Petrochemical engineering pipeline leakage intelligent detection and repair system and method thereof

Abstract

The invention belongs to the technical field of petrochemical engineering pipeline monitoring, and discloses an intelligent detection and repair system and method for petrochemical engineering pipeline leakage, and the method comprises the following modules: a data collection module which comprehensively monitors the leakage condition and environmental parameters in a pipeline by deploying a high-precision and low-power-consumption multi-parameter sensor network; the data transmission module is responsible for safely and reliably transmitting the collected data to the cloud processing center; the data processing and analyzing module is used for cleaning, processing and analyzing the collected data and extracting feature information; the leakage identification module is used for identifying and judging leakage events based on various algorithms; the leakage positioning module is used for accurately determining a leakage position; and the leakage evaluation module is used for evaluating the leakage degree, type and potential risk.

Description

Technical Field

[0001] This application relates to the field of pipeline monitoring technology, and more specifically, to an intelligent detection and repair system and method for leaks in petrochemical pipelines; Background Technology

[0002] Petrochemical pipelines are a vital means of modern energy transmission, and their safe operation is crucial for economic development and environmental protection. Existing petrochemical pipeline detection technologies primarily rely on physical sensors and data analysis techniques. While these technologies can detect leaks to a certain extent, they have significant limitations in providing follow-up handling recommendations and process guidance. Due to factors such as long-term operation, material aging, corrosion, and external damage, pipeline leaks occur frequently, causing not only economic losses but also potential environmental pollution and safety incidents.

[0003] Existing pipeline leak detection technologies mainly include pressure monitoring, flow monitoring, and fiber optic sensing, but these methods suffer from low detection sensitivity, high false alarm rates, and poor location accuracy. Meanwhile, traditional leak repair relies on manual inspection and repair, which is inefficient and poses safety risks.

[0004] Therefore, developing a high-precision, intelligent leak detection and repair system for petrochemical pipelines is of great significance for ensuring the safe operation of petrochemical pipelines.

[0005] In view of this, we propose an intelligent detection and repair system and method for leaks in petrochemical pipelines; Summary of the Invention

[0006] 1. Technical problems to be solved The purpose of this application is to provide an intelligent detection and repair system and method for pipeline leaks in petrochemical industries. By constructing a multi-level intelligent sensor network and deep learning algorithms, the system can achieve real-time monitoring, precise location and intelligent repair of pipeline leaks, thereby significantly improving pipeline safety and operational efficiency.

[0007] 2. Technical Solution This application provides an intelligent detection and repair system for leaks in petrochemical pipelines, comprising the following modules: a data acquisition module, which comprehensively monitors the leak status and environmental parameters within the pipeline by deploying a high-precision, low-power multi-parameter sensor network; a data transmission module, responsible for securely and reliably transmitting the acquired data to a cloud processing center; a data processing and analysis module, which cleans, processes, and analyzes the acquired data to extract feature information; a leak identification module, which identifies and judges leak events based on various algorithms; a leak location module, which accurately determines the leak location; a leak assessment module, which assesses the degree, type, and potential risks of the leak; a repair decision module, which formulates the optimal repair plan based on the leak situation; an automatic repair execution module, which controls the intelligent repair equipment to perform repair operations; a human-machine interaction module, which provides maintenance personnel with an intuitive system operation interface; and a system self-learning module, which continuously optimizes the algorithms and decision models.

[0008] Furthermore, the data acquisition module includes: a multi-parameter sensor network, including pressure sensors, flow sensors, temperature sensors, acoustic sensors, gas concentration sensors, strain sensors, and corrosion sensors; a sensor deployment optimization unit, which optimizes the sensor placement and density based on pipeline structure, material, and operating conditions; a low-power design unit, which uses low-power chips and intelligent sleep strategies to extend sensor lifespan; and a self-calibration mechanism, where sensors periodically self-calibrate to ensure long-term monitoring accuracy.

[0009] Furthermore, the data transmission module includes: a field communication network that adopts industrial IoT technologies such as LoRaWAN and NB-IoT low-power wide area network technologies; a data encryption unit that encrypts transmitted data to ensure data security; a multi-path transmission strategy unit that adopts a multi-path transmission strategy for underground installations and complex environments; and a network status monitoring unit that monitors network status in real time and activates local caching and backup communication schemes in the event of communication interruption.

[0010] Furthermore, the leak identification module includes: a multi-model fusion identification engine, including identification algorithms based on pressure waves, sound waves, flow balance, and gas concentration; a deep learning identification unit, employing deep learning algorithms such as convolutional neural networks (CNN) and long short-term memory networks (LSTM); an anomaly detection unit, utilizing unsupervised learning algorithms to detect abnormal data points; and a decision fusion unit, integrating the results of multiple identification methods to improve identification accuracy and reliability.

[0011] Furthermore, the leak location module includes: a wave velocity calculation unit, which calculates the distance between the leak point and the sensor based on the propagation characteristics of pressure waves and sound waves; a multi-point cross-location algorithm unit, which uses the signal time difference of multiple sensors to determine the location of the leak point; a pipeline digital twin model, which combines the precise three-dimensional model of the pipeline to optimize the location algorithm; and a location accuracy evaluation and correction unit, which evaluates the reliability of the location results and performs necessary corrections.

[0012] Furthermore, the automatic repair execution module includes: an intelligent repair robot, including an in-pipe inspection robot, a sealing and repair robot, and an external wrapping robot; a repair material selection unit, which selects the most suitable repair material based on the leakage characteristics and pipe material; a repair process monitoring unit, which monitors the repair process in real time to ensure repair quality; and a post-repair verification unit, which verifies the repair effect to ensure that the leak is completely eliminated.

[0013] A method for intelligent detection and repair of leaks in petrochemical pipelines includes the following steps: system initialization and parameter setting, setting system parameters and establishing a digital twin model of the pipeline based on the characteristics and operating conditions of the pipeline system; data acquisition and transmission, continuously acquiring pipeline status parameters through a multi-parameter sensor network and transmitting them to a cloud processing center; data processing and analysis, preprocessing the raw data, extracting key features, and constructing a comprehensive pipeline status image; leak identification and location, determining the presence of a leak through a multi-model fusion identification engine and calculating the location of the leak point; leak assessment, evaluating the scale, type, risk level, and development trend of the leak; repair decision-making and execution, selecting the most suitable repair scheme and controlling an intelligent repair robot to perform the repair operation; and system self-learning and optimization, continuously updating the model and optimizing system parameters based on new monitoring data and maintenance experience.

[0014] Furthermore, in the leak identification process, the following weighted fusion results of algorithms are used: a leak identification algorithm based on pressure wave propagation characteristics; a leak identification algorithm based on flow balance principle; a leak identification algorithm based on acoustic wave characteristics; a leak identification algorithm based on gas concentration changes; and a comprehensive identification algorithm based on deep learning.

[0015] Furthermore, during the leak location process, the leak location is determined through the following steps: calculating the propagation speeds of pressure waves and sound waves; recording the time difference between the leak signals received by different sensors; initially determining the leak location using triangulation or polygonal positioning algorithms; performing position correction using a pipeline digital twin model; evaluating the positioning accuracy, and, if necessary, deploying an in-pipeline inspection robot for precise positioning.

[0016] Furthermore, during the remediation decision-making and execution process, different remediation strategies are selected based on the scale, type, and location of the leak: for minor leaks, internal remediation is performed using pipeline sealing robots; for moderate leaks, external remediation is performed using external wrapping robots; and for severe leaks, emergency plans are activated, combining automated and manual remediation.

[0017] 3. Beneficial effects One or more technical solutions provided in this application have at least the following technical effects or advantages: The intelligent detection and repair system and method for leaks in petrochemical pipelines disclosed in this application have the following beneficial effects: This application significantly improves the sensitivity and accuracy of leak detection through multi-parameter sensor networks and multi-model fusion, reducing the detection limit from 1%~2% of traditional methods to below 0.1%. It achieves real-time leak detection and automatic alarm, drastically shortening response time from hours to minutes. By combining multiple location algorithms and pipeline digital twin models, leak location accuracy is improved to the meter level. The system automatically selects the optimal repair solution and executes the repair through an intelligent repair robot, reducing human intervention and improving repair efficiency and quality. The system possesses self-learning capabilities, continuously optimizing algorithms and decision models based on actual operating data to continuously improve system performance.

[0018] This application can significantly reduce the incidence and impact of leakage accidents, ensuring safe production. It reduces leakage losses and maintenance costs, extends pipeline service life, and significantly reduces total life cycle costs; it also enables timely detection and repair of leaks, reducing environmental pollution caused by media leakage. Attached Figure Description

[0019] Figure 1 This is a flowchart illustrating a preferred embodiment of the intelligent detection method for leaks in petrochemical pipelines disclosed in this application. Detailed Implementation

[0020] The present application will be further described in detail below with reference to the accompanying drawings; Reference Figure 1 This application provides an intelligent detection and repair system for leaks in petrochemical pipelines, comprising the following modules: The system comprises several modules: a data acquisition module, a data processing and analysis module, and an automatic repair module. The data acquisition module uses a high-precision, low-power multi-parameter sensor network to comprehensively monitor pipeline leaks and environmental parameters. The data transmission module reliably transmits the collected data to the cloud processing center. The data processing and analysis module cleans, processes, and analyzes the collected data to extract feature information. The leak identification module uses various algorithms to identify and determine leak events. The leak location module accurately pinpoints the leak location. The leak assessment module evaluates the extent, type, and potential risks of the leak. The repair decision module develops the optimal repair plan based on the leak situation. The automatic repair execution module controls intelligent repair equipment to perform repair operations. The human-machine interface module provides maintenance personnel with an intuitive system operation interface. The system self-learning module continuously optimizes algorithms and decision models.

[0021] The data acquisition module includes: Multi-parameter sensor network: Pressure sensor: monitors changes in medium pressure within the pipeline; Flow sensor: monitors changes in medium flow rate within the pipeline; Temperature sensor: monitors temperature distribution within the pipeline; Acoustic sensor: detects acoustic signals generated by leaks; Gas concentration sensor: detects the concentration of leaked gas; Strain sensor: monitors pipeline deformation; Corrosion sensor: monitors pipeline wall thickness and corrosion status.

[0022] Sensor Deployment Optimization Unit: Based on pipeline structure, material, and operating conditions, optimize the placement and density of sensors to ensure comprehensive coverage of key locations.

[0023] Low power consumption design: Employs low power chips and intelligent sleep strategies to extend sensor lifespan.

[0024] Self-calibration mechanism: The sensor self-calibrates periodically to ensure the accuracy of long-term monitoring.

[0025] The data transmission module includes: Field Communication Network: Employs Industrial Internet of Things (IIoT) technologies, such as LoRaWAN and NB-IoT (Local Low Power Wide Area Network), to ensure reliable transmission of sensor data. Data Encryption Unit: Encrypts transmitted data to ensure data security. Multi-path Transmission Strategy: Utilizes a multi-path transmission strategy for underground installations and complex environments to ensure reliable data transmission. Network Status Monitoring: Monitors network status in real time and activates local caching and backup communication schemes in the event of communication interruption.

[0026] The data processing and analysis module includes: Data preprocessing unit: Filters, denoises, and standardizes the raw data. Feature extraction unit: Extracts key features reflecting the pipeline's condition from multi-source heterogeneous data. Data fusion unit: Integrates data from multiple sensors to construct a comprehensive pipeline condition image. Time series analysis unit: Analyzes the time-series changes of parameters to identify abnormal trends. Multidimensional correlation analysis: Analyzes the correlations between parameters to discover hidden association patterns.

[0027] The leak identification module includes: a multi-model fusion identification engine: a pressure wave-based identification algorithm that analyzes pressure wave propagation characteristics to identify leaks; a sound wave-based identification algorithm that analyzes sound wave characteristics to identify leaks; a flow balance-based identification algorithm that analyzes flow differences to identify leaks; and a gas concentration-based identification algorithm that monitors changes in specific gas concentrations to identify leaks. A deep learning identification unit uses a combination of Convolutional Neural Networks (CNN) and Long Short-Term Memory Networks (LSTM) with deep learning algorithms to learn leak characteristic patterns from historical data. An anomaly detection unit uses unsupervised learning algorithms, such as autoencoders and isolated forests, to detect anomalous data points. A decision fusion unit integrates the results of multiple identification methods to improve identification accuracy and reliability.

[0028] The leak location module includes: a wave velocity calculation unit: calculating the distance between the leak point and the sensor based on the propagation characteristics of pressure waves and sound waves; a multi-point cross-location algorithm: determining the leak point location using the signal time differences from multiple sensors through triangulation or polygonal positioning algorithms; a pipeline digital twin model: optimizing the location algorithm and improving location accuracy by combining a precise 3D model of the pipeline; and a location accuracy evaluation and correction unit: evaluating the reliability of the location results and performing necessary corrections.

[0029] The leak assessment module includes: a leak size assessment unit: assessing the leak size based on parameters such as pressure drop rate and flow rate difference; a leak type identification unit: distinguishing between different types of leaks, such as cracks, perforations, and connection point leaks; a risk assessment unit: assessing the potential impact of the leak on the environment, safety, and production, and determining the risk level; and a development trend prediction unit: predicting the development trend of the leak to provide a basis for emergency decision-making.

[0030] The remediation decision-making module includes: a remediation solution library containing standard remediation solutions for various leakage scenarios; an intelligent decision engine that selects the most suitable remediation solution based on leakage type, scale, location, and risk level; a resource allocation optimization unit that optimizes the allocation of remediation equipment and human resources; and a remediation time assessment unit that assesses the time required for remediation and guides production adjustments.

[0031] The automatic repair execution module includes: Intelligent Repair Robots: Pipeline Inspection Robot: Enters the pipeline to precisely locate the leak point; Sealing and Repair Robot: Carries repair materials for automatic repair; External Covering Robot: Performs temporary sealing or permanent repair on the outside of the pipeline. Repair Material Selection Unit: Selects the most suitable repair material based on the leak characteristics and pipeline material. Repair Process Monitoring Unit: Monitors the repair process in real time to ensure repair quality. Post-Repair Verification Unit: Verifies the repair effect to ensure the leak is completely eliminated.

[0032] The human-machine interface module includes: a visual interface that intuitively displays pipeline status, leak location, and repair progress; an alarm management unit for hierarchical management and push notifications of alarm information; a remote control interface that supports remote control of repair equipment by maintenance personnel; and a report generation unit that automatically generates monitoring reports and maintenance records.

[0033] The system's self-learning module includes: a model update unit that continuously updates the identification and decision-making models based on new monitoring data and maintenance experience; a parameter self-optimization unit that automatically optimizes system parameters to improve system performance; a knowledge graph construction unit that builds a pipeline leakage knowledge graph to support intelligent decision-making; and a performance evaluation and improvement unit that periodically evaluates system performance and proposes improvement plans.

[0034] A method for intelligent detection and repair of leaks in petrochemical pipelines includes the following steps: Step 1: System initialization and parameter setting: Set system parameters according to the characteristics and operating conditions of the pipeline system.

[0035] Establish a digital twin model of the pipeline; initialize the operating parameters of each module.

[0036] Step 2: Data Acquisition and Transmission: The multi-parameter sensor network continuously collects pipeline status parameters; the data is transmitted to the cloud processing center via the data transmission module; in the event of a communication interruption, local caching and backup communication schemes are activated.

[0037] Step 3: Data Processing and Analysis: Preprocess the raw data, including filtering, denoising, and standardization; extract key features reflecting the pipeline status; perform data fusion to construct a comprehensive pipeline status image; analyze the time series changes of parameters and identify abnormal trends.

[0038] Step 4: Leak Identification and Location: The multi-model fusion identification engine analyzes and processes the data to determine whether a leak exists; if a leak is detected, the leak location module calculates the location of the leak point; the location result is optimized by combining the pipeline digital twin model; the location accuracy is evaluated and necessary corrections are made.

[0039] During the leak identification process, the following weighted fusion results of algorithms are used: leak identification algorithm based on pressure wave propagation characteristics; leak identification algorithm based on flow balance principle; leak identification algorithm based on acoustic wave characteristics; leak identification algorithm based on gas concentration change; and comprehensive identification algorithm based on deep learning.

[0040] During the leak location process, the leak location is determined through the following steps: calculating the propagation speed of pressure waves and sound waves; recording the time difference between the leak signals received by different sensors; initially determining the leak location using triangulation or polygonal positioning algorithms; performing position correction by combining the pipeline digital twin model; evaluating the positioning accuracy, and, if necessary, dispatching an in-pipeline inspection robot for precise positioning.

[0041] Step 5: Leakage Assessment: Assess the scale of the leak and distinguish the type of leak; analyze the potential impact of the leak on the environment, safety and production, and determine the risk level; predict the development trend of the leak.

[0042] Step Six: Repair Decision and Execution: The intelligent decision engine selects the most suitable repair solution; optimizes the allocation of repair equipment and human resources; controls the intelligent repair robot to perform repair operations; monitors the repair process to ensure repair quality; and verifies the repair effect to ensure that the leak is completely eliminated.

[0043] During the remediation decision-making and execution process, different remediation strategies are selected based on the scale, type, and location of the leak: for minor leaks, internal remediation is carried out using pipeline sealing robots; for medium-sized leaks, external remediation is carried out using external wrapping robots; and for severe leaks, emergency plans are activated, combining automated and manual remediation.

[0044] Step 7: System self-learning and optimization: Based on new monitoring data and maintenance experience, update the identification model and decision-making model; automatically optimize system parameters to improve system performance; regularly evaluate system performance and propose improvement plans.

[0045] Example 1: Intelligent Detection and Repair System for Crude Oil Pipeline Leaks; This embodiment is applicable to long-distance crude oil transportation pipelines, and the system deployment is as follows: 1. Data acquisition module: A set of sensing nodes containing pressure sensors, flow sensors and acoustic sensors are arranged every 500 meters; a comprehensive monitoring station is set up every 5 kilometers, with gas concentration sensors and corrosion sensors added; strain sensors are added at key pipeline nodes (such as valves, elbows, etc.).

[0046] 2. Leakage identification employs an integrated model: combining a pressure wave propagation model with a flow balance model; using a deep learning model to analyze time-series data; and using confidence weights to fuse the results of each model.

[0047] 3. Location method: A combination of negative pressure wave location method and acoustic wave location method is used; once the leak is confirmed, a pipeline inspection robot is dispatched for precise location.

[0048] 4. Repair Execution: Select different repair robots according to the scale of the leak; for minor leaks, use internal sealing repair; for medium leaks, use external encapsulation repair; for severe leaks, activate the emergency plan and arrange manual repair.

[0049] Example 2: Intelligent detection and repair system for leaks in complex pipeline networks in chemical industrial parks; This embodiment is applicable to complex pipeline networks within chemical industrial parks, and the system deployment is as follows: 1. Data acquisition module: Construct a high-density sensor network with an average spacing of 100 meters; add infrared thermal imaging monitoring equipment to monitor pipeline temperature anomalies in real time; and add soil gas concentration sensor arrays in underground pipeline areas.

[0050] 2. Data transmission adopts a multi-level network architecture: the field level uses an industrial wireless sensor network; the control level uses an industrial Ethernet; and the management level uses a combination of enterprise private network and cloud platform.

[0051] 3. Leak identification employs multiple safeguards: gas concentration monitoring is the preferred detection method; image recognition algorithms analyze thermal imaging data; and changes in soil gas concentration serve as a key indicator of underground pipeline leaks.

[0052] 4. Repair strategy: Establish a tiered response mechanism; small leaks should be repaired automatically; medium leaks should be repaired semi-automatically with remote control; large leaks should be repaired by activating the emergency plan and combining automatic and manual repair.

Claims

1. A smart detection and repair system for leaks in petrochemical pipelines, characterized in that, Includes the following modules: The data acquisition module comprehensively monitors pipeline leakage and environmental parameters by deploying a high-precision, low-power multi-parameter sensor network. The data transmission module is responsible for securely and reliably transmitting the collected data to the cloud processing center. The data processing and analysis module cleans, processes, and analyzes the collected data, extracting feature information. The leak detection module identifies and judges leak events based on multiple algorithms; Leak location module to determine the location of the leak; The leakage assessment module evaluates the extent, type, and potential risks of leaks. The remediation decision module formulates the optimal remediation plan based on the leakage situation. The automatic repair execution module controls the intelligent repair equipment to perform repair operations; the human-machine interaction module provides maintenance personnel with an intuitive system operation interface. The system has a self-learning module that continuously optimizes algorithms and decision-making models.

2. The intelligent detection and repair system for petrochemical pipeline leaks according to claim 1, characterized in that, The data acquisition module includes: a multi-parameter sensor network, which includes pressure sensors, flow sensors, temperature sensors, acoustic sensors, gas concentration sensors, strain sensors, and corrosion sensors; and a sensor deployment optimization unit, which optimizes the sensor placement and density based on the pipeline structure, material, and operating conditions.

3. The intelligent detection and repair system for petrochemical pipeline leaks according to claim 1, characterized in that, The data transmission module includes: a field communication network that adopts industrial IoT technologies such as LoRaWAN and NB-IoT low-power wide area network technologies; a data encryption unit that encrypts transmitted data to ensure data security; a multi-path transmission strategy unit that adopts multi-path transmission strategies for underground installations and complex environments; and a network status monitoring unit that monitors network status in real time and activates local caching and backup communication schemes in the event of communication interruption.

4. The intelligent detection and repair system for petrochemical pipeline leaks according to claim 1, characterized in that, The leak identification module includes: a multi-model fusion identification engine, including identification algorithms based on pressure waves, sound waves, flow balance, and gas concentration; a deep learning identification unit, which uses a deep learning algorithm combining convolutional neural networks and long short-term memory networks; an anomaly detection unit, which uses unsupervised learning algorithms to detect abnormal data points; and a decision fusion unit, which integrates the results of multiple identification methods to improve the accuracy and reliability of identification.

5. The intelligent detection and repair system for petrochemical pipeline leaks according to claim 1, characterized in that, The leak location module includes: a wave velocity calculation unit, which calculates the distance between the leak point and the sensor based on the propagation characteristics of pressure waves and sound waves; a multi-point cross-location algorithm unit, which uses the signal time difference of multiple sensors to determine the location of the leak point; a pipeline digital twin model, which combines the pipeline's precise three-dimensional model to optimize the location algorithm; and a location accuracy evaluation and correction unit, which evaluates the reliability of the location results and performs necessary corrections.

6. The intelligent detection and repair system for petrochemical pipeline leaks according to claim 1, characterized in that, The automated repair execution module includes: an intelligent repair robot, comprising an in-pipe inspection robot, a sealing and repair robot, and an external wrapping robot; a repair material selection unit, which selects the most suitable repair material based on the leakage characteristics and pipe material; a repair process monitoring unit, which monitors the repair process in real time to ensure repair quality; and a post-repair verification unit, which verifies the repair effect to ensure that the leak is completely eliminated.

7. A method for intelligent detection and repair of leaks in petrochemical pipelines, characterized in that, Includes the following steps: S1 system initialization and parameter setting: Based on the characteristics and operating conditions of the pipeline system, system parameters are set, and a digital twin model of the pipeline is established. S2 data acquisition and transmission continuously collects pipeline status parameters through a multi-parameter sensor network and transmits them to the cloud processing center; S3 data processing and analysis preprocesses the raw data, extracts key features, and constructs a comprehensive pipeline status image; S4 leak detection and location uses a multi-model fusion detection engine to determine whether a leak exists and calculate the location of the leak point; S5 leak assessment evaluates the size, type, risk level, and development trend of a leak. S6 Repair Decision and Execution: Selects the most suitable repair solution and controls the intelligent repair robot to perform repair operations; The S7 system is self-learning and optimizing, constantly updating models and optimizing system parameters based on new monitoring data and maintenance experience.

8. The intelligent detection and repair method for petrochemical pipeline leaks according to claim 7, characterized in that, During the leak identification process, the following weighted fusion results of algorithms are used: leak identification algorithm based on pressure wave propagation characteristics; leak identification algorithm based on flow balance principle; leak identification algorithm based on acoustic wave characteristics; leak identification algorithm based on gas concentration change; and comprehensive identification algorithm based on deep learning.

9. The intelligent detection and repair method for petrochemical pipeline leaks according to claim 7, characterized in that, During the leak location process, the leak location is determined through the following steps: calculating the propagation speed of pressure waves and sound waves; recording the time difference between the leak signals received by different sensors; initially determining the leak location using triangulation or polygonal positioning algorithms; performing position correction by combining the pipeline digital twin model; evaluating the positioning accuracy, and, if necessary, dispatching an in-pipeline inspection robot for precise positioning.

10. The intelligent detection and repair method for petrochemical pipeline leaks according to claim 7, characterized in that, During the remediation decision-making and execution process, different remediation strategies are selected based on the scale, type, and location of the leak: for minor leaks, internal remediation is carried out using pipeline sealing robots; for medium-sized leaks, external remediation is carried out using external wrapping robots; and for severe leaks, emergency plans are activated, combining automated and manual remediation.

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