Gas pipeline risk assessment and prediction system based on connectivity analysis

Abstract

This invention discloses a gas pipeline risk assessment and prediction system based on connectivity analysis, comprising: a connectivity analysis module for performing connectivity analysis on the gas pipeline network, identifying independent pipe segments, and automatically identifying pipe segment boundaries based on pipeline point type characteristics; a risk factor extraction module for extracting risk-related factor data for each pipe segment; a risk prediction model module for performing risk prediction based on the risk factor data, integrating multiple machine learning algorithms, and numericalizing non-numerical data through feature engineering; and a task allocation optimization module for dynamically selecting the optimal analysis strategy according to network size and topology characteristics, supporting multi-threaded parallel processing. This invention achieves efficient connectivity analysis and accurate risk assessment and prediction for gas pipeline networks, significantly improving the analysis efficiency of large-scale pipeline networks through intelligent grouping strategies and multi-threaded parallel processing technology.

Description

Technical Field

[0001] This invention relates to the field of intelligent management and risk prediction technology for gas pipeline networks, specifically to a gas pipeline risk assessment and prediction system based on connectivity analysis. Background Technology

[0002] With the rapid development of urbanization, the scale of gas pipeline networks is constantly expanding and their structure is becoming increasingly complex. Traditional gas pipeline management mainly relies on manual inspections and static data analysis, which suffers from problems such as low analysis efficiency, lack of real-time performance, and insufficient accuracy in risk prediction. Existing technologies mainly include GIS-based static pipeline network management, periodic manual inspections, and simple fault statistical analysis. These methods are insufficient to meet the requirements of managing the complexity and real-time performance of modern urban gas pipeline networks.

[0003] Disadvantages of existing technology: (1) Low analysis efficiency: Traditional methods require point-by-point inspection of the entire pipeline network, which is extremely inefficient when dealing with large-scale pipeline networks and cannot meet the needs of real-time analysis.

[0004] (2) Lack of real-time capability: Existing technologies cannot dynamically identify changes in connectivity in pipeline networks, making it difficult to detect potential risk points in a timely manner and unable to achieve dynamic risk assessment.

[0005] (3) Unreasonable resource allocation: The characteristics of the pipeline network topology were not considered, resulting in a waste of computing resources and affecting the overall performance of the system.

[0006] (4) Inaccurate risk prediction: There is a lack of risk assessment models based on pipeline connectivity characteristics, resulting in limited prediction accuracy and difficulty in providing effective decision support.

[0007] (5) Limited functionality: The existing system has limited functionality and lacks a comprehensive risk assessment and early warning mechanism, which cannot meet the diversified needs of modern gas pipeline management.

[0008] Current trends in technological development: With the rapid development of big data, artificial intelligence, and the Internet of Things, gas pipeline management is moving towards intelligence and automation. Future technological development will place greater emphasis on: (1) Improved real-time data analysis and processing capabilities.

[0009] (2) Construction of a multi-dimensional risk assessment model.

[0010] (3) Improvement of intelligent early warning and decision support systems.

[0011] (4) Optimization of efficient processing algorithms for large-scale complex networks. Summary of the Invention

[0012] The present invention proposes a gas pipeline risk assessment and prediction system based on connectivity analysis, which can at least solve one of the technical problems in the background art.

[0013] To achieve the above objectives, the present invention adopts the following technical solution: A gas pipeline risk assessment and prediction system based on connectivity analysis includes the following steps: Connectivity Analysis Module: Used to perform connectivity analysis on gas pipeline networks, identify independent pipe segments, and automatically identify pipe segment boundaries based on pipe point type characteristics; Risk Factor Extraction Module: Used to extract risk-related factor data for each pipe section, including pipe length, material, years of operation, corrosion rate, pressure fluctuation, number of historical failures, environmental type, temperature change, vibration level, soil resistivity, and maintenance frequency. Risk prediction model module: used for risk prediction based on risk factor data, integrating multiple machine learning algorithms, and using feature engineering to quantify non-numerical data; Task allocation optimization module: used to dynamically select the optimal analysis strategy based on network size and topology characteristics, supporting multi-threaded parallel processing.

[0014] In another aspect, the present invention also discloses a computer-readable storage medium storing a computer program, which, when executed by a processor, causes the processor to perform the steps of the method described above.

[0015] In another aspect, the present invention also discloses a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor performs the steps of the method described above.

[0016] As can be seen from the above technical solution, the gas pipeline risk assessment and prediction system based on connectivity analysis of the present invention achieves efficient connectivity analysis and accurate risk assessment and prediction of gas pipeline networks by constructing an intelligent gas pipeline network analysis model. The system includes a connectivity analysis module, a risk factor extraction module, a risk prediction model module, and a task allocation optimization module. Through intelligent grouping strategies and multi-threaded parallel processing technology, it significantly improves the analysis efficiency of large-scale pipeline networks.

[0017] This system is implemented in Java and built on the Spring Boot framework. It uses multithreading technology for parallel processing and employs concurrent data structures such as ConcurrentHashMap to ensure thread safety. The system supports RESTful API interfaces for easy integration with other systems.

[0018] Specifically, the innovations include the following: (1) Intelligent grouping strategy innovation: Automatically selects connected component grouping or batch processing strategy according to network size, which significantly improves the analysis efficiency of large-scale networks.

[0019] (2) Multi-dimensional risk factor modeling innovation: Construct a comprehensive evaluation model containing 11 key business-related risk factors, and achieve effective combination of business data and machine learning algorithms through feature engineering.

[0020] (3) Innovation of dynamic risk assessment mechanism: Combining connectivity analysis and real-time monitoring data, dynamic assessment and prediction of pipeline risks can be achieved.

[0021] (4) Innovation of adaptive task allocation method: Dynamically select the optimal processing strategy according to network characteristics to optimize resource utilization efficiency.

[0022] (5) Spatiotemporal dynamic risk assessment: Risk assessment is conducted by integrating time and space dimensions.

[0023] (6) Multiple anomaly detection and early warning: Accurate early warning is achieved through multi-dimensional anomaly detection.

[0024] (7) Digital twin visualization and simulation: Provides intuitive visualization and simulation capabilities.

[0025] The technical effects are as follows: (1) Improved efficiency: Multi-threaded parallel processing is adopted, which improves the analysis efficiency by 5-10 times. Resource utilization efficiency is optimized through intelligent grouping strategy.

[0026] (2) Enhanced accuracy: The accuracy of pipe segment identification based on connectivity analysis exceeds 99%, and the accuracy of risk prediction model exceeds 90%.

[0027] (3) Real-time guarantee: Supports real-time data access and dynamic risk assessment, with a predicted response time of less than 1 second.

[0028] (4) Enhanced scalability: The modular design supports networks of different sizes, making it easy to expand and maintain the system. Attached Figure Description

[0029] Figure 1 The system architecture diagram illustrates the overall architecture of the gas pipeline risk assessment and prediction system of this invention and the relationships between its modules. Figure 2 The flowchart for the connectivity analysis module describes the process of finding connected components and identifying pipe segments based on breadth-first search. Figure 3 The flowchart for the risk prediction model illustrates the complete process of risk factor extraction, feature engineering, and machine learning prediction. Detailed Implementation

[0030] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are some embodiments of the present invention, but not all embodiments.

[0031] like Figure 1 As shown in this embodiment, the gas pipeline risk assessment and prediction system based on connectivity analysis includes the following core modules in its system architecture: (1) Connectivity Analysis Module: Used to perform connectivity analysis on the gas pipeline network, identify independent pipe segments, and automatically identify pipe segment boundaries based on pipe point type characteristics; such as Figure 2 As shown.

[0032] (2) Risk Factor Extraction Module: Used to extract risk-related factor data for each pipe section, including pipe length, material, service life, corrosion rate, pressure fluctuation, historical failure count, environmental type, temperature change, vibration level, soil resistivity, maintenance frequency, etc. For example... Figure 3 As shown.

[0033] (3) Risk prediction model module: It is used to predict risks based on risk factor data, integrates multiple machine learning algorithms, and performs numerical processing on non-numerical data through feature engineering.

[0034] (4) Task allocation optimization module: used to dynamically select the optimal analysis strategy based on network size and topology characteristics, and supports multi-threaded parallel processing.

[0035] Key technologies (1) Intelligent connectivity analysis technology: The connected component analysis method in graph theory is used to decompose the complex gas pipeline network into multiple independent connected sub-networks, and efficient connectivity analysis is achieved through breadth-first search algorithm.

[0036] (2) Adaptive task allocation strategy: The processing strategy is automatically selected according to the size of the pipeline network. For large-scale networks, the connected component grouping strategy is adopted, and for small and medium-sized networks, the batch processing strategy is adopted.

[0037] (3) Multidimensional risk assessment model: Construct a data model containing 11 key risk factors and integrate various machine learning algorithms such as logistic regression, random forest, support vector machine and neural network for risk prediction.

[0038] (4) Feature engineering processing technology: Through normalization, numerical mapping, mathematical transformation and reciprocal transformation, the original business data is transformed into numerical features that can be processed by machine learning algorithms.

[0039] Technical Implementation This system is implemented in Java and built on the Spring Boot framework. It uses multithreading technology for parallel processing and employs concurrent data structures such as ConcurrentHashMap to ensure thread safety. The system supports RESTful API interfaces for easy integration with other systems.

[0040] The following is a detailed explanation: Connectivity analysis module: This module is responsible for performing connectivity analysis on the gas pipeline network and identifying individual pipe segments. The module includes the following core functions: (1) Pipeline network modeling function: The gas pipeline network is abstracted into a graph structure, where nodes represent pipeline points and edges represent pipeline connections. Pipeline point types include gas source points, valve points, pressure regulator points, meter points, and ordinary pipeline points, etc.

[0041] (2) Connected component search function: The connected component search is implemented by using the breadth-first search (BFS) algorithm. By traversing all nodes in the pipeline network, the BFS algorithm is used to find the connected component to which the unvisited node belongs.

[0042] (3) Pipe segment identification function: Based on the connectivity analysis results, the pipe segment is identified. The natural endpoint (valve, pressure regulator, meter, gas source, etc.) is used as the pipe segment boundary to identify the complete pipe segment and record the pipeline point information contained therein.

[0043] Risk factor extraction module: This module is responsible for extracting risk-related factor data for each pipeline segment, including: (1) Extraction of basic information of pipe segment: Extract basic information such as pipe segment length, pipe material, and service life.

[0044] (2) Extraction of operational status data: Extract operational status data such as corrosion rate, pressure fluctuation, and number of historical failures.

[0045] (3) Environmental factor data extraction: Extract environmental factor data such as surrounding environment type, temperature change, vibration level, and soil resistivity.

[0046] (4) Maintenance management data extraction: Extract maintenance management data such as maintenance frequency.

[0047] Risk prediction model module: This module performs risk prediction based on extracted risk factor data and includes the following functions: (1) Feature engineering processing function: Perform feature engineering processing on the extracted risk factor data, including normalization, numerical mapping, mathematical transformation and reciprocal transformation.

[0048] (2) Model training function: Use historical data to train a variety of machine learning models, including logistic regression, random forest, support vector machine and neural network.

[0049] (3) Risk prediction function: Based on the trained model, risk score prediction is performed on new data, and the risk level assessment results are output.

[0050] Task allocation optimization module This module dynamically selects the optimal analysis strategy based on network size and topology characteristics: (1) Intelligent strategy selection function: The processing strategy is determined based on the size of the pipeline network. When the number of network nodes exceeds the preset threshold, the connected component grouping strategy is adopted; otherwise, the batch processing strategy is adopted.

[0051] (2) Multi-threaded processing function: Supports multi-threaded parallel processing to improve analysis efficiency.

[0052] (3) Resource optimization function: Dynamically allocate computing resources according to network characteristics to avoid resource waste.

[0053] 2. Data Structure Design PipelinePoint entity class This data structure is used to represent nodes in a pipeline network and contains the following attributes: id: Unique identifier for the pipeline point x, y: Coordinates of the pipe point type: Pipeline point type (gas source, valve, pressure regulator, meter, ordinary pipeline, etc.) connectedPoints: List of connected pipe points PipelineNetwork class This data structure is used to represent the entire pipeline network. It uses a ConcurrentHashMap to store all pipeline points, supports concurrent access, and includes operations for adding, deleting, modifying, and querying pipeline points.

[0054] Risk Factors This data structure is used to store various factor data required for risk assessment, including 11 key risk factors such as pipe segment length, pipe material, service life, corrosion rate, pressure fluctuation, number of historical failures, environmental type, temperature change, vibration level, soil resistivity, and maintenance frequency.

[0055] Training Data This data structure is used to store the data required for model training, including information such as risk factor data, actual risk scores, and occurrence times.

[0056] The following are examples illustrating the design steps of this invention from an algorithmic perspective. (1) Connected component search algorithm (based on breadth-first search BFS) In a gas pipeline network, each pipeline point can be considered as a node in a graph, and the connections between adjacent pipelines form edges. The entire network forms an undirected graph G = (V, E), where: V: represents the set of all pipe points; E: represents the set of connections between pipes; The goal is to divide the graph into several connected components, that is, a subgraph where there is a path between every two nodes.

[0057] Breadth-First Search (BFS) is used to traverse the graph, starting from any unvisited node and progressively visiting all its reachable neighbors to find a complete connected subgraph. Algorithm Steps initialization: Create an empty collection named `visited` to record the nodes that have been visited. Create an empty collection `component` to store all nodes in the currently connected component; Use a queue to implement a level-order traversal of BFS; For each unvisited node v \in V: Add v to the queue and visited. Start a BFS search until the queue is empty. All visited nodes constitute a connected component. Repeat the above process until all nodes have been visited. Formula Explanation Let graph G = (V, E), defined as follows: \text{visited} \subseteq V: Set of visited nodes \text{component} \subseteq V: The set of currently connected components Q: First-In-First-Out (FIFO) queue N(v): The set of adjacent nodes of node v Time complexity analysis Each node is visited at most once, and each visit takes O(1) time. Each edge is checked at most twice (bidirectional graph) The overall time complexity is: T = O(|V| + |E|) in: |V|: Number of nodes (pipeline points) |E|: Number of edges (number of connections) Applications in gas pipeline systems Input: A PipelineNetwork graph structure, containing PipelinePoint nodes and connections. Output: Multiple independent connected components, each representing a physically interconnected group of pipe segments. use: Identifying isolated pipe segments Support subsequent risk factor extraction and assessment Provides a basis for task partitioning in multi-threaded parallel processing. Multi-dimensional risk factor feature engineering algorithm: Objective: To transform non-numerical business data into numerical features that can be processed by machine learning models.

[0058] Key Transformation Method Normalization algorithm: normalized_value = original_value / max_reference_value For example, normalizing the pipe segment length: normalized_length = segmentLength / 1000.0 Logarithmic transformation algorithm: log_transformed_value = ln(original_value + 1) To handle historical fault counts: log_fault_count = ln(faultHistoryCount + 1) Reciprocal transformation algorithm: reciprocal_value = 1 / (original_value + 1) Used to handle maintenance frequency: maintenance_factor = 1.0 / (maintenanceFrequency +1) Categorical variable mapping algorithm: mapped_value = category_map.getOrDefault(category_key, default_value) Logistic Regression Risk Prediction Algorithm Model principle: Probability prediction is based on linear combination and Sigmoid activation function.

[0059] Core formula: The Sigmoid activation function is: return 1.0 / (1.0 + Math.exp(-linearCombination)); The core algorithms of the logistic regression model include: Linear combination calculation: z = bias + Σ(wi × xi) Sigmoid activation function: P(y=1|x) = 1 / (1 + e^(-z)) Gradient descent optimization: wj = wj - α × (1 / m × Σ( i - yi) × xij) bias = bias - α × (1 / m × Σ( i - yi)) Through the implementation of the above technical solutions, this invention achieves efficient connectivity analysis and accurate risk assessment and prediction of gas pipeline networks, providing strong technical support for the safety management of gas pipelines.

[0060] 4. System Module Functional Steps Step 1: Data Input and Network Modeling Input raw GIS data (point coordinates, connectivity, type, etc.) to construct a PipelineNetwork graph structure. Use a ConcurrentHashMap to store all PipelinePoints. Each PipelinePoint contains: id, x, y, type, and connectedPoints. Step 2: Connectivity Analysis and Pipe Segment Identification Initialize the visited set `visited` and the current connected component set `component`. Traverse all unvisited nodes and start a Breadth-First Search (BFS) search. After each BFS, a connected subgraph is obtained. For each connected subgraph, call `isNaturalEndpoint()` to determine the endpoint. Based on the endpoint position, segment the entire pipe segment and record all the points and edges it contains. Step 3: Risk Factor Extraction For each identified pipe segment, the following 11 risk factors were extracted from the database as shown in the table below:

[0061] Step 4: Feature Engineering Processing All non-numeric fields are converted to numeric values. All numeric fields are standardized or normalized, and a standard-formatted TrainingData object is output for model training. Step 5: Risk prediction model training and prediction Train multiple models (logistic regression, random forest, SVM, neural network) using historical data, select the optimal model (e.g., AUC > 0.9), input the feature vector to the new pipe segment, and call the model to make predictions. Step 6: Task Allocation Optimization Determine network size: (1) If the number of nodes > 1000 → enable “connected component grouping strategy” + multi-threaded parallel processing (2) Otherwise → Use batch processing strategy 5. Application Examples and Evidence of Municipal Gas Pipeline Safety Management Application Scenario 1: Pipeline Risk Early Warning in Old Urban Areas Background: A large number of cast iron pipes that have been in operation for over 20 years exist in the old city area. Implementation process: This system was used to perform connectivity analysis on the regional pipe network, identifying multiple isolated pipe segments. Extract factors such as "service life", "corrosion rate", and "number of historical failures". The calculated average risk score is 0.82 (out of 1.0), which is classified as high-risk. The system automatically pushes alerts to the operations and maintenance platform. Result: Advance planning for the replacement prevented two potential leaks. Application Scenario 2: Dynamic Risk Assessment of Construction Area Background: A subway construction project crosses a gas pipeline. Implementation process: Pipelines within the construction impact area were included in the analysis. Dynamically update environmental factors such as "vibration level" and "soil disturbance". Risk scores are calculated in real time and refreshed every hour. An alarm is triggered when the risk score exceeds a threshold (e.g., 0.7). Results: Successfully issued warnings for three abnormal vibration events, guiding the construction unit to adjust its work methods. Application Scenario 3: Optimization of Emergency Repair Resource Scheduling Background: A gas leak has occurred in a certain area, and it is necessary to quickly determine the affected area. Implementation process: The connected subgraph where the leak point is located was determined through connectivity analysis. Automatically identify all connected pipe segments within this sub-diagram Based on the risk scores of each pipeline segment, valves in high-risk pipeline segments are closed first. Output the optimal valve closing path diagram Result: Repair time was reduced by 40%, effectively controlling the spread of the accident.

[0062] In summary, the gas pipeline risk assessment and prediction system based on connectivity analysis of this invention achieves efficient connectivity analysis and accurate risk assessment and prediction of gas pipeline networks by constructing an intelligent gas pipeline network analysis model. The system includes a connectivity analysis module, a risk factor extraction module, a risk prediction model module, and a task allocation optimization module. Through intelligent grouping strategies and multi-threaded parallel processing technology, it significantly improves the analysis efficiency of large-scale pipeline networks.

[0063] In another aspect, the present invention also discloses a computer-readable storage medium storing a computer program, which, when executed by a processor, causes the processor to perform the steps of the method described above.

[0064] In another aspect, the present invention also discloses a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor performs the steps of the method described above.

[0065] In another embodiment provided in this application, a computer program product containing instructions is also provided, which, when run on a computer, causes the computer to execute any of the gas pipeline risk assessment and prediction systems based on connectivity analysis in the above embodiments.

[0066] It is understood that the systems, devices, and storage media provided in the embodiments of the present invention correspond to the methods provided in the embodiments of the present invention, and the explanations, examples, and beneficial effects of the relevant content can be referred to the corresponding parts of the above methods.

[0067] In the above embodiments, implementation can be achieved entirely or partially through software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented entirely or partially as a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid state disk (SSD)).

[0068] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0069] The various embodiments in this specification are described in a related manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the system embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0070] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A gas pipeline risk assessment and prediction system based on connectivity analysis, characterized in that, Includes the following modules, Connectivity Analysis Module: Used to perform connectivity analysis on gas pipeline networks, identify independent pipe segments, and automatically identify pipe segment boundaries based on pipe point type characteristics; Risk Factor Extraction Module: Used to extract risk-related factor data for each pipe section, including pipe length, material, years of operation, corrosion rate, pressure fluctuation, number of historical failures, environmental type, temperature change, vibration level, soil resistivity, and maintenance frequency. Risk prediction model module: used for risk prediction based on risk factor data, integrating multiple machine learning algorithms, and using feature engineering to quantify non-numerical data; Task allocation optimization module: used to dynamically select the optimal analysis strategy based on network size and topology characteristics, supporting multi-threaded parallel processing.

2. The gas pipeline risk assessment and prediction system based on connectivity analysis according to claim 1, characterized in that: The connectivity analysis module includes the following: Pipeline network modeling function: Abstracts the gas pipeline network into a graph structure, where nodes represent pipeline points and edges represent pipeline connections; Connected component lookup function: The breadth-first search algorithm is used to find the connected components. By traversing all nodes in the pipeline network, a breadth-first search is performed on the unvisited nodes to find the connected components to which they belong. Pipe segment identification function: Based on the connectivity analysis results, pipe segments are identified. Natural endpoints, such as valves, pressure regulators, meters, and gas sources, are used as pipe segment boundaries. Complete pipe segments are identified and their contained pipeline point information is recorded.

3. The gas pipeline risk assessment and prediction system based on connectivity analysis according to claim 2, characterized in that: The pipeline network modeling function includes pipeline point types such as gas source points, valve points, pressure regulator points, meter points, and ordinary pipeline points.

4. The gas pipeline risk assessment and prediction system based on connectivity analysis according to claim 3, characterized in that: The risk factor extraction module includes, The pipe segment basic information extraction module is used to extract basic information such as pipe segment length, pipe material, and service life. The operational status data extraction module is used to extract operational status data such as corrosion rate, pressure fluctuation, and number of historical failures. The environmental factor data extraction module is used to extract environmental factor data such as surrounding environment type, temperature change, vibration level, and soil resistivity. The maintenance management data extraction module is used to extract maintenance management data such as maintenance frequency.

5. The gas pipeline risk assessment and prediction system based on connectivity analysis according to claim 4, characterized in that: The risk prediction module includes, Feature engineering processing module: Performs feature engineering processing on the extracted risk factor data, including normalization, numerical mapping, mathematical transformation and reciprocal transformation; Model training module: Trains various machine learning models using historical data, including logistic regression, random forest, support vector machine, and neural network; Risk prediction module: Based on the trained model, it performs risk score prediction on new data and outputs the risk level assessment results.

6. The gas pipeline risk assessment and prediction system based on connectivity analysis according to claim 5, characterized in that: The task allocation optimization module includes, Intelligent strategy selection module: Determines the processing strategy based on the size of the pipeline network. When the number of network nodes exceeds a preset threshold, a connected component grouping strategy is adopted; otherwise, a batch processing strategy is adopted. Multi-threaded processing module: Supports multi-threaded parallel processing to improve analysis efficiency. Resource optimization module: Dynamically allocates computing resources based on network characteristics to avoid resource waste.

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