Smart city lifeline engineering emergency supervision method and Internet of Things large model system

Abstract

The invention provides a smart city lifeline engineering emergency supervision method and an Internet of Things large model system, and relates to the technical field of emergency supervision. The Internet of Things large model system comprises an emergency supervision user platform, an emergency supervision service platform, an emergency supervision management platform, an emergency supervision sensing network platform and an emergency supervision object platform. The method is executed by an emergency supervision and management platform, and comprises the following steps: in response to a condition that gas environment characteristics corresponding to a target pipeline meet early warning conditions, determining a target sensor and acquiring corresponding air flow data based on space communication information corresponding to the target pipeline every preset period; determining an estimated diffusion amplitude of the target gas based on the gas transportation data, the space communication information, the air flow data and the regional soil characteristics of the target pipeline; determining inspection frequency and sampling frequency based on the estimated diffusion amplitude so as to obtain a soil sample during inspection; and receiving a leak warning. According to the invention, accurate judgment and timely warning of the leakage risk can be realized.

Description

Technical Field

[0001] The present invention relates to the technical field of emergency supervision, and particularly relates to an emergency supervision method for lifeline projects in smart cities and an Internet of Things large model system. Background Art

[0002] In urban lifeline projects, the management of various pipelines, including gas pipelines, is a key link. Since many pipelines are laid in underground spaces, the toxic, harmful, flammable, or explosive gases contained in the pipelines, such as natural gas, hydrogen sulfide, coal gas, chlorine gas, etc., may diffuse into the surrounding underground pipe trenches through tiny leaks and even penetrate into the soil environment, forming potential safety hazards such as endangering residents' health or causing serious safety accidents. Currently, robot patrol mainly focuses on checking for pipeline leaks, but relatively ignores the gas monitoring in the soil or pipe trenches. How to evaluate the gas diffusion amplitude of different pipelines and the surrounding soil and achieve targeted prevention and control of different regions is a problem that needs to be solved.

[0003] Therefore, it is necessary to provide an emergency supervision method for lifeline projects in smart cities, which can adjust the working parameters of the patrol robot in real time according to the actual situation of gas diffusion around the pipeline, so as to achieve accurate discrimination and timely warning of leakage risks and realize effective emergency supervision. Summary of the Invention

[0004] In order to solve the problem of how to evaluate the diffusion amplitude of leaked gas from different pipelines and the surrounding soil, the present invention provides an emergency supervision method for lifeline projects in smart cities and an Internet of Things large model system.

[0005] The summary of the invention includes an emergency supervision method for lifeline projects in smart cities, and the method includes: in response to the gas environment characteristics corresponding to the target pipeline satisfying the early warning conditions, at every preset period: based on the spatial connectivity information corresponding to the target pipeline, determine the target sensor and obtain the air flow data corresponding to the target sensor; obtain the regional soil characteristics corresponding to the target pipeline; based on the gas transportation data, the spatial connectivity information, the air flow data, and the regional soil characteristics of the target pipeline, determine the estimated diffusion amplitude of the target gas; based on the estimated diffusion amplitude, determine the patrol frequency and sampling frequency, and send them to the emergency supervision object platform to control the patrol robot to patrol based on the patrol frequency and sample the soil based on the sampling frequency during the patrol to obtain soil samples; and receive the leakage warning sent by the patrol robot when the soil sample is in an abnormal state.

[0006] The invention content includes an Internet of Things large model system for emergency supervision of smart city lifeline projects. The Internet of Things large model system includes an emergency supervision user platform, an emergency supervision service platform, an emergency supervision management platform, an emergency supervision sensing network platform, and an emergency supervision object platform; the emergency supervision management platform is configured to execute the emergency supervision method for smart city lifeline projects.

[0007] The beneficial effects brought by the above invention content include but are not limited to: (1) Based on the estimated diffusion amplitude, controlling the inspection frequency and sampling frequency of inspection robots, being able to adjust the working parameters of inspection robots in real time according to the actual situation of gas diffusion around pipelines, so as to achieve accurate discrimination and timely warning of leakage risks, realize effective emergency supervision, and reduce the harm of gas leakage; (2) Adjusting the estimated diffusion amplitude based on the diffusion amplitude of the target gas corresponding to the connected position can improve the accuracy of the estimated diffusion amplitude; (3) Dynamically generating the display frequency and display color of the positioning component based on the first diffusion amplitude in the diffusion stage can dynamically respond to diffusion changes and achieve risk visualization hierarchical warning; (4) By determining the placement points of the positioning component, the suction power and suction period of the negative pressure suction device, it is possible to achieve accurate positioning of gas leakage risks, dynamic hierarchical response, and automated emergency disposal, thereby quickly suppressing gas diffusion, optimizing resource allocation, and reducing the risks of casualties and environmental pollution. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] The present invention will be further described by way of exemplary embodiments, and these exemplary embodiments will be described in detail through the drawings. These embodiments are not restrictive. In these embodiments, the same numbers represent the same structures, where: Figure 1 is a system structure diagram of the Internet of Things large model system for emergency supervision of smart city lifeline projects shown in some embodiments of the present invention; Figure 2 is an exemplary flowchart of the emergency supervision method for smart city lifeline projects shown in some embodiments of the present invention; Figure 3 is an exemplary schematic diagram of determining the first diffusion amplitude shown in some embodiments of the present invention; Figure 4 is an exemplary flowchart of determining the estimated diffusion amplitude shown in some embodiments of the present invention; Figure 5 is an exemplary schematic diagram of determining the second diffusion amplitude shown in some embodiments of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0009] The following will briefly introduce the drawings required for the description of the embodiments. The drawings do not represent all the implementation manners.

[0010] When describing the operations performed step by step in the embodiments of the invention, unless otherwise specified, the order of the steps can be adjusted, steps can be omitted, and other steps can also be included during the operation.

[0011] Figure 1 It is the system structure diagram of the Internet of Things large model system for emergency supervision of smart city lifeline projects shown in some embodiments of the present invention.

[0012] In some embodiments, as Figure 1 shown, the Internet of Things large model system 100 for emergency supervision of smart city lifeline projects includes an emergency supervision user platform 110, an emergency supervision service platform 120, an emergency supervision management platform 130, an emergency supervision sensing network platform 140, and an emergency supervision object platform 150.

[0013] The emergency supervision user platform refers to the platform that initiates emergency supervision requirements and receives emergency supervision feedback information, and can be configured as a user terminal. For example, devices with input and / or output functions such as computers.

[0014] The emergency supervision service platform refers to an interactive service platform that receives and transmits data, and can include communication terminals. For example, wireless mobile phones, video monitors, multimedia computers, etc.

[0015] In some embodiments, the emergency supervision service platform 120 interacts with the emergency supervision user platform 110 upward and with the emergency supervision management platform 130 downward.

[0016] The emergency supervision management platform refers to a comprehensive platform for processing and managing emergency supervision data, and can include a processor, a storage device, etc.

[0017] In some embodiments, the emergency supervision management platform 130 is configured to execute the emergency supervision method for smart city lifeline projects. For more content about this method, see Figures 2 - 5 the relevant description.

[0018] The emergency supervision sensing network platform refers to a management platform for transmitting emergency supervision-related sensing data or information, and can include a communication network or gateway, a network interface, etc.

[0019] In some embodiments, the emergency supervision sensing network platform 140 can interact with the emergency supervision management platform 130 upward and with the emergency supervision object platform 150 downward.

[0020] The emergency supervision object platform 150 refers to a platform for collecting emergency supervision data and implementing execution instructions, including inspection robots, etc. In some embodiments, the inspection robots in the emergency supervision object platform 150 are configured to perform inspections based on the inspection frequency, sample the soil based on the sampling frequency during inspections to obtain soil samples, and send leakage warnings to the emergency supervision management platform when the soil samples are in an abnormal state.

[0021] In some embodiments of the present invention, the emergency supervision Internet of Things large model system for smart city lifeline projects can form an information operation closed-loop among various functional platforms, and operate coordinately and regularly under the unified management of the emergency supervision management platform. By efficiently and accurately dynamically adjusting the inspection frequency and sampling frequency of inspection robots, the processing efficiency for emergency scenarios can be improved.

[0022] Figure 2 It is an exemplary flowchart of the emergency supervision method for smart city lifeline projects shown in some embodiments of the present invention. In some embodiments, the emergency supervision method process for smart city lifeline projects can be executed by the emergency supervision management platform. As Figure 2 shown, the emergency supervision method process for smart city lifeline projects includes: in response to the gas environment characteristics corresponding to the target pipeline satisfying the warning conditions, the emergency supervision management platform executes the following steps S210 - step S250 every preset period.

[0023] The target pipeline refers to a pipeline that needs to be monitored for gas leakage.

[0024] The gas environment characteristics refer to the characteristics related to the gas in the target area where the target pipeline is located. For example, the concentration of the target gas in the soil of the target area, the air flow velocity in the target area, etc. The target area refers to the area within the first preset range around the target pipeline. The first preset range can be preset manually according to experience.

[0025] In some embodiments, the gas environment characteristics corresponding to the target pipeline can be monitored by inspection robots and uploaded to the emergency supervision management platform in real time.

[0026] The target gas refers to the gas being transported in the current target pipeline, which can be represented by the main components of the gas. For example, if the target pipeline transports natural gas, the corresponding target gas is methane.

[0027] The warning condition refers to the condition for initiating emergency inspections. The warning condition can be that the concentration of the target gas in the soil of the target area is greater than the corresponding first concentration threshold, and / or the air flow velocity exceeds the reference flow velocity range. When the gas environment characteristics corresponding to the target pipeline satisfy the warning conditions, it indicates that there is probably a gas leakage problem, and it is necessary to control the inspection robot to perform enhanced inspections to further determine the location of the gas leakage.

[0028] In some embodiments, both the first concentration threshold corresponding to the target gas and the reference flow rate range can be preset manually.

[0029] In some embodiments, the preset period can be set manually based on historical experience or historical data. For example, 5 minutes, 10 minutes, etc.

[0030] Step S210: Based on the spatial connectivity information corresponding to the target pipeline, determine the target sensor and obtain the air flow data corresponding to the target sensor.

[0031] Spatial connectivity information refers to the relevant information on the spatial connectivity of the target pipeline in the target area. For example, the connectivity relationship between the target pipeline and structures such as underground pipe galleries, pipe trenches, and manholes in the target area, including the connectivity direction and the coordinates of the connectivity position, etc.

[0032] In some embodiments, the emergency supervision and management platform can obtain the spatial connectivity information corresponding to the target pipeline based on the pre-stored as-built drawings of underground pipelines.

[0033] In some embodiments, the emergency supervision and management platform can determine the sensors deployed in underground pipe galleries, pipe trenches, manholes, etc. in the target area as target sensors. The types of target sensors include thermos anemometers, vane anemometers, etc.

[0034] The air flow data corresponding to the target sensor includes the air flow velocity and air flow direction collected by the target sensor.

[0035] Step S220: Obtain the regional soil characteristics corresponding to the target pipeline.

[0036] Regional soil characteristics include the soil characteristics at multiple points in the target area. Soil characteristics include soil density, soil porosity, soil water content, etc.

[0037] In some embodiments, for one point, the emergency supervision and management platform can determine the mean value of the soil characteristics obtained from the historical nearest M soil samplings at this point as the soil characteristic of this point, and the sequence of the soil characteristics at multiple points in the target area constitutes the regional soil characteristics. The historical nearest M soil samplings refer to the nearest M historical soil samplings from the current moment. M can be selected manually according to the actual situation, for example, 3, 5, etc.

[0038] The inspection robot is equipped with a sampling device (such as a robotic arm) and various types of sensors (such as soil moisture sensors, density sensors, etc.) to perform soil sampling, analyze and obtain soil characteristics, and upload them to the emergency supervision and management platform.

[0039] In some embodiments, the emergency supervision and management platform may evenly divide the target area to obtain multiple points. For example, the target area is divided into multiple sub-areas with equal areas, and the center point of each sub-area is a point.

[0040] Step S230, based on the gas transportation data, spatial connectivity information, air flow data, and regional soil characteristics of the target pipeline, determine the estimated diffusion range of the target gas.

[0041] The gas transportation data may include the type, flow rate, transportation pressure, etc. of the target gas transported by the target pipeline, and can be detected and obtained by sensors (such as, composition detectors, flow meters, pressure sensors, etc.) arranged in the target pipeline.

[0042] The estimated diffusion range may include the estimated diffusion range, estimated diffusion speed, and estimated diffusion volume, etc. of the target gas.

[0043] The diffusion range can be represented by the area of the region where the target gas diffuses. The diffusion speed can be represented by the area diffused per unit time. The diffusion volume can be represented by the leakage volume of the target gas.

[0044] In some embodiments, the emergency supervision and management platform may determine the estimated diffusion range by querying the first preset table based on the gas transportation data, spatial connectivity information, air flow data, and regional soil characteristics.

[0045] The first preset table includes the corresponding relationship between the gas transportation data, spatial connectivity information, air flow data, regional soil characteristics, and the estimated diffusion range. The first preset table can be constructed manually based on historical data. For example, the first preset table is constructed by using the historical gas transportation data, historical spatial connectivity information, historical air flow data, and historical regional soil characteristics recorded during multiple historical monitors, and the corresponding historical actual diffusion range.

[0046] In some embodiments, the estimated diffusion range includes the first diffusion range corresponding to the diffusion stage, and the emergency supervision and management platform may determine the first diffusion range through a division model. For more content on this part, see Figure 3 and related descriptions.

[0047] In some embodiments, the emergency supervision and management platform may determine the second diffusion range corresponding to the connection position based on the gas transportation data, spatial connectivity information, air flow data, connection characteristics corresponding to the connection position, and position soil characteristics. For more content, see Figure 4 and related descriptions.

[0048] Step S240: Based on the estimated diffusion amplitude, determine the inspection frequency and sampling frequency, and send them to the emergency supervision object platform to control the inspection robot to conduct inspections based on the inspection frequency and take soil samples based on the sampling frequency during inspections to obtain soil samples.

[0049] The inspection frequency refers to the number of inspections the inspection robot conducts per unit time. For example, 10 times per hour. The sampling frequency refers to the number of soil samplings during a single inspection by the inspection robot.

[0050] In some embodiments, the emergency supervision management platform can determine the inspection frequency and sampling frequency by querying the second preset table based on the estimated diffusion amplitude.

[0051] The second preset table includes the corresponding relationships between the estimated diffusion amplitude, inspection frequency, and sampling frequency. The second preset table can be constructed manually based on historical experience or historical data.

[0052] Step S250: Receive the leakage warning sent by the inspection robot when the soil sample is in an abnormal state.

[0053] In some embodiments, in response to the concentration of the target gas in the soil samples at N consecutive points being greater than the first concentration threshold, the inspection robot determines that the soil sample is in an abnormal state, generates a leakage warning, and sends it to the emergency supervision management platform. Among them, N consecutive points can refer to N points where the inspection robot continuously conducts soil sampling. The value of N and the first concentration threshold can be set manually based on historical experience.

[0054] The leakage warning is used to remind the user that the target pipeline may have a gas leak. The leakage warning includes the gas leakage point and the concentration of the target gas in the corresponding soil sample, etc. The gas leakage point refers to the point where the pipeline has a gas leak. In some embodiments, there are multiple pipeline preset points on the target pipeline, and the pipeline preset points can be pre-calibrated manually. The emergency supervision management platform can determine the point with the highest concentration of the target gas among the N consecutive points where the soil sample is in an abnormal state, and determine the pipeline preset point closest to it as the gas leakage point.

[0055] In some embodiments of the present invention, controlling the inspection frequency and sampling frequency of the inspection robot based on the estimated diffusion amplitude can, according to the actual situation of gas diffusion around the pipeline, adjust the working parameters of the inspection robot in real time to achieve accurate discrimination and timely warning of leakage risks, realize effective emergency supervision, and reduce the harm of gas leakage.

[0056] Figure 3 It is an exemplary schematic diagram for determining the first diffusion amplitude shown in some embodiments of the present invention.

[0057] In some embodiments, the estimated diffusion amplitude includes a first diffusion amplitude corresponding to the diffusion stage.

[0058] For more information about the estimated diffusion amplitude, see Figure 2 and related descriptions.

[0059] The diffusion stage refers to different stages of the target gas diffusing in the soil. For example, the emergency supervision and management platform can take the gas leakage point as the center and divide it into multiple concentric circles with preset different distances as the radii. The multiple rings from the inside to the outside are multiple diffusion ranges. The process of the target gas diffusing into a diffusion range until the target gas fills the diffusion range is recorded as the diffusion stage corresponding to this diffusion range, that is, one diffusion range corresponds to one diffusion stage, and each diffusion stage has its corresponding diffusion speed and diffusion amount.

[0060] The first diffusion amplitude refers to the diffusion amplitude corresponding to the target gas in the diffusion stage. The diffusion time period, diffusion range, diffusion speed, and diffusion amount corresponding to each diffusion stage constitute the first diffusion amplitude corresponding to this diffusion stage. The estimated diffusion amplitude includes the first diffusion amplitudes corresponding to multiple diffusion stages.

[0061] The diffusion time period corresponding to the diffusion stage refers to the time period from when the target gas diffuses into the diffusion range corresponding to this diffusion stage until the target gas fills the diffusion range.

[0062] In some embodiments, as Figure 3 shown, the emergency supervision and management platform can determine the first diffusion amplitude 370 corresponding to the diffusion stage based on the gas transportation data 310, spatial connectivity information 320, air flow data 330, and regional soil characteristics 340 through the partitioning model 350; and adjust the first diffusion amplitude 370 based on the density difference 360 between the target gas and air.

[0063] The partitioning model is a model used to determine the first diffusion amplitude. In some embodiments, the partitioning model is a machine learning model. For example, a Deep Neural Networks (DNN) model, etc.

[0064] The inputs of the partitioning model include gas transportation data, spatial connectivity information, air flow data, and regional soil characteristics, and the output of the partitioning model includes the first diffusion amplitude corresponding to the diffusion stage.

[0065] For more information about the gas transportation data, spatial connectivity information, air flow data, and regional soil characteristics, see Figure 2 and related descriptions.

[0066] In some embodiments, the emergency supervision and management platform may train a partitioning model based on multiple first training samples with first labels. The emergency supervision and management platform may input the first training samples into an initial partitioning model, construct a loss function based on the first labels and the output of the initial partitioning model, iteratively update the parameters of the initial partitioning model based on the loss function, and end the iteration when the iteration end condition is met to obtain a trained partitioning model. Among them, the methods for iterative update include, but are not limited to, the gradient descent method, and the iteration end condition may be that the loss function converges or the number of iterations reaches a threshold.

[0067] The first training samples can be obtained based on historical data. The first training samples include historical gas transportation data, historical spatial connectivity information, historical air flow data, and historical regional soil characteristics corresponding to the historical target pipelines.

[0068] The first labels include the reference first diffusion amplitudes corresponding to the historical target pipelines in multiple reference diffusion stages.

[0069] There are multiple historical leakage processes for the historical target pipeline corresponding to one first training sample, and each historical leakage process has corresponding historical gas leakage points.

[0070] For each historical gas leakage point, the emergency supervision and management platform can construct multiple clustering vectors based on the historical diffusion range, historical diffusion period, historical diffusion speed, and historical diffusion volume corresponding to the historical gas leakage point in multiple historical leakage processes. One clustering vector is composed of the historical diffusion range, historical diffusion period, historical diffusion speed, and historical diffusion volume corresponding to one historical leakage process; cluster the multiple clustering vectors based on the historical diffusion speed and historical diffusion volume to obtain multiple clustering clusters.

[0071] For each clustering cluster, determine the average value of the historical diffusion ranges corresponding to the multiple clustering vectors in the clustering cluster as a reference diffusion range, that is, determine a reference diffusion stage, determine the union of the historical diffusion periods corresponding to the multiple clustering vectors in the clustering cluster as the reference diffusion period corresponding to the reference diffusion stage, and determine the average value of the historical diffusion speeds and the average value of the historical diffusion volumes corresponding to the multiple clustering vectors in the clustering cluster as the reference diffusion speed and reference diffusion volume corresponding to the reference diffusion stage, respectively.

[0072] Multiple clustering clusters obtain multiple reference diffusion stages and the corresponding multiple reference diffusion ranges, multiple reference diffusion speeds, and multiple reference diffusion volumes, and thus can construct multiple reference first diffusion amplitudes corresponding to the multiple reference diffusion stages as the first labels.

[0073] The methods for clustering include, but are not limited to, the K-Means clustering algorithm, the DBSCAN clustering algorithm, etc.

[0074] In some embodiments, the emergency supervision and management platform can directly obtain the density of the target gas and the density of air pre-uploaded manually, and determine the absolute value of the difference between the two as the density difference between the target gas and air.

[0075] In some embodiments, the emergency supervision and management platform can determine the diffusion speed influence value and the diffusion amount influence value by querying a third preset table based on the density difference between the target gas and air.

[0076] The third preset table includes the corresponding relationship between the density difference between the target gas and air and the diffusion speed influence value and the diffusion amount influence value. The greater the density difference between the target gas and air, the greater the diffusion speed influence value and the greater the diffusion amount influence value. The third preset table can be constructed manually based on historical experience.

[0077] The diffusion speed influence value and the diffusion amount influence value respectively reflect the influence degree of the density difference between the target gas and air on the diffusion speed and diffusion amount of the target gas.

[0078] In some embodiments, the emergency supervision and management platform can determine the adjusted diffusion speed by multiplying the diffusion speed by the diffusion speed influence value, and determine the adjusted diffusion amount by multiplying the diffusion amount by the diffusion amount influence value, thereby obtaining the adjusted first diffusion amplitude.

[0079] The density difference between the target gas and air will significantly affect the penetration resistance of the target gas in the soil, thereby affecting the diffusion speed and diffusion amount of the target gas. In some embodiments of the present invention, adjusting the first diffusion amplitude based on the density difference between the target gas and air can improve the reliability of predicting the diffusion amplitude.

[0080] Figure 4 is an exemplary flowchart for determining the estimated diffusion amplitude according to some other embodiments of the present invention. As Figure 4 shown, the process of determining the estimated diffusion amplitude includes the following steps S410-step S430. In some embodiments, the process of determining the estimated diffusion amplitude can be executed by the emergency supervision and management platform.

[0081] Step S410, based on the spatial connectivity information and the regional soil characteristics, determine the location soil characteristics corresponding to the connectivity location.

[0082] For more content about the estimated diffusion amplitude, spatial connectivity information, and regional soil characteristics, see Figure 2 and related descriptions.

[0083] The location soil characteristics refer to the soil characteristics corresponding to the connectivity location. In some embodiments, the emergency supervision and management platform can determine the soil characteristics of the point closest to the connectivity location as the location soil characteristics corresponding to the connectivity location.

[0084] In some embodiments, the emergency supervision and management platform may determine a preset number of adjacent points at the connected location and the corresponding location soil characteristics based on the spatial connectivity information and the regional soil characteristics; and determine the location soil characteristics corresponding to the connected location based on the location soil characteristics corresponding to the adjacent points.

[0085] In some embodiments, the preset number is related to the regional soil characteristics corresponding to the target pipeline. The larger the standard deviation or variance of the soil characteristics of multiple points in the target area, the more complex the distribution of soil components and types in the target area, and the larger the preset number, so as to improve the reliability of the location soil characteristics.

[0086] In some embodiments, the emergency supervision and management platform may determine the preset number of points closest to the connected location as the adjacent points, and determine the average value of the soil characteristics corresponding to the preset number of adjacent points as the location soil characteristics corresponding to the connected location.

[0087] In some embodiments of the present invention, the location soil characteristics corresponding to the connected location are determined based on the soil characteristics of multiple points near the connected location, and the obtained location soil characteristics are more accurate.

[0088] Step S420: Determine the second diffusion amplitude corresponding to the connected location based on the gas transportation data, spatial connectivity information, air flow data, the connected characteristics corresponding to the connected location, and the location soil characteristics.

[0089] The connected characteristics refer to the characteristics related to the connection situation of the connected location. For example, the connected characteristics include whether there is an opening at the connection, whether it is in direct contact with the soil, etc. The connected characteristics corresponding to the connected location can also be determined based on the underground pipeline as-built drawing.

[0090] The second diffusion amplitude refers to the diffusion amplitude of the target gas at the connected location. For example, the second diffusion amplitude includes the estimated diffusion range, estimated diffusion speed, estimated diffusion amount, etc. of the target gas at the connected location.

[0091] In some embodiments, the emergency supervision and management platform may determine the second diffusion amplitude by querying the fourth preset table based on the gas transportation data, spatial connectivity information, air flow data, the connected characteristics corresponding to the connected location, and the location soil characteristics.

[0092] The fourth preset table includes the corresponding relationship between the gas transportation data, spatial connectivity information, air flow data, the connected characteristics corresponding to the connected location, the location soil characteristics, and the second diffusion amplitude. The fourth preset table can be constructed manually based on historical data. For example, the fourth preset table is constructed from the historical gas transportation data, historical spatial connectivity information, historical air flow data, historical connected characteristics corresponding to the historical connected location, historical location soil characteristics, and the corresponding historical actual second diffusion amplitude recorded during multiple historical monitoring.

[0093] In some embodiments, the emergency supervision and management platform may construct a gas flow map based on gas transportation data, spatial connectivity information, air flow data, connectivity characteristics corresponding to the connected positions, and location soil characteristics; based on the gas flow map, through a prediction model, determine a second diffusion amplitude corresponding to the connected position. For more details, see Figure 5 and related descriptions.

[0094] Step S430, adjust the estimated diffusion amplitude based on the second diffusion amplitude.

[0095] In some embodiments, the emergency supervision and management platform may determine the overlapping part of the estimated diffusion range corresponding to the estimated diffusion amplitude determined by querying the first preset table and the estimated diffusion range corresponding to the second diffusion amplitude as the adjusted diffusion range; perform a weighted sum of the estimated diffusion speed corresponding to the estimated diffusion amplitude determined by querying the first preset table and the estimated diffusion speed corresponding to the second diffusion amplitude, and determine the weighted sum result as the adjusted diffusion amplitude. The weight of the estimated diffusion speed corresponding to the estimated diffusion amplitude is much greater than the weight of the estimated diffusion speed corresponding to the second diffusion amplitude. The adjustment method of the diffusion amount is the same and will not be elaborated here, and thus the adjusted estimated diffusion amplitude can be obtained.

[0096] In some embodiments of the present invention, by adjusting the estimated diffusion amplitude based on the diffusion amplitude of the target gas corresponding to the connected position, the accuracy of the estimated diffusion amplitude can be improved.

[0097] Figure 5 is an exemplary diagram showing the determination of the second diffusion amplitude according to some embodiments of the present invention.

[0098] In some embodiments, as Figure 5 shown, the emergency supervision and management platform may construct a gas flow map 530 based on gas transportation data 310, spatial connectivity information 320, air flow data 330, connectivity characteristics 510 corresponding to the connected positions, and location soil characteristics 520; based on the gas flow map 530, through a prediction model 540, determine a second diffusion amplitude 550 corresponding to the connected position.

[0099] The gas flow map refers to a map that describes the flow path and characteristics of the target gas and is composed of nodes and directed edges.

[0100] In some embodiments, the nodes of the gas flow map include the connected positions and gas leakage points.

[0101] The node characteristics corresponding to the connected positions may include the connectivity characteristics of the connected positions and the location soil characteristics.

[0102] The node characteristics corresponding to the gas leakage points may include gas transportation data.

[0103] For more information on gas transportation data, connection locations, connection characteristics, and location soil characteristics, see Figure 2 and Figure 4 the relevant descriptions.

[0104] In some embodiments, for any node in the gas flow map, the node characteristics of the node further include the standard deviation of the location soil characteristics of multiple points within a preset range corresponding to the node.

[0105] In some embodiments, the preset range corresponding to the node may refer to a range area at a preset distance from the node. The preset distance can be preset manually. The preset range can be smaller than the first preset range above.

[0106] In some embodiments of the present invention, taking the standard deviation of the location soil characteristics of multiple points within the preset range corresponding to the node as the node characteristics can more accurately reflect the local complexity of the soil composition and soil structure near the node, thereby improving the accuracy of determining the diffusion rate of the target gas through the prediction model subsequently and providing more reliable data support for emergency supervision decisions.

[0107] In some embodiments, the directed edges of the gas flow map include underground pipe galleries, pipe trenches, manholes, etc. between nodes, and the direction of the edge is the gas flow direction.

[0108] The edge characteristics include the air flow data of the target sensors in the underground pipe galleries, pipe trenches, manholes, etc. corresponding to the edges. For more information on air flow data, see Figure 2 the relevant descriptions.

[0109] The prediction model is a model for predicting the second diffusion amplitude. In some embodiments, the prediction model can be a machine learning model, for example, a Graph Neural Networks (GNN) model, etc. In some embodiments, the input of the prediction model can include the gas flow map, and the output can include the second diffusion amplitude corresponding to the connection location.

[0110] In some embodiments, the prediction model can be trained based on a large number of second training samples with second labels. The second training samples can be obtained based on historical data. The second training samples can include the historical gas flow maps corresponding to the historical target pipelines, and the second labels can be the historical actual second diffusion amplitudes corresponding to each historical connection location.

[0111] In some embodiments, there are multiple historical leakage processes for the historical target pipeline corresponding to a second training sample, and each historical leakage process has a corresponding historical gas flow pattern. For each historical connection location, calculate the mean value of the historical actual second diffusion amplitude at this historical connection location in multiple historical gas flow patterns, and determine it as the historical actual second diffusion amplitude at this historical connection location, then the second label can be obtained. The historical actual second diffusion amplitude is obtained from the historical record data of historical inspections and manually labeled as the second label. The training process of the prediction model can refer to Figure 3 the training process of the partitioning model in

[0112] In some embodiments of the present invention, by constructing a gas flow pattern and determining the second diffusion amplitude corresponding to the connection location based on the prediction model, and by integrating multi-dimensional dynamic data such as gas transportation data, connection characteristics, and soil characteristics, the risk of target gas diffusion can be evaluated more accurately, and different pipeline network topologies and leakage scenarios can be adapted, so as to optimize inspection and emergency management and improve the safety of urban lifeline projects.

[0113] In some embodiments, in response to a gas leak occurring in the target pipeline, the emergency supervision and management platform can, based on the inspection data of the inspection robot, determine the target points where the gas concentration in the soil is greater than the preset concentration threshold, and / or based on the second diffusion amplitude corresponding to the connection location, determine the target connection locations where the diffusion amount is greater than the preset diffusion threshold, and determine the target points and / or target connection locations as the placement points of the positioning components; based on the air flow data, generate the suction power and suction period of the negative pressure suction device; send the placement points, suction power, and suction period to the emergency supervision object platform to control the inspection robot to place the positioning components at the placement points, and control the negative pressure suction device to perform air extraction and / or air inhalation based on the suction power and suction period.

[0114] In some embodiments, after receiving the leakage warning sent by the inspection robot, the emergency supervision and management platform can determine whether the pressure of the target pipeline is less than the pressure threshold. If the pressure of the target pipeline is less than the pressure threshold, it is determined that a gas leak has occurred in the target pipeline. The pressure threshold can be preset manually according to prior experience or set by default in the system. The pressure of the target pipeline can be detected and obtained by a pressure sensor arranged inside the pipeline and uploaded to the emergency supervision and management platform.

[0115] The inspection data refers to the data obtained by the inspection robot during the inspection process. For example, the inspection data can include the concentration of the target gas in the soil samples at each point. In some embodiments, the inspection data can be uploaded by the inspection robot to the emergency supervision and management platform.

[0116] The target point refers to the point that needs to be monitored and / or processed during the inspection process. In some embodiments, the emergency supervision and management platform may determine the point where the concentration of the target gas in the soil sample is greater than the preset concentration threshold as the target point.

[0117] The target connection position refers to the connection position that needs to be monitored and / or processed. In some embodiments, the emergency supervision and management platform may determine the connection position with a diffusion amount greater than the preset diffusion threshold as the target connection position based on the first diffusion amplitude corresponding to the connection position.

[0118] In some embodiments, both the preset concentration threshold and the preset diffusion threshold can be preset manually according to experience. The preset concentration threshold can be greater than the first concentration threshold.

[0119] In some embodiments, the preset concentration threshold and the preset diffusion threshold are related to the population density and / or building density.

[0120] The population density refers to the number of permanent or real-time monitored people per unit area; the building density refers to the number of buildings per unit area or the proportion of building floor area. In some embodiments, the inspection robot may also be configured with an image acquisition device (such as a camera, etc.) for acquiring images and uploading them to the emergency supervision and management platform, and the emergency supervision and management platform may perform image recognition on the images to obtain the population density and building density. The method of image recognition can be a computer vision method, a deep learning model, etc.

[0121] In some embodiments, the preset concentration threshold and the preset diffusion threshold may be negatively correlated with the population density and / or building density. For example, the greater the population density and / or building density, the greater the risk of gas poisoning, and the smaller the preset concentration threshold and the preset diffusion threshold need to be.

[0122] In some embodiments of the present invention, determining the preset concentration threshold and the preset diffusion threshold through the population density and / or building density can achieve dynamic risk classification management, thereby optimizing the allocation of emergency resources while ensuring public safety.

[0123] The positioning component refers to a component used to mark the area with a risk of gas leakage, such as a warning light, a warning sign, etc.

[0124] The placement point refers to the point where the positioning component is placed.

[0125] In some embodiments, the emergency supervision and management platform may directly determine the target point and / or the target connection position as the placement point of the positioning component.

[0126] A negative pressure suction device refers to a device that extracts and collects gases and / or particles by generating negative pressure. For example, a negative pressure air extractor, etc. The negative pressure suction device can be arranged at a position near the exit in a trench, an underground pipe gallery, or a manhole.

[0127] In some embodiments, the emergency supervision management platform can, based on spatial connectivity information and air flow data, determine whether there is a target gas flowing towards an area where the population density is greater than the population threshold and / or the building density is greater than the building threshold. If so, it turns on multiple negative pressure suction devices arranged at the positions where the target gas flows through in the underground pipe gallery, trench, or manhole, controls the negative pressure suction devices to perform air extraction and / or inhalation based on the suction power and suction period, changes the air flow direction and air flow rate in the underground space, and ensures that the flow direction of the target gas is changed, thereby ensuring the safety of the area where people and / or buildings gather. Among them, the population threshold and the building threshold can be preset according to prior experience.

[0128] The suction power refers to the power of the negative pressure suction device when it is working. The suction period refers to the operation period of the negative pressure suction device.

[0129] In some embodiments, the magnitude of the suction power is dynamically adjusted. The emergency supervision management platform can control the negative pressure suction device to increase the power starting from the standard suction power until the air flow direction changes, and determine the power of the negative pressure suction device at this time as the stable suction power. The negative pressure suction device will continuously suck within the suction period according to the stable suction power. Among them, the standard suction power can be preset according to prior experience.

[0130] In some embodiments, the emergency supervision management platform can determine the moment when the target gas flows through each position according to the air flow rate; for each position, the corresponding negative pressure suction device is pre-activated within a preset period before the corresponding moment until the air flow direction at this position changes and becomes stable, and then the corresponding negative pressure suction device is turned off. The period from when the negative pressure suction device is turned on to when it is turned off is the suction period. Among them, the duration of the preset period can be preset manually according to experience.

[0131] In some embodiments, the emergency supervision management platform can generate the display frequency and display color corresponding to the placement points based on the first diffusion amplitude corresponding to the diffusion stage, and send them to the emergency supervision object platform to control the positioning component to blink based on the display frequency and emit light based on the display color.

[0132] For more content about the diffusion stage and the first diffusion amplitude, see Figure 3 and the relevant descriptions.

[0133] In some embodiments, the emergency supervision management platform may obtain the diffusion stage to which the positioning component belongs based on the diffusion range to which the placement point of the positioning component belongs, and determine the display frequency and display color of the positioning component through a fifth preset table. The fifth preset table includes the diffusion stage, display frequency, and display color. The earlier the diffusion stage, that is, the closer the diffusion range is to the gas leakage point, the greater the display frequency and the more prominent the display color may be.

[0134] In some embodiments of the present invention, dynamically generating the display frequency and display color of the positioning component based on the first diffusion amplitude of the diffusion stage can dynamically respond to diffusion changes and achieve risk visualization hierarchical early warning.

[0135] In some embodiments of the present invention, by determining the placement point of the positioning component and the suction power and suction period of the negative pressure suction device, it is possible to achieve precise positioning of the gas leakage risk, dynamic hierarchical response, and automated emergency disposal, thereby quickly suppressing gas diffusion, optimizing resource allocation, and reducing the risks of casualties and environmental pollution.

[0136] Certain features, structures, or characteristics in one or more embodiments of the present invention may be appropriately combined.

[0137] In addition, unless clearly stated in the claims, the order of the processing elements and sequences, the use of numbers, letters, or other names in the present invention is not used to limit the order of the processes and methods of the present invention. Although various examples are discussed in the above disclosure for some currently considered useful embodiments of the invention, it should be understood that such details are for illustrative purposes only. The appended claims are not limited to the disclosed embodiments. On the contrary, the claims are intended to cover all modifications and equivalent combinations that conform to the essence and scope of the embodiments of the present invention. For example, although the system components described above can be implemented by hardware devices, they can also be implemented only through software solutions, such as installing the described system on existing servers or mobile devices.

[0138] If there are inconsistencies or conflicts between the descriptions, definitions, and / or uses of terms in the materials cited in the present invention and the content described in the present invention, the descriptions, definitions, and / or uses of terms in the present invention shall prevail.

Claims

1. An emergency supervision method for the lifeline project of a smart city, characterized in that, The method is executed by an emergency supervision management platform in an Internet of Things large model system for emergency supervision of smart city lifeline projects. The method includes: In response to the gas environment characteristics corresponding to the target pipeline meeting the warning conditions, at every preset cycle: Based on the spatial connectivity information corresponding to the target pipeline, determine the target sensor and obtain the air flow data corresponding to the target sensor; Obtain the regional soil characteristics corresponding to the target pipeline; Based on the gas transportation data, the spatial connectivity information, the air flow data, and the regional soil characteristics of the target pipeline, determine the estimated diffusion range of the target gas; Based on the estimated diffusion range, determine the inspection frequency and sampling frequency, and send them to the emergency supervision object platform to control the inspection robot to perform inspections based on the inspection frequency and take soil samples based on the sampling frequency during inspections to obtain soil samples; and, Receive the leakage warning sent by the inspection robot when the soil sample is in an abnormal state.

2. The method according to claim 1, wherein The estimated diffusion range includes a first diffusion range corresponding to the diffusion stage; the determining of the estimated diffusion range of the target gas based on the gas transportation data, the spatial connectivity information, the air flow data, and the regional soil characteristics of the target pipeline includes: Based on the gas transportation data, the spatial connectivity information, the air flow data, and the regional soil characteristics, determine the first diffusion range corresponding to the diffusion stage through a division model, and the division model is a machine learning model; Adjust the first diffusion range based on the density difference between the target gas and air.

3. The method according to claim 1, characterized in that, The determining of the estimated diffusion range of the target gas based on the gas transportation data, the spatial connectivity information, the air flow data, and the regional soil characteristics further includes: Based on the spatial connectivity information and the regional soil characteristics, determine the position soil characteristics corresponding to the connection position; Based on the gas transportation data, the spatial connectivity information, the air flow data, the connection characteristics corresponding to the connection position, and the position soil characteristics, determine the second diffusion range corresponding to the connection position; and, Adjust the estimated diffusion range based on the second diffusion range.

4. The method according to claim 3, wherein The determining of the second diffusion range corresponding to the connection position based on the gas transportation data, the spatial connectivity information, the air flow data, the connection characteristics corresponding to the connection position, and the position soil characteristics includes: Based on the gas transportation data, the spatial connectivity information, the air flow data, the connection characteristics corresponding to the connection position, and the position soil characteristics, construct a gas flow map; Based on the gas flow map, determine the second diffusion range corresponding to the connection position through a prediction model, and the prediction model is a machine learning model.

5. The method according to claim 1, characterized in that, The method further includes: In response to a gas leak occurring in the target pipeline, Determine target points where the gas concentration in the soil is greater than a preset concentration threshold based on the inspection data of the inspection robot, and / or determine target connection positions where the diffusion amount is greater than a preset diffusion threshold based on the second diffusion amplitude corresponding to the connection positions, and determine the placement positions of the positioning components as the target points and / or the target connection positions; Generate the suction power and suction period of the negative pressure suction device based on the air flow data; Send the placement positions, the suction power, and the suction period to the emergency supervision object platform to control the inspection robot to place the positioning components at the placement positions, and control the negative pressure suction device to perform air extraction and / or air inhalation based on the suction power and the suction period.

6. An Internet of Things large model system for emergency supervision of lifeline projects in a smart city, characterized in that, Including an emergency supervision user platform, an emergency supervision service platform, an emergency supervision management platform, an emergency supervision sensing network platform, and an emergency supervision object platform; The emergency supervision management platform is configured to: In response to the gas environment characteristics corresponding to the target pipeline meeting the warning conditions, at every preset cycle: Determine target sensors based on the spatial connection information corresponding to the target pipeline, and obtain the air flow data corresponding to the target sensors; Obtain the regional soil characteristics corresponding to the target pipeline; Determine the estimated diffusion amplitude of the target gas based on the gas transportation data, the spatial connection information, the air flow data, and the regional soil characteristics of the target pipeline; Determine the inspection frequency and sampling frequency based on the estimated diffusion amplitude, and send them to the emergency supervision object platform; the inspection robot in the emergency supervision object platform is configured to perform inspections based on the inspection frequency, take soil samples during inspections based on the sampling frequency to obtain soil samples, and send leakage warnings to the emergency supervision management platform when the soil samples are in an abnormal state.

7. The Internet of Things large model system according to claim 6, wherein The estimated diffusion amplitude includes a first diffusion amplitude corresponding to the diffusion stage; the emergency supervision management platform is further configured to: Determine the first diffusion amplitude corresponding to the diffusion stage based on the gas transportation data, the spatial connection information, the air flow data, and the regional soil characteristics through a division model, and the division model is a machine learning model; Adjust the first diffusion amplitude based on the density difference between the target gas and air.

8. The Internet of Things large model system according to claim 6, characterized in that, The emergency supervision management platform is further configured to: Determine the position soil characteristics corresponding to the connection positions based on the spatial connection information and the regional soil characteristics; Determine the second diffusion amplitude corresponding to the connection positions based on the gas transportation data, the spatial connection information, the air flow data, the connection characteristics corresponding to the connection positions, and the position soil characteristics; and, Adjust the estimated diffusion amplitude based on the second diffusion amplitude.

9. The Internet of Things large model system according to claim 8, characterized in that, The emergency supervision management platform is further configured to: Construct a gas flow map based on the gas transportation data, the spatial connection information, the air flow data, the connection characteristics corresponding to the connection positions, and the position soil characteristics; Based on the gas flow map, through a prediction model, determine the second diffusion amplitude corresponding to the connection position, and the prediction model is a machine learning model.

10. The Internet of Things large model system according to claim 6, characterized in that, The emergency supervision management platform is further configured to: In response to a gas leak occurring in the target pipeline, Based on the inspection data of the inspection robot, determine the target points where the gas concentration in the soil is greater than the preset concentration threshold, and / or based on the second diffusion amplitude corresponding to the connection position, determine the target connection positions where the diffusion amount is greater than the preset diffusion threshold, and determine the placement points of the positioning components as the target points and / or the target connection positions; Generate the suction power and suction period of the negative pressure suction device based on the air flow data; Send the placement points, the suction power, and the suction period to the emergency supervision object platform to control the inspection robot to place the positioning components at the placement points, and control the negative pressure suction device to perform air extraction and / or air inhalation based on the suction power and the suction period.

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