knowledge, etc., the logical relationship between each sub-event is determined, thereby determining the M key node fault event logic gates to clarify the logical relationship between each fault event.

[0072] Based on the M key node fault event logic gates, the M key node fault operation feature sets are cascaded and constructed. During the construction process, the various features and events are connected to form a tree structure according to the relationship of the logic gates. At the same time, the constructed fault analysis tree is optimized to remove repeated logical branches and simplify complex logical relationships. Finally, M key node fault analysis trees are obtained and stored in the data analysis unit.

[0073] The fault analysis tree constructed through the above steps comprehensively and accurately describes the potential failure modes of each node, providing an intuitive and effective analysis tool for subsequent fault diagnosis and improving the accuracy and efficiency of fault identification.

[0074] Furthermore, the risk parameter analysis module 40 of the embodiment of the present application is also used to perform the following steps:

[0075] Step P41: Obtain a gas pipeline network failure risk data set through the information management cloud center, perform diversion training on the gas pipeline network failure risk data set according to the M gas pipeline network key node sets, and obtain M key node failure risk analysis networks.

[0076] Step P42: Conduct a failure risk impact analysis on the set of M key nodes of the gas pipeline network to obtain information on the impact factors of the M key nodes.

[0077] Step P43: Based on the information on the impact factors of the M key nodes, integrate and fuse the failure risk analysis networks of the M key nodes to generate an adaptive network for failure risk analysis.

[0078] Step P44: Based on the adaptive network for failure risk analysis, conduct an integrated analysis on the set of failure characteristics of the K nodes to obtain the failure risk parameters of the gas pipeline network.

[0079] Specifically, the information management cloud center obtains a failure risk data set of the gas pipeline network from sources such as historical failure records and sensor data. This failure risk data set of the gas pipeline network is a collection of risk data on the occurrence of failures of the gas pipeline network under different conditions, including information such as the frequency of failure occurrence, the scope of influence, and the degree of loss. The failure risk data set is divided according to the set of M key nodes of the gas pipeline network, and the data of each key node is trained separately to obtain failure risk analysis networks for different nodes. These failure risk analysis networks for key nodes can be constructed based on machine learning algorithms such as neural networks and decision trees, and can predict the failure risk of the node according to the input node failure characteristic data. For example, for each subset of failure risk data of the key nodes after splitting, a neural network is used for training, and the structure of the network (such as the number of nodes in the input layer, hidden layer, and output layer, etc.) and training parameters (such as the learning rate, the number of iterations, etc.) are set, and M failure risk analysis networks for key nodes are obtained through iterative training.

[0080] The information on the impact factors of key nodes is information on various factors describing the impact of key node failures on the gas pipeline network, such as the size of the influence scope and the severity of the influence degree. For the set of M key nodes of the gas pipeline network, a failure risk impact analysis is conducted by analyzing factors such as the geographical location, connection relationship, and importance of the supply area of each node to determine the possible impact of each key node on the entire gas pipeline network in case of failure, including factors such as the influence scope and the influence degree. For example, for a key node located in the city center, due to the dense population and the large scope of gas supply influence, its impact factor may be relatively high; while for a node with fewer connected pipelines, its failure risk impact is relatively small. Through the failure risk impact analysis, information on the impact factors of the M key nodes is obtained.

[0081] According to the influence factor information of M key nodes, the fault risk analysis networks of M key nodes are integrated and fused. A weighted fusion method can be adopted, that is, different weights are assigned to each key node fault risk analysis network according to the size of the key node influence factor. The fault risk analysis network corresponding to the key node with a large influence factor is given a larger weight during integration and fusion. In this way, each network is combined and optimized to generate an adaptive network for fault risk analysis. This adaptive network for fault risk analysis synthesizes the advantages of each key node fault risk analysis network, can adaptively adjust the analysis strategy according to different fault feature data, and accurately predict the fault risk of the gas pipeline network.

[0082] Input the fault feature sets of K nodes into the adaptive network for fault risk analysis. The network performs integrated analysis on these fault feature sets according to its internal algorithms and structures. For example, the network will calculate the fault risk parameters of the gas pipeline network such as the probability of fault occurrence and the possible impact range caused by the fault according to different fault features and their relationships with key nodes.

[0083] By constructing an adaptive network for fault risk analysis, a comprehensive assessment of the overall fault risk of the gas pipeline network is realized, providing a quantitative basis for the analysis of operation and maintenance strategies, and enhancing the dynamic risk adaptation ability and operation and maintenance decision-making support ability of the gas pipeline network.

[0084] Furthermore, the operation and maintenance strategy analysis module 50 in the embodiment of the present application is further used to perform the following steps:

[0085] Step P51: Analyze the operation and maintenance strategies for the gas pipeline network fault risk parameters to obtain the target fault operation and maintenance strategies, and construct a fault operation and maintenance strategy space based on the target fault operation and maintenance strategies.

[0086] Step P52: Fit to obtain a fault operation and maintenance effect evaluation function, and randomly select multiple fault operation and maintenance strategy parameters in the fault operation and maintenance strategy space.

[0087] Step P53: Use the fault operation and maintenance effect evaluation function to evaluate the effects of the multiple fault operation and maintenance strategy parameters to obtain the fitness of the multiple fault operation and maintenance strategy parameters.

[0088] Step P54: Based on the fitness of the multiple fault operation and maintenance strategy parameters, perform cross-variation expansion and global comparison optimization on the multiple fault operation and maintenance strategy parameters to determine the target fault operation and maintenance strategy parameters.

[0089] Specifically, according to the failure risk parameters of the gas pipeline network, considering factors such as the operation requirements, safety standards, and cost limitations of the gas pipeline network, the operation and maintenance strategy is analyzed, and the best operation and maintenance plan in historical cases is extracted as the target failure operation and maintenance strategy. This target failure operation and maintenance strategy is a preliminary framework for maintenance and operation strategies formulated for the failure risk situation of the gas pipeline network. Based on this target failure operation and maintenance strategy, the value ranges of various possible strategy parameters (such as the number of maintenance personnel, types of maintenance equipment, maintenance time arrangements, etc.) are determined, thereby constructing the failure operation and maintenance strategy space and determining the range for subsequent searching for the optimal failure operation and maintenance strategy parameters.

[0090] Analyze historical operation and maintenance data to determine various factors affecting the failure operation and maintenance effect (such as maintenance cost, maintenance efficiency, impact on users, etc.). Based on the multi-objective optimization algorithm, establish a failure operation and maintenance effect evaluation function. For example, a comprehensive evaluation value can be obtained by weighted summation of the quantified values of maintenance cost, maintenance time, and impact on users. At the same time, within the failure operation and maintenance strategy space, use tools such as random number generators to randomly select multiple failure operation and maintenance strategy parameters, which represent different failure operation and maintenance strategies.

[0091] Substitute the randomly selected multiple failure operation and maintenance strategy parameters into the failure operation and maintenance effect evaluation function for calculation to determine the fitness of each failure operation and maintenance strategy parameter corresponding to the failure operation and maintenance strategy parameter. The higher the fitness, the more compliant the strategy parameter is and the more effective it is in solving the failure operation and maintenance problem of the gas pipeline network.

[0092] According to the fitness of multiple failure operation and maintenance strategy parameters, use genetic algorithms or similar optimization algorithms for crossover and mutation expansion. For example, select the failure operation and maintenance strategy parameters with higher fitness for crossover operations, exchange some of their strategy parameter values, and at the same time perform mutation operations to randomly change some strategy parameter values to expand the diversity of strategy parameters. Then, compare the expanded failure operation and maintenance strategy parameters globally to find the strategy parameter with the highest fitness and determine it as the target failure operation and maintenance strategy parameter.

[0093] By analyzing and optimizing the operation and maintenance strategy, the target failure operation and maintenance strategy parameters are determined, and the optimal or near-optimal strategy parameters can be found within the failure operation and maintenance strategy space, so that the failure handling effect reaches the best, improving the failure operation and maintenance efficiency of the gas pipeline network and the operation safety of the pipeline network.

[0094] In summary, the information management system for gas pipeline networks provided by the embodiments of the present application has the following technical effects:

[0095] The key node analysis module 10 uses GIS technology to extract entities and build a topological model of the gas pipeline network, constructs a visualization model of the gas pipeline network, and accurately identifies the key node set through dynamic simulation and criticality evaluation screening. This step provides a precise target area for subsequent monitoring and management, optimizes resource allocation, and improves the pertinence and accuracy of monitoring. Next, the IoT endpoint deployment module 20 deploys IoT endpoints at key nodes to collect multi-dimensional operation data in real time, providing a rich data basis for fault identification and risk assessment. The fault identification module 30 accurately identifies the fault nodes and their characteristics through associated feature extraction, correlation analysis, and fault analysis tree construction, providing a reliable basis for risk assessment and operation and maintenance strategy formulation. The risk parameter analysis module 40 uses the data in the information management cloud center to generate an adaptive network for fault risk analysis through shunt training, fault risk impact analysis, and integration and fusion, comprehensively evaluating the fault risk of the gas pipeline network and early warning potential risks. Finally, the operation and maintenance strategy analysis module 50 determines the optimal fault operation and maintenance strategy parameters through operation and maintenance strategy analysis, effect evaluation function fitting, and global comparison optimization, guiding the operation and maintenance emergency management of the gas pipeline network, improving the emergency response ability, and reducing the probability of accidents.

[0096] Overall, the embodiments of the present application realize the intelligent monitoring and management of the gas pipeline network, not only improving the timeliness and accuracy of fault detection, but also enhancing the emergency response ability of the gas pipeline network, effectively reducing the time and cost of fault troubleshooting. At the same time, by formulating operation and maintenance strategies through in-depth analysis of data, potential risks can be early warned, accidents can be prevented, and thus the operation safety and reliability of the gas pipeline network are significantly improved, providing a strong guarantee for the stability of urban energy supply.

[0097] Embodiment 2, as Figure 4 shown, the embodiments of the present application provide an information management method for a gas pipeline network, and the method includes:

[0098] Step S1: Obtain the distribution characteristic information of the gas pipeline network, perform key node analysis on the distribution characteristic information of the gas pipeline network, and obtain M key node sets of the gas pipeline network.

[0099] Step S2: Sequentially deploy IoT endpoints for the M key node sets of the gas pipeline network to obtain M key node IoT endpoint sets, and each endpoint in the M key node IoT endpoint sets includes a sensor acquisition unit, a data communication unit, and a data analysis unit.

[0100] Step S3: Obtain M multi-dimensional operation data streams of the gas pipeline network through the sensor acquisition unit, and perform fault identification on the M multi-dimensional operation data streams of the gas pipeline network respectively based on the data analysis unit to obtain K node fault feature sets.

[0101] Step S4: Wirelessly transmit the K node fault feature sets to the information management cloud center through the data communication unit for integrated analysis to obtain the gas pipeline network fault risk parameters.

[0102] Step S5: Analyze the operation and maintenance strategies for the gas pipeline network fault risk parameters to obtain the target fault operation and maintenance strategy parameters, and perform operation and maintenance emergency management for the gas pipeline network based on the target fault operation and maintenance strategy parameters.

[0103] Furthermore, step S1 of the embodiment of the present application includes:

[0104] Use GIS to perform entity extraction and topological modeling on the gas pipeline network distribution characteristic information to obtain a gas pipeline network visualization model; determine a preset grid size according to the gas pipeline network information management requirements, and perform grid segmentation on the gas pipeline network visualization model according to the preset grid size to obtain an initial grid node set; perform dynamic simulation on the gas pipeline network visualization model to obtain pipeline network flow simulation information, pressure change simulation information, and pipeline network leakage simulation information; based on the pipeline network flow simulation information, pressure change simulation information, and pipeline network leakage simulation information, perform criticality evaluation and screening on the initial grid node set to obtain the M gas pipeline network key node sets.

[0105] Furthermore, obtaining the gas pipeline network visualization model includes:

[0106] Construct a gas pipeline network entity label library, perform entity extraction on the gas pipeline network distribution characteristic information based on the gas pipeline network entity label library to obtain a gas pipeline network entity set; obtain gas pipeline network entity attribute information, where the gas pipeline network entity attribute information includes entity type, structural dimensions, spatial location, and distribution trend; perform attribute marking on the gas pipeline network entity set in sequence according to the gas pipeline network entity attribute information to obtain a gas pipeline network entity attribute parameter set; create an entity element symbol library, and use GIS to perform annotation topological modeling on the gas pipeline network entity attribute parameter set based on the entity element symbol library to obtain the gas pipeline network visualization model.

[0107] Furthermore, step S3 of the embodiment of the present application includes:

[0108] Respectively extract the correlation features of the M gas pipeline network multi-dimensional operation data streams to obtain M gas pipeline network correlation operation feature sets; perform correlation analysis and feature selection on the M gas pipeline network correlation operation feature sets to determine M gas pipeline network key operation feature sets; obtain the M key node fault analysis trees of the M key node Internet of Things endpoints of the M key node sets through the data analysis unit; based on the M key node fault analysis trees, perform fault identification and integration on the M gas pipeline network key operation feature sets to obtain the K node fault feature sets.

[0109] Further, the obtaining of the M key-node fault analysis trees of the M key-node Internet of Things endpoint sets by the data analysis unit includes:

[0110] Collecting and obtaining, by the data analysis unit, the M key-node fault operation feature sets of the M key-node Internet of Things endpoint sets; respectively extracting top events from the M key-node fault operation feature sets to determine M key-node fault top events; logically decomposing the M key-node fault operation data sets step by step based on the M key-node fault top events to determine M key-node fault event logic gates; cascading and optimizing the M key-node fault operation feature sets based on the M key-node fault event logic gates to obtain M key-node fault analysis trees and storing them in the data analysis unit.

[0111] Further, step S4 of the embodiment of the present application includes:

[0112] Obtaining a gas pipeline network fault risk data set through the information management cloud center, splitting and training the gas pipeline network fault risk data set according to the M gas pipeline network key-node sets to obtain M key-node fault risk analysis networks; performing a fault risk impact analysis on the M gas pipeline network key-node sets to obtain M key-node impact factor information; integrating and fusing the M key-node fault risk analysis networks based on the M key-node impact factor information to generate a fault risk analysis adaptive network; performing an integrated analysis on the K-node fault feature sets based on the fault risk analysis adaptive network to obtain the gas pipeline network fault risk parameters.

[0113] Further, step S5 of the embodiment of the present application includes:

[0114] Analyzing the operation and maintenance strategies of the gas pipeline network fault risk parameters to obtain target fault operation and maintenance strategies, and constructing a fault operation and maintenance strategy space based on the target fault operation and maintenance strategies; fitting to obtain a fault operation and maintenance effect evaluation function, and randomly selecting multiple fault operation and maintenance strategy parameters in the fault operation and maintenance strategy space; using the fault operation and maintenance effect evaluation function to evaluate the effects of the multiple fault operation and maintenance strategy parameters to obtain the fitness of the multiple fault operation and maintenance strategy parameters; performing cross-variation expansion and global comparison optimization on the multiple fault operation and maintenance strategy parameters based on the fitness of the multiple fault operation and maintenance strategy parameters to determine the target fault operation and maintenance strategy parameters.

[0115] Through the foregoing detailed description of an information management system for a gas pipeline network in this specification, those skilled in the art can clearly know an information management method for a gas pipeline network in this embodiment. For the method disclosed in Embodiment 2, since it corresponds to the system disclosed in Embodiment 1 and has corresponding execution steps and beneficial effects, the relevant parts can be referred to the description of the system part.

[0116] The foregoing description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to these embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. An information management system for a gas pipeline network, characterized in that, The system includes: A key node analysis module, which is used to obtain the distribution characteristic information of the gas pipeline network, perform key node analysis on the distribution characteristic information of the gas pipeline network, and obtain M key node sets of the gas pipeline network; An Internet of Things endpoint deployment module, which is used to sequentially perform Internet of Things endpoint deployment on the M key node sets of the gas pipeline network to obtain M key node Internet of Things endpoint sets, and each endpoint in the M key node Internet of Things endpoint sets includes a sensor acquisition unit, a data communication unit, and a data analysis unit; A fault identification module, which is used to obtain M multi-dimensional operation data streams of the gas pipeline network through the sensor acquisition unit, and perform fault identification on the M multi-dimensional operation data streams of the gas pipeline network respectively based on the data analysis unit to obtain K node fault feature sets; A risk parameter analysis module, which is used to wirelessly transmit the K node fault feature sets to an information management cloud center through the data communication unit for integrated analysis to obtain gas pipeline network fault risk parameters; An operation and maintenance strategy analysis module, which is used to analyze the operation and maintenance strategy of the gas pipeline network fault risk parameters to obtain target fault operation and maintenance strategy parameters, and perform operation and maintenance emergency management of the gas pipeline network based on the target fault operation and maintenance strategy parameters.

2. The information management system for a gas pipeline network according to claim 1, characterized in that, The key node analysis module is further used to perform the following steps: Use GIS to perform entity extraction and topological modeling on the distribution characteristic information of the gas pipeline network to obtain a gas pipeline network visualization model; According to the information management requirements of the gas pipeline network, determine a preset grid size, and perform grid division on the gas pipeline network visualization model according to the preset grid size to obtain an initial grid node set; Perform dynamic simulation on the basis of the gas pipeline network visualization model to obtain pipeline network flow simulation information, pressure change simulation information, and pipeline network leakage simulation information; Based on the pipeline network flow simulation information, pressure change simulation information, and pipeline network leakage simulation information, perform criticality evaluation and screening on the initial grid node set to obtain the M key node sets of the gas pipeline network.

3. The information management system for a gas pipeline network according to claim 2, wherein The key node analysis module is further used to perform the following steps: Construct a gas pipeline network entity tag library, and perform entity extraction on the distribution characteristic information of the gas pipeline network based on the gas pipeline network entity tag library to obtain a gas pipeline network entity set; Obtain the gas pipeline network entity attribute information, where the gas pipeline network entity attribute information includes entity type, structural size, spatial position, and distribution trend; Perform attribute marking on the gas pipeline network entity set in sequence according to the gas pipeline network entity attribute information to obtain a gas pipeline network entity attribute parameter set; Create an entity element symbol library, and use GIS to perform annotation topological modeling on the gas pipeline network entity attribute parameter set based on the entity element symbol library to obtain the gas pipeline network visualization model.

4. An information management system for a gas pipeline network as described in claim 1, characterized in that, The fault identification module is further used to perform the following steps: Extract correlation features from the M multi-dimensional operation data streams of the gas pipeline network respectively to obtain M associated operation feature sets of the gas pipeline network; Perform correlation analysis and feature selection on the M associated operation feature sets of the gas pipeline network to determine M key operation feature sets of the gas pipeline network; Obtain the M key-node fault analysis trees of the M key-node Internet of Things endpoint sets through the data analysis unit; Based on the M key-node fault analysis trees, perform fault identification and integration on the M key operating characteristic sets of the gas pipeline network to obtain the K node fault characteristic sets.

5. The information management system for a gas pipeline network according to claim 4, characterized in that, The fault identification module is further configured to perform the following steps: Collect and obtain the M key-node fault operation characteristic sets of the M key-node Internet of Things endpoint sets through the data analysis unit; Extract top events from the M key-node fault operation characteristic sets respectively to determine the M key-node fault top events; Logically decompose the M key-node fault operation data sets step by step based on the M key-node fault top events to determine the M key-node fault event logic gates; Based on the M key-node fault event logic gates, perform cascade construction and optimization on the M key-node fault operation characteristic sets to obtain M key-node fault analysis trees and store them in the data analysis unit.

6. The information management system for a gas pipeline network according to claim 1, wherein, The risk parameter analysis module is further configured to perform the following steps: Obtain the gas pipeline network fault risk data set through the information management cloud center, and split and train the gas pipeline network fault risk data set according to the M gas pipeline network key node sets to obtain M key-node fault risk analysis networks; Perform fault risk impact analysis on the M gas pipeline network key node sets to obtain M key-node impact factor information; Based on the M key-node impact factor information, perform integrated fusion on the M key-node fault risk analysis networks to generate a fault risk analysis adaptive network; Based on the fault risk analysis adaptive network, perform integrated analysis on the K node fault characteristic sets to obtain the gas pipeline network fault risk parameters.

7. The information management system for a gas pipeline network according to claim 6, wherein, The operation and maintenance strategy analysis module is further configured to perform the following steps: Analyze the operation and maintenance strategy of the gas pipeline network fault risk parameters to obtain the target fault operation and maintenance strategy, and based on the target fault operation and maintenance strategy, construct a fault operation and maintenance strategy space; Fit to obtain a fault operation and maintenance effect evaluation function, and randomly select multiple fault operation and maintenance strategy parameters in the fault operation and maintenance strategy space; Use the fault operation and maintenance effect evaluation function to evaluate the effects of the multiple fault operation and maintenance strategy parameters to obtain the fitness of the multiple fault operation and maintenance strategy parameters; Based on the fitness of the multiple fault operation and maintenance strategy parameters, perform cross-variation expansion and global comparison optimization on the multiple fault operation and maintenance strategy parameters to determine the target fault operation and maintenance strategy parameters.

8. An information management system for a gas pipeline network, characterized in that, The method is executed by an information management system for gas pipeline network according to any one of claims 1-7, including: Obtain the distribution characteristic information of the gas pipeline network, perform key node analysis on the distribution characteristic information of the gas pipeline network to obtain M gas pipeline network key node sets; Successively deploy Internet of Things endpoints for the M gas pipeline network key node sets to obtain M key-node Internet of Things endpoint sets, and each endpoint in the M key-node Internet of Things endpoint sets includes a sensor acquisition unit, a data communication unit, and a data analysis unit; Obtain M multi-dimensional operation data streams of the gas pipeline network through the sensor acquisition unit, and respectively perform fault identification on the M multi-dimensional operation data streams of the gas pipeline network based on the data analysis unit to obtain K node fault feature sets; Wirelessly transmit the K node fault feature sets to the information management cloud center through the data communication unit for integrated analysis to obtain gas pipeline network fault risk parameters; Analyze the operation and maintenance strategies for the gas pipeline network fault risk parameters to obtain target fault operation and maintenance strategy parameters, and perform operation and maintenance emergency management of the gas pipeline network based on the target fault operation and maintenance strategy parameters.

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