A Real-Time Pipeline Safety Early Warning Method and System

Abstract

This invention relates to computer data processing technology, and more particularly to a real-time pipeline safety early warning method and system. The method includes: performing computer data processing based on pipeline topology information to divide risk units and generate risk unit session configuration structures; performing automated feature extraction to generate real-time risk feature vectors; fusing real-time features with historical baseline features through machine learning fusion modeling to construct dual-time-scale fused feature vectors; using a preset intelligent model for reasoning and event determination, and generating a structured event log and resource adjustment parameters to drive periodic self-optimization of sampling configuration, model thresholds, and early warning strategies, thereby achieving closed-loop intelligent monitoring and early warning of pipeline auxiliary equipment status. This invention can effectively improve early warning accuracy, significantly reduce false alarms and missed alarms, and enhance system adaptability through a closed-loop learning mechanism, providing an efficient solution for the application of next-generation information network industries in related fields.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of computer data processing, and particularly to a real-time pipeline safety early warning method and system. BACKGROUND

[0002] In the technical field of computer data processing, especially in the field of intelligent monitoring and repair early warning of pipeline auxiliary equipment, the existing scheme of long-distance pipeline and station pipeline safety early warning usually carries out basic modeling around pipeline topological data and monitoring devices laid along the line, carries out state monitoring according to artificially divided monitoring sections and empirically set risk levels, and mainly adopts fixed section division, real-time monitoring data analysis of a single time scale and simple alarm threshold strategy, which has limitations such as coarse risk section division granularity, insufficient use of historical risk event distribution, and loose correlation between optical fiber laying information and pipeline topological data. The existing method mainly constructs a data acquisition and monitoring link through distributed optical fiber detection devices, pressure and flow monitoring devices and a monitoring center platform, and relies on periodic sampling and sliding window statistics to complete abnormal fluctuation recognition and alarm level determination. In the scene where the pipeline operating condition changes frequently, the environmental interference source is complex, and the noise of multi-source sensing data is superimposed, frequent alarm fluctuation and serious risk recognition lag are prone to occur, and it is difficult to meet the requirements of generating a risk unit session configuration structure based on pipeline topological data, historical risk event distribution data and optical fiber laying information along the line, and stably supporting the implementation of subsequent early warning processing link. For the joint processing of pipeline topological data, historical risk event distribution data, optical fiber laying information along the line and real-time collected sensing data in time and space dimensions, the existing technology generally lacks systematic fusion modeling means between real-time risk feature vectors and historical risk baseline statistics, lacks structured event flow account records and resource adjustment parameter statistics mechanisms between early warning evaluation results and disposal behavior records, and is difficult to form a continuous processing flow covering risk unit session configuration, real-time risk feature vector structure generation, double time scale fusion feature vector structure generation and event flow account and resource adjustment parameter structure output in the real-time pipeline safety early warning application scene, which leads to the difficulty in accurately depicting the risk evolution law based on the double time scale fusion feature vector structure and generating the event flow account and resource adjustment parameter structure, the insufficient matching degree between the pipeline safety early warning result and the disposal resource input, and the difficulty in objectively adjusting the early warning strategy and configuration parameter in the long-period operation process according to the accumulated event records. The above deficiencies are prone to cause adverse effects on pipeline operation safety, on-site inspection arrangement and inspection and maintenance resource overall arrangement in the background of long-distance pipeline transportation and complex working conditions along the line. SUMMARY

[0003] In order to solve the above technical problems, the present application provides a real-time pipeline safety early warning method, comprising:

[0004] Based on pipeline topology data, historical risk event distribution data, and fiber optic cable laying information, risk segment division, risk level labeling, fiber optic measurement point mapping, and time anchor point configuration are performed to generate a risk unit session configuration structure.

[0005] Based on the risk unit session configuration structure, segment matching, time window slicing and noise reduction are performed, and integrity verification, statistical feature calculation and suspected abnormal segment marking are executed to generate a real-time risk feature vector structure.

[0006] Based on the real-time risk feature vector structure, risk unit matching and working condition screening are performed, and time merging, feature statistical aggregation and dual time scale fusion modeling are performed to generate a dual time scale fusion feature vector structure.

[0007] Based on the dual-timescale fusion feature vector structure, model inference and event judgment processing are performed, and early warning strategy matching and resource adjustment parameter statistical processing are executed to generate event log and resource adjustment parameter structure.

[0008] Furthermore, pipeline topology data, historical risk event distribution data, and fiber optic cable laying information include:

[0009] The pipeline topology data includes the pipeline's starting and ending station numbers, pipe diameter, wall thickness, burial depth, material, elbow and tee locations, valve chamber and station locations, crossing point types, and spatial relationship information with key geographical features along the pipeline.

[0010] The historical risk event distribution data includes the occurrence time, mileage of the event, event type, event severity level, and handling time of historical leakage events, third-party construction disturbance events, geological disaster impact events, and equipment failure events;

[0011] The fiber optic cable laying information includes the fiber optic cable start and end point mileage, fiber optic cable laying path, channel number, measurement spacing, resolution level, turnaround point location, distance from the duct body, and relative orientation.

[0012] Furthermore, the process of risk segment division, risk level labeling, fiber optic measurement point mapping, and time anchor point configuration also includes:

[0013] The risk segmentation process includes determining the risk unit boundaries based on the pipeline topology and the distribution of historical risk events, and performing segmentation optimization based on spatial continuity and risk accumulation probability.

[0014] The risk level labeling process includes assigning risk levels based on the frequency and severity of historical events, and dynamically adjusting them considering operating conditions and environmental factors;

[0015] The fiber optic measurement point mapping process includes spatially associating fiber optic measurement points with risk units and establishing a many-to-one mapping relationship between measurement points and risk units.

[0016] The time anchor configuration process includes setting the timing baseline for data acquisition and processing, and defining the start and end anchor points of the sliding time window.

[0017] Furthermore, the process of segment matching, time window slicing, and noise reduction also includes:

[0018] The segment matching process includes matching real-time data streams with risk units in the risk unit session configuration structure and verifying data integrity and spatial coverage.

[0019] Time window slicing involves dividing the data stream using a sliding time window and setting window overlap and step size parameters;

[0020] Noise reduction processing includes using filtering algorithms and outlier removal methods to reduce data noise.

[0021] Furthermore, the process of performing integrity verification, statistical feature calculation, and suspected abnormal segment marking also includes:

[0022] The integrity verification process includes data quality checks, missing data imputation, and outlier marking, as well as statistical feature calculations.

[0023] The statistical feature calculation and processing includes calculating energy, frequency band distribution, duration of continuous fluctuations, and correlation features of adjacent windows, and extracting time-domain and frequency-domain statistics;

[0024] The suspected abnormal segment marking process includes marking segments based on statistical features that deviate from a threshold, and recording the start time and duration of the abnormal segments.

[0025] Furthermore, the process of risk unit matching and operating condition screening also includes:

[0026] The risk unit matching process includes aligning the real-time risk feature vector with the historical risk baseline feature vector by risk unit and verifying data consistency.

[0027] The operating condition screening process includes filtering relevant data based on pressure, flow rate, and temperature conditions, and excluding invalid operating conditions.

[0028] Furthermore, the process of performing time merging, feature statistical aggregation, and dual-timescale fusion modeling also includes:

[0029] Time merging involves aggregating data from different time scales into a unified timeline and resolving timestamp conflicts;

[0030] Feature statistical aggregation processing includes calculating mean, variance, and quantile statistics, and generating a baseline distribution model;

[0031] The dual-timescale fusion modeling process includes calculating the sequence features of the historical risk baseline feature vector, such as deviation amount, deviation rate, and deviation acceleration, and includes similarity calculation with historical event templates. It also records the baseline version number and modeling strategy identifier to support auditable multi-version baseline linkage.

[0032] Furthermore, the process of model reasoning and event determination also includes:

[0033] Model reasoning and event determination processing includes using intelligent models to determine event types and risk levels, and outputting the confidence level of the determination.

[0034] Furthermore, the process of matching early warning strategies and statistically processing resource adjustment parameters also includes:

[0035] The early warning strategy matching process includes selecting an early warning strategy based on the event type and risk level, and matching the notification channels and handling templates.

[0036] The resource adjustment parameter statistical processing includes generating an event log with a unified event primary key and event state machine. The event log contains event lifecycle records and sub-instruction entries. Based on the event log, resource adjustment parameters are statistically analyzed, including suggestions for sampling frequency adjustment, sliding time window length adjustment, risk level threshold adjustment, and notification channel adjustment, to drive the periodic self-optimization of sampling configuration, model threshold, and notification strategy.

[0037] Furthermore, a real-time pipeline safety early warning system, applied to any of the methods described above, includes:

[0038] The risk unit configuration unit is used to receive pipeline topology data, historical risk event distribution data and fiber optic laying information, complete risk section division, risk level labeling, fiber optic measurement point mapping and detection section aggregation, time anchor point setting and sliding time window parameter registration, and output the risk unit session configuration structure.

[0039] The real-time session acquisition unit is used to receive monitoring data from the distributed optical fiber detection unit according to the risk unit session configuration structure, complete segment matching and time window slicing, generate real-time session raw records, and submit the risk unit identifier field, time anchor point field and sensor data sequence field carried in the real-time session raw records to the back-end processing flow to form inputs for subsequent cleaning and feature calculation.

[0040] The historical risk baseline modeling unit is used to read historical records associated with the risk unit from the real-time risk feature vector structure and the historical database, construct historical risk candidate records, perform time merging and feature statistical aggregation on the normal operation fragments and archived event fragments fields in the historical risk candidate records, and generate historical risk baseline feature vectors.

[0041] The dual-timescale fusion processing unit is used to receive the real-time risk feature vector structure and the historical risk baseline feature vector, complete the corresponding relationship matching according to the risk unit identification field, perform deviation calculation, change trend analysis and event template similarity analysis on the statistical features within the short window and the statistical features of the long-term baseline, construct the dual-timescale fusion feature vector structure, record the baseline version information and modeling strategy identifier in the structure, and output the dual-timescale fusion feature vector structure for the early warning judgment link to call.

[0042] The early warning determination unit is used to obtain the dual-timescale fusion feature vector structure, and to infer and determine the events based on the fusion features of different risk units according to the preset early warning model, and generate an early warning assessment record. The early warning assessment record includes an event type field, a risk level field, and a spatial location field, and is stored in association with the corresponding risk unit identifier field.

[0043] The early warning strategy and response unit is used to read the event type field, risk level field, and spatial location field from the early warning assessment record, perform early warning strategy matching and response template selection according to the early warning strategy configuration and response template library, generate an early warning response instruction set, and send the early warning response instruction set to the monitoring and dispatch platform, on-site execution terminal, or mobile terminal to complete the issuance of audible and visual alarms, inspection arrangements, pressure reduction, or power outage control commands, and return the generated early warning response instruction set for use in event recording;

[0044] The event log and resource statistics unit is used to receive early warning assessment records and early warning handling instruction sets, establish event lifecycle records according to event primary keys, uniformly write event status changes, handling process records and feedback information, generate event log and resource adjustment parameter structures, and statistically analyze the event occurrence frequency, response time, false alarms and missed alarms of different risk units in the event log and resource adjustment parameter structures, and output the event log and resource adjustment parameter structures for reference in the pipeline safety early warning system parameter adjustment and resource allocation strategy revision stages.

[0045] The key innovations of this invention include:

[0046] (1) Based on the unified access to pipeline topology data, historical risk event distribution data and fiber optic laying information, a risk unit session configuration structure is generated through risk segment division, risk level labeling, fiber optic measurement point mapping and time anchor point configuration. Under the constraints of the risk unit session configuration structure, segment matching, time window slicing, noise reduction, integrity verification, statistical feature calculation and suspected abnormal segment marking are performed to generate a real-time risk feature vector structure with risk unit as the spatial scale, forming a top-down risk unit session configuration and real-time risk feature extraction link.

[0047] (2) Based on the real-time risk feature vector structure, historical data segments related to the target risk unit and its operating conditions are selected through risk unit matching and operating condition screening. Based on time merging and feature statistical aggregation, a historical risk baseline feature vector is constructed. Through dual-time scale fusion modeling, the real-time risk features in the short window and the historical risk baseline features in the long-term statistical sense are uniformly organized in the dual-time scale fusion feature vector structure, and the risk unit, operating conditions and time scale are integrated into a feature expression.

[0048] (3) Based on the dual-timescale fusion feature vector structure, the early warning output containing the event judgment result is generated through model reasoning and event judgment processing. With the cooperation of early warning strategy matching and resource adjustment parameter statistical processing, the event-related judgment information and early warning strategy execution information are uniformly solidified into the event log and resource adjustment parameter structure, and a continuous data statistics and backtracking link is established between the dual-timescale fusion feature vector structure and the event log and resource adjustment parameter structure.

[0049] The following are its main beneficial effects:

[0050] (1) Compared with the pipeline safety early warning scheme based on manual delineation of monitoring sections and experience-based setting of alarm thresholds in the background technology, this innovation constructs a risk unit session configuration structure and generates a real-time risk feature vector structure under the constraints of the risk unit session configuration structure. This enables pipeline topology data, historical risk event distribution data and fiber optic laying information to form a unified data organization unit in the process of risk section division, risk level labeling and real-time monitoring data processing. This is beneficial for completing section matching, time window slicing, noise reduction and integrity verification in the presence of multi-source noise interference and missing data collection. It also reduces the risk identification deviation caused by coarse section division granularity and insufficient data alignment, and provides stable input for subsequent risk unit matching and dual time scale fusion modeling based on the real-time risk feature vector structure.

[0051] (2) Compared with the existing technology, which usually only performs statistical analysis on real-time monitoring data at a single time scale and is difficult to systematically utilize the distribution of historical risk events, this innovation performs time merging and feature statistical aggregation based on risk unit matching and operating condition screening, and constructs a dual-time scale fusion feature vector structure through dual-time scale fusion modeling. This enables the real-time risk feature vector structure and the historical risk baseline features to form a fusion feature expression that can be directly called under the same risk unit dimension and operating condition. As a result, model reasoning and event judgment processing can run under the premise of simultaneously considering short-term abnormal changes and long-term operating characteristics, reducing the sensitivity of single-time scale analysis to operating condition fluctuations and seasonal changes. This is conducive to maintaining the stability and consistency of pipeline safety early warning judgment results under complex operating conditions and long-term operating scenarios.

[0052] (3) Compared with existing technologies that simply record alarm trigger facts and lack a scheme to integrate the early warning judgment results and disposal behavior into a structured event record and carry out resource adjustment parameter statistics, this innovation performs model reasoning and event judgment processing on the basis of dual-time-scale fused feature vector structure, and combines early warning strategy matching and resource adjustment parameter statistical processing to generate event log and resource adjustment parameter structure. This allows event-related judgment information and resource adjustment parameters to be recorded and statistically analyzed in the same data structure, which is convenient for analyzing the early warning strategy execution and resource input and use based on the event log and resource adjustment parameter structure during the long-term operation of the pipeline. It provides verifiable data basis for early warning strategy matching and resource adjustment parameter statistical processing, reduces the reliance of early warning strategies and configuration parameters on experience adjustment, and is conducive to forming a continuous processing flow of generating event log and resource adjustment parameter structure around dual-time-scale fused feature vector structure in real-time pipeline safety early warning application scenarios. Attached Figure Description

[0053] Figure 1 A flowchart illustrating a real-time pipeline safety early warning method provided in this application embodiment;

[0054] Figure 2 This is a structural block diagram of a real-time pipeline safety early warning system provided in an embodiment of this application. Detailed Implementation

[0055] Example 1: In the field of computer data processing technology, particularly in the field of intelligent monitoring and repair early warning of pipeline auxiliary equipment, referring to... Figure 1 This is a flowchart illustrating a real-time pipeline safety early warning method provided in an embodiment of the present invention. The process may include at least steps S100-S400:

[0056] S100, based on pipeline topology data, historical risk event distribution data and fiber optic laying information, performs risk segment division, risk level labeling, fiber optic measurement point mapping and time anchor point configuration processing to generate risk unit session configuration structure.

[0057] S200: Based on the risk unit session configuration structure, segment matching, time window slicing and noise reduction processing are performed, and integrity verification, statistical feature calculation and suspected abnormal segment marking processing are executed to generate a real-time risk feature vector structure.

[0058] S300: Based on the real-time risk feature vector structure, risk unit matching and working condition screening are performed, and time merging, feature statistical aggregation and dual time scale fusion modeling are performed to generate a dual time scale fusion feature vector structure.

[0059] S400 performs model inference and event judgment processing based on a dual-timescale fusion feature vector structure, and performs early warning strategy matching and resource adjustment parameter statistical processing to generate an event log and resource adjustment parameter structure.

[0060] Step S100 includes at least steps S110-S130:

[0061] S110. Obtain pipeline topology data, historical risk event distribution data and fiber optic cable laying information, perform risk section division and risk level labeling processing, and obtain the initial risk unit division table.

[0062] In one specific implementation, the pipeline safety early warning system includes a pipeline data management unit, a risk analysis unit, and a fiber optic layout management unit. The pipeline data management unit periodically synchronizes pipeline topology data through data interfaces with the design management system, Geographic Information System (GIS), and production scheduling system. This pipeline topology data includes at least the pipeline's starting and ending chainages, pipe diameter, wall thickness, burial depth, material, elbow and tee locations, valve chamber and station locations, crossing point types, and spatial relationship information with key geographic features along the pipeline route. Historical risk event distribution data is extracted from a historical event database by the risk analysis unit. This data includes fields such as the occurrence time, mileage of the event, event type, severity level, and response time for historical leakage events, third-party construction disturbance events, geological disaster impact events, and equipment failure events. Through unified coordinate system transformation and mileage calibration, event locations from different sources are uniformly mapped to the same pipeline mileage reference system. The fiber optic cable laying information is read from the fiber optic monitoring subsystem by the fiber optic layout management unit. This information includes fields such as the fiber optic start and end point mileage, fiber optic laying path, channel number, measurement spacing, resolution level, turnaround point location, distance from the pipeline body, and relative orientation. When the system initiates this step, the task scheduling module performs data synchronization operations based on configured trigger conditions. These trigger conditions can include the commissioning of a new pipeline section, the appearance of incremental records in the historical risk event database, changes to the fiber optic monitoring system's laying plan, or a recalculation command initiated by maintenance personnel in the configuration interface. After data acquisition, the pipeline data management unit first performs integrity checks on the pipeline topology data. For abnormal data such as missing key mileage points, abnormal burial depth, or discontinuous pipe diameter, the rule engine automatically marks them, and if necessary, includes the corresponding row records in the manual correction task list. For data that passes the integrity check, the system generates a continuous mileage sequence according to the pipeline route and constructs a spatial index for valve chambers, stations, crossing points, and geologically sensitive areas along the route. Based on uniformly processed historical risk event distribution data, the risk analysis unit statistically analyzes the frequency, temporal distribution characteristics, and spacing between adjacent events of different types of risk events within each mileage segment. Combined with population density around the pipeline, distribution of environmentally sensitive targets, and access status of important users, a multi-dimensional reference indicator set is formed for risk segmentation. On this basis, the risk analysis unit performs preliminary equidistant segmentation of the pipeline according to preset risk segmentation rules. Then, based on event density, event type combinations, and environmental sensitivity, the preliminary segments are further subdivided or merged. For example, in mileage segments with a high concentration of historical events and proximity to urban residential areas, the basic segment is further divided into multiple shorter risk units; in desert segments with sparse historical events and a relatively homogeneous surrounding environment, adjacent segments can be merged into longer risk units.During the above-mentioned risk classification process, the system records the starting mileage, ending mileage, length, type of nearby key facilities, and corresponding historical event statistical characteristics of each candidate risk unit, providing input for subsequent risk level labeling. Subsequently, the risk analysis unit calculates the risk level of each candidate risk unit according to the configured risk level labeling rules. These rules comprehensively consider factors such as historical event frequency, the severity of the highest event, sensitivity of the surrounding environment, pipeline operating pressure level, hazardousness of the transported medium, and fiber optic coverage quality. Risk score intervals are calculated using dimensionless methods and weighted summation. Risk units are then labeled into several levels, such as high-level, medium-level, and low-level, based on these risk score intervals. High-level risk units cover areas with dense historical events, high-incidence areas of geological disasters, and densely populated areas; medium-level risk units cover conventional risk areas; and low-level risk units cover areas with simple environments and very few historical events. After completing the risk level labeling, the risk analysis unit writes the number, starting mileage, ending mileage, spatial geometric representation, risk level, corresponding fiber optic coverage status, pipeline identifier, and metadata from the classification and labeling process of all risk units into the configuration database, forming the "Initial Risk Unit Classification Table." The table uses a structured data structure and supports the management of version number, effective time, and expiration time fields. Each re-division generates a new version record while retaining the old version for audit traceability. In this embodiment, the initial risk unit division table is used as the output field name for this step. Subsequently, S120 extracts the risk unit spatial range field from the initial risk unit division table to perform fiber optic measurement point mapping processing. At the same time, the risk level information and spatial range information are used in subsequent S200, S300, and S400 to configure the sampling frequency, modeling priority, and early warning strategy weight, thus forming a spatial and risk basic configuration link that runs through the entire process of this invention.

[0063] S120. Extract the risk unit spatial range field from the initial risk unit division table, perform fiber optic measurement point mapping and detection segment aggregation processing, and generate a risk unit-detection segment mapping table.

[0064] In this embodiment, triggered by the task scheduling module, the fiber optic detection mapping unit reads the latest effective version of the aforementioned initial risk unit partitioning table from the configuration database. Simultaneously, it accesses the fiber optic laying information registered in the fiber optic layout management unit, associating risk units with actual fiber optic measurement points through a unified spatial reference system. The risk unit spatial range field includes at least the risk unit's starting mileage, ending mileage, mileage bandwidth, corresponding pipeline geometric segment, buffer width, and geometric object identifiers for visualization. The fiber optic measurement point data in the fiber optic laying information includes the fiber optic channel number, measurement mileage location, measurement point spacing, sampling frequency capability, and horizontal and vertical distances from the pipeline centerline for each measurement point. The fiber optic detection mapping unit first performs coordinate transformation and spatial buffering operations on the risk unit spatial range field, converting the linear mileage segment of each risk unit into a geometric segment or strip area in the GIS environment, and generating a geometric index for spatial querying for each risk unit. Subsequently, the system uses the fiber optic measurement point mileage as the primary key to traverse all fiber optic measurement points. It compares the measurement point mileage with the start and end mileage of each risk unit. If a measurement point falls within the mileage range of a risk unit, it is assigned to the candidate measurement point set for that risk unit. For measurement points close to the risk unit boundary and with measurement errors or mileage calibration deviations, the system determines whether the measurement point can be assigned to multiple adjacent risk units based on a configured tolerance threshold. If necessary, it performs a secondary check by comparing the measurement point with the node position in the pipeline topology to reduce duplicate assignments. For engineering scenarios where multiple optical fibers are laid in parallel or where the fiber optic route deviates from the pipeline direction, the fiber optic detection and mapping unit also combines the distance and orientation fields from the fiber optic laying information to determine the spatial distance between the measurement point and the pipeline centerline. Measurement points that significantly deviate from the pipeline monitoring objective are filtered out of the candidate set, and the reasons for filtering are recorded to prevent irrelevant measurement points from entering the subsequent early warning process. After assigning measurement points, the system performs detection segment aggregation processing on the candidate measurement point set within each risk unit. Using fiber optic channel number and measurement point continuity as aggregation dimensions, it aggregates measurement point sequences with continuous mileage and similar sampling capabilities into a single detection segment. The system also calculates parameters for each detection segment, including the starting and ending measurement point mileage, coverage length, number of measurement points, and average sampling capability. For areas with missing measurement points or measurement point spacing significantly exceeding design values, the system marks the length and location of the detection blind zone during aggregation and writes this information into the attribute fields of the detection segment for reference by the subsequent early warning model when assessing risk credibility. In one engineering embodiment, in a mountainous section of an oil pipeline, the fiber optic detection mapping unit, through the above mapping and aggregation processing, aggregates densely packed measurement points within a high-risk unit traversing a landslide into multiple short detection segments. Simultaneously, it marks sparsely populated areas in the shaded region of the mountain as detection blind zones, providing input for subsequent acquisition strategy adjustments and early warning weight settings.After aggregation, the fiber optic detection mapping unit generates a "Risk Unit-Detection Segment Mapping Table" using the risk unit number as the primary key. This table records a list of detection segments corresponding to each risk unit, including the channel number, start and end mileage, number of measurement points, sampling capacity, detection blind zone information, and the associated pipeline number for each detection segment. A version identifier and effective time period are assigned to the entire mapping table to facilitate version management and auditing during changes to fiber optic laying schemes or pipeline rerouting. This Risk Unit-Detection Segment Mapping Table serves as the output field name for this step. It is subsequently directly obtained by S130 for time anchor point configuration and serves as the basis for segment matching in S210, guiding the generation of real-time session raw records. It is also used as static constraint information between risk units and monitoring capabilities in S300 and S400.

[0065] S130. Perform time anchor point configuration and sliding time window parameter registration on the risk unit-detection section mapping table to generate the risk unit session configuration structure.

[0066] In this step, the session configuration management unit reads the currently active version of the aforementioned risk unit-detection section mapping table from the configuration database. Simultaneously, it reads the risk level field from the initial risk unit partitioning table and the typical operating condition information provided by the operating condition management unit. Based on different risk levels, detection section monitoring capabilities, and pipeline operation modes, it constructs an appropriate time anchor plan and sliding time window parameters for each risk unit. The session configuration management unit first determines the session granularity for each risk unit and its subordinate detection sections based on the risk level and historical event characteristics. For risk units with higher risk levels, the session granularity is set more finely, and the time anchor interval is shorter, so that the system can form more intensive monitoring sessions in high-risk sections. For risk units with lower risk levels, the session granularity can be set relatively coarser, reducing the real-time computation load by increasing the time anchor interval and window length. In this invention, time anchors are used to identify the reference time for the start and end of real-time sessions. The session configuration management unit selects the hour, half-hour, or time aligned with the operating condition switching time as the base anchor based on the synchronization results between the system master clock and the acquisition system clock. Then, according to the session granularity set by each risk unit, a series of time anchor sequence is generated near the base anchor, and each time anchor is associated with the corresponding risk unit and detection section. Considering the characteristics of pipeline operation, such as batch transportation and significant day-night temperature differences, the session configuration management unit also makes condition-sensitive adjustments to some time anchors based on the operating condition switching plan provided by the operating condition management unit. For example, in areas where leaks are difficult to identify through background noise at night, the density of nighttime time anchors is appropriately increased, thereby forming a session configuration adapted to the differences in noise background at different times.

[0067] After completing the time anchor plan construction, the session configuration management unit calculates sliding time window parameters for each "risk unit - detection section" combination. These parameters include at least the window length, window step size, window overlap ratio, window alignment strategy, and anomaly trigger extension length. The window length is set by comprehensively considering factors such as the flow velocity of the medium in the pipeline, the minimum observation time required by the early warning model, and the fiber optic sampling period. This ensures that a single window contains sufficient observation data to reflect the event's evolution without causing excessive response delay due to an excessively long window. The window step size controls the time interval between adjacent windows; when the step size is less than the window length, overlapping windows are formed, which helps to fine-tune the event's start and end boundaries. The window overlap ratio field describes the degree of window overlap, and the window alignment strategy field describes the correspondence between the time anchor and the window's starting point. In some implementations, the window's starting point is aligned with the time anchor; in others, a rolling method is used to gradually move forward with the sampling clock. The anomaly trigger extension length field describes whether to extend the window beyond the current boundary for a certain duration when a sudden, significant anomaly occurs during real-time monitoring, capturing the transition data before and after the anomaly. The calculation of the above parameters is automatically executed by the parameter calculation subunit in the session configuration management unit. During the parameter calculation process, if it is found that the fiber sampling capability of certain detection segments cannot support the set window length and step size combination, the system will record the parameter conflict and adjust the window configuration through parameter rollback rules, such as increasing the window length or step size, until the parameter combination meets the sampling capability constraints. For records marked with detection blind spots in the risk unit-detection segment mapping table, the session configuration management unit will write the blind spot location into the additional description field of the window configuration when generating the sliding time window parameters, indicating that the subsequent real-time session generation process will not establish a normal session in this area, but will instead use interpolation or event masking strategies. After generating the aforementioned time anchor plan and sliding time window parameters, the session configuration management unit aggregates all configuration fields related to risk units, detection segments, time anchors, and windows to form a "risk unit session configuration structure." This risk unit session configuration structure uses a session-oriented configuration data structure. Each record includes at least the following fields: risk unit number, risk level, list of detection segment numbers, time anchor sequence description, window length, window step size, window overlap ratio, window alignment strategy, anomaly trigger extension length, configuration version number, effective time, and expiration time. The session configuration management unit writes the risk unit session configuration structure into the configuration database and simultaneously pushes it to the real-time stream processing engine through the configuration distribution interface. When executing S210, the real-time stream processing engine reads this structure and performs segment matching and time window slicing on the monitoring data stream from the distributed fiber optic detection unit according to the time anchor and sliding time window parameters described therein, generating the original real-time session record.On the other hand, the risk level and window configuration fields in the risk unit session configuration structure are also referenced by the risk baseline modeling unit in S300 and the early warning strategy engine in S400 to distinguish the modeling strategies and early warning strategy priorities of different risk units. To support long-term operation and the auditability of configuration evolution, the session configuration management unit generates a new configuration version for each update, marks the old version of the risk unit session configuration structure as a historical version, and writes the version switch time into the event log record, enabling those skilled in the art to reconstruct the monitoring session construction logic at the time of the event based on the configuration version at that time. The technical effect of this step can be summarized as follows: By uniformly constructing the risk unit session configuration structure at the risk unit level, the pipeline space division, fiber optic detection section layout, and time anchor points and sliding time window parameters are combined into a set of session configuration data that can be directly consumed by the real-time engine. This allows real-time monitoring data to be organized and sliced ​​according to the granularity of the risk unit session from the initial acquisition stage. Compared to the existing sensor-centric configuration method, this step forms a unified spatiotemporal management foundation for risk units, providing a coherent and consistent context for subsequent dual-timescale feature construction and intelligent early warning decision-making.

[0068] Step S200 includes at least steps S210-S230:

[0069] S210. Obtain the risk unit session configuration structure, perform segment matching and time window slicing processing to obtain the real-time session raw record;

[0070] In one specific implementation, the pipeline safety early warning system deploys a flow data access unit, a session scheduling unit, and a section matching unit. The flow data access unit continuously receives vibration signals, temperature signals, and environmental interference signals from distributed optical fiber detection units along the pipeline via a communication network to form a monitoring data stream. The session scheduling unit loads the risk unit session configuration structure generated in S130 from the configuration database. The risk unit session configuration structure records fields such as risk unit number, risk level, corresponding detection section number list, time anchor sequence, sliding time window length, time window step size, window overlap ratio, window alignment strategy, and abnormal trigger extension length. During system operation, the task scheduling module triggers the execution of this step according to preset rules. Triggering conditions include the accumulation of a specified number of new samples in the monitoring data stream, the current time entering a new time anchor interval, or maintenance personnel initiating an offline recalculation task in the monitoring platform. When any condition is met, the session scheduling unit reads the currently effective version of the risk unit session configuration structure from the configuration database and completes the loading of the configuration version in the internal cache.

[0071] Specifically, the segment matching unit first parses each data frame from the distributed optical fiber detection unit into a data record in an internally unified format. This data record includes at least the optical fiber channel number, measurement time, measurement point mileage, original vibration amplitude sequence, original temperature sequence, and equipment status identifier fields. The segment matching unit uses the risk unit-detection segment mapping information in the risk unit session configuration structure to find the detection segment and risk unit to which each data record belongs. By comparing the measurement point mileage with the start and end mileage of the detection segment, the data record is bound to the corresponding detection segment. When the data record mileage is located at the boundary of multiple detection segments, the segment matching unit selects the attribution relationship based on the blind zone marker and priority marker in the detection segment definition, and simultaneously writes a boundary marker in the additional fields to facilitate the subsequent cleaning and feature calculation modules in identifying boundary data. In a long-distance oil and gas pipeline engineering scenario, there is local overlap in the optical fiber laying in mountainous bends. The segment matching unit, through the detection segment priority field in the configuration, assigns data records in overlapping areas to detection segments with higher monitoring capabilities, avoiding duplicate processing.

[0072] After completing the segment matching, the session scheduling unit performs time window slicing processing for each risk unit and probe segment combination, based on the time anchor sequence and sliding time window parameters in the risk unit session configuration structure. The session scheduling unit first aligns the current system clock with the time anchor sequence in the risk unit session configuration structure, assigning newly accessed data records to the corresponding time anchor interval according to their measurement time. Then, within each risk unit, a series of sliding time windows are constructed backward from the latest time anchor point according to the window length and window step size. Each sliding time window covers a continuous time range, with the ends of the time range determined by both the time anchor point and the window length. When a risk unit is configured to have overlapping windows, the session scheduling unit uses the window overlap ratio field in the configuration to control the offset duration of the new window start point when constructing adjacent time windows. This results in some sampled data appearing repeatedly in two windows, providing initial support for subsequent detailed analysis of event start and end boundaries.

[0073] For short-term strong interference events occurring during data acquisition, this embodiment sets up anomaly trigger detection logic in the monitoring data stream parsing stage. When the vibration amplitude or temperature change of a single measuring point exceeds the threshold, an extended time window is temporarily generated based on the current risk unit session configuration structure. The original window is extended by a fixed length at both ends to accommodate the transitional data before and after the anomaly. This extension strategy is recorded in the time window metadata field for reference by subsequent modules. In actual engineering deployments, for urban road crossings, short-term high-amplitude vibrations are generated when vehicles pass through in concentrated traffic. The system uses this extended time window mechanism to completely preserve the background data before and after the interference, facilitating the subsequent differentiation between construction excavation events and regular vehicle interference.

[0074] After the time window is constructed, the session scheduling unit calls the session identifier generation logic to assign a unique session identifier to each risk unit, detection segment, and time window combination. Subsequently, the segment matching unit aggregates the data records belonging to that time window based on the session identifier, forming a structured session-level data record. This record, in addition to storing the original monitoring data sequence, also includes fields such as risk unit number, detection segment number, session start time, session end time, time anchor point identifier, window length, window step size, window overlap ratio, and configuration version number. All session-level data records are streamed into the buffer queue inside the stream processing engine and simultaneously written to the raw layer of the time-series database or real-time database, forming the "real-time session raw record." The real-time session raw record, as the output field name of this step, is used as the input data source by the cleaning and processing unit in subsequent S220. Noise reduction and integrity verification operations are performed on the sensor data sequence within it. Simultaneously, the session identifier, risk unit number, and time anchor point fields in the real-time session raw record are used in S300 to construct an index of historical risk candidate records, thus forming an important intermediate product of the risk unit session-based acquisition link in this invention.

[0075] S220. Extract the risk unit identifier field, time anchor point field and sensor data sequence field from the real-time session raw record, perform noise reduction and integrity verification processing, and generate a real-time session clean record.

[0076] In this embodiment, the real-time cleaning unit is deployed inside the stream processing engine and is responsible for noise reduction and data integrity verification of the real-time session raw records output by S210, realizing automated preprocessing of fiber optic monitoring data in complex field environments. The real-time cleaning unit starts the cleaning process under the trigger of the task scheduling module. The triggering conditions include the number of real-time session raw records in the buffer queue reaching a preset batch threshold, the time elapsed since the last cleaning operation exceeding a preset time threshold, or the early warning model requesting a new batch of cleaned data. When the triggering conditions are met, the real-time cleaning unit reads a batch of real-time session raw records from the buffer queue and parses the risk unit number, time anchor point identifier, session start time, session end time, and original sensor data sequence fields into the internal data structure.

[0077] Specifically, the noise reduction subunit first performs a channel status check on the sensor data sequence within each real-time session's original record. Using the device status identifier field and the embedded self-test result field, it determines whether there are device-level issues such as fiber breakage, saturation, or abnormal gain in the current session. Session records with obvious device faults are marked as device faulty and processed separately in subsequent calculations. For session records with normal device status, the noise reduction subunit selects an appropriate filtering strategy based on the risk level and detection segment characteristics recorded in the risk unit session configuration structure. For example, a noise suppression process that preserves high-frequency characteristics is used for high-risk units, while a smoothing process that suppresses high-frequency interference is used for low-risk units. The filtering strategy is implemented in a multi-level cascade manner. First, a coarse trend removal process is performed to eliminate baseline drift introduced by slow temperature changes. Then, local anomaly point replacement processing is performed, interpolating and replacing isolated points that significantly deviate from surrounding samples. Finally, narrowband noise suppression processing is performed to suppress typical noise sources such as power frequency interference and communication noise. During the cleaning process, the noise reduction subunit records the parameters of each filtering action and the sample location affected, writing relevant information into the session-level cleaning metadata field for easy subsequent debugging and traceability.

[0078] Furthermore, the integrity verification subunit performs a sampling integrity check on the noise-reduced sensor data sequence. Based on the time anchor identifier, session start time, session end time, and expected sampling period, it calculates the number of sampling points that the current session should include. It compares the actual number of sampling points with the expected number to identify sampling loss or duplicate sampling. For session records with detected consecutive missing segments, the integrity verification subunit inserts a missing segment marker field into the session record, recording the start position, length, and cause of the missing segment. If necessary, it marks the time period corresponding to the missing segment as not participating in subsequent statistical feature calculations. For cases with short-term abnormal sampling intervals but where the overall data volume meets the minimum requirements, the integrity verification subunit can call an optional interpolation strategy to reconstruct the time axis of locally discontinuous samples, remapping irregular interval samples to a regular sampling time axis to improve the consistency of subsequent feature calculations. This interpolation strategy can be enabled or disabled in the configuration, depending on the actual engineering needs.

[0079] In a specific engineering embodiment, for the risk unit traversing an urban overpass section, the session scheduling unit generates a real-time session raw record containing vehicle traffic and environmental noise in stage S210. The real-time cleaning unit identifies a large number of short-period, high-amplitude interferences in stage S220. These interferences correspond to the vibrations of vehicles passing over the bridge. The noise reduction subunit distinguishes these interferences from continuous vibration patterns through trend removal and local anomaly replacement processing, while retaining low-frequency continuous vibrations that may be related to construction and excavation. In the same embodiment, the integrity verification subunit detects fiber optic channel breaks during a certain period. Based on the difference between the number of sampling points and the expected number, and the device status identifier field, it determines that the session record data is incomplete, marks the corresponding session as an abnormal device state, and simultaneously marks the record in the event log for subsequent maintenance personnel to inspect the equipment.

[0080] After noise reduction and integrity verification, the real-time cleaning unit generates a new structured record for each session record, retaining the risk unit number, time anchor identifier, session start time, session end time, and processed sensor data sequence. It also adds equipment anomaly markers, missing segment markers, interpolation processing markers, and cleaning parameter metadata fields. These records constitute the "real-time session cleaning record." The real-time session cleaning record is written to the cleaning layer of the real-time database via a stream processing engine and cached in memory for use by the subsequent feature calculation module. This real-time session cleaning record serves as the output field name for this step. In subsequent step S230, it is used as direct input by the feature calculation unit for statistical feature calculation and suspected anomaly segment marking operations. Simultaneously, the marker fields in the real-time session cleaning record are used in step S300 as the sample selection basis when constructing the historical risk baseline feature vector, forming a crucial transition layer from raw monitoring data to feature modeling.

[0081] S230. Perform statistical feature calculation and suspected abnormal segment marking on the real-time session cleaning record to generate a real-time risk feature vector structure.

[0082] In this embodiment, the feature calculation unit and the anomaly marking unit together constitute the real-time analysis layer. The layer receives the real-time session cleaning records output by S220 and converts the session-level sensor data into feature vector representations that can be directly consumed by subsequent intelligent models through unified feature construction rules. Triggered by the task scheduling module, the feature calculation unit reads real-time session cleaning records in batches from the real-time database cleaning layer or memory cache. First, it groups the records according to risk unit number and time anchor identifier, forming a time series of consecutive session records within the same risk unit, thus creating an input structure that facilitates subsequent multi-session correlation analysis.

[0083] At the single-session granularity, the feature calculation unit constructs multiple statistical features for the sensor data sequences in the real-time session cleaning record. The statistical feature set includes time-domain amplitude statistical features, trend features, and local fluctuation features. The time-domain amplitude statistical features describe the overall vibration or temperature level of the current session by calculating indicators such as average amplitude, maximum amplitude, minimum amplitude, amplitude distribution quantile, and amplitude standard deviation within the session. The trend features characterize the slow strengthening or weakening trend within the session by calculating the difference between the average values ​​at the beginning and end of the session, the proportion of monotonically rising or falling segments, and multi-segment difference values. The local fluctuation features characterize the significance of short-term impacts and periodic vibrations by statistically analyzing the number of instantaneous impacts exceeding a set amplitude threshold, the distribution characteristics of differences between adjacent samples, and the duration of short-term oscillations. For specific event-sensitive frequency bands predefined in the configuration, the feature calculation unit can also call the frequency band energy analysis sub-process in the current step to project the sensing data sequence onto the corresponding frequency band and calculate the energy ratio and time proportion of the frequency band in order to enhance the ability to identify specific damage modes. This frequency band energy analysis step is a preferred extended action and can be enabled as needed during engineering deployment.

[0084] After obtaining the aforementioned statistical characteristics, the anomaly marking unit calculates a suspected anomaly score for each real-time session cleaning record based on the risk level and historical experience rules recorded in the risk unit session configuration structure. The scoring rules comprehensively utilize multi-dimensional features. For example, if the number of short-term impacts in a session is significantly higher than the statistical level of recent sessions in the same risk unit, or if a continuously enhancing pattern appears in the trend characteristics and the magnitude exceeds a threshold, the anomaly marking unit marks the session as a suspected anomaly session and adds a suspected anomaly marking field to the real-time session cleaning record. For session records with missing test fragments, the anomaly marking unit reduces their weight during the scoring evaluation or requires the features to meet multiple conditions simultaneously before assigning an anomaly mark, thereby reducing the interference of equipment defects on anomaly judgment. In some implementations, the anomaly marking unit also supports multi-session correlation analysis. When sessions corresponding to multiple consecutive time anchor points show a gradually deteriorating trend in the same risk unit, even if the characteristics of a single session do not reach the anomaly threshold, the entire group of sessions will be marked as a candidate set of suspected anomaly events through trend aggregation logic.

[0085] In one engineering embodiment, for a high-risk unit traversing a riverbed area, the feature calculation unit observes a slow increase in the average vibration amplitude of multiple consecutive sessions in stage S230, along with a significant increase in the number of short-term impacts and local fluctuation characteristics. The anomaly marking unit uses trend aggregation logic to mark these sessions as a suspected anomalous session group, recording the group identifier and associated session number in the suspected anomalous marking field. Subsequently, in stages S300 and S400, the historical risk baseline modeling unit and the intelligent early warning model utilize this group of suspected anomalous sessions as key candidate events, combining historical risk baseline feature vectors and historical event templates for more refined classification and risk level determination, thereby achieving early identification of the risk of pipeline suspension caused by riverbed scouring.

[0086] After completing the single-session feature construction and suspected anomaly marking, the feature calculation unit packages the statistical features and marking information corresponding to each real-time session cleaning record into a structured feature vector. The feature vector includes at least the risk unit number, time anchor identifier, session start time, session end time, time domain amplitude statistical feature field, trend feature field, local fluctuation feature field, frequency band energy feature field (when frequency band analysis is enabled), equipment anomaly marking field, missing segment marking field, and suspected anomaly marking field. All feature vectors constitute a "real-time risk feature vector structure," which is stored in the feature layer of the real-time database and simultaneously provided to the S300's historical risk candidate record construction unit via a memory interface. The real-time risk feature vector structure is the output field name of this step. It is used as direct input for risk unit matching and operating condition screening in subsequent step S310, providing a unified real-time feature representation for the construction of historical risk baseline feature vectors. In this invention, the closed loop of the entire "real-time session-based acquisition and real-time risk feature generation" step S200 is formed by sequentially connecting the original real-time session records output from S210, the real-time session cleansing records output from S220, and the real-time risk feature vector structure output from this step. It also forms a clear data interface with the historical modeling and intelligent early warning processes in S300 and S400. The technical effect of this step can be summarized as follows: By performing multi-dimensional statistical feature calculations and marking of suspected abnormal segments on real-time monitoring data at the session level, complex and noisy fiber optic monitoring data is transformed into a structured real-time risk feature vector structure. It retains a detailed description of trends and local impacts at the feature level. Compared to existing solutions that rely solely on simple threshold judgments, this step provides high-quality input that can be directly utilized by subsequent dual-timescale modeling and intelligent early warning models.

[0087] Step S300 includes at least steps S310-S330:

[0088] S310. Obtain the real-time risk feature vector structure, perform risk unit matching and working condition screening to obtain historical risk candidate records.

[0089] In one specific implementation, the historical data retrieval unit is deployed in the historical analysis subsystem of the pipeline safety early warning system. It retrieves the real-time risk feature vector structure generated by S230 from the real-time database feature layer via an internal bus. This real-time risk feature vector structure, written by the feature calculation unit, includes risk unit number, time anchor point identifier, session start time, session end time, time domain amplitude statistical feature field, change trend feature field, local fluctuation feature field, frequency band energy feature field, equipment anomaly marker field, missing segment marker field, and suspected anomaly marker field. The historical data retrieval unit initiates this step under the trigger of the task scheduling module. Triggering conditions can include the number of accumulated real-time risk feature vectors in the real-time database reaching a preset batch threshold, a historical analysis task reaching its scheduled execution time, or maintenance personnel initiating a baseline reconstruction task for a specific pipeline segment through the monitoring platform. When any triggering condition is met, the historical data retrieval unit groups the real-time risk feature vector structure according to the risk unit number, sorts the feature records belonging to the same risk unit by time, forming a risk unit feature time sequence, providing input for subsequent alignment with historical operation records.

[0090] Specifically, the historical data retrieval unit first loads the session configuration version information of the corresponding risk unit from the risk unit session configuration structure based on the risk unit number and time anchor identifier, obtaining configuration fields such as window length, window step size, window overlap ratio, and abnormal trigger extension length used by the risk unit in different time periods. The historical data retrieval unit compares the session start time and session end time in the real-time risk feature vector structure with the effective time and expiration time of the configuration version, matching the correct session configuration version number for each feature record, so that the meaning and scale of the feature values ​​can be understood based on the configuration environment at that time during the subsequent screening process. In addition, the historical data retrieval unit accesses the operating condition archives provided by the operating condition management unit. The operating condition archives record the operating conditions of the pipeline in different time periods, such as the operating pressure level, the state of the transported medium, the change of transport direction, the start and stop plans, and the state of the surrounding environment. By aligning the time axis, the time periods in the operating condition archives are mapped to the time range of the risk unit feature time series. An operating condition identifier field and an operating condition classification field are added to each feature record to form a dataset to be screened containing real-time features and operating condition information.

[0091] Furthermore, the historical data retrieval unit performs risk unit matching and operating condition filtering based on a pre-configured historical modeling strategy. For each risk unit, the historical data retrieval unit groups the corresponding feature records according to the operating condition classification provided by the operating condition management unit. For example, in the long-distance oil and gas pipeline scenario, subsequences of conventional steady-state transportation operating conditions, start-stop switching operating conditions, and low-flow maintenance operating conditions can be constructed separately. In each group of operating condition subsequences, the historical data retrieval unit applies time range filtering rules to retain only feature records located within a set historical analysis interval. This historical analysis interval can be specified by the system administrator in the configuration interface, such as the most recent year or several recent operating cycles. For feature records with equipment anomaly markers or missing test segment markers, the historical data retrieval unit decides whether to directly remove or downgrade the markers according to the configuration rules. For example, equipment anomaly records are usually removed during the baseline construction stage, while some records containing equipment anomalies can be retained during the event template construction stage and processed differently in subsequent steps.

[0092] In one engineering embodiment, for a high-risk pipe section traversing a mountain tunnel, the historical data retrieval unit selects feature records from the past two years of the high-risk unit from the real-time risk feature vector structure in S310. The operating condition management unit identifies two types of operating conditions: routine transportation and high groundwater levels during the rainy season, and constructs feature time series for each type. For the high groundwater level during the rainy season, the system also retrieves time periods from the historical event database where leakage events occurred, using these as key intervals for subsequent historical risk candidate records. Finally, the historical data retrieval unit encapsulates the set of feature records filtered by risk unit, operating condition, and time range into "historical risk candidate records." Each record contains information such as the risk unit number, operating condition classification, time anchor identifier, feature field set, and source version number. The historical risk candidate record serves as the output field name for this step. In subsequent step S320, it is used as input by the historical baseline modeling unit to extract normal operation segments and archived event segments. In step S300, it acts as an intermediate bridge connecting the real-time risk feature vector structure and the historical risk baseline feature vector, while providing an auditable historical candidate sample pool for the intelligent early warning analysis layer in S400.

[0093] S320. Extract the normal operation segment and archived event segment fields from the historical risk candidate records, perform time merging and feature statistical aggregation processing, and generate the historical risk baseline feature vector.

[0094] In this embodiment, the historical baseline modeling unit is deployed in the historical analysis subsystem and is responsible for constructing baseline features for the historical risk candidate records output by S310. The historical baseline modeling unit initiates this step under the trigger of the task scheduling module. Triggering conditions include the number of historical risk candidate record samples corresponding to a certain risk unit reaching the baseline update threshold, the operating condition management unit reporting the appearance of a new operating condition type, or the event log database adding a new important event type. The historical baseline modeling unit first reads the risk unit number, operating condition classification, time anchor identifier, and feature field set from the historical risk candidate records. Combining this with the event log database and the historical event template library, it categorizes and labels each candidate record with normal operation segments and archived event segments.

[0095] Specifically, the historical baseline modeling unit accesses the event log database, which is continuously written to by the S430 during real-time alerts. This database records fields such as the event primary key, event type, risk level, spatial location, handling process, and closure time for each alert assessment record. The historical baseline modeling unit compares the session time range of each record in the historical risk candidate records with the time range of closed events in the event log using a time interval matching method. When the session time range overlaps with the occurrence time and preceding / following buffer periods of an event, the session record is marked as an archived event segment, and the event primary key and event type fields are appended to the candidate record. For candidate records that do not overlap with any event time period and whose suspected anomaly marker field is unmarked, the historical baseline modeling unit categorizes them into the normal operation segment set. For records whose suspected anomaly marker field is marked but does not correspond to an actual event, they can be classified into a separate "suspected anomaly unconfirmed segment" according to configuration for subsequent manual review or use as a soft label sample. Through this cross-referencing mechanism with the event log, the historical baseline modeling unit clearly distinguishes between the true normal operation status and the confirmed risk event status in the historical risk candidate records.

[0096] After completing the segment classification, the historical baseline modeling unit performs time merging processing on the normal operation segment set for each risk unit and operating condition classification combination. During the time merging process, the historical baseline modeling unit scans adjacent normal operation sessions in chronological order. For sessions that are temporally adjacent and whose characteristic fluctuation amplitude is within a set range, they are merged into longer normal operation segment intervals, thus forming continuous normal operation segments on the time axis. For areas with short-term missing data or slight abnormal fluctuations, they can be included in the merged normal operation segments based on the missing data segment markers and characteristic fluctuation features, without disrupting the overall trend. These segments are then assigned lower weights to facilitate subsequent statistical processing using a hierarchical weighted approach. Similarly, for archived event segments, the historical baseline modeling unit merges the corresponding session records according to the event primary key to form event evolution time segments. These segments record three time sub-intervals: the normal characteristics before the event, the characteristics of the event development stage, and the characteristics of the event recovery stage, which serve as the basis for event template construction.

[0097] The historical baseline modeling unit performs feature statistical aggregation on the merged set of normal operation segments. Under each risk unit and operating condition combination, the historical baseline modeling unit statistically aggregates the feature fields of all sessions within the merged segment. It describes the vibration or temperature level range under normal conditions by calculating the central tendency and dispersion of the time-domain amplitude features; it describes the typical pattern of trend changes under normal conditions by calculating the average rate of change and frequency of change patterns; and it describes the upper limit of the impact level under normal conditions by statistically analyzing the distribution interval of the number of short-term impacts and the duration of fluctuations in local fluctuation features. For the frequency band energy feature field, when this field is enabled in the system configuration, the historical baseline modeling unit also statistically analyzes the energy proportion of each sensitive frequency band, generating statistical parameters reflecting the spectral structure under normal conditions. The above statistical aggregation results are recorded in a structured manner, including the representative interval of each feature field under normal operation conditions, the long-term drift trend description field, and the abnormal sensitivity reference field, constituting the normal part of the "historical risk baseline feature vector".

[0098] For archived event fragments, the historical baseline modeling unit, during feature statistical aggregation, summarizes the feature change trajectories of different stages in the event evolution process, forming an evolutionary pattern description corresponding to the event type. This includes a summary of pre-event background features, a summary of peak event development features, and a summary of event recovery stage features. This event evolution pattern, along with the baseline features of normal operation, is stored in the historical risk baseline feature vector for reference in subsequent dual-timescale fusion modeling and intelligent early warning analysis. In a real-world scenario, for a suburban section frequently experiencing third-party construction interference, the historical baseline modeling unit summarizes the feature evolution trajectories of multiple archived construction events in stage S320. It finds that the number of short-term impacts increases and the intensity of local fluctuations increases before the construction event, while the overall trend changes relatively little. During the event, the frequency and amplitude of short-term impacts further increase, and some features gradually return to normal levels in the later stages of the event. This summary result is written into the historical risk baseline feature vector for the corresponding event type for subsequent use.

[0099] The historical baseline modeling unit assembles the normal operation statistics and event evolution pattern statistics for each risk unit and operating condition category into a "historical risk baseline feature vector." Each vector contains the risk unit number, operating condition category, normal operation feature statistics field, long-term drift description field, event type list and corresponding evolution pattern statistics field, sample quantity statistics field, and baseline version number field. The historical risk baseline feature vector is stored in the historical database baseline layer and provided to the S330 fusion modeling unit through an internal interface. Simultaneously, the baseline version number field in the historical risk baseline feature vector is referenced during the model inference stage of the S400 to record the baseline version information used in model judgment in the early warning assessment record, enabling traceable management of the historical baseline evolution process. The output field of this step is named "historical risk baseline feature vector," which is used as the core input for constructing dual-timescale fusion features in the S330.

[0100] S330. Perform dual-time-scale fusion modeling on the historical risk baseline feature vector to generate a dual-time-scale fusion feature vector structure.

[0101] In this embodiment, the fusion modeling unit is deployed at the intersection of the intelligent analysis layer and the historical analysis layer, responsible for establishing a dual-timescale correlation description between real-time risk features and historical risk baselines. The fusion modeling unit reads the historical risk baseline feature vector output by S320 from the historical database baseline layer via an internal interface, and simultaneously obtains the latest real-time risk feature vector structure from the real-time database feature layer as needed. Based on the risk unit number and operating condition classification, the two types of features are matched to form fusion modeling input pairs. The processing flow of the fusion modeling unit can be executed in batches periodically, or it can be triggered on demand by the early warning engine when a suspected abnormal session occurs in a certain risk unit. The triggering conditions and execution frequency can be configured by the system to adapt to the balance requirements of real-time performance and resource consumption in different pipeline scenarios.

[0102] Specifically, the fusion modeling unit first filters records matching the target risk unit from the historical risk baseline feature vector according to the risk unit number. Then, based on the operating condition classification field recorded in the real-time risk feature vector structure, it selects the baseline record closest to the current operating condition from the historical baselines. When multiple candidate baseline records are returned, the fusion modeling unit prioritizes the latest record with sufficient sample size based on the baseline version number and sample quantity statistics field, while retaining the candidate baseline list in the metadata field for interpretation and auditing. After matching, the fusion modeling unit constructs a dual-timescale feature input object, which includes one or more sets of real-time risk feature vectors (e.g., features of the current session and several previous sessions), the corresponding historical risk baseline feature vectors, and optional historical event evolution pattern statistics fields. Subsequently, the fusion modeling unit performs dimensional unification and scale alignment on the two types of features. For feature fields with different dimensions, normalization or interval mapping is used to transform them into a unified comparison space. During the alignment process, the reference benchmark of the real-time features is adjusted by referring to the normal state feature interval and long-term drift description field recorded in the historical baseline feature vector.

[0103] After alignment, the fusion modeling unit constructs dual-timescale fusion features for each risk unit and operating condition combination. These fusion features include descriptions of the magnitude of deviation between real-time and baseline, the rate of deviation change, and the degree of similarity to typical event evolution patterns. The magnitude of deviation is described by comparing the positional relationship between real-time and baseline features in the same feature dimension. For example, it calculates the degree of deviation of real-time amplitude statistical features relative to the representative range of normal operation, the degree of exceedance of real-time local fluctuation features relative to the normal upper limit, and the degree of difference between real-time trend change patterns and normal change patterns. Structured fields are used to record the direction and level of deviation during the description process. The rate of deviation change is constructed based on multi-session continuous features. By observing the change trajectories of features corresponding to multiple continuous time anchors, it provides a trend description and magnitude description of the increase or decrease in deviation over time, used to characterize the speed of risk evolution. The degree of similarity to typical event evolution patterns is constructed by comparing the current continuous feature trajectory with the event evolution pattern statistical fields recorded in the historical risk baseline feature vector. When the similarity is high, similar event type and similarity level fields are added to the fusion features to provide input for subsequent event type determination.

[0104] In one engineering embodiment, for the aforementioned high-risk unit traversing the riverbed area, the fusion modeling unit obtains the latest set of real-time risk feature vector structures for the risk unit during a specific rainy season and finds the historical risk baseline feature vectors corresponding to the rainy season conditions in the historical database. Through dual-timescale fusion modeling processing, the fusion modeling unit discovers that the average vibration amplitude and local fluctuation features in the current real-time features continuously deviate from the normal baseline. The deviation magnitude description field shows that multiple feature dimensions have multiple consecutive time anchor points in a high deviation state, and the deviation change rate description field indicates that the degree of deviation is accelerating. Simultaneously, it exhibits a high degree of similarity to the evolution pattern of the archived historical event "riverbed scouring causing pipeline suspension." The fusion modeling unit records this similar event type and corresponding similarity level in the dual-timescale fusion features and passes this fusion feature object to the subsequent model inference unit in S410. During model inference, this dual-timescale fusion feature is used to determine the event type and risk level.

[0105] After completing the aforementioned feature construction, the fusion modeling unit solidifies the fusion feature objects under each risk unit and operating condition combination into a structured record, forming a "dual-timescale fusion feature vector structure." This structure includes at least the risk unit number, operating condition classification field, real-time feature summary field, baseline feature summary field, deviation magnitude description field, deviation change rate description field, similar event type field, similarity level field, and baseline version number field. It also records the fusion modeling time and the matching strategy identifier used for retrospective analysis during subsequent model inference and auditing. The dual-timescale fusion feature vector structure generated in this step is stored in the fusion feature layer of the analysis database and is accessible to the S410 model inference and event determination units via an internal interface. The dual-timescale fusion feature vector structure, as the output field name of this step, is a key product connecting real-time analysis and historical baselines in the entire S300 process. It is also utilized in S400 for early warning assessment record construction and event log statistical analysis. In summary, the technical effects of this step are as follows: By performing dual-timescale fusion modeling on the feature vectors of historical risk baselines, real-time risk characteristics are uniformly described with long-term historical baselines and event evolution patterns within the same structure. This enables subsequent model inference to not only perceive the degree of anomaly within the current window, but also to understand deviation trends and typical risk patterns based on historical context.

[0106] Step S400 includes at least steps S410-S430:

[0107] S410. Obtain the dual-timescale fused feature vector structure, perform model inference and event judgment processing, and obtain early warning assessment records;

[0108] In one specific implementation, the model inference unit is deployed in the intelligent analysis layer of the pipeline safety early warning system. It obtains the dual-timescale fusion feature vector structure generated by S330 from the fusion feature layer via an internal data bus. This dual-timescale fusion feature vector structure records information such as risk unit number, operating condition classification field, real-time feature summary field, baseline feature summary field, deviation size description field, deviation change rate description field, similar event type field, similarity level field, baseline version number field, fusion modeling time field, and fusion modeling strategy identifier field. The model inference unit executes this step under the trigger of the real-time task scheduling module. The triggering condition can be a data write event generated when the fusion modeling unit writes a new batch of dual-timescale fusion feature vector structures to the fusion feature layer, an on-demand re-judgment task initiated by the early warning system for a specified risk unit, or offline re-judgment by maintenance personnel manually specifying a time period and a pipe section through the monitoring platform interface. The model inference unit first extracts the objects to be judged from the dual-timescale fused feature vector structure according to the risk unit number and time order. Multiple fused feature records belonging to the same risk unit and adjacent in time are combined into a judgment session set, which is used to comprehensively consider the evolution information of multiple time anchors during the inference stage.

[0109] Specifically, the model inference unit loads the currently active intelligent model configuration at the start of each inference task. This configuration includes model version identifiers for the event type discrimination sub-model, risk level assessment sub-model, and spatial impact range correction sub-model, as well as input feature mapping tables, output field definitions, and threshold segmentation configurations. The model inference unit first extracts the corresponding feature fields from the dual-timescale fused feature vector structure based on the input feature mapping table, and arranges and transforms them according to the sub-model input requirements. Missing fields are handled using pre- and post-session interpolation, default value filling, or sample discarding strategies. These strategies are uniformly managed by the missing data strategy field in the model configuration. For feature fields with anomaly markers, the model inference unit can choose to use weight reduction processing (including the field in inference but attenuating its weight in the internal inference) or directly mask the field, depending on the configuration. Then, the model inference unit inputs the processed feature vectors into the event type discrimination sub-model, which outputs candidate event type labels and corresponding confidence scores, and records the output candidate label list in a temporary inference result structure. Subsequently, the model inference unit inputs the same input feature vector into the risk level assessment sub-model to generate a risk level recommendation value and a risk level confidence field. It then combines the event type discrimination results to perform a consistency check on the risk level. For example, for high-level recommendations with low confidence, manual review can be added in subsequent stages.

[0110] Furthermore, the model inference unit can also invoke the spatial influence range correction sub-model. This sub-model, based on the risk unit number and the dual-timescale fusion feature vector structure of its adjacent risk units in the current time period, determines whether the anomaly exhibits a cross-unit expansion trend. If the deviation magnitude description field and deviation change rate description field of multiple adjacent risk units simultaneously show significant deviations within the same time window, the spatial influence range correction sub-model will append an expanded influence range field to the inference result, marking the affected risk unit set, and writing this set into the subsequent early warning assessment record. For fusion features containing a similar event type field, the model inference unit will also incorporate the similar event type field and similarity level field into the inference result to indicate the degree of correlation between this judgment and a certain type of historical event. In one engineering embodiment, for a high-pressure pipeline traversing the core urban area, when the dual-timescale fused feature vector structure shows that the deviation magnitude description field at multiple consecutive time anchor points is consistently in a high deviation state, and the similarity level field with the historical "third-party mechanical construction damage" event is high, the model inference unit records the "third-party construction interference" candidate type in the event type discrimination sub-model output, records the risk level suggestion value close to the highest level in the risk level assessment sub-model output, and calls the spatial influence range correction sub-model to confirm that the abnormal influence range has not crossed the current risk unit, which constitutes the core judgment basis for this inference.

[0111] After completing the sub-model inference, the model inference unit integrates the outputs of each sub-model into a structured "early warning assessment record." This early warning assessment record includes at least the following fields: event primary key field, risk unit number field, operating condition classification field, main event type field, alternative event type list field, risk level field, spatial location field, spatial impact range field, baseline version number field, model version number field, inference time field, inference trigger source field, inference confidence level field, and manual review requirement marker field. The event primary key field is generated by the model inference unit according to the timestamp and risk unit number, and is used as a unified association key for event log records and early warning handling instruction sets in subsequent S420 and S430. The spatial location field calculates the mileage location result based on the risk unit number and pipeline topology information, and the spatial impact range field corrects the set of affected risk units in the sub-model output record based on the spatial impact range. After the early warning assessment record is generated, it is written to the early warning assessment buffer and pushed to the early warning strategy engine through the internal message queue. The early warning strategy matching and handling template in S420 selects and calls the event type field, risk level field, and spatial location field in the record. The same early warning assessment record will also be written into the event log and referenced in the resource adjustment parameter statistical processing in S430, as one of the inputs to the event log and resource adjustment parameter structure.

[0112] S420. Extract the event type field, risk level field, and spatial location field from the early warning assessment record, perform early warning strategy matching and handling template selection processing, and generate an early warning handling instruction set.

[0113] In this embodiment, the early warning strategy engine is deployed on the backend of the monitoring and scheduling platform and consists of a strategy matching unit and a handling template management unit. The strategy matching unit reads the early warning assessment records output by S410 from the early warning assessment buffer through an internal interface. Under the trigger of the task scheduling module, it processes the records in chronological order or according to risk level priority. Triggering conditions include the cumulative number of records in the early warning assessment buffer reaching a certain threshold, the presence of high-risk records in the buffer, or maintenance personnel actively initiating a strategy rematch operation for a specific event. The strategy matching unit extracts the event type field, risk level field, spatial location field, spatial impact range field, operating condition classification field, and inference trigger source field from each early warning assessment record as input variables for strategy decision-making and loads the currently effective early warning strategy configuration. The early warning strategy configuration is maintained by system administrators in the strategy configuration interface and is stored internally in the form of rule entries. Rule conditions can combine multiple conditions such as event type, risk level, sensitive area category of spatial location, time period, operating condition classification, and inference trigger source. Rule actions define parameters such as notification channel combination, notification target role list, automatic instruction distribution scope, handling time limit requirements, and manual review requirements.

[0114] Specifically, for each early warning assessment record, the strategy matching unit iterates through the early warning strategy rule set according to preset priorities, using conditional expression matching to determine whether the event type, risk level, and spatial location fields in the record meet the rule conditions. When a rule's conditions completely match the key fields of the current early warning assessment record, the strategy matching unit adds the action portion of that rule to the strategy matching result set for this event. When multiple rules simultaneously meet the conditions, the strategy matching unit filters based on the rule priority field and the rule conflict handling strategy field. It can use the highest priority rule overriding method or a rule action merging method, combining the notification channels, targets, and handling requirements of multiple rules to generate richer actions. If no rule matches, the strategy matching unit reverts to the system default strategy, such as recording the event but only providing a low-priority prompt on the monitoring interface. Subsequently, the strategy matching unit passes the matching results to the handling template management unit, which selects the corresponding standard handling template from the handling template library based on the event type and risk level fields. The handling template library predefines standard operating procedures for different event types and risk level combinations, including on-site inspection procedures, remote valve operation sequence, pressure regulation strategies, data verification actions, and a list of notifications to collaborating departments.

[0115] In one engineering embodiment, for a long-distance oil and gas pipeline traversing a densely populated residential area, when the event type field in the early warning assessment record is "suspected pipeline leak," the risk level field is at the highest level, and the spatial location field corresponds to the sensitive area category as "residential area," the strategy matching unit selects an emergency response rule based on the early warning strategy configuration. The action portion of this rule requires immediate notification via a pop-up window on the monitoring and dispatch platform's alarm interface, pushing a high-priority alarm message to the duty mobile application, sending alarm information to the duty terminals of relevant functional departments, and triggering a pressure reduction command from the remote valve chamber control system. Simultaneously, the "pipeline leak emergency response template" is selected in the response template management unit. Based on this, the response template management unit generates a standard operating procedure that includes on-site inspection routes, on-site leak detection equipment configuration, remote control operation steps, and information reporting paths. This procedure is then combined with the notification channel configuration selected by the strategy matching unit to form a structured "early warning response instruction set." The early warning and handling instruction set includes fields such as event primary key, risk unit number, event type, risk level, target system identifier, target role identifier, notification channel, instruction type, instruction parameters, handling time limit, whether manual confirmation is required, and instruction version number. For less urgent events, such as third-party construction interference events of a general risk level, the early warning strategy engine can generate only monitoring platform interface alarms and end-of-day summary report instructions according to rules, without triggering remote control operations. After the early warning and handling instruction set is generated, it is written to the instruction team list and pushed to each target system and mobile terminal through the system's internal messaging mechanism. These target systems then execute pipeline control, on-site inspection dispatch, and information notification operations. At the same time, the early warning and handling instruction set, along with the original early warning assessment record, is transmitted to the event management and audit unit in S430. The event management and audit unit registers a new handling session in the event log based on the event primary key and provides the resource assessment unit with the original action record for calculating resource adjustment parameters.

[0116] S430. Write event logs and perform resource adjustment parameter statistical processing on the early warning response instruction set and early warning assessment record to generate event log and resource adjustment parameter structure.

[0117] In this implementation, the event management and auditing unit is deployed at the system management layer and consists of an event log writing subunit and a resource assessment subunit. The event log writing subunit obtains information such as the event primary key field, risk unit number field, event type field, risk level field, spatial location field, early warning strategy matching result field, and handling template identifier field from the early warning handling instruction set output by S420 and the early warning assessment record output by S410, thereby uniformly modeling the event lifecycle. When the monitoring and scheduling platform backend service starts, the event log writing subunit establishes an event state machine. The state machine defines states such as "automatic discovery," "pending confirmation," "in handling," "recovered," and "closed," as well as state transition conditions. State transition events include early warning assessment record generation, completion of manual confirmation operation, reporting of handling instruction execution results, and closure operation by maintenance personnel. Whenever an early warning handling instruction set is generated, the event log writing subunit automatically creates or updates the corresponding event record based on the event primary key, sets the current state to "automatic discovery," and records the discovery time field, trigger source field, and initial inference result summary field. Subsequently, when the early warning and handling instruction set is sent to each target system through the message bus, the event log writing sub-unit generates several sub-instruction records. Each sub-instruction record includes the target system identifier, target role identifier, instruction type, instruction parameters, and handling time limit requirements, and is recorded as an action item in the event log.

[0118] Furthermore, upon receiving the early warning and handling instruction set, each target system returns the execution progress and results to the event management and auditing unit via acknowledgment messages. The event log writing sub-unit listens to these acknowledgment messages and updates the execution status, start time, end time, and result summary fields of the sub-instruction records in the event log accordingly. For operations requiring manual confirmation, it also records the confirming personnel's identification and shift information. When all critical handling actions meet the predetermined completion conditions, the event log writing sub-unit switches the event state machine status from "handling" to "recovered" or "closed" according to the configuration, and records the recovery time and closure time fields. In a real-world engineering scenario, for a suspected leak incident in a high-risk urban pipeline section, the event management and auditing unit automatically creates an event record in the event log after receiving the early warning and handling instruction set, and generates three sub-instruction entries: "Remote Pressure Reduction Instruction," "On-site Inspection Dispatch," and "Supervisor Notification," corresponding to the remote valve chamber control system, the inspection work order system, and the mobile messaging system, respectively. As the remote valve chamber control system reports successful pressure reduction, the inspection work order system reports that the on-site inspection has been completed and no further signs of leakage have been found, and the mobile message system reports that the notification has been delivered, the event log is written to the sub-unit to update the status of each sub-instruction entry in sequence, and the event status is set to "closed" after all key actions are completed.

[0119] Based on the continuous writing of event logs, the resource assessment subunit periodically performs statistical analysis on the event logs and executes statistical processing of resource adjustment parameters. The resource assessment subunit extracts event type, risk level, spatial location, handling instruction structure, event status, discovery time, handling completion time, and manual review result fields from the event logs. For each risk unit, each work condition category, and each early warning strategy configuration version, it calculates statistical indicators such as response time distribution, handling time distribution, false alarm frequency, and missed alarm backtracking records, and derives resource adjustment parameters that can be used to adjust sampling configurations, model thresholds, and early warning strategies. The minimum set of resource adjustment parameters can include suggested fields for adjusting sampling frequency, sliding time window length, risk level classification threshold, and notification channel for each risk unit. These fields are generated by the resource assessment subunit based on the statistical results of the event logs. For example, after a long period of operation, if a risk unit frequently triggers high-risk level warnings under a certain type of working condition, and subsequent manual review results repeatedly determine that the risk is not substantial, the resource assessment subunit will generate threshold increase suggestion fields and strategy downgrade suggestion fields in the resource adjustment parameter structure corresponding to the event type and risk level. Conversely, if some serious events are discovered through manual investigation afterward, but no corresponding high-level alarms or no alarms are generated in the warning assessment record, the resource assessment subunit will record the underreporting statistics and provide threshold reduction suggestions and sampling frequency increase suggestions.

[0120] The resource adjustment parameter structure generated by the resource assessment subunit is referenced by the configuration management unit and the historical baseline modeling unit in subsequent periodic optimization processes. The configuration management unit can create new versions of risk unit session configuration structures, model threshold configurations, and early warning strategy configurations based on the suggested fields in the resource adjustment parameter structure, and store these new versions in the configuration version repository, recording the version effective time and associated resource adjustment parameter reference records to achieve traceable management of configuration changes. In the next round of S320 and S330 processing, the historical baseline modeling unit can use the statistical information on sample quality and event distribution in the resource adjustment parameter structure to adjust the selection strategy for historical risk candidate records and the baseline statistical aggregation method. The event log and the resource adjustment parameter structure are used together as the output field name for this step. The event log structure is used in S320 to identify archived event fragments and label the event attributes of historical risk candidate records. The resource adjustment parameter structure is used by the configuration management unit and operations personnel in subsequent configuration optimization steps.

[0121] In summary, the technical effects of this step are as follows: By introducing a unified event primary key and state machine management during the event log writing process, and constructing a quantifiable parameter set for sampling configuration, model thresholds, and early warning strategies in the resource adjustment parameter statistical processing, this step tightly couples early warning assessment records with actual handling behaviors, enabling the pipeline safety early warning system to form an auditable and measurable closed-loop operation mechanism, and providing structured input for resource optimization and strategy adjustment based on operational data.

[0122] Example 2: Figure 2 A structural block diagram of a real-time pipeline safety early warning system according to an embodiment of the present invention is shown. Figure 2 As shown, the structure may include:

[0123] Risk Unit Configuration Unit 01 is used to receive pipeline topology data, historical risk event distribution data, and fiber optic cable laying information. It completes risk section division, risk level labeling, fiber optic measuring point mapping and detection section aggregation, time anchor point setting and sliding time window parameter registration, and outputs the risk unit session configuration structure. Specifically, the risk unit configuration unit receives fields related to pipeline direction, station location, burial depth, geological conditions along the route, and river and road crossing locations from the pipeline topology data formed during the design phase; it receives event record fields related to third-party damage, geological disasters, corrosion leaks, and equipment failures from the historical risk event distribution data from the operation and maintenance platform; and it receives fields related to fiber optic cable path, loop number, measuring point spacing, and reflection positioning step size from the fiber optic cable laying information from the fiber optic monitoring system. It completes the field naming standardization and coordinate unification of the three types of data through a unified interface, so that all the above fields are mapped to a unified mileage reference system and spatial reference system. The risk unit configuration unit divides the pipeline into several risk sections based on the sensitivity of the environment along the pipeline, the pipeline structural characteristics, and the event density in the historical risk event distribution data. Shorter section lengths are set in high-risk areas, while longer section lengths are used in general areas. Each section is assigned an independent risk unit number. The risk level labeling logic marks risk units at locations of historically high-incidence events as high-level risk units and areas with fewer events as medium- or low-level risk units. The risk unit configuration unit also parses the location of fiber optic measurement points in the mileage reference system from the fiber optic laying information, maps each measurement point to the corresponding risk unit, and aggregates adjacent measurement points into detection segments according to fiber optic loops and geographical continuity, so that a risk unit is associated with one or more detection segments. On this basis, the risk unit configuration unit determines the time anchor interval and sliding time window length for each risk unit according to the risk level. Shorter time anchor intervals and smaller sliding time windows are registered in high-level risk units, and longer time anchor intervals and larger sliding time windows are registered in low-level risk units. The risk unit number, spatial range, risk level, detection segment number list, time anchor parameters, and sliding time window parameters are arranged into a structured risk unit session configuration structure. During the runtime phase, the risk unit configuration unit receives the event log and resource adjustment parameter structure output by the event log and resource statistics unit. It reads the frequency of events, response time, false alarms and missed alarms of each risk unit from the structure, and revises the risk segment division strategy, risk level labeling strategy and time anchor setting parameters. When generating a new risk unit session configuration structure, it attaches a configuration version identifier and broadcasts the latest configuration version to the real-time session acquisition unit, so that the real-time session acquisition unit can perform subsequent acquisition processes based on the current version of the risk unit session configuration structure.

[0124] The real-time session acquisition unit 02 receives monitoring data from the distributed optical fiber detection unit according to the risk unit session configuration structure, completes segment matching and time window slicing, generates real-time session raw records, and submits the risk unit identifier field, time anchor point field, and sensor data sequence field carried in the real-time session raw records to the back-end processing flow to form inputs for subsequent cleaning and feature calculation. Specifically, during the startup phase, the real-time session acquisition unit reads the risk unit session configuration structure output by the risk unit configuration unit, loads the mapping relationship between the risk unit number and the detection segment number, the time anchor point parameters, and the sliding time window parameters, and receives continuous vibration data and temperature data streams along the line through a long connection established with the distributed optical fiber detection unit. The real-time session acquisition unit performs preliminary diversion of the monitoring data from the optical fiber detection unit according to the detection segment number, so that the data stream of the same detection segment queues in the same processing thread to enter the time window slicing logic. Then, according to the time anchor point parameters in the risk unit session configuration structure, a reference anchor point is established for the data of each detection segment on the time axis, dividing the monitoring data into adjacent sliding time window segments. During time window slicing, the real-time session acquisition unit sets overlapping areas for adjacent windows of the same risk unit according to the configured sliding step size, ensuring continuous observation segments between them, and adds a time anchor field and a window number field to each window. After completing segment matching, the real-time session acquisition unit merges sensor data sequences belonging to multiple detection segments within the same time window under the corresponding risk unit name according to the mapping between risk units and detection segments. It organizes vibration and temperature sequences using a unified field order, generating a real-time session raw record object with a risk unit identifier field, a time anchor field, and a sensor data sequence field. The real-time session acquisition unit caches the real-time session raw record object in chronological order into a streaming buffer, and simultaneously submits events to the backend processing flow, triggering noise reduction and integrity verification actions. It marks abnormal interruptions, timestamp jumps, and missing data in the cache, and writes the unprocessed real-time session raw record along with the corresponding risk unit identifier field, time anchor field, and sensor data sequence field into a data area for subsequent cleaning and feature calculation. The field group output from this data area constitutes the pre-stage input for the subsequent real-time risk feature vector structure. After receiving the new version of the risk unit session configuration structure pushed by the risk unit configuration unit, the real-time session acquisition unit switches the configuration version at the window boundary and marks the newly generated real-time session original records with the corresponding configuration version identifier, so that the historical risk baseline modeling unit and the dual time scale fusion processing unit can restore the acquisition conditions at that time according to the configuration version when reading the real-time risk feature vector structure.

[0125] The historical risk baseline modeling unit 03 is used to read historical records associated with risk units from the real-time risk feature vector structure and the historical database, construct historical risk candidate records, and perform time merging and feature statistical aggregation on the normal operation segments and archived event segments in the historical risk candidate records to generate historical risk baseline feature vectors. Specifically, the historical risk baseline modeling unit reads records carrying risk unit identifier fields, time anchor fields, and multi-dimensional statistical feature fields from the real-time risk feature vector structure output from the real-time session acquisition link backend, and retrieves historical feature records and event records related to the same risk unit in the historical database according to the risk unit identifier field, placing short-term real-time risk features and long-term stored historical features into the same candidate set. Based on the event primary keys recorded in the event log and resource adjustment parameter structure, the historical risk baseline modeling unit maps segments marked as archived events in the historical database to real-time risk features within the corresponding time period, and marks event type information and risk level information in the historical risk candidate records. For time periods not marked by the event log, the historical risk baseline modeling unit marks them as normal operation segments and divides them into different operating condition groups according to conditions such as risk unit identifier fields, operating conditions, and seasonal environment. After constructing historical risk candidate records, the historical risk baseline modeling unit performs time merging of real-time risk features in chronological order within the normal operation segment for each risk unit. This integrates short-timescale feature sequences into longer-timescale statistical features, calculating the mean, extreme values, variation amplitude, and duration of continuous abnormal periods for each feature component within a statistical period. It also constructs feature trajectories for different stages before, during, and after the event within the archived event segments. After time merging, the historical risk baseline modeling unit aggregates the statistical results from the normal operation segment into a historical risk baseline feature vector and aggregates the feature trajectories from the archived event segments into event feature templates. These vectors and templates are stored in the historical database according to the risk unit identifier field, operating condition field, and risk level field. Simultaneously, it outputs the historical risk baseline feature vector of the current risk unit to the dual-timescale fusion processing unit as the historical input for constructing the dual-timescale fusion feature vector structure.

[0126] The dual-timescale fusion processing unit 04 receives the real-time risk feature vector structure and the historical risk baseline feature vector, completes the corresponding relationship matching based on the risk unit identifier field, performs deviation calculation, trend analysis, and event template similarity analysis on the statistical features within the short-term window and the statistical features of the long-term baseline, constructs a dual-timescale fusion feature vector structure, records the baseline version information and modeling strategy identifier in the structure, and outputs the dual-timescale fusion feature vector structure for the early warning judgment stage to call. Specifically, the dual-timescale fusion processing unit receives the real-time risk feature vector structure of each risk unit at the current moment from the back end of the real-time session acquisition link, and receives the historical risk baseline feature vector of the same risk unit under the current operating conditions from the historical risk baseline modeling unit. It completes one-to-one matching through the risk unit identifier field and the configuration version identifier, so that each real-time risk feature record is associated with a corresponding historical risk baseline feature record and event feature template set. The dual-timescale fusion processing unit, for matched record pairs, calculates the deviation of various statistical feature components in the real-time risk features relative to the historical risk baseline features within the current window. It introduces a time anchor field and a window number field to construct a short-term trend sequence, and analyzes the changing trend of the deviation sequence over several consecutive windows to identify the direction and duration of deviation. The dual-timescale fusion processing unit then compares the current short-term feature sequence with the feature trajectories of each stage in past typical events using similarity analysis logic with the event feature template set in the historical database. It calculates the matching degree and occurrence stage of similar trajectories, obtaining similarity indices related to several event types. Based on the above processing, the dual-timescale fusion processing unit combines the feature components in the real-time risk feature vector structure, the baseline statistical features, deviation indices, and changing trend indices in the historical risk baseline feature vector, and the event template similarity index to form a dual-timescale fusion feature vector structure. It also appends the source version number of the historical risk baseline feature vector and the modeling strategy identifier to the structure, enabling the early warning judgment unit to select different judgment rules according to the baseline version and modeling strategy during subsequent inference. The dual-timescale fusion processing unit pushes the dual-timescale fusion feature vector structure to the early warning judgment unit according to the risk unit identifier field and the time anchor point field. At the same time, it records the correspondence between the dual-timescale fusion feature vector structure and the configuration version and event log records at that time in the historical database, providing data support for subsequent retrospective analysis and strategy revision.

[0127] The early warning determination unit 05 is used to acquire the dual-timescale fusion feature vector structure, and to perform inference and event determination on the fusion features of different risk units according to the preset early warning model, generating an early warning assessment record. The early warning assessment record includes an event type field, a risk level field, and a spatial location field, and is stored in association with the corresponding risk unit identifier field. Specifically, the early warning determination unit continuously receives the dual-timescale fusion feature vector structure of each risk unit from the dual-timescale fusion processing unit, sorts the fusion features according to the time anchor point field and the risk unit identifier field, and puts them into the inference queue. According to the model structure and parameters defined in the preset early warning model, it reads the real-time statistical component, historical baseline component, deviation component, change trend component, and event template similarity component in the fusion features as model input. During the model inference phase, the early warning determination unit performs classification and level determination operations for each fused feature record. It uses multi-layered feature combination logic within the model to determine whether an anomaly exists in the current risk unit. Through multi-classification output, it provides an event type field and a risk level field for each risk unit. In cases where adjacent risk units trigger anomalies simultaneously within a short period, the early warning determination unit performs joint analysis on multiple fused feature records before outputting, merging multiple risk unit identifier fields and spatial location fields into a set of spatial location fields under the same event primary key. This allows subsequent early warning strategies and response units to handle situations on an event-by-event basis. After completing model inference, the early warning determination unit constructs structured early warning assessment records based on the risk unit identifier field, event type field, risk level field, and spatial location field. Each early warning assessment record is assigned an event primary key and a time anchor field, and the event primary key and current status field are registered in the event trigger queue. The early warning judgment unit sends the early warning assessment record to the early warning strategy and handling unit, which is used as the basis for strategy matching and handling template selection. At the same time, the same early warning assessment record is written into the event log and resource statistics unit, which is used as the starting input for event lifecycle recording, and event source data is formed in the event log and resource adjustment parameter structure.

[0128] The early warning strategy and response unit 06 is used to read the event type field, risk level field, and spatial location field from the early warning assessment record, perform early warning strategy matching and response template selection according to the early warning strategy configuration and response template library, generate an early warning response instruction set, and send the early warning response instruction set to the monitoring and dispatch platform, the field execution terminal, or the mobile terminal to complete the issuance of audible and visual alarms, inspection arrangements, pressure reduction, or transmission stop control commands, and return the generated early warning response instruction set for use in the event record. Specifically, after receiving the early warning assessment record from the early warning judgment unit, the early warning strategy and response unit searches for strategy entries that meet the current event conditions in the early warning strategy configuration according to the event type field, risk level field, and spatial location field, and searches for the set of response templates associated with the strategy entry in the response template library. The early warning strategy and response unit divides the response templates into three categories according to the notification level, notification target role, and response time limit configured in the strategy entry: monitoring and dispatch platform notification templates, field execution terminal control templates, and mobile terminal prompt templates. For each type of template, it fills in the event primary key, event type field, risk level field, and spatial location field in the early warning assessment record to generate the corresponding early warning response instruction entry. After generating early warning and handling instruction entries, the early warning strategy and handling unit writes the early warning and handling instruction sets for the monitoring and dispatching platform into the monitoring screen pop-up queue via an internal message channel, writes the early warning and handling instruction sets for the field execution end into the field control system via the control interface, and distributes the early warning and handling instruction sets for mobile terminals to each terminal via a message push gateway. All early warning and handling instruction sets carry event primary key and strategy number fields during generation, for the event log and resource statistics unit to record the corresponding relationships later. Upon receiving the instruction acceptance status from the monitoring and dispatching platform, the control execution feedback from the field execution end, and the read feedback from the mobile terminal, the early warning strategy and handling unit associates the above feedback information with the corresponding early warning and handling instruction set record and sends the complete record, including the event primary key, the early warning and handling instruction set content, and the execution feedback fields, to the event log and resource statistics unit as the data source for the handling process in the event lifecycle record.

[0129] The event log and resource statistics unit 07 is used to receive the early warning assessment record and the early warning handling instruction set, establish an event lifecycle record according to the event primary key, uniformly write event status changes, handling process records and feedback information, generate an event log and resource adjustment parameter structure, and statistically analyze the event occurrence frequency, response time, false alarms and missed alarms of different risk units in the event log and resource adjustment parameter structure, and output the event log and resource adjustment parameter structure for reference in the pipeline safety early warning system parameter adjustment and resource allocation strategy revision stages; specifically, the event log and resource statistics unit receives early warning assessment records containing event primary key, event type field, risk level field and spatial location field from the early warning judgment unit, and records containing event primary key and multiple types of early warning handling instruction sets and their execution feedback from the early warning strategy and handling unit. In the internal event index structure, the two types of records are merged into a single event lifecycle record according to the event primary key. Status fields are set for the status change process of the event from generation, notification, control execution, on-site feedback to closure, and timestamp and operation source are recorded for each status change. The event log and resource statistics unit archives the risk level field given in the early warning assessment record and the control action type in the handling process record within the event lifecycle record. This ensures that each event lifecycle record includes an event type field, a risk level field, a spatial location field, a set of risk unit identifier fields involved in the control, and a sequence of handling actions. Based on the review markers submitted by maintenance personnel during the event closure phase, the event log and resource adjustment parameter structure records statistical results for each risk unit, including event frequency, average response time, number of false alarms, and number of missed alarms within the statistical period. Based on the aforementioned statistical results, the event log and resource statistics unit constructs a set of resource adjustment parameters, including suggestions for adjusting sampling strategies, time anchor parameters, and model thresholds for high-frequency event risk units. Statistical period and version identifiers are appended to the event log and resource adjustment parameter structure. This structure is then output to the risk unit configuration unit and early warning judgment unit, serving as the data basis for subsequent revisions to the risk unit session configuration structure and early warning model parameters. Simultaneously, the old version of the event log and resource adjustment parameter structure is archived to support comparative analysis and strategy backtracking of the pipeline safety early warning system before and after parameter adjustments.

Claims

1. A real-time pipeline safety alerting method, characterized in that, Comprise: Based on pipeline topology data, historical risk event distribution data and optical fiber along the line laying information, risk section division, risk level labeling, optical fiber measuring point mapping and time anchor configuration processing are carried out, and risk unit session configuration structure is generated; wherein, the risk section division, risk level labeling, optical fiber measuring point mapping and time anchor configuration processing include: Risk section division processing includes determining risk unit boundary according to pipeline topology structure and historical risk event distribution, and segment optimization based on spatial continuity and risk accumulation probability; Risk level labeling processing includes assigning risk level based on historical event frequency and severity, and dynamically adjusting considering working condition and environmental factors; Optical fiber measuring point mapping processing includes spatially correlating optical fiber measuring point with risk unit, and establishing many-to-one mapping relationship between measuring point and risk unit; Time anchor configuration processing includes setting time sequence reference of data acquisition and processing, and defining start and end anchors of sliding time window; Based on risk unit session configuration structure, section matching, time window slicing and noise reduction processing are carried out, and integrity checking, statistical feature calculation and suspected abnormal segment marking processing are executed, and real-time risk feature vector structure is generated; wherein, the section matching, time window slicing and noise reduction processing include: Section matching processing includes matching real-time data stream with risk unit in risk unit session configuration structure, and verifying data integrity and spatial coverage; Time window slicing processing includes dividing data stream in sliding time window manner, and setting window overlap rate and step length parameters; Noise reduction processing includes reducing data noise by using filtering algorithm and outlier rejection method; Based on real-time risk feature vector structure, risk unit matching, working condition screening processing are carried out, and time merging, feature statistical aggregation and double time scale fusion modeling processing are executed, and double time scale fusion feature vector structure is generated; wherein, the time merging, feature statistical aggregation and double time scale fusion modeling processing include: Time merging processing includes aggregating data of different time scales to unified time axis, and solving timestamp conflict; Feature statistical aggregation processing includes calculating mean, variance and quantile statistics, and generating baseline distribution model; Double time scale fusion modeling processing includes calculating sequence features of deviation amount, deviation speed and deviation acceleration on historical risk baseline feature vector, and includes similarity calculation with historical event template, while recording baseline version number and modeling strategy identification to support auditable multi-version baseline linkage; Based on double time scale fusion feature vector structure, model reasoning and event judgment processing are carried out, and early warning strategy matching, resource adjustment parameter statistical processing are executed, and event stream account and resource adjustment parameter structure are generated.

2. The method of claim 1, wherein, Pipeline topology data, historical risk event distribution data and optical fiber along the line laying information include: The pipeline topology data includes pipeline start and end stake number, pipe diameter of each section, wall thickness, burial depth, material, elbow and tee position, valve chamber and station position, crossing point type and spatial relationship information with key geographical elements along the line; The historical risk event distribution data includes occurrence time, mileage, event type, event severity level, and disposal time consumption of historical leakage events, third-party construction disturbance events, geological disaster influence events, and equipment failure events; The optical fiber along the line laying information includes optical fiber starting point and ending point mileage, optical fiber laying path, each channel number, measurement interval, resolution level, turnaround point position, distance from the pipeline body, and relative orientation.

3. The method of claim 1, wherein, The process of performing integrity check, statistical feature calculation, and suspected abnormal segment marking processing further includes: The integrity check processing includes data quality check, missing data interpolation, and abnormal data marking, and performs statistical feature calculation processing; The statistical feature calculation processing includes calculating energy, frequency band distribution, continuous fluctuation duration, and adjacent window correlation features, and extracting time domain and frequency domain statistics; The suspected abnormal segment marking processing includes marking based on statistical feature deviation threshold, and recording the start time and duration of the abnormal segment.

4. The method of claim 1, wherein, The process of performing risk unit matching and working condition screening processing further includes: The risk unit matching processing includes aligning real-time risk feature vectors and historical risk baseline feature vectors by risk unit, and checking data consistency; The working condition screening processing includes screening related data according to pressure, flow, and temperature conditions, and excluding invalid working conditions.

5. The method of claim 1, wherein, The process of performing model reasoning and event determination processing further includes: The model reasoning and event determination processing includes using intelligent models to determine event type and risk level, and outputting determination confidence.

6. The method of claim 1, wherein, The process of performing early warning strategy matching and resource adjustment parameter statistical processing further includes: The early warning strategy matching processing includes selecting early warning strategies according to event type and risk level, and matching notification channels and disposal templates; The resource adjustment parameter statistical processing includes generating a unified event primary key and an event state machine event flow account, the event flow account including event life cycle records and sub-instruction entries, and based on the event flow account, statistical resource adjustment parameters, including sampling frequency adjustment suggestions, sliding time window length adjustment suggestions, risk level threshold adjustment suggestions, and notification channel adjustment suggestions, to drive periodic self-optimization of sampling configuration, model threshold, and notification strategy.

7. A real-time pipeline safety warning system applied to the method of any one of claims 1-6, characterized in that, It includes: A risk unit configuration unit configured to receive pipeline topology data, historical risk event distribution data, and optical fiber along the line laying information, complete risk section division, risk level labeling, optical fiber measurement point mapping and detection section aggregation, time anchor point setting and sliding time window parameter registration, and output risk unit session configuration structure; A real-time session acquisition unit configured to receive monitoring data from distributed optical fiber detection units according to the risk unit session configuration structure, complete section matching and time window slicing, and generate real-time session raw records; A historical risk baseline modeling unit configured to read historical records associated with risk units from real-time risk feature vector structures and historical databases, construct historical risk candidate records, perform time merging and feature statistical aggregation on the historical risk candidate records, and generate historical risk baseline feature vectors; The double-time-scale fusion processing unit is configured to receive a real-time risk feature vector structure and a historical risk baseline feature vector, perform correspondence matching according to a risk unit identification field, execute deviation calculation, change trend analysis and event template similarity analysis, and construct a double-time-scale fusion feature vector structure. The early warning judgment unit is configured to obtain the double-time-scale fusion feature vector structure, perform reasoning and event judgment according to a preset early warning model, and generate an early warning evaluation record. The early warning strategy and disposal unit is configured to read an event type field, a risk level field and a spatial positioning field from the early warning evaluation record, perform early warning strategy matching and disposal template selection, and generate an early warning disposal instruction set. The event flow account and resource statistics unit is configured to receive the early warning evaluation record and the early warning disposal instruction set, establish an event life cycle record, and generate an event flow account and resource adjustment parameter structure.

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