Pipeline gallery flooding prediction and response optimization method and system

Abstract

The present invention relates to the technical field of smart city infrastructure management and optimization, and in particular to a method and system for optimizing pipeline corridor flooding prediction and response. The present invention optimizes the data fusion process by introducing graph database technology, uses reinforcement learning to adaptively optimize the prediction model, and combines digital twin technology for real-time simulation and verification to form a complete pipeline corridor flooding prediction and response system. The core process includes: real-time data acquisition and preliminary fusion, advanced feature extraction, prediction model training and optimization, simulation and prediction verification, and dynamic emergency response generation and adjustment. The present invention significantly improves the accuracy of flooding prediction and the real-time nature of response measures, can effectively reduce the impact of flooding events on urban infrastructure, and enhance the city's ability to respond to sudden floods.

Description

Technical Field

[0001] The present invention relates to the technical field of smart city infrastructure management and optimization, and in particular to a method and system for pipe gallery flooding prediction and response optimization. Background Art

[0002] As a critical component of urban infrastructure, the safety of utility corridor systems is directly linked to the stability of urban operations and public safety. Flooding incidents in utility corridors can not only cause significant property damage but also threaten the lives of citizens. Therefore, an efficient and accurate flooding prediction and response optimization system is crucial for proactively identifying risks and implementing appropriate measures.

[0003] However, existing flooding prediction technologies face numerous challenges. First, traditional methods often rely on basic data fusion techniques, such as simple data aggregation and average calculation, which greatly limits the depth and breadth of data analysis. Furthermore, these traditional prediction models are not flexible enough in terms of parameter adjustment and adaptation to new environmental data, making them difficult to cope with complex or rapidly changing real-world situations. Furthermore, existing methods lack effective simulation verification mechanisms to ensure the reliability of model predictions in complex or unexpected situations. Finally, existing emergency response strategies are too rigid and lack the ability to dynamically adjust to real-time conditions.

[0004] Existing technologies (Chinese invention patent, publication number CN114252128B) exhibit significant deficiencies in the face of rapidly changing environments: basic data fusion technology cannot fully utilize the complex data collected by modern sensor networks; it lacks an adaptive mechanism and cannot efficiently utilize new data to optimize prediction accuracy; it fails to verify the authenticity of prediction results, which reduces the basis for decision-making; and pre-set response measures are difficult to adjust in a timely manner to adapt to actual flooding events. Summary of the Invention

[0005] In response to the many problems existing in the above-mentioned existing technologies, the present invention provides a method and system for predicting and optimizing the waterlogging response of a pipeline corridor. By integrating advanced data fusion technology and graph databases, it conducts in-depth analysis of environmental and monitoring data and constructs a complex data relationship model, thereby improving data utilization efficiency and prediction accuracy. At the same time, the introduction of reinforcement learning algorithms allows the prediction model to automatically optimize parameters when receiving new data, improving its adaptability and accuracy. Digital twin technology is used to simulate the actual pipeline corridor system, verify and optimize the prediction results in real time, and ensure the practicality and reliability of the prediction.

[0006] A pipeline corridor flooding prediction and response optimization method includes the following steps:

[0007] Through the deployed sensor network and distributed acoustic sensing technology, raw environmental data and fiber optic signal data inside and outside the tunnel are collected. The raw environmental data and fiber optic signal data are de-noised, missing value processed, and time series synchronized to obtain primary fused data.

[0008] Based on the primary fusion data, a relational model between data is constructed in the graph database, the graph relationships and nodes of the relational model are analyzed, and environmental features are extracted;

[0009] Using environmental feature data to train a reinforcement learning model, enabling the model to adjust its prediction strategy through online learning and self-optimization, thereby obtaining a trained prediction model for real-time flooding prediction;

[0010] The latest environmental data is fed into the prediction model for real-time flooding predictions, while digital twin technology is used to simulate scenarios and verify the prediction results, by identifying inconsistencies or errors in the simulation and making adjustments;

[0011] Based on the adjusted prediction results and simulation feedback reports, the decision support system generates and implements corresponding emergency response measures, and optimizes the response measures through continuous monitoring and evaluation to improve the system's response efficiency and accuracy.

[0012] Preferably, the raw environmental data monitored by the sensor network include: water level data, groundwater pressure, temperature, humidity, rainfall, wind speed and direction, and geological data; distributed acoustic sensing technology uses optical fibers to capture changes in sound waves or vibration signals to detect unconventional activities and water flow anomalies.

[0013] Preferably, based on the primary fusion data, the data from at least two sensors are integrated and mapped so that each data point has a unique identification and measurement attributes; graph database technology is used to establish data nodes and edges according to the correlation and spatial position relationship between the data, and the construction of the relational model in the graph database is completed by defining nodes to represent each monitoring point or sensor, and edges to represent the association between nodes based on physical location or data behavior; the relational model is used to represent the connection and interaction between different environmental data points.

[0014] Preferably, analyzing the graph relationships and nodes of the relationship model and extracting environmental features includes:

[0015] Perform path analysis and network flow analysis in the relational model to identify and quantify interactions between nodes and calculate key metrics in the graph database, including path strength and clustering coefficient, which are used to reveal close relationships and potential groups between data points.

[0016] Select nodes with relatively high centrality in the relationship model or nodes directly related to important environmental events as key nodes;

[0017] Analyze the properties of all key nodes, including the centrality of the nodes and the characteristics of adjacent nodes;

[0018] Based on the geographic information, physical properties and attributes of key nodes in the graph database, water flow dynamic characteristics and geological change characteristics are extracted and integrated into a set of environmental characteristics. The environmental characteristics include: water flow dynamic characteristics, geological change characteristics, and other environmental factors that have a significant impact on the safety and operation of the pipeline corridor system. The environmental characteristics are used to predict and assess potential risks faced by the pipeline corridor, optimize pipeline corridor maintenance strategies and reduce potential economic losses.

[0019] Preferably, the use of environmental feature data to train the reinforcement learning model includes:

[0020] The environmental feature data is input into the model in batches for training, and the reinforcement learning algorithm is used to learn the optimal strategy through interaction with the environment;

[0021] Evaluate the model's prediction performance after each training cycle and adjust the model parameters based on the evaluation results. Adjusting the model parameters includes modifying the reward function or learning rate to optimize the model's prediction performance.

[0022] Learning is performed based on the input environmental characteristics, and the prediction strategy and parameters are optimized through repeated evaluation and adjustment to obtain a fully trained prediction model.

[0023] Preferably, the scene simulation using digital twin technology includes:

[0024] The scenario is simulated using a digital twin model of the tunnel, which is a virtual copy of the tunnel entity, including all relevant physical and environmental characteristics of the tunnel entity;

[0025] The prediction results are input into the digital twin model, including: predicted water level, water flow dynamics, prediction time and its affected area;

[0026] The predicted flooding events were simulated in the digital twin model, and the process of water flow affecting the pipe gallery structure, including the speed and range of water level rise, was observed and recorded in real time to obtain simulation results.

[0027] Preferably, by comparing the data of the simulation results with the prediction results, verifying the consistency and accuracy of the two, checking whether all predicted parameters are reasonably reflected in the simulation, identifying any inconsistencies or errors in the simulation, analyzing the causes and making targeted adjustments, updating the prediction results, the adjusted prediction results match the simulation results, and generating a simulation feedback report;

[0028] The simulation feedback report includes: the simulation process, observed phenomena, identified errors, and problems identified during the simulation process and optimization suggestions.

[0029] Preferably, emergency response measures are formulated based on the adjusted forecast results and the simulation feedback report, wherein the emergency response measures are dynamically adjusted based on the optimization suggestions to address potential changes or factors not fully considered in the forecast;

[0030] The monitoring system tracks the implementation of emergency response measures in real time, collects feedback data during the implementation process, and evaluates the actual effect of the response. The feedback data includes: the implementation status, time, efficiency and actual control effect of the emergency response measures on the impact of flooding.

[0031] Preferably, the updated prediction results are compared with the actual effects, and the emergency response strategy and prediction model are optimized based on the comparison results and feedback data.

[0032] A pipe gallery flooding prediction and response optimization system, comprising:

[0033] The data acquisition module is deployed inside and outside the tunnel to collect raw environmental data and optical fiber signal data inside and outside the tunnel;

[0034] A data processing module, connected to the data acquisition module, is used to perform denoising, missing value processing, and time series synchronization on the collected raw environmental data to generate primary fused data;

[0035] The relational model building module builds a relational model between data in the graph database based on the primary fusion data, analyzes the graph relationships and nodes of the relational model, and extracts environmental feature data;

[0036] The reinforcement learning model training module uses the extracted environmental feature data to train the reinforcement learning model. Through online learning and self-optimization, the model can adjust the prediction strategy to obtain a trained prediction model.

[0037] A real-time prediction module that feeds the latest environmental data into the prediction model for real-time flooding predictions. It also uses digital twin technology to simulate scenarios and verify prediction results, identifying inconsistencies or errors in the simulation and making adjustments.

[0038] The decision support module generates and implements corresponding emergency response measures based on the adjusted forecast results and simulation feedback reports;

[0039] The monitoring and evaluation module optimizes response measures through continuous monitoring and evaluation, thereby improving the system's response efficiency and accuracy.

[0040] Compared with the prior art, the advantages and beneficial effects of the present invention are:

[0041] The present invention uses complex data fusion strategies and graph database technology to achieve detailed analysis of advanced features such as water flow dynamics and geological changes, improving the input accuracy and complexity processing capabilities of the prediction model;

[0042] This invention uses reinforcement learning and model self-optimization technology to enable the prediction model to self-adjust the prediction strategy based on real-time feedback, significantly improving the performance of the model in dynamic environments.

[0043] This invention uses digital twin technology to not only simulate prediction scenarios but also verify the authenticity and accuracy of prediction results, ensuring the reliability of model output;

[0044] The present invention uses real-time monitoring and dynamic emergency response technology to dynamically generate and adjust emergency response measures based on verified prediction results, thereby improving the efficiency and accuracy of responding to future events. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 Schematic diagram of the process of the present invention;

[0046] Figure 2 Schematic diagram of the data processing flow of the present invention;

[0047] Figure 3 It is a structural block diagram of the system of the present invention. DETAILED DESCRIPTION

[0048] Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. However, it should be understood that these descriptions are merely exemplary and are not intended to limit the scope of the present disclosure. In the following detailed description, for ease of explanation, many specific details are set forth to provide a comprehensive understanding of the embodiments of the present disclosure. However, it is apparent that one or more embodiments may also be implemented without these specific details. In addition, in the following description, descriptions of well-known structures and technologies are omitted to avoid unnecessary confusion of the concepts of the present disclosure.

[0049] The terms used herein are only for describing specific embodiments and are not intended to limit the present disclosure. The terms "comprise," "include," etc. used herein indicate the presence of the features, steps, operations, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, or components.

[0050] All terms used herein (including technical and scientific terms) have the meanings commonly understood by those skilled in the art unless otherwise defined. It should be noted that the terms used herein should be interpreted as having a meaning consistent with the context of this specification and should not be interpreted in an idealized or overly rigid manner.

[0051] When expressions such as “at least one of A, B, and C, etc.” are used, they should generally be interpreted in accordance with the meaning commonly understood by those skilled in the art (for example, “a system having at least one of A, B, and C” should include but is not limited to systems having A alone, B alone, C alone, A and B, A and C, B and C, and / or A, B, and C, etc.). When expressions such as “at least one of A, B, or C, etc.” are used, they should generally be interpreted in accordance with the meaning commonly understood by those skilled in the art (for example, “a system having at least one of A, B, or C” should include but is not limited to systems having A alone, B alone, C alone, A and B, A and C, B and C, and / or A, B, and C, etc.).

[0052] The accompanying drawings illustrate some block diagrams and / or flow charts. It should be understood that some blocks in the block diagrams and / or flow charts, or combinations thereof, may be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when executed by the processor, these instructions may create a device for implementing the functions / operations described in these block diagrams and / or flow charts. The techniques of the present disclosure may be implemented in the form of hardware and / or software (including firmware, source code, etc.). In addition, the techniques of the present disclosure may take the form of a computer program product on a computer-readable storage medium storing instructions, which may be used by or in conjunction with an instruction execution system.

[0053] like Figure 1-Figure 2 As shown, a pipeline corridor flooding prediction and response optimization method includes the following steps:

[0054] Through the deployed sensor network and distributed acoustic sensing technology, raw environmental data and fiber optic signal data inside and outside the tunnel are collected. The raw environmental data and fiber optic signal data are de-noised, missing value processed, and time series synchronized to obtain primary fused data.

[0055] In this invention, data collection is the foundation of the entire prediction system. A sensor network is deployed at key locations inside and outside the tunnel, such as low-lying areas, water inlets and outlets, and areas potentially affected by moisture. These sensors collect a variety of raw environmental data, including but not limited to water levels, groundwater pressure, temperature, humidity, and other meteorological conditions. For example, a water level gauge can monitor the water level in the tunnel in real time. When the water level exceeds a safe threshold, the system automatically triggers an alarm.

[0056] Distributed acoustic sensing (DAS) uses laser light propagating through optical fibers to detect minute changes caused by sound waves or vibrations. Water and other media (such as air) absorb and scatter sound waves differently, causing variations in the frequency content of the sound or vibration signals detected by the fiber. DAS optical fibers deployed along pipe corridors can precisely locate the source of sound or vibration, which is crucial for detecting unauthorized intrusion, pipe wall breaches, or abnormal water flow dynamics.

[0057] The collected raw data often contains noise, which may be caused by sensor failure, external interference, or data transmission errors. The purpose of data denoising is to filter out these unrealistic changes to ensure the accuracy of subsequent analysis. Denoising methods include using techniques such as sliding average and median filtering to smooth the data series. At the same time, there may be missing values ​​in the raw data. This situation is more common in environmental monitoring and may be caused by temporary sensor failure or data loss. Missing value processing techniques, such as linear interpolation or nearest neighbor interpolation, can be used to estimate these missing data points to maintain data integrity.

[0058] By combining sensor networks with DAS technology, this system not only monitors common environmental parameters but also detects more subtle physical changes, such as minute fluctuations in water flow or micro-tremors in pipe walls, which traditional sensors may not be able to capture. De-noising and processing missing values ​​makes the data more accurate and reliable, providing high-quality input for the system, enabling more precise prediction and response to potential flooding events. Real-time data processing and synchronization enable the system to rapidly respond to environmental changes and provide timely warnings, effectively mitigating or preventing damage caused by flooding.

[0059] For example, during a heavy rainstorm, DAS technology could detect abnormal acoustic patterns in specific areas, indicating possible accelerated water flow or pipe wall damage. Simultaneously, water level gauges within the sensor network could indicate a sharp rise in water levels. After synchronizing and processing this information, the system could quickly identify high-risk areas and initiate early warning and emergency response measures, such as activating pumping stations or shutting off water intakes in certain areas to mitigate the impact of flooding.

[0060] In this way, the overall monitoring and response capabilities of the system are significantly enhanced, which can more effectively protect the safety of the pipeline corridor and reduce maintenance costs and potential economic losses.

[0061] Preferably, the raw environmental data monitored by the sensor network include: water level data, groundwater pressure, temperature, humidity, rainfall, wind speed and direction, and geological data; distributed acoustic sensing technology uses optical fibers to capture changes in sound waves or vibration signals to detect unconventional activities and water flow anomalies.

[0062] Water level data is continuously monitored inside and outside the tunnel using water level sensors such as float gauges, pressure sensors, or ultrasonic sensors. This data directly reflects whether the tunnel is at risk of flooding.

[0063] Groundwater pressure is measured using pressure sensors. Abnormal changes in groundwater pressure may indicate abnormal groundwater flow or changes in geological structure, which are potential flooding risk factors.

[0064] Temperature and humidity: Temperature and humidity sensors monitor ambient and internal tunnel temperatures and humidity. These parameters help assess the risk of condensation within the tunnel and its potential impact on the structure.

[0065] Rainfall, rain gauges are used to monitor rainfall. Continuous or heavy rainfall is one of the main causes of flooding, especially in areas with limited drainage capacity.

[0066] Wind speed and direction: Anemometers and wind vanes are used to record wind speed and direction. Although this data has little direct impact on flooding, it is of great value in environmental analysis of open tunnels or related to meteorological conditions.

[0067] Geological data, which is usually obtained through geological surveys, includes soil type, rock structure, etc. These data are crucial for predicting groundwater flow and determining the geological stability of the pipeline corridor.

[0068] The DAS system detects and locates acoustic or vibration events along the fiber path by analyzing minute changes in the reflected laser signal propagating through the fiber. These changes can be caused by unusual water flow (such as rapid flow or dripping) or physical contact (such as excavation). DAS technology does not rely on traditional acoustic sensors, but instead uses the optical fiber itself as the sensing medium. This technology offers wide coverage and high sensitivity, enabling continuous monitoring of long-distance pipeline corridors.

[0069] By combining geographic and meteorological data with acoustic wave monitoring, the system can comprehensively monitor the environment and underground activities and conduct real-time assessments of flooding risks. Through real-time data analysis, the system can provide early warnings before flooding occurs, allowing preventive measures to mitigate potential damage. The introduction of DAS technology is particularly suitable for monitoring unconventional activities such as illegal excavation, which may cause damage to pipeline corridors and are difficult to detect through traditional methods.

[0070] For example, during heavy rainstorms, sensor networks can monitor dramatic changes in water levels and groundwater pressure in real time, while meteorological sensors can track rainfall and wind direction. Simultaneously, a DAS system might detect changes in acoustic patterns caused by increased groundwater velocity. The system integrates and analyzes this data to identify high-risk areas and activates an early warning system, notifying relevant personnel and agencies to take measures, such as activating water pumps or closing valves in certain areas, to prevent or minimize the impact of flooding.

[0071] The present invention uses multi-source data, and the model can more accurately predict when and where flooding may occur, and its possible severity. Based on real-time data and prediction results, the system can dynamically adjust response strategies, such as promptly activating drainage systems and adjusting flood control measures. Through timely early warnings and effective emergency responses, direct and indirect losses that may be caused by flooding incidents can be reduced. Continuous data collection and analysis facilitate long-term risk assessment and maintenance planning for pipeline corridors.

[0072] For example, in a pipeline corridor area with a history of frequent flooding, the forecasting system, by integrating data on water levels, rainfall, and groundwater pressure, can predict the potential flooding risk before heavy rainfall. This enables management to activate drainage pumps, deploy flood gates, or issue evacuation warnings hours in advance, significantly reducing the risk of personal injury and property damage.

[0073] Through the detailed collection and intelligent analysis of these original environmental data, the pipeline corridor flooding prediction and response optimization method can not only respond to flooding risks in real time, but also provide a scientific basis for the long-term maintenance and management of the pipeline corridor.

[0074] Based on the primary fusion data, a relational model between data is constructed in the graph database, the graph relationships and nodes of the relational model are analyzed, and environmental features are extracted;

[0075] In a graph database, nodes must first be created. Each node represents an independent data point, such as that collected by a specific sensor. A node contains all primary fused data, such as water level, temperature, and humidity, which has been processed for noise reduction and time series synchronization. Node attributes can include timestamps, geographic locations, and measured environmental parameters.

[0076] In a graph database, nodes are connected by edges, which define the relationships between them. Relationships are based on physical connections between data (such as geographical proximity) and logical connections (such as similar data trends or environmental influences). For example, if two water level sensors frequently record rising water levels at the same time, a graph database edge might connect these two nodes, indicating that they were affected by the same rainfall event.

[0077] Use path and network flow analysis within a graph database to identify possible water flow paths or potentially high-risk areas. These analyses help determine how water flows through the pipeline corridor system and which areas might be affected first. For example, path analysis can reveal which connected nodes (sensor locations) experience the first rise in water levels during a heavy rain event, thereby predicting the direction and speed of water flow.

[0078] Perform cluster analysis on nodes to identify groups of nodes that exhibit similar environmental responses. These groups may indicate they are located in similar microclimates or influenced by the same groundwater source. Calculate node centrality to determine which nodes occupy a central position in the graph database and may be key monitoring or control points.

[0079] Through detailed graph relationship and node analysis, this invention can more accurately understand and predict water flow dynamics and environmental changes in the pipeline corridor system. For example, by identifying key nodes and main water flow paths, protective measures in these areas can be prioritized for monitoring and strengthened. Based on the environmental characteristics extracted from the graph database, response strategies can be customized and optimized. For example, if graph analysis predicts that a certain area may be flooded in the next few hours, drainage facilities in that area can be adjusted in advance, or even an emergency warning system can be activated.

[0080] In one example, graph database analysis revealed that the nodes connecting Area A and Area B exhibited highly synchronized water level rises during past rainfall events. Future emergency preparedness measures include installing a more efficient water pumping system between the two areas and increasing monitoring frequency to detect problems in advance.

[0081] In summary, the relational model and environmental feature extraction based on the graph database in this invention not only enhance the understanding of the behavior of the pipeline corridor system, but also greatly improve the accuracy of flooding prediction and the timeliness of emergency response, providing a powerful decision support tool for pipeline corridor management.

[0082] Preferably, based on the primary fusion data, the data from at least two sensors are integrated and mapped so that each data point has a unique identification and measurement attributes; graph database technology is used to establish data nodes and edges according to the correlation and spatial position relationship between the data, and the construction of the relational model in the graph database is completed by defining nodes to represent each monitoring point or sensor, and edges to represent the association between nodes based on physical location or data behavior; the relational model is used to represent the connection and interaction between different environmental data points.

[0083] The present invention collects primary fused data from different sensors. After noise removal, missing value processing, and time series synchronization, each data point is assigned a unique identifier. This unique identifier typically includes information such as the sensor ID of the data source, a timestamp, and geographic location, ensuring data traceability and uniqueness. The integration operation involves aligning data points from at least two different sensors onto a unified timeline, allowing comparison and analysis of environmental conditions at different locations at the same point in time.

[0084] In a graph database, each monitoring point or sensor is defined as a node. Each node contains detailed information about the monitoring point, such as the monitored environmental parameter (water level, temperature, etc.), geographic location, and timestamp. Edges represent connections between nodes in a graph database. These connections can be based on physical location (such as water flow connecting two water level sensors) or data behavior (such as two sensors displaying similar response patterns during a certain event). Edges are defined based on pre-set logical relationships or through data-driven analysis. For example, if two nodes always experience a simultaneous increase in water level during a rainfall event, an edge is created between the two nodes.

[0085] Each node represents a monitoring point that obtains data through different types of sensors (such as water level gauges, thermometers and hygrometers). The attributes of the node should reflect the key characteristics of the sensor, including:

[0086] (1) Type: the type of sensor, such as water level meter, thermometer, and hygrometer, etc. This helps to filter data by type during analysis;

[0087] (2) Frequency, the frequency of data collection, such as once a minute, hourly, or daily; this property is critical for processing time series data and synchronization;

[0088] (3) Accuracy: The accuracy of the sensor indicates the reliability and accuracy of the data. High-precision sensors provide more reliable input for the model.

[0089] (4) Geographic location, the specific location of the sensor, which is the basis for defining the edges between nodes, because the physical location relationship directly affects how to connect these nodes;

[0090] (5) Status: The working status of the sensor, such as normal, under maintenance, or faulty, which affects the validity of the data and the preprocessing measures that need to be taken.

[0091] When applying graph database technology to integrate pipeline corridor monitoring data, it is necessary to ensure the consistency and integrity of the data when facing data from multiple sensors.

[0092] Ensure that all sensor data has a unified format and structure before entering the graph database. This includes a unified timestamp format, measurement unit, data type, etc. Such preprocessing reduces the complexity of the subsequent integration process. In a graph database, each sensor or monitoring point is regarded as a node, and the node contains all relevant sensor data attributes. The edges between nodes are established based on the physical location or data correlation between sensors. The network diagram formed in this way can intuitively reflect the relationship between data points. For time series data, time alignment processing is performed to ensure that data from different sensors are aligned on the time axis. This is usually achieved through interpolation or resampling methods so that all data points are compared and analyzed at the same time point.

[0093] During data integration, data conflicts are first identified, such as when two adjacent sensors report significantly different data at the same time. Such conflicts may be caused by sensor failure, data transmission errors, or errors due to environmental factors. Before resolving conflicting data, the reliability and historical performance of the sensors should be assessed. For example, if a sensor has a history of frequent failures or generally poor data quality, its data may be given lower priority during conflict resolution. Data fusion techniques are used to resolve data conflicts. Depending on the reliability of the sensors and the statistical characteristics of the data, weighted average, median, or more complex data fusion algorithms can be used to synthesize a single data value that most likely reflects the true environmental state. In some cases, machine learning models can be used to predict plausible data values, particularly when dealing with missing data or outliers. After conflict resolution, data should be validated to ensure that the newly generated data value is logically consistent with the other data. This validation can be performed by comparing historical data, data from adjacent sensors, or through an expert system. Based on the validation results, data processing strategies can be adjusted to continuously optimize the data integration and conflict resolution process.

[0094] In one embodiment, a geographical area is frequently affected by heavy rains. Multiple monitoring points, including water level gauges and rainfall gauges, are located in the area. In a graph database, each monitoring point is a node, and edges between nodes are established based on geographic location and water flow direction.

[0095] The risk identification process includes:

[0096] (1) In the graph database, real-time data from each monitoring point, such as water level and rainfall, are first integrated. Node attribute updates include the latest water level and the accumulated rainfall in the past 24 hours.

[0097] (2) Using graph database analysis tools, identify nodes where water levels are rising rapidly and analyze their upstream nodes (determined by the direction of water flow). Calculate the clustering coefficient of the adjacent nodes of key nodes (nodes where water levels are rising rapidly) to determine potential risk areas.

[0098] (3) Based on model analysis, if multiple adjacent nodes in a certain area show rising water levels at the same time and rainfall continues to increase, the system will automatically trigger a flood risk warning for that area. The warning information includes the expected flooding area, possible flooding depth, and recommended emergency measures.

[0099] When integrating data from different sensors into corresponding nodes, it is important to ensure data consistency. For example, if two sensors both measure temperature but with different accuracies, a consistent standard (such as higher accuracy or an average value) should be selected to represent the temperature attribute of the node.

[0100] If two sensors are geographically adjacent or in the same water area, their nodes should be connected by an edge. This connection reflects the similarity or continuity of environmental conditions that may exist in these adjacent areas.

[0101] Data behavior associations are defined by analyzing patterns of data change across different nodes. For example, if two sensors frequently record rising water levels simultaneously, an edge can be created to represent their hydrological correlation. This edge helps the model identify possible water flow paths or areas commonly affected by a particular environmental factor.

[0102] Edge weights can be defined based on the strength or importance of the connection between two nodes. For example, an edge between two geographically close water level gauge nodes might have a higher edge weight than one between two geographically distant nodes. Directionality indicates the direction of data flow or influence, such as water flow, and is important when simulating and analyzing water flow dynamics.

[0103] Through such detailed definitions, the relational model in the graph database can accurately map the structure and dynamics of the environmental monitoring network, greatly enhancing the depth and breadth of data analysis and providing solid data support for pipeline corridor flooding prediction.

[0104] Under extreme weather conditions, dynamic risk assessment requires the model to be able to receive and process data from various sensors in real time, and quickly update the risk assessment results based on the latest data.

[0105] As data continues to flow in, node attributes (such as water levels and rainfall) in the graph database are constantly updated, ensuring that risk assessments are based on the latest environmental conditions. Automatic data update mechanisms and triggers are established to ensure that the risk levels of relevant nodes and edges are immediately reassessed if data exceeds preset thresholds.

[0106] When predicting an impending extreme weather event, such as a predicted heavy rainstorm, simulations are run using historical data and similar weather scenarios to assess the worst-case scenario. The simulation results help determine which areas are most likely to be affected and how severe the impacts might be.

[0107] Based on the results of dynamic risk assessments, emergency response measures can be adjusted, such as starting water pumps in high-risk areas and shutting down critical facilities in vulnerable areas. This response strategy, based on real-time analysis and prediction, makes the response more precise and timely.

[0108] Through this in-depth relationship model analysis and dynamic risk assessment approach, the utility corridor flooding prediction and response optimization system can effectively address the challenges posed by extreme weather and mitigate its potential impact on utility corridor systems. This approach not only enhances prediction accuracy but also improves the timeliness and effectiveness of response measures.

[0109] Through the above strategies and technologies, the present invention not only effectively integrates and manages data from different sensors, but also handles potential data conflicts and ensures data consistency and integrity. This provides a solid foundation for subsequent data analysis and flooding prediction.

[0110] The relational model allows for a clear view of how different environmental data points interact and influence each other. This model not only helps understand the behavior of individual data points, but also reveals the complex interactions between multiple data points, which is crucial for predicting and responding to events in the tunnel system.

[0111] This invention leverages the relational model within a graph database to accurately simulate and predict dynamic changes in the tunnel system, such as flooding paths, impact speed, and potential impact range. Insights gained from the relational model enable more refined and targeted emergency response measures. For example, if graph analysis reveals that sensors in a particular area are frequently reporting high water levels, additional emergency resources can be prioritized for deployment in that area.

[0112] In one example, within a large utility corridor system, graph database analysis revealed that sensors in Areas A and B often displayed similar, rapid water level rises after heavy rain. By establishing edges between these sensors, it was possible to predict that if the water level in Area A began to rise, Area B would soon be affected. This knowledge enabled the proactive deployment of flood control measures in Area B, minimizing potential flooding damage.

[0113] In summary, by building sophisticated relational models in a graph database and analyzing these models, the effectiveness of pipeline corridor flooding prediction and response optimization methods can be greatly improved, enabling them to respond to potential environmental threats more intelligently and promptly.

[0114] Preferably, analyzing the graph relationships and nodes of the relationship model and extracting environmental features includes:

[0115] Perform path analysis and network flow analysis in the relational model to identify and quantify interactions between nodes and calculate key metrics in the graph database, including path strength and clustering coefficient, which are used to reveal close relationships and potential groups between data points.

[0116] Select nodes with relatively high centrality in the relationship model or nodes directly related to important environmental events as key nodes;

[0117] Analyze the properties of all key nodes, including the centrality of the nodes and the characteristics of adjacent nodes;

[0118] Based on the geographic information, physical properties and attributes of key nodes in the graph database, water flow dynamic characteristics and geological change characteristics are extracted and integrated into a set of environmental characteristics. The environmental characteristics include: water flow dynamic characteristics, geological change characteristics, and other environmental factors that have a significant impact on the safety and operation of the pipeline corridor system. The environmental characteristics are used to predict and assess potential risks faced by the pipeline corridor, optimize pipeline corridor maintenance strategies and reduce potential economic losses.

[0119] Path analysis and network flow analysis are used in graph databases to identify and quantify interactions between nodes. These analytical methods help determine the paths that information or materials (such as water flow) take through a network. Path analysis focuses on the strength and reliability of single or multiple shortest paths, while network flow analysis assesses the flow performance of an entire network under specific conditions. For pipeline flooding prediction, these analyses can be used to simulate the likely paths and velocities of water flowing through a pipe network, as well as how water will spread within the system during rainfall events or other flooding scenarios.

[0120] Path analysis primarily determines the shortest or most efficient paths between nodes, helping to understand how an event (such as flooding) propagates from one point to another in a tunnel system. Dijkstra's algorithm or the Floyd-Warshall algorithm can be used to find the shortest path between two points in a graph database. These algorithms can help identify critical transmission paths, such as those likely to serve as primary water flow channels in flood simulations.

[0121] Network flow analysis is used to assess the handling capacity of an entire network under a specific load. It is particularly useful for evaluating the drainage capacity of pipeline corridors during extreme rainfall events. The maximum flow and minimum cut theorem is applied to determine the network's maximum carrying capacity. The Ford-Fulkerson method or its modifications, such as the Edmonds-Karp algorithm, is used to calculate maximum flows, which helps identify nodes or connections that may become bottlenecks under high-flow conditions.

[0122] Key metrics in graph databases, such as path strength and clustering coefficient, are important tools for measuring the closeness of connections between nodes and the structure of groups. Path strength indicates the ability to transmit information or resources along a path, while the clustering coefficient reflects the degree of interconnectedness between a node's neighbors. A high clustering coefficient generally indicates a tightly connected group. In a utility corridor system, areas with a high clustering coefficient may indicate shared hydrogeological characteristics or similar risk profiles, which is crucial for identifying potential high-risk areas and developing appropriate protective measures.

[0123] Centrality metrics help identify the most influential nodes in a network. In a pipe corridor system, this might be the area with the highest water flow or the area most susceptible to influence.

[0124] The selection criteria and analysis process of key nodes include:

[0125] (1) Degree centrality: Calculates how many direct connections (i.e., edges) a node has. The higher the degree centrality of a node, the more influential it is in the network. It is suitable for identifying nodes that may be key intersections.

[0126] (2) Betweenness centrality: This measures how often a node appears on the shortest path between all pairs of nodes. Nodes with high betweenness centrality play a key role in connecting different parts of the network.

[0127] (3) Closeness centrality: Considering the distance from a node to all other nodes, a node with high closeness centrality means it is more likely to quickly influence other nodes in the network.

[0128] Based on the calculated centrality and the node's strategic location (e.g., proximity to important pipeline corridor facilities or historically flooded areas), nodes with high centrality and strategic locations are selected as key nodes. External factors, such as historical data, geographic factors, and the impact of recent engineering activities on centrality, are considered to dynamically adjust the selection of key nodes.

[0129] This invention not only increases the transparency of theoretical and practical operations, but also helps achieve more accurate and effective monitoring and risk management of pipeline corridor systems. If necessary, I can further expand on the implementation details of the specific algorithm or provide case studies of actual applications.

[0130] In a graph database, nodes with high centrality or those directly related to significant environmental events are selected as key nodes. Nodes with high centrality play a crucial role in the network and significantly influence the distribution of information or material flows. For example, a water level gauge at the intersection of a pipe corridor might be considered a key node due to its locational importance (high centrality). Analyzing the properties of these nodes, such as the characteristics of their neighboring nodes, can help predict the potential impact of a problem at that node on the entire system.

[0131] Based on the geographic information, physical properties, and attributes of key nodes in the graph database, key environmental features, such as water flow dynamics and geological changes, are extracted. These features comprehensively reflect the environmental status and potential risks of the pipeline corridor system. These extracted environmental features are used to assess risks faced by the pipeline corridor and optimize maintenance strategies. For example, if analysis reveals that the water flow dynamics in a certain area indicate a rapid rise in water levels after heavy rain, additional drainage facilities or geological reinforcement can be prioritized in this area to reduce potential economic losses.

[0132] Through the aforementioned in-depth graph relationship and node analysis, the present invention's pipeline corridor flooding prediction and response optimization method can more accurately identify and predict flooding risks and promptly respond to potential environmental threats. This not only enhances the safety of the pipeline corridor system, but also improves operational efficiency and disaster response capabilities, significantly reducing the losses that could result from failure to promptly predict and respond to flooding events. This approach enables managers to make data-driven decisions, optimize resource allocation, and take preventative measures when necessary to ensure the long-term stable operation of the pipeline corridor system.

[0133] Using environmental feature data to train a reinforcement learning model, enabling the model to adjust its prediction strategy through online learning and self-optimization, thereby obtaining a trained prediction model for real-time flooding prediction;

[0134] Reinforcement learning is a machine learning method in which a model learns to maximize a reward signal through interaction with its environment. In the context of pipeline corridor flooding prediction, this model can optimize performance by continuously receiving real-time environmental data and adjusting its prediction strategy. The model predicts future flooding events by analyzing environmental characteristics (such as water levels, rainfall, and groundwater flow) and adjusts and improves its prediction algorithm based on actual events (e.g., flooding).

[0135] Online learning refers to a model's ability to receive new data in real time and instantly update its learning results. Self-optimization refers to the model's ability to continuously adjust its internal parameters, such as the learning rate or decision threshold, during the learning process to improve prediction accuracy. Reinforcement learning algorithms such as Q-learning and Deep Q-Network (DQN) are used, which are capable of handling complex state spaces and finding optimal action strategies. The model self-corrects by comparing predicted results with actual results.

[0136] In one example, a model predicted flooding in an area within the next 24 hours, but flooding didn't actually occur. The model used this as negative feedback to adjust its prediction algorithm to reduce similar errors in the future. This continuous learning and adjustment process makes the model increasingly accurate, improving the reliability of predictions and the system's responsiveness.

[0137] In another example, the model can monitor environmental data in real time and predict areas most likely to flood. This is crucial for proactively deploying flood prevention measures and preparing for emergency responses. Over time, as the reinforcement learning model accumulates more data and experience, its prediction accuracy and operational efficiency will significantly improve, helping to reduce property damage and maintenance costs caused by flooding.

[0138] This invention allows tunnel system managers to leverage real-time data and advanced machine learning techniques to predict and address flooding risks, significantly improving their ability to prevent and respond to floods. Furthermore, this technology helps optimize tunnel operation and maintenance strategies, resulting in more efficient and cost-effective management.

[0139] Preferably, the use of environmental feature data to train the reinforcement learning model includes:

[0140] The environmental feature data is input into the model in batches for training, and the reinforcement learning algorithm is used to learn the optimal strategy through interaction with the environment;

[0141] Evaluate the model's prediction performance after each training cycle and adjust the model parameters based on the evaluation results. Adjusting the model parameters includes modifying the reward function or learning rate to optimize the model's prediction performance.

[0142] Learning is performed based on the input environmental characteristics, and the prediction strategy and parameters are optimized through repeated evaluation and adjustment to obtain a fully trained prediction model.

[0143] In reinforcement learning, batch processing of data is an effective training strategy, especially when the dataset is large or updated in real time. This approach allows the model to gradually absorb new information and update its strategy, rather than processing all data at once, thereby improving learning efficiency and effectiveness. In the pipeline corridor flooding prediction model, environmental characteristic data (such as water level, rainfall, temperature and humidity) is fed into the model in batches. This allows the model to update instantly upon receiving new flooding event data, reflecting the latest environmental changes.

[0144] Reinforcement learning optimizes decision-making strategies through interactive learning with the environment. After each decision, the model receives feedback (rewards or penalties) from the environment and adjusts its behavior based on this feedback to maximize future rewards. In applications such as predicting flood risk, the model might receive rewards or penalties based on the accuracy of its predictions. For example, if the model accurately predicts the time and location of a flood event, it receives a positive reward; if the prediction is incorrect, it receives a negative reward.

[0145] When the model's predictions match the actual events (e.g., accurately predicting the time and location of flooding), the model should receive positive rewards. When the model's predictions fail (e.g., failing to predict the actual flooding or incorrectly predicting that flooding did not occur), the model should receive negative rewards.

[0146] The size of the reward is generally proportional to the accuracy and timeliness of the prediction. For example, the closer the prediction is to the actual flooding event, the larger the reward. Conversely, the greater the error, the more severe the penalty.

[0147] The reward function may need to be adjusted at different stages of training or for different types of water events to reflect different learning priorities. For example, in the early stages, more positive rewards may be given to encourage model exploration. As the model matures, the proportion of penalties can be increased to improve prediction accuracy.

[0148] The learning rate determines how quickly the model updates its parameters when it receives new information. A suitable learning rate adjustment strategy is crucial for fast model convergence and avoiding overfitting.

[0149] As training progresses, gradually reducing the learning rate can help the model make more subtle adjustments when approaching the optimal solution and avoid oscillation near the optimal solution. You can use strategies such as exponential decay and step descent. For example, reduce the learning rate by 10% after every 1000 training cycles.

[0150] If the model's performance on the validation set doesn't improve over several epochs, increasing the learning rate may help the model escape the local minimum. Use performance-triggered adjustments (e.g., increase the learning rate when validation error stops decreasing) or increase the learning rate if the error is decreasing but too slowly.

[0151] Reinforcement learning models not only effectively adjust their behavior based on environmental feedback but also maintain good adaptability and efficient learning speed across a variety of training environments. The implementation of these strategies directly impacts the model's performance in real-world applications, especially in complex and dynamically changing environments, such as pipeline corridor flooding prediction systems.

[0152] The performance of a reinforcement learning model depends on its parameter settings, such as the form of the reward function and the learning rate. The learning rate determines how quickly the model updates its knowledge base, while the reward function defines the metric by which the model's behavior is evaluated. These parameters are adjusted after each training cycle by analyzing the model's predictions. For example, if the model is found to be too slow to respond to certain types of flooding events, the reward strength for these events might be increased or the learning rate adjusted to encourage the model to more quickly learn the importance of these events.

[0153] Through continuous online learning and self-optimization, the model can adapt to environmental changes, such as seasonal climate or geographical variations, thereby maintaining the timeliness and accuracy of the forecasting strategy. Over time and with the accumulation of data, the model will become increasingly accurate in predicting pipeline corridor flooding events, reducing false positives and missed alerts, and effectively improving the reliability of the early warning system.

[0154] In one example, after a major flood event, the model failed to accurately predict flooding in a low-lying area. By analyzing data from this event, the model learned the vulnerability of low-lying areas to extreme rainfall and subsequently adjusted its parameters to assign higher risk scores to such areas. Under similar climate conditions in the future, the model-based early warning system can issue earlier alerts, prompting timely response measures.

[0155] Through the continuous learning and adjustment of the present invention, the enhanced learning model not only improves the ability to identify and respond to potential risks in the pipeline corridor system, but also helps managers develop more effective maintenance strategies and disaster reduction measures, thereby significantly reducing the economic losses caused by flooding.

[0156] The latest environmental data is fed into the prediction model for real-time flooding predictions, while digital twin technology is used to simulate scenarios and verify the prediction results, by identifying inconsistencies or errors in the simulation and making adjustments;

[0157] Real-time flooding prediction involves feeding a predictive model with real-time environmental data, including but not limited to water levels, rainfall, and groundwater flow. Based on patterns derived from historical data, the model analyzes current data in real time to predict possible future flooding events. For example, if the model detects a persistent rise in water levels at a particular location and rainfall exceeds the threshold that typically causes flooding, the model will predict flooding risk based on this real-time data and issue an early warning.

[0158] Digital twin technology creates a virtual replica of a physical system, allowing real-world scenarios to be simulated and analyzed in a virtual environment. By inputting the same data as the actual environment, digital twin models can test and validate predictions in a safe simulation environment. In the case of a utility corridor system, digital twins can simulate water flow dynamics under specific rainfall and water level conditions, as well as the potential extent and depth of flooding.

[0159] The flooding predictions generated by the predictive model are fed into the digital twin model, and the predicted flooding event is simulated to verify the accuracy of the predictions. This includes verifying that the water flow path and affected areas are consistent with the predictions. If the simulation reveals discrepancies between the predictions and the actual possible scenario, such as a discrepancy between the predicted water level and the simulated flooding range, the model can be adjusted. This may include adjusting certain model parameters, such as threshold settings, or refining the data input and processing logic.

[0160] In one example, during a heavy rainstorm warning period for a large utility corridor system, real-time data indicated a sharp rise in water levels in a certain area. This data was fed into a forecasting model, which predicted severe flooding in that area within the next few hours. This prediction was then validated against a digital twin model. The simulation revealed that the predicted flooding area was larger than the actual area, suggesting that the model may have overreacted to certain input variables. Based on this finding, the model's sensitivity parameters were adjusted to ensure more accurate forecasts.

[0161] Combining real-time flooding prediction with digital twin technology not only enables highly accurate risk prediction, but also enables the validation and refinement of prediction strategies within a secure simulation environment. This approach significantly enhances the reliability and adaptability of the prediction system, providing a powerful decision-making tool for utility corridor management, ultimately aiming to reduce potential damage and economic losses caused by flooding. This technology makes it possible to address complex and dynamic environmental challenges, and is particularly valuable in responding to extreme climate events and other unpredictable situations.

[0162] Preferably, the scene simulation using digital twin technology includes:

[0163] The scenario is simulated using a digital twin model of the tunnel, which is a virtual copy of the tunnel entity, including all relevant physical and environmental characteristics of the tunnel entity;

[0164] The prediction results are input into the digital twin model, including: predicted water level, water flow dynamics, prediction time and its affected area;

[0165] The predicted flooding events were simulated in the digital twin model, and the process of water flow affecting the pipe gallery structure, including the speed and range of water level rise, was observed and recorded in real time to obtain simulation results.

[0166] Digital twin technology involves creating a highly accurate virtual model of a physical system—in this case, a utility corridor. This model not only includes the corridor's geometry and location but also meticulously simulates various physical and environmental characteristics, such as material properties, structural integrity, and the water absorption of the surrounding soil. By integrating real-time data from sensors (such as water levels and flow rates), the digital twin model can reflect the behavior and response of the actual utility corridor under specific conditions.

[0167] The predictive model generates flooding predictions based on real-time environmental data, including predicted water levels, flow dynamics, and the expected time and area of ​​impact. These predictions are fed into the digital twin model. For example, if the predictive model predicts that a certain area will experience flooding based on continuous rainfall data, this data is used to set the corresponding initial and boundary conditions in the digital twin model.

[0168] The digital twin model uses predicted inputs to simulate how water flows through the actual terrain and pipe network, including flow speed, direction, and the extent of water level rise. The simulation can show how water flows from overflow areas to low-lying areas and how water levels change over time in different areas.

[0169] The digital twin model uses the principles of fluid dynamics to simulate the behavior of water in the pipeline corridor system. This involves solving the Navier-Stokes equations, which describe the motion of fluids and account for factors such as velocity, pressure, density, and viscosity. The model can simulate complex flow phenomena such as eddies and turbulence at varying flow rates and flow directions, as well as their specific manifestations in pipe corridor bends, branches, narrowing, and expansion sections.

[0170] The interaction between water flow and the pipe corridor structure primarily involves impact on the pipe walls, friction, and pressure changes. These interactions are simulated in the model based on the physical properties of the pipe material (such as hardness, elasticity, and friction coefficient). The model allows observation of how the pressure of water on the pipe walls at high flow rates can lead to structural stress, deformation, or, in extreme cases, failure.

[0171] Interfacial interaction effects include erosion of the pipe gallery wall by water flow and localized flow anomalies caused by pipe material or structural defects. The model simulates how water flow can accelerate wear or widen cracks in areas of soft or cracked pipe walls.

[0172] The physical properties of a tunnel include its material's compressive strength, corrosion resistance, density, and thermal conductivity. These properties determine its stability and durability under varying environmental conditions. Digital twins use physical modeling to ensure these properties are accurately reflected. For example, finite element analysis (FEA) techniques are used to simulate the material's behavior under varying pressures and temperatures.

[0173] Environmental characteristics include the surrounding soil's water absorption and permeability, geological structure, and climatic conditions such as rainfall and temperature. In the model, rainfall is simulated by setting rainfall intensity and duration, and its impact on water level fluctuations is predicted using soil infiltration models and groundwater flow models. Geological changes, such as fluctuations in the groundwater level and soil erosion, are also taken into account to assess their potential impact on the tunnel's structural stability.

[0174] Simulations conducted within the digital twin model allow engineers and decision-makers to observe the development of a predicted flooding event in real time, including how water levels will rise and the maximum extent they may reach. Detailed results from these simulations, such as maps of water velocity and depth of the affected area, provide invaluable information to emergency response teams.

[0175] In one example, a digital twin model simulated a severe storm event in an urban area with a history of frequent flooding. The simulation revealed that the drainage system in a newly developed area might be overwhelmed. Based on this information, the local government could adjust drainage plans, deploy mobile pumping stations, or evacuate residents in advance of the storm.

[0176] The key to ensuring that prediction results (such as water levels and flow dynamics) are accurately input into the digital twin model is to establish a reliable data interface that can capture and transmit prediction data in real time. Data synchronization between the model and the prediction system can be achieved through APIs (application programming interfaces) or data middleware. These tools can help ensure the immediate update and integrity of data during transmission, preventing data loss or delays.

[0177] After running a simulation in the digital twin model, compare the simulation results with the actual predicted data and analyze any discrepancies. If discrepancies are found between the prediction and the simulation, examine the data input process, the model parameter settings, or the prediction algorithm itself. Depending on the specific discrepancies, it may be necessary to adjust certain model parameters (such as boundary conditions and initial settings) or optimize the prediction algorithm to improve accuracy.

[0178] In a digital twin model, dynamic changes in water flow can be observed through real-time visualization tools. These tools typically include graphical interfaces that display water levels, flow rates, and their changing trends. They can even provide 3D visualization to more intuitively illustrate the distribution and movement of water within the pipeline corridor system. This functionality can be achieved using GIS (Geographic Information Systems) and advanced simulation software such as ANSYS or COMSOL, which can process complex data sets and provide real-time feedback.

[0179] The simulation results are evaluated by a professional team, who compare them to historical data and on-site conditions to verify the accuracy of the simulation. Once the simulation results are verified, the data is used to support emergency response decisions. For example, if the simulation shows that water levels in a certain area are rising rapidly and may overflow, the emergency management team can deploy flood control measures in advance, such as strengthening levees, activating drainage pumping stations, or evacuating residents.

[0180] By comparing the simulation results of the digital twin model with the actual occurrence of the event, the accuracy of the prediction model can be verified. If significant differences are found between the prediction and the actual situation, the model parameters can be adjusted to address the inconsistencies and improve the accuracy of future predictions.

[0181] Digital twin technology provides a powerful tool for predicting pipeline corridor flooding. It not only serves as a predictive validation and decision support tool, but also enhances the effectiveness of emergency response. This technology allows for the prediction and rehearsal of various scenarios before risks occur, significantly improving the proactive and targeted nature of disaster prevention and mitigation efforts.

[0182] Preferably, by comparing the data of the simulation results with the prediction results, verifying the consistency and accuracy of the two, checking whether all predicted parameters are reasonably reflected in the simulation, identifying any inconsistencies or errors in the simulation, analyzing the causes and making targeted adjustments, updating the prediction results, the adjusted prediction results match the simulation results, and generating a simulation feedback report;

[0183] The simulation feedback report includes: the simulation process, observed phenomena, identified errors, and problems identified during the simulation process and optimization suggestions.

[0184] In digital twin technology, by inputting predicted results (such as water levels and flow dynamics) into a digital model for simulation, predicted flooding events can be observed and simulated in real time. Comparing these simulation results with actual predicted data is a key step in verifying the model's accuracy. Implementing this step involves two key activities: first, reproducing the predicted conditions in a simulated environment, and then recording and analyzing the results. Simulations can fully demonstrate the predicted flooding process, including the speed and extent of the water flow, in a controlled environment.

[0185] Identifying inconsistencies or errors between simulations and predictions can reveal flaws in the predictive model or issues with the data input. This step is crucial for model iteration and optimization, ensuring the accuracy and reliability of the forecast results. For example, if the predictive model fails to accurately predict the rate of water level rise in a certain area, this discrepancy will be flagged during the simulation. This analysis may reveal data processing errors or insufficient model response to certain variables.

[0186] These errors often arise from model assumptions, data input errors, or limitations of the simulation software. For example, if a water level sensor is inaccurately calibrated, it can lead to systematic overestimation or underestimation of the water level. These errors arise from fluctuations in natural variables or random noise in the measurements. For example, rapid changes in rainfall or occasional fluctuations in sensor readings. Data validation and error detection algorithms, such as outlier detection, are used to identify inconsistencies in the data. Statistical methods, such as standard deviation and analysis of variance, can be used to assess random errors.

[0187] Use statistical software (such as R or Python's SciPy library) to perform regression analysis to quantify the relationship between input variables and simulation outputs and identify possible sources of systematic error. Use linear regression analysis of predicted and actual water level data to identify trends in model deviations, allowing adjustments to input parameters or model assumptions to reduce these deviations.

[0188] A simulation feedback report consolidates key observations, identified errors, and optimization recommendations from the simulation process. This report not only summarizes the simulation process but also provides specific guidance for improving the predictive model. The report typically includes a detailed description of the simulation, observed phenomena, a detailed analysis of any issues, and recommended adjustments based on the observations. This information helps decision makers understand the model's behavior and guide future optimization efforts.

[0189] Specific strategies for targeted adjustments include:

[0190] Create a decision tree or flowchart to clearly define the response steps after a specific error is identified. For example, if a data entry error is discovered, the flowchart can guide re-entering the data; if the error is caused by a model parameter, it can guide parameter adjustment. Use flowchart software (such as Microsoft Visio or an online flowchart tool) to create and share these flowcharts to ensure that all team members understand the action steps after an error is identified.

[0191] If the simulation shows that flooding in a particular area is significantly greater than predicted, adjustments to the terrain data or water velocity parameters may be necessary. After making these adjustments, rerun the simulation to verify the effectiveness of the adjustments. Record the results before and after the adjustments to evaluate their effectiveness. Provide a detailed comparative analysis and subsequent optimization recommendations in the simulation feedback report.

[0192] This systematic comparative analysis and feedback mechanism allows for continuous improvement in the accuracy of the forecast model. With each application of feedback, the model is gradually optimized, and the forecast results are increasingly close to actual conditions. In one real-world application, simulations revealed that the forecast model was overly sensitive, overreacting to small-scale rainfall events and resulting in unnecessary warnings. The feedback report recommended adjusting the model's sensitivity threshold, which reduced the false alarm rate in subsequent forecasts.

[0193] Through this in-depth analysis and continuous optimization, digital twin technology not only improves the disaster prevention capabilities of the utility corridor system but also provides a highly reliable support tool for corridor management, making response strategies more scientific and effective. The application of this method has greatly improved the safety of the utility corridor system and the proactiveness of disaster response.

[0194] Based on the adjusted prediction results and simulation feedback reports, the decision support system generates and implements corresponding emergency response measures, and optimizes the response measures through continuous monitoring and evaluation to improve the system's response efficiency and accuracy.

[0195] The Decision Support System (DSS) is an integrated platform that combines real-time data analytics, predictive models, and digital twin technology to provide evidence-based decision support. The system uses adjusted forecasts and simulation feedback reports to generate emergency response measures for impending flooding events. The system adjusts response strategies by analyzing the accuracy of forecasts and the effectiveness of simulations. For example, if simulations indicate that flood control measures in a particular area are ineffective, the system may recommend strengthening flood control facilities in that area.

[0196] Based on the simulation results from the digital twin model, a decision support system can plan and implement specific emergency response measures in detail, such as starting pumps, closing valves, or evacuating people. These response measures are optimized based on continuous monitoring and evaluation. The monitoring system tracks the effectiveness of implemented measures in real time, assesses the consistency of data with the predictive model, and adjusts operations in a timely manner to improve response efficiency and accuracy.

[0197] The system's optimized response measures enable faster and more accurate responses to flooding incidents, mitigating potential damage. For example, by monitoring water level rise in real time and using model predictions, pumping stations can be activated hours in advance, preventing dangerous water levels from reaching critical levels. With each incident, the system accumulates more data, optimizing the model's accuracy and making future predictions and responses even more precise.

[0198] In one example, a decision support system in a city with a history of frequent flooding used data collected from a previous flood and simulation feedback to adjust the city's drainage system design. During the subsequent rainy season, the system successfully predicted and responded to multiple potential flooding events, significantly reducing property damage and improving public safety by dynamically adjusting pump station operations and issuing timely evacuation orders.

[0199] Decision support systems not only provide real-time and dynamic emergency response, but also, through continuous learning and adjustment, improve the overall efficiency and effectiveness of the system, significantly enhancing public safety. The implementation of such systems is a key innovation in modern urban management, particularly in the field of disaster management.

[0200] Preferably, emergency response measures are formulated based on the adjusted forecast results and the simulation feedback report, wherein the emergency response measures are dynamically adjusted based on the optimization suggestions to address potential changes or factors not fully considered in the forecast;

[0201] The monitoring system tracks the implementation of emergency response measures in real time, collects feedback data during the implementation process, and evaluates the actual effect of the response. The feedback data includes: the implementation status, time, efficiency and actual control effect of the emergency response measures on the impact of flooding.

[0202] Emergency response plans are developed based on adjusted forecasts and simulation feedback reports. These reports provide detailed information about the predicted flooding event, such as the likely water level, impacted areas, and potential risks. Based on this data, decision makers can plan specific response measures, such as activating pumps, shutting off valves, and evacuating personnel. Emergency response measures must be dynamically adjusted based on real-time data and ongoing assessments. This means that response plans must be flexible and able to quickly adapt to new or changing circumstances, such as forecast errors and unexpected weather changes.

[0203] By monitoring the emergency response in real time, the system collects detailed data on each measure, including the time, efficiency, and effectiveness of each action. This data is used to assess the effectiveness of the response and identify any implementation issues or deficiencies. This information is then used to further optimize the response strategy.

[0204] By dynamically adjusting response measures based on real-time predictions and feedback, response teams can more precisely handle flooding incidents, reducing potential losses and impacts. The collected performance data helps decision-makers understand which strategies are most effective and which require improvement, thereby improving their ability to handle future incidents.

[0205] In one example, a new flooding prediction model predicted a widespread flooding event within a city's pipeline corridor system. Based on the prediction results and previous simulation feedback, the emergency response center quickly initiated pre-defined drainage measures and issued evacuation notices for the surrounding area. As the incident progressed, the monitoring system indicated that some drainage pumps were not meeting efficiency standards. Immediate adjustments were made, including adding additional pumping stations and optimizing coordination between pumping stations, effectively controlling the rate of water level rise.

[0206] Preferably, the updated prediction results are compared with the actual effects, and the emergency response strategy and prediction model are optimized based on the comparison results and feedback data.

[0207] This process involves comparing updated forecasts with the effects of actual observed flooding events. The goal is to validate the accuracy and practicality of the forecast model, particularly its performance in real-world applications. Actual flooding data (such as water level measurements, rainfall records, and the extent of the affected area) is collected and compared with the corresponding data predicted by the model. This includes an accurate match of the predicted water height, arrival time, duration, and affected area.

[0208] Insights gained from comparing forecasts with actual outcomes can be used to adjust and optimize emergency response strategies and forecast models. This can include improving data inputs, adjusting model parameters, and redefining key variables and thresholds in forecast algorithms. Based on this feedback, if the rate of water level rise in a particular region is systematically underestimated, it may be necessary to adjust the sensitivity of the terrain data for that region or improve the model's responsiveness to rainfall inputs.

[0209] Through continuous comparison and adjustment, the forecasting model gradually improves its ability to predict future events, reduces errors, and increases the accuracy of prediction results. Accurate forecasts make emergency responses more targeted and timely, improving the effectiveness and efficiency of response measures.

[0210] Based on the comparison of actual events with predicted data, model parameters are adjusted to improve forecast accuracy and timely response. Model adjustment involves modifying the model's sensitivity, threshold settings, or introducing new variables to better reflect real-world conditions. Machine learning algorithms are used to automatically adjust model parameters, such as using gradient descent to optimize forecast error or genetic algorithms to find the optimal parameter combination.

[0211] Sensitivity analysis is to determine the degree to which the model responds to small changes in input variables. If the model is too sensitive to certain inputs, it may be necessary to reduce this sensitivity to avoid prediction errors caused by small external fluctuations. The setting of thresholds directly affects the triggering conditions of the prediction. For example, adjusting the water level threshold for flood warning may be based on the difference between the actual observed water level and the predicted water level in the recent flood event. The adjustment process should be based on the actual data collected, such as data from historical events, accuracy assessment of simulation feedback, and post-evaluation of the response effect. These data support decision makers in understanding which parameter adjustments are necessary.

[0212] Regularly review existing emergency response processes and assess their performance in actual emergencies. Use simulation drills and feedback from actual incidents to identify bottlenecks or deficiencies in the process. Based on this feedback, optimize the process by streamlining the decision chain, improving response speed, or reconfiguring resource allocation to ensure critical resources reach where they are most needed.

[0213] Ensure that all feedback data is systematically recorded and analyzed. Use a data management system (such as a CRM or ERP system) to track and store all relevant information, including response time, effectiveness of implementation measures, and effectiveness of flood control. Establish a continuous improvement mechanism to convert feedback data into actionable improvement measures. Regularly update prediction models and response strategies to ensure the system can adapt to changes in environmental and socioeconomic conditions.

[0214] In one example, a traditional flooding prediction model in a coastal city frequently misjudged flooding due to irregular rainfall patterns caused by climate change. By implementing the aforementioned comparison and optimization process, city management adjusted their prediction model, particularly its response to rainfall intensity. During the next storm season, the model successfully predicted flooding risks in several key areas, enabling timely preventative measures to be initiated, effectively avoiding significant property damage and casualties.

[0215] Through such in-depth analysis and continuous optimization, the pipeline corridor flooding prediction and response system of the present invention can more accurately respond to future challenges, improve its adaptability to complex environmental changes, and provide solid scientific and technological support for the safe management of urban infrastructure.

[0216] like Figure 3 As shown, a pipeline corridor flooding prediction and response optimization system includes:

[0217] The data acquisition module, deployed inside and outside the tunnel, collects raw environmental data and fiber optic signal data. This module deploys a variety of sensors, such as water level sensors, temperature and humidity sensors, and meteorological sensors, to collect real-time data on the tunnel environment. It also uses optical fibers to capture changes in acoustic or vibration signals. These technologies continuously monitor various environmental parameters and provide raw input for the data processing module. By accurately monitoring environmental changes in real time, this module ensures that the system captures all key environmental indicators from the very beginning of data acquisition, which is the basis for accurately predicting flooding risks.

[0218] The data processing module, connected to the data acquisition module, removes noise, addresses missing values, and synchronizes time series on the collected raw environmental data to generate primary fused data. Connected to the sensor network, this module performs preliminary processing on the received raw environmental data, including removing noise, filling missing values, and synchronizing time series to generate primary fused data. These processing steps are necessary to improve data quality and lay the foundation for further complex analysis. The resulting primary fused data is cleaner and more consistent, providing a reliable foundation for advanced data analysis and feature extraction.

[0219] The Relationship Model Building module, based on the primary fused data, constructs a relational model between data within a graph database, analyzes the graph relationships and nodes within the relational model, and extracts environmental feature data. Based on the primary fused data, this module constructs a relational model between data within the graph database. By analyzing the graph relationships and nodes within these relational models, the module can extract deeper environmental features from the data, such as water flow dynamics and geological changes. This approach not only captures direct relationships between data but also reveals hidden patterns and connections, which is critical for predicting complex environmental events such as flooding.

[0220] The reinforcement learning model training module uses the extracted environmental feature data to train the reinforcement learning model. Through online learning and self-optimization, the model adjusts its prediction strategy to obtain a trained prediction model. The reinforcement learning model is trained using the environmental feature data extracted from the relational model. This model type continuously adjusts its prediction strategy to adapt to new data inputs through online learning and self-optimization. The reinforcement learning model can adaptively improve its performance, ensuring high prediction accuracy even when environmental conditions change.

[0221] The real-time prediction module feeds the latest environmental data into the prediction model for real-time flooding predictions. It also uses digital twin technology to simulate scenarios and verify the prediction results, identifying inconsistencies or errors in the simulation and making adjustments. The module also feeds the latest environmental data into the trained prediction model for real-time flooding predictions. Simultaneously, digital twin technology is used to simulate scenarios and verify the prediction results, identifying inconsistencies or errors in the simulation and making adjustments. This integrated real-time prediction and simulation verification ensures the reliability of the prediction results and allows the system to quickly adjust when problems are discovered.

[0222] The decision support module generates and implements appropriate emergency response measures based on adjusted forecast results and simulation feedback reports. Based on verified forecast results and simulation feedback reports, this module generates and implements appropriate emergency response measures, which are customized based on current risk assessments and resource availability. This enables rapid and accurate response, significantly improving the timeliness and effectiveness of emergency response and reducing potential losses and impacts.

[0223] The monitoring and evaluation module optimizes response measures through continuous monitoring and evaluation, improving the efficiency and accuracy of the system's response. Through continuous monitoring and evaluation, this module continuously optimizes response measures, collects feedback, and adjusts prediction models and response strategies. This ensures that the entire system continuously learns and adapts to new challenges, improving the efficiency and accuracy of the entire prediction and response system.

[0224] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0225] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems) and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0226] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0227] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0228] In a typical configuration, a computing device includes one or more processors (CPUs), input / output interfaces, network interfaces, and memory.

[0229] Memory includes non-permanent storage in a computer-readable medium, in the form of random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of a computer-readable medium.

[0230] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can be implemented using any method or technology for information storage. Information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase-change RAM (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices, or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include transitory computer-readable media, such as modulated data signals and carrier waves.

[0231] It should also be noted that the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, commodity, or apparatus that includes a series of elements includes not only those elements but also other elements not explicitly listed, or includes elements inherent to such process, method, commodity, or apparatus. In the absence of further limitations, an element defined by the phrase "comprises a ..." does not exclude the presence of other identical elements in the process, method, commodity, or apparatus that includes the element.

[0232] The above are merely embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various modifications and variations. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application should be included within the scope of the claims of the present application.

Claims

1. A pipeline corridor flooding prediction and response optimization method, characterized in that: The following steps are involved: Through the deployed sensor network and distributed acoustic sensing technology, raw environmental data and fiber optic signal data inside and outside the tunnel are collected. The raw environmental data and fiber optic signal data are de-noised, missing value processed, and time series synchronized to obtain primary fused data. Denoising methods include using sliding average and median filtering techniques to smooth the data series. Based on the primary fusion data, a relational model between data is constructed in the graph database, the graph relationships and nodes of the relational model are analyzed, and environmental features are extracted; Using environmental feature data to train a reinforcement learning model, enabling the model to adjust its prediction strategy through online learning and self-optimization, thereby obtaining a trained prediction model for real-time flooding prediction; The latest environmental data is fed into the prediction model for real-time flooding predictions, while digital twin technology is used to simulate scenarios and verify the prediction results, by identifying inconsistencies or errors in the simulation and making adjustments; Based on the adjusted prediction results and simulation feedback reports, the decision support system generates and implements corresponding emergency response measures, and optimizes the response measures through continuous monitoring and evaluation to improve the system's response efficiency and accuracy. The raw environmental data monitored by the sensor network includes: water level data, groundwater pressure, temperature, humidity, rainfall, wind speed and direction, and geological data; distributed acoustic sensing technology uses optical fibers to capture changes in sound waves or vibration signals to detect irregular activities and abnormal water flows. Distributed acoustic sensing technology uses optical fibers arranged along the pipeline corridor to accurately locate the source of sound or vibration.

2. The pipeline corridor flooding prediction and response optimization method according to claim 1 is characterized in that: Based on the primary fusion data, integrating and mapping the data from at least two sensors so that each data point has a unique identity and measurement attributes; Graph database technology is used to establish data nodes and edges based on the correlation and spatial location relationship between data. By defining nodes to represent each monitoring point or sensor, and edges to represent the association between nodes based on physical location or data behavior, the construction of the relational model in the graph database is completed; the relational model is used to represent the connection and interaction between different environmental data points.

3. The pipeline corridor flooding prediction and response optimization method according to claim 2 is characterized in that: Analyzing the graph relationships and nodes of the relationship model and extracting environmental features includes: Perform path analysis and network flow analysis in the relational model to identify and quantify interactions between nodes and calculate key metrics in the graph database, including path strength and clustering coefficient, which are used to reveal close relationships and potential groups between data points. Select nodes with relatively high centrality in the relationship model or nodes directly related to important environmental events as key nodes; Analyze the properties of all key nodes, including the centrality of the nodes and the characteristics of adjacent nodes; Based on the geographic information, physical properties and attributes of key nodes in the graph database, water flow dynamic characteristics and geological change characteristics are extracted and integrated into a set of environmental characteristics. The environmental characteristics include: water flow dynamic characteristics, geological change characteristics, and other environmental factors that have a significant impact on the safety and operation of the pipeline corridor system. The environmental characteristics are used to predict and assess potential risks faced by the pipeline corridor, optimize pipeline corridor maintenance strategies and reduce potential economic losses.

4. The pipeline corridor flooding prediction and response optimization method according to claim 1 is characterized in that: The use of environmental feature data to train the reinforcement learning model includes: The environmental feature data is input into the model in batches for training, and the reinforcement learning algorithm is used to learn the optimal strategy through interaction with the environment; Evaluate the model's prediction performance after each training cycle and adjust the model parameters based on the evaluation results. Adjusting the model parameters includes modifying the reward function or learning rate to optimize the model's prediction performance. Learning is performed based on the input environmental characteristics, and the prediction strategy and parameters are optimized through repeated evaluation and adjustment to obtain a fully trained prediction model.

5. The pipeline corridor flooding prediction and response optimization method according to claim 1 is characterized in that: The scenario simulation using digital twin technology includes: The scenario is simulated using a digital twin model of the tunnel, which is a virtual copy of the tunnel entity, including all relevant physical and environmental characteristics of the tunnel entity; The prediction results are input into the digital twin model, including: predicted water level, water flow dynamics, prediction time and its affected area; The predicted flooding events were simulated in the digital twin model, and the process of water flow affecting the pipe gallery structure, including the speed and range of water level rise, was observed and recorded in real time to obtain simulation results.

6. The pipeline corridor flooding prediction and response optimization method according to claim 5 is characterized in that: By comparing the simulation results with the predicted results, verifying their consistency and accuracy, checking whether all predicted parameters are reasonably reflected in the simulation, identifying any inconsistencies or errors in the simulation, analyzing the causes and making targeted adjustments, updating the predicted results, ensuring that the adjusted predicted results match the simulation results, and generating a simulation feedback report; The simulation feedback report includes: the simulation process, observed phenomena, identified errors, and problems identified during the simulation process and optimization suggestions.

7. The pipeline corridor flooding prediction and response optimization method according to claim 6 is characterized in that: Formulate emergency response measures based on the adjusted forecast results and simulation feedback reports. Dynamically adjust emergency response measures based on optimization recommendations to address potential changes or factors not fully considered in the forecast; The monitoring system tracks the implementation of emergency response measures in real time, collects feedback data during the implementation process, and evaluates the actual effect of the response. The feedback data includes: the implementation status, time, efficiency and actual control effect of the emergency response measures on the impact of flooding.

8. The pipeline corridor flooding prediction and response optimization method according to claim 7 is characterized in that: Compare the updated prediction results with the actual effects, and optimize the emergency response strategy and prediction model based on the comparison results and feedback data.

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