Urban lifeline gas pipeline emergency supervision internet of things large model system and method

Abstract

The application provides an urban lifeline gas pipeline emergency supervision Internet of Things large model system and method, the system comprises an emergency supervision management platform, the emergency supervision management platform is configured to: control the distributed optical fiber to collect the first stress distribution and the first displacement distribution of the buried pipeline; based on the first stress distribution and the first displacement distribution, determine the corresponding to-be-detected area of the buried pipeline; control the unmanned vehicle to detect the buried pipeline in the to-be-detected area and obtain detection data; based on the detection data, determine the target valve and send a valve control instruction; based on the valve control instruction, control the target valve to close. The method can reasonably allocate detection resources, timely find defects of the buried pipeline and perform targeted maintenance, and improve the safety of the buried pipeline.

Description

Technical Field

[0001] This specification relates to the field of gas pipeline supervision, and in particular to a large-scale IoT model system and method for emergency supervision of urban lifeline gas pipelines. Background Technology

[0002] As urban gas pipeline networks continue to expand, the real-time monitoring and emergency response to deeply buried underground gas pipelines, due to their concealment and complex operating environment, have become crucial for ensuring gas supply security. Despite continuous advancements in monitoring and inspection technologies, practical applications still face challenges such as limited coverage and insufficient response efficiency, particularly in rapid location and emergency monitoring during emergencies, which still require improvement.

[0003] Therefore, there is a need to provide a large-scale IoT model system for emergency monitoring of urban lifeline gas pipelines. Through data interaction between IoT platforms, the system can monitor relevant parameters of deeply buried gas pipelines and take corresponding measures to improve the safety of urban gas use. Summary of the Invention

[0004] The invention includes a large-scale IoT model system for emergency monitoring of urban lifeline gas pipelines. The system includes an emergency monitoring and management platform configured to: control distributed optical fibers to collect first stress distribution and first displacement distribution data of buried pipelines; wherein the distributed optical fibers are located on the buried pipelines; determine the detection area corresponding to the buried pipeline based on the first stress distribution and first displacement distribution; control an unmanned vehicle to probe the buried pipeline within the detection area and acquire detection data; determine the target valve based on the detection data and send a valve control command; and control the target valve to close based on the valve control command.

[0005] The invention includes an emergency monitoring method for urban lifeline gas pipelines. The method is executed by an emergency monitoring management platform and includes: controlling distributed optical fibers to collect a first stress distribution and a first displacement distribution of the buried pipeline; wherein the distributed optical fibers are located on the buried pipeline; determining the detection area corresponding to the buried pipeline based on the first stress distribution and the first displacement distribution; controlling an unmanned vehicle to probe the buried pipeline within the detection area and acquire detection data; determining the target valve based on the detection data and sending a valve control command; and controlling the target valve to close based on the valve control command.

[0006] Based on the content of this invention, all-weather remote monitoring of buried pipelines is achieved by deploying a distributed optical fiber network. This automatically identifies areas of abnormal vibration and stress change, accurately locates potentially risky areas to be inspected, and uses unmanned vehicles to perform detailed detection of these areas. Based on the detection data, valves in these areas can be inspected and repaired. This allows for the rational allocation of detection resources, timely detection of defects in buried pipelines, and targeted maintenance, thereby improving the safety of buried pipelines. Attached Figure Description

[0007] This specification will be further described by way of exemplary embodiments, which will be described in detail with reference to the accompanying drawings. These embodiments are not limiting; in these embodiments, the same reference numerals denote the same structures, wherein:

[0008] Figure 1 This is a schematic diagram of the platform structure of a large-scale IoT model system for emergency monitoring of urban lifeline gas pipelines, based on some embodiments of this specification.

[0009] Figure 2 This is an exemplary flowchart of an emergency monitoring method for urban lifeline gas pipelines, as shown in some embodiments of this specification.

[0010] Figure 3 This is a schematic diagram of the deployment of gas pipelines according to some embodiments of this specification;

[0011] Figure 4 This is a schematic diagram of a prediction model based on some embodiments of this specification;

[0012] Figure 5 This is an exemplary flowchart illustrating pulse width adjustment according to some embodiments of this specification. Detailed Implementation

[0013] To more clearly illustrate the technical solutions of the embodiments in this specification, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are merely some examples or embodiments of this specification. For those skilled in the art, these drawings can be applied to other similar scenarios without creative effort. Unless obvious from the context or otherwise specified, the same reference numerals in the drawings represent the same structures or operations.

[0014] It should be understood that the terms “system,” “device,” “unit,” and / or “module” used herein are one way to distinguish different components, elements, parts, sections, or assemblies at different levels. However, if other terms can achieve the same purpose, they may be replaced by other expressions.

[0015] Unless the context clearly indicates an exception, words such as "a," "an," "a kind," and / or "the" do not specifically refer to the singular and may also include the plural. Generally speaking, the terms "comprising" and "including" only indicate the inclusion of explicitly identified steps and elements, which do not constitute an exclusive list, and the method or apparatus may also include other steps or elements.

[0016] Flowcharts are used in this specification to illustrate the operations performed by the system according to embodiments of this specification. It should be understood that the preceding or following operations are not necessarily performed in exact order. Instead, the steps can be processed in reverse order or simultaneously. Furthermore, other operations can be added to these processes, or one or more steps can be removed from them.

[0017] Some embodiments in this specification use stress and displacement distribution data collected by distributed optical fibers to identify potential anomalies in buried pipelines and dispatch unmanned vehicles (UAVs) based on the characteristics of these areas to conduct mobile, refined detection, obtaining more accurate on-site information. Furthermore, based on the detection data fed back by the UAVs, the system identifies target valves that require emergency closure and sends valve control commands to automatically shut them down, thereby achieving closed-loop management from status awareness to emergency response.

[0018] Figure 1 This is a schematic diagram of the platform structure of a large-scale IoT model system for emergency monitoring of urban lifeline gas pipelines, based on some embodiments of this specification.

[0019] In some embodiments, such as Figure 1 As shown, the large-scale IoT model system 100 for emergency monitoring of urban lifeline gas pipelines may include an emergency monitoring user platform 110, an emergency monitoring service platform 120, an emergency monitoring management platform 130, an emergency monitoring sensor network platform 140, and an emergency monitoring object platform 150.

[0020] The emergency monitoring user platform 110 refers to a platform that interacts with users (such as regulatory personnel or citizens). In some embodiments, the emergency monitoring user platform 110 includes terminal devices. For example, the terminal devices may include mobile devices, tablet computers, consoles, etc. The emergency monitoring user platform 110 can interact bidirectionally with the emergency monitoring service platform 120.

[0021] Emergency monitoring service platform 120 refers to a platform used for receiving and transmitting data and / or information. In some embodiments, emergency monitoring service platform 120 is configured as a server or processor. Emergency monitoring service platform 120 can interact bidirectionally with emergency monitoring user platform 110 and emergency monitoring management platform 130.

[0022] The emergency monitoring and management platform 130 refers to a comprehensive management platform that manages and coordinates the connections and collaboration between multiple platforms. In some embodiments, the emergency monitoring and management platform 130 is configured as a server or processor. The emergency monitoring and management platform 130 can interact bidirectionally with the emergency monitoring service platform 120 and the emergency monitoring sensor network platform 140. For example, the emergency monitoring and management platform 130 can obtain detection data from the emergency monitoring target platform 150 through the emergency monitoring sensor network platform 140; or, for example, the emergency monitoring and management platform 130 can issue control commands (such as valve control commands) to the emergency monitoring target platform 150 through the emergency monitoring sensor network platform 140 to control the emergency monitoring target platform 150 to perform operations such as closing target valves based on the control commands. More details about the emergency monitoring target platform 150 performing operations such as closing target valves can be found in the following text and related descriptions.

[0023] The emergency monitoring sensor network platform 140 refers to a platform used for the comprehensive management of sensor information. In some embodiments, the emergency monitoring sensor network platform 140 is configured as a communication network or gateway, etc. The emergency monitoring sensor network platform 140 can interact bidirectionally with the emergency monitoring management platform 130 and the emergency monitoring object platform 150.

[0024] The emergency monitoring object platform 150 refers to a platform for generating monitoring information and executing control information. In some embodiments, the emergency monitoring object platform 150 is configured as a distributed fiber optic sensor, an unmanned vehicle, etc. The emergency monitoring object platform 150 can interact bidirectionally with the emergency monitoring sensor network platform 140. For example, the emergency monitoring object platform 150 can acquire detection data of multiple target locations of buried pipelines and upload it to the emergency monitoring management platform 130 through the emergency monitoring sensor network platform 140; or, for another example, the emergency monitoring object platform 150 can respond to control commands issued by the emergency monitoring management platform 130 via the emergency monitoring sensor network platform 140 and execute relevant control operations.

[0025] A distributed fiber optic sensor is a device that senses environmental changes by analyzing changes in the transmission characteristics of optical signals in optical fibers. In some embodiments, the distributed fiber optic sensor can collect multiple stress values ​​(first stress distribution, second stress distribution) and multiple displacement values ​​(first displacement distribution, second displacement distribution) of buried pipelines. More details can be found below. Figure 2 , Figure 5 And related descriptions.

[0026] For more information about the above platforms, please see the following: Figures 2 to 5 And related descriptions.

[0027] In some embodiments of this specification, the Urban Lifeline Gas Pipeline Emergency Monitoring IoT Big Data Model System 100 can form an information operation closed loop among various functional platforms and operate in a coordinated and regular manner under the unified management of the emergency monitoring and management platform, thereby realizing the informatization and intelligentization of emergency monitoring of urban buried gas pipelines.

[0028] It should be noted that the above description of the Urban Lifeline Gas Pipeline Emergency Monitoring IoT Large-Scale Model System 100 and its platform is for ease of description only and should not limit this specification to the scope of the embodiments described. It is understood that those skilled in the art, after understanding the principles of this system, may arbitrarily combine the various platforms or construct subsystems connected to other platforms without departing from these principles.

[0029] Figure 2 This is an exemplary flowchart of an emergency monitoring method for urban lifeline gas pipelines, as shown in some embodiments of this specification.

[0030] In some embodiments, emergency monitoring methods for urban lifeline gas pipelines can be implemented by an emergency monitoring management platform. For example... Figure 2 As shown, process 200 includes the following steps.

[0031] Step 210: Control the distributed optical fiber to acquire the first stress distribution and the first displacement distribution of the buried pipeline.

[0032] Distributed optical fiber, also known as distributed optical fiber sensor, refers to a sensor that monitors buried gas pipelines along the optical fiber transmission path by using distributed optical fiber detection technology.

[0033] Buried pipelines refer to gas pipelines or ancillary facilities buried underground.

[0034] The first stress distribution refers to the distribution of stress changes along the buried pipeline relative to the initially laid pipeline. In some embodiments, the first stress distribution may include stress values ​​of multiple sections of buried pipeline or pipeline nodes.

[0035] A pipe node is a node that connects multiple pipes. In some embodiments, a valve may be installed at a pipe node.

[0036] The first displacement distribution refers to the distribution of displacement of the buried pipeline relative to its initial laying position, obtained along the buried pipeline. In some embodiments, the first displacement distribution may include the displacement of multiple pipeline segments or pipeline nodes.

[0037] In some embodiments, the emergency monitoring and management platform can acquire the first stress distribution and the first displacement distribution through distributed optical fibers.

[0038] Step 220: Determine the area to be inspected corresponding to the buried pipeline based on the first stress distribution and the first displacement distribution.

[0039] An area to be inspected refers to an area that may pose a danger and requires further inspection. In some embodiments, the area to be inspected may be an area containing a portion of a buried pipeline.

[0040] In some embodiments, the emergency monitoring and management platform may designate the area enclosed by pipes or pipe nodes in the first stress distribution whose stress values ​​exceed a first preset threshold, or pipes or pipe nodes in the first displacement distribution whose displacements exceed a second preset threshold, as the area to be monitored. The pipes or pipe nodes are part of a buried pipeline. The first and second preset thresholds can be set based on experience.

[0041] Step 230: Control the unmanned vehicle to detect the buried pipelines in the area to be tested and obtain detection data.

[0042] An unmanned vehicle refers to a mobile detection platform with autonomous exploration capabilities. In some embodiments, an unmanned vehicle can be equipped with a variety of sensors (e.g., ultrasonic devices, ground-penetrating radar, etc.).

[0043] Detection data refers to data obtained from detecting buried pipelines. In some embodiments, detection data includes metal loss of the buried pipeline or pipeline joints, integrity of the anti-corrosion layer, concentration of combustible gas, temperature, etc.

[0044] In some embodiments, the detection data can be obtained by unmanned vehicles detecting buried pipelines.

[0045] Step 240: Based on the detection data, determine the target valve and send a valve control command.

[0046] A target valve refers to a valve that needs to be regulated. In some embodiments, the target valve may be a valve that needs to be closed.

[0047] Valve control commands are commands used to regulate the opening and closing of valves. In some embodiments, valve control commands include commands to close the target valve.

[0048] In some embodiments, the emergency monitoring and management platform can determine the target valve based on the detection data using a vector matching method.

[0049] For example, the emergency monitoring and management platform can use historical data that meets the screening criteria as the first sample data. The screening criteria may include the fact that no accident occurred within a first preset time period after the valve was closed in the historical data. The first sample data includes the detection data of the area to be monitored and the valves that were actually closed.

[0050] The current detection data is used to construct a first target vector, and each detection data point in the first sample data is used to construct a first target vector. Multiple first similarities are determined between the first target vector and the multiple first target vectors. The valve that is actually closed corresponding to the first target vector with the highest first similarity is determined as the target valve. The first similarity can be Euclidean distance, cosine similarity, etc.

[0051] For more information on identifying the target valve, please see [link / reference]. Figure 3 And its related descriptions.

[0052] Step 250: Based on the valve control command, control the target valve to close.

[0053] In some embodiments, the emergency monitoring and management platform can control the target valve to close based on valve control commands.

[0054] In some embodiments of this specification, the emergency monitoring and management platform enables all-weather remote monitoring of buried pipelines by deploying a distributed fiber optic network, automatically identifying areas of abnormal vibration and stress changes, accurately locating potentially risky inspection areas, and using unmanned vehicles to perform detailed detection of the inspection areas. Based on the detection data, valves in the inspection areas are then inspected and repaired. This allows for the rational allocation of detection resources, timely detection of defects in buried pipelines, and targeted maintenance, thereby improving the safety of buried pipelines.

[0055] In some embodiments, the emergency monitoring and management platform can determine pipeline anomalies in buried pipelines based on detection data; and identify target valves based on these anomalies. For more information on detection data, buried pipelines, and target valves, please refer to [link to relevant documentation]. Figure 2 And its related descriptions.

[0056] A pipeline anomaly point refers to a location in a buried pipeline where a problem may exist. In some embodiments, a pipeline anomaly point can be a section of the buried pipeline or a pipeline node.

[0057] In some embodiments, the emergency monitoring and management platform identifies pipelines or pipeline nodes that meet at least one of the following criteria from the detection data: metal loss greater than a loss threshold, corrosion protection layer integrity lower than an integrity threshold, gas concentration higher than a concentration threshold, or temperature higher than a temperature threshold, as pipeline anomalies. The loss threshold, integrity threshold, concentration threshold, and temperature threshold can be preset based on experience.

[0058] In some embodiments, the emergency monitoring and management platform can determine the target valve by querying a first preset table, wherein the first preset table includes pipeline anomalies and corresponding target valves, and the first preset table can be constructed by technicians based on historical data and prior experience.

[0059] In some embodiments, the emergency monitoring and management platform can determine the regulation radiation range of candidate valves; wherein, candidate valves are generated based on pipeline anomalies; based on the regulation radiation range, the radiation impact value and coverage data of candidate valves are determined; based on pipeline anomalies, the control impact value of candidate valves is determined; based on the weighted average of the radiation impact value and the control impact value, a comprehensive score of candidate valves is determined; and based on the comprehensive score and coverage data, a target valve is determined.

[0060] Candidate valves are valves that may be identified as target valves. Candidate valves can be preset by the system or randomly generated by the system.

[0061] In some embodiments, the emergency monitoring and management platform can generate candidate valves based on pipeline anomalies. For example, the platform can identify all upstream valves of the anomaly as candidate valves. Alternatively, it can identify a predetermined number of valves extending in the opposite direction of gas flow from the anomaly as candidate valves. This predetermined number can be pre-set by the system.

[0062] The regulated radiation range refers to the buried pipeline affected by the valve. In some embodiments, the regulated radiation range may include the number of buried pipelines upstream and downstream of the valve affected by the valve. Figure 3 This is a schematic diagram of the gas pipeline deployment, such as... Figure 3 As shown, valves 311 to 314, pipeline anomaly points 321 to 323, and pipelines 331 to 334 are included. Valves and pipelines not involved in this embodiment are not numbered. The arrows indicate the direction of gas flow. The adjustment range of valve 312 can include upstream pipelines 333 and 334, and downstream pipelines 332 and 335. That is, the above-mentioned pipelines are the buried pipelines affected by valve 312.

[0063] In some embodiments, the radiation range can be preset by the system.

[0064] In some embodiments, the emergency monitoring and management platform can acquire historical data of candidate valves that meet preset conditions; wherein the historical data includes first pipeline data and second pipeline data; based on the historical data, the upstream critical pipeline and downstream critical pipeline of the candidate valve are determined; based on the upstream critical pipeline and downstream critical pipeline, the regulation radiation range of the candidate valve is generated.

[0065] Preset conditions refer to the conditions used to screen valves.

[0066] In some embodiments, preset conditions may include the valve being located within a preset radius of a pipeline anomaly point, the preset radius being set based on experience.

[0067] Historical data refers to data related to all buried pipelines and valves stored in the system. For example, historical data includes valve regulation data. In some embodiments, historical data includes first pipeline data and second pipeline data.

[0068] The first pipeline data refers to the data generated before the candidate valve is adjusted. In some embodiments, the first pipeline data includes data such as gas flow rate and gas pressure in all buried pipelines when the candidate valve is opened.

[0069] The second pipeline data refers to the data generated after the candidate valves are adjusted. This data includes gas flow rate, gas pressure, and other data for all buried pipelines within a second preset time period after the candidate valves are closed. The second preset time is set based on experience.

[0070] The upstream critical pipeline refers to the farthest pipeline affected by the opening (or closing) of a candidate valve, extending in the opposite direction of gas flow from the candidate valve. The impact can be caused by the gas flow rate difference, pressure difference, or other factors exceeding a corresponding threshold before and after the valve opening.

[0071] Downstream critical pipeline refers to the farthest pipeline affected by the opening (or closing) of a candidate valve, extending along the gas flow direction from the candidate valve.

[0072] In some embodiments, the emergency monitoring and management platform can extend along the opposite direction of gas flow from the candidate valve. When the change in flow velocity and gas pressure in a certain section of the pipeline before and after valve adjustment first falls below a first preset threshold, that section of the pipeline is considered the upstream critical pipeline. Extending along the gas flow direction from the candidate valve, when the change in flow velocity and gas pressure in a certain section of the pipeline before and after valve adjustment first falls below a second preset threshold, that section of the pipeline is considered the downstream critical pipeline. The first and second preset thresholds can be set empirically.

[0073] In some embodiments, the regulation radiation range of candidate valves can be generated based on the number of pipes between the upstream critical pipe and the valve, and the number of pipes between the downstream critical pipe and the valve.

[0074] In some embodiments of this specification, by mining the first and second pipeline data in the historical control data of candidate valves, the upstream critical pipeline and the downstream critical pipeline are accurately identified, and the control radiation range adapted to the valve is dynamically generated to ensure the accuracy of emergency control.

[0075] Radiation impact value is a parameter that measures the impact of valve opening (or closing) on ​​buried pipelines.

[0076] In some embodiments, the radiation impact value may be the sum of the number of upstream and downstream pipes affected by the valve opening (or closing).

[0077] Coverage data refers to data related to abnormal points in pipelines that can block gas flow after the valve is closed.

[0078] In some embodiments, the coverage data can characterize the situation of all pipeline anomalies downstream of a candidate valve. For example, such as... Figure 3 As shown, there are no pipeline anomalies downstream of valve 311, and the coverage data for valve 311 indicates that there are no pipeline anomalies. The pipeline anomalies downstream of valve 312 include pipeline anomaly 321, and the coverage data for valve 312 indicates pipeline anomaly 321.

[0079] The control impact value is the number of abnormal points in the pipeline that can directly block the flow of gas when the valve is closed.

[0080] In some embodiments, the control impact value can be the number of pipeline anomalies in the coverage data. For example, such as Figure 3 As shown, there are no abnormal points in the pipeline downstream of valve 311, and the control impact value of valve 311 is 0. The abnormal points in the pipeline downstream of valve 312 include pipeline abnormal point 321, and the control impact value of valve 312 is 1.

[0081] The overall score is a parameter that measures the control efficiency when pipeline anomalies have been inspected and risks have been eliminated.

[0082] In some embodiments, the emergency monitoring and management platform can first normalize the control impact value and radiation impact value of candidate valves, then sum them with weights, and use the result as a comprehensive score. The weights can be set based on experience.

[0083] In some embodiments, the emergency monitoring and management platform may select the candidate valve with the highest comprehensive score as the target valve.

[0084] In some embodiments of this specification, the control effectiveness of candidate valves at pipeline anomaly points is accurately grasped by covering data and control impact values, the impact range of valve control on buried pipelines is reflected by radiation impact values, and further combined with comprehensive scoring, the balance between control effectiveness and pipeline disturbance impact is reasonably controlled.

[0085] In some embodiments of this specification, pipeline anomalies are quickly located by detecting data, thereby accurately matching target valves, effectively improving defect detection efficiency and valve maintenance targeting, reducing leakage risks, and ensuring safe pipeline operation.

[0086] Figure 4 This is a schematic diagram of a prediction model shown according to some embodiments of this specification.

[0087] In some embodiments, the emergency monitoring and management platform can control unmanned vehicles to conduct burial depth detection of buried pipelines in the area to be inspected, and obtain burial depth data; construct a buried pipeline map based on the detection data and burial depth data; and determine pipeline anomaly points based on the buried pipeline map through a prediction model.

[0088] Burial depth data is used to describe the situation where buried pipelines are buried deep underground.

[0089] In some embodiments, burial depth data may include the burial depth of the pipe or pipe node of the buried pipeline.

[0090] In some embodiments, burial depth data can be obtained by unmanned vehicles.

[0091] Prediction model 420 refers to a model used to identify pipeline anomalies. In some embodiments, the parameter determination model is a machine learning model, such as a graph neural network (GNN) model.

[0092] In some embodiments, the input to the prediction model 420 may include a buried pipeline map 410, and the output may be pipeline outliers 430. The pipeline outliers 430 may include the pipeline outlier value of each node and / or edge corresponding to the pipeline outlier point in the buried pipeline map.

[0093] Pipeline outliers are parameters that describe whether a pipeline has an anomaly. In some embodiments, pipeline outliers can be Boolean values, for example, a pipeline outlier can be a Boolean value of 0 or 1, where 1 indicates that the pipeline has an anomaly and 0 indicates that the pipeline has no anomaly.

[0094] The buried pipeline map 410 refers to a map describing the condition of buried pipelines. The buried pipeline map 410 can be composed of at least one node 411 and at least one edge 412.

[0095] In some embodiments, a node corresponds to a pipeline node of a buried pipeline. The node attributes may include whether there are valves in the ancillary facilities at the pipeline node, the number of upstream pipelines connected to the node, the number of downstream pipelines, etc.

[0096] In some embodiments, an edge corresponds to a segment of a buried pipeline. An edge exists between two pipeline nodes corresponding to two nodes. The attributes of the edge include pipeline detection data and pipeline burial depth data.

[0097] In some embodiments, the predictive model can be trained using a large amount of labeled second sample data. The second sample data may include a sample buried pipeline map constructed based on historical data, and the labels may be pipeline anomalies found at each node and along each edge of the sample buried pipeline map during subsequent investigations. In some embodiments, the second sample data may be constructed based on historical data, and the labels corresponding to the second sample data may be obtained by labeling pipeline anomalies at each node and along each edge of the sample buried pipeline map in the historical data.

[0098] In some embodiments, the emergency monitoring and management platform can train a prediction model based on second sample data and labels. Training methods may include, but are not limited to, gradient descent. As an example only, the emergency monitoring and management platform can input multiple second sample data into an initial prediction model, construct a loss function based on the labels and the output of the initial prediction model, and then iteratively update the parameters of the initial prediction model based on the loss function. When training conditions are met, model training is complete, and a trained prediction model is obtained. These training conditions may include loss function convergence, the number of iterations reaching a preset threshold, etc.

[0099] In some embodiments, the emergency monitoring and management platform can identify nodes and / or edges with anomaly values ​​of 1 in the buried pipeline map output by the prediction model as pipeline anomaly points.

[0100] In some embodiments of the specification, by considering the physical positional relationship between multiple upstream and downstream pipelines in a buried pipeline, and by using detection data and burial depth data, a prediction model is used to determine whether there are any anomalies in the pipeline or pipeline nodes, thereby quickly and accurately locating problems in the buried pipeline, which is beneficial for maintaining the safety of the buried pipeline.

[0101] Figure 5 This is an exemplary flowchart illustrating pulse width adjustment according to some embodiments of this specification.

[0102] In some embodiments, the pulse width adjustment method can be executed by the emergency monitoring and management platform. For example... Figure 5 As shown, process 500 includes the following steps.

[0103] Step 510: Determine the pulse width adjustment zone of the buried pipeline based on the area to be tested.

[0104] The pulse width adjustment region refers to the area of ​​the optical fiber involved in the adjustment of the distributed optical fiber pulse.

[0105] In some embodiments, the pulse width adjustment zone can be a portion of the area where the buried pipeline is located.

[0106] In some embodiments, the pulse width adjustment area can be a user-specified region within the detection area, determined by user input.

[0107] In some embodiments, the emergency monitoring and management platform can directly use the area to be detected as the pulse width adjustment area.

[0108] Step 511: Determine the correlation radius based on the number of pipeline anomalies in the area to be detected.

[0109] The correlation radius is a parameter that measures the extent of the impact of pipeline anomalies.

[0110] In some embodiments, the emergency monitoring and management platform can determine the correlation radius based on the positive correlation between the correlation radius and the number of pipeline anomalies in the area to be monitored.

[0111] In some embodiments, the number of pipeline anomalies within the detection area is an indicator for assessing the overall anomaly level of the detection area; the more pipeline anomalies in the detection area, the greater the anomaly level within the detection area. In some embodiments, the emergency monitoring and management platform can count the number of nodes and / or edges with a pipeline anomaly value of 1 in the detection area to determine the number of pipeline anomalies within the detection area. Pipeline anomaly values ​​are determined by a prediction model; for more related content, please refer to [link to relevant documentation]. Figure 4 Corresponding description.

[0112] By appropriately adjusting the correlation radius based on the number of abnormal points in the pipeline within the detection area, the judgment range of the pulse width adjustment area can be adjusted, thereby improving the spatial detection resolution of distributed optical fibers over a wider range.

[0113] Step 512: Determine the pipe segment within the associated radius as the pulse width adjustment zone.

[0114] In some embodiments, the emergency monitoring and management platform can use the geometric distribution center of all pipeline anomalies in the detection area as the center of a circle, and define the area enclosed by the pipeline segments within the radius associated with that center as the pulse width adjustment area.

[0115] In some embodiments of the specification, the pulse width adjustment zone is dynamically adjusted according to the number of pipeline anomalies in the area to be detected, so that the distributed optical fiber can intelligently adjust the pulse width adjustment zone according to the actual pipeline anomalies, thereby specifically enhancing the spatial defect identification capability and overall monitoring reliability of the distributed optical fiber, and improving the monitoring accuracy of the distributed optical fiber.

[0116] In some embodiments, due to the different burial depths, blind spots in the detection of unmanned vehicles can easily be caused, so step 513 can be performed to correct the pulse width adjustment area.

[0117] Step 513: Based on the burial depth of the pipeline corresponding to the pulse width adjustment zone, correct the pulse width adjustment zone. The burial depth of the pipeline corresponding to the pulse width adjustment zone can be obtained by calling pre-stored data in the system or by unmanned vehicle detection. For more information on burial depth, please refer to [link to relevant documentation]. Figure 3And its related descriptions.

[0118] In some embodiments, the emergency monitoring and management platform can determine the correction range by querying a second preset table based on the burial depth of the pipeline corresponding to the pulse width adjustment zone. The second preset table includes the burial depth and the corresponding correction range, showing a positive correlation between the burial depth and the corresponding correction range. The second preset table can be set based on experience.

[0119] In some embodiments, the emergency monitoring and management platform can determine the adjusted correlation radius based on the correlation radius determined in step 511 and the correction magnitude determined in step 513 (for example, multiply the correlation radius determined in step 511 and the correction magnitude determined in step 513 to determine the adjusted correlation radius), and determine a new pulse width adjustment zone based on the adjusted correlation radius according to step 512, so as to realize the correction pulse width adjustment zone.

[0120] In some embodiments of the specification, by taking into account the burial depth and correcting the associated radius based on the burial depth to correct the pulse width adjustment zone, the signal transmission attenuation differences caused by different geological structures can be dynamically compensated, effectively eliminating the detection blind zone caused by the burial depth and significantly improving the detection accuracy of distributed optical fibers for underground pipelines.

[0121] Step 520: Determine the target pulse width corresponding to the pulse width adjustment region and generate a pulse width adjustment command.

[0122] The target pulse width refers to the monitoring pulse width of the distributed optical fiber.

[0123] In some embodiments, the target pulse width may include the pulse width to which the distributed optical fiber needs to be adjusted within the pulse width adjustment region.

[0124] In some embodiments, the emergency monitoring and management platform can determine the pulse width adjustment range based on the number of pipeline anomalies in the area to be monitored, and generate a target pulse width based on the pulse width adjustment range and the current pulse width.

[0125] For example, the target pulse width is generated by multiplying the pulse width adjustment range by the current pulse width. The pulse width adjustment range is less than 1 and is positively correlated with the number of pipeline anomalies in the area to be detected. In some embodiments, the target pulse width is less than the current pulse width.

[0126] Pulse width adjustment commands are used to adjust the pulse width of distributed optical fibers.

[0127] In some embodiments, the pulse width adjustment command may include a corresponding command to adjust the target pulse width.

[0128] In some embodiments, the pulse width adjustment instruction can be determined by querying a third preset table based on the target pulse width corresponding to the pulse width adjustment area. The third preset table includes the target pulse width and the corresponding pulse width adjustment instruction, and the third preset table can be set based on experience.

[0129] In some embodiments, the emergency monitoring and management platform can control the laser in the pulse width adjustment zone to operate with a current corresponding to the target pulse width based on the pulse width adjustment command. The current corresponding to the target pulse width can be determined by querying a fourth preset table based on the target pulse width. This fourth preset table includes the pulse width adjustment command and its corresponding current, and can be set empirically.

[0130] In some embodiments, the emergency monitoring and management platform can send pulse width adjustment commands to the laser via an unmanned vehicle; the unmanned vehicle and the laser are communicatively connected. For more information about unmanned vehicles, please see [link to relevant documentation]. Figure 2 And its related descriptions.

[0131] In some embodiments, after receiving a pulse width adjustment command, the laser adjusts the pulse width of the distributed optical fiber to operate with a current corresponding to the target pulse width.

[0132] In some embodiments of the specification, the functions of data detection and pulse width adjustment command transmission are integrated into the unmanned vehicle, which not only improves the utilization rate of the unmanned vehicle, but also ensures the reliability of the emergency management platform and distributed optical fiber communication.

[0133] In some embodiments of the specification, the emergency monitoring and management platform determines at least one pulse width adjustment zone of the buried pipeline through the area to be detected and adjusts the pulse width of the distributed optical fiber, which can improve the spatial resolution accuracy while maintaining the signal-to-noise ratio threshold, thereby improving the monitoring accuracy of the distributed optical fiber.

[0134] In some embodiments, after updating the pulse width of the distributed optical fiber, the buried pipeline can be re-monitored based on the updated pulse width, thereby updating the monitoring results.

[0135] In some embodiments, the emergency monitoring and management platform can obtain the second stress distribution and the second displacement distribution through distributed optical fibers; based on the second stress distribution and the second displacement distribution, the area to be inspected for the buried pipeline is determined.

[0136] The second stress distribution refers to the distribution of stress changes in the buried pipeline relative to the initially laid pipeline, obtained by monitoring the target pulse width using distributed optical fibers.

[0137] The second displacement distribution refers to the displacement distribution of the buried pipeline relative to the initially laid pipeline, obtained by monitoring the target pulse width using distributed optical fibers.

[0138] The second stress distribution is similar to the first stress distribution, and the second displacement distribution is similar to the first displacement distribution. The methods for obtaining the second stress distribution and the second displacement distribution are similar to those for obtaining the first stress distribution and the first displacement distribution. For more information on the second stress distribution and the second displacement distribution, please refer to [link to relevant documentation]. Figure 2 The relevant content of the first stress distribution and the first displacement distribution in step 210.

[0139] The method for determining the inspection area of ​​a buried pipeline based on the second stress distribution and the second displacement distribution is similar to the method for determining the inspection area of ​​a buried pipeline based on the first stress distribution and the first displacement distribution. For details, please refer to [link to relevant documentation]. Figure 2 Step 210 involves determining the relevant information about the area to be inspected for the buried pipeline.

[0140] In some embodiments of the specification, by adjusting the pulse width of the distributed optical fiber for re-monitoring and determining the detection area based on the second stress distribution and the second displacement distribution, the monitoring accuracy can be improved in a timely manner, making the determined detection area more accurate.

Claims

1. A large-scale IoT model system for emergency monitoring of urban lifeline gas pipelines, characterized in that, The system includes an emergency monitoring and management platform, which is configured as follows: The distributed optical fiber is used to collect the first stress distribution and the first displacement distribution of the buried pipeline; wherein the distributed optical fiber is located on the buried pipeline. Based on the first stress distribution and the first displacement distribution, the area to be inspected corresponding to the buried pipeline is determined; The unmanned vehicle is controlled to detect the buried pipeline in the area to be detected and to acquire detection data; Based on the detection data, the pipeline anomaly points of the buried pipeline are determined; Determine the regulation radiation range of candidate valves, wherein the candidate valves are generated based on the pipeline anomaly points; Based on the adjusted radiation range, the radiation impact value and coverage data of the candidate valves are determined; Based on the pipeline anomaly points, determine the control impact value of the candidate valves; The comprehensive score of the candidate valve is determined by weighting the radiation impact value and the control impact value. Based on the comprehensive score and the coverage data, the target valve is determined and a valve control command is sent. Based on the valve control command, the target valve is controlled to close.

2. The system according to claim 1, characterized in that, The emergency monitoring and management platform is further configured as follows: Obtain historical data of the candidate valves that meet preset conditions; wherein the historical data includes first pipeline data and second pipeline data; Based on the historical data, the upstream critical pipeline and downstream critical pipeline of the candidate valve are determined; The regulating radiation range of the candidate valve is generated based on the upstream critical pipe and the downstream critical pipe.

3. The system according to claim 1, characterized in that, The emergency monitoring and management platform is further configured as follows: The unmanned vehicle is controlled to detect the burial depth of the buried pipeline in the area to be inspected, and to obtain burial depth data. Based on the detection data and the burial depth data, a buried pipeline map is constructed; Based on the buried pipeline map, the pipeline anomalies are determined by a prediction model, which is a machine learning model.

4. The system according to claim 1, characterized in that, The emergency monitoring and management platform is further configured as follows: The correlation radius is determined based on the number of abnormal points in the pipeline within the area to be detected; The pipe segment within the range of the associated radius is defined as the pulse width adjustment zone of the buried pipeline; The target pulse width corresponding to the pulse width adjustment region is determined, and a pulse width adjustment command is generated to control the laser in the pulse width adjustment region to operate with the current corresponding to the target pulse width.

5. The system according to claim 4, characterized in that, The emergency monitoring and management platform is further configured as follows: The second stress distribution and the second displacement distribution are obtained through the distributed optical fiber; Based on the second stress distribution and the second displacement distribution, the area to be detected of the buried pipeline is determined.

6. A method for emergency monitoring of urban lifeline gas pipelines, characterized in that, The method is executed by the emergency monitoring and management platform described in claim 1, and includes: The distributed optical fiber is used to collect the first stress distribution and the first displacement distribution of the buried pipeline; wherein the distributed optical fiber is located on the buried pipeline. Based on the first stress distribution and the first displacement distribution, the area to be inspected corresponding to the buried pipeline is determined; The unmanned vehicle is controlled to detect the buried pipeline in the area to be detected and to acquire detection data; Based on the detection data, the pipeline anomaly points of the buried pipeline are determined; Determine the regulation radiation range of candidate valves, wherein the candidate valves are generated based on the pipeline anomaly points; Based on the adjusted radiation range, the radiation impact value and coverage data of the candidate valves are determined; Based on the pipeline anomaly points, determine the control impact value of the candidate valves; The comprehensive score of the candidate valve is determined by weighting the radiation impact value and the control impact value. Based on the comprehensive score and the coverage data, the target valve is determined and a valve control command is sent. Based on the valve control command, the target valve is controlled to close.

7. An emergency monitoring device for urban lifeline gas pipelines, characterized in that, The device includes at least one processor and at least one memory; The at least one memory is used to store computer instructions; The at least one processor is used to execute the computer instructions to implement the method as described in claim 6.

8. A computer-readable storage medium, characterized in that, The storage medium stores computer instructions. When the computer reads the computer instructions from the storage medium, the computer executes the method described in claim 6.

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