Gas pipe network operation state simulation method and system based on digital twinning

Abstract

The invention provides a gas pipe network operation state simulation method and system based on digital twinning, and relates to the technical field of gas pipe network simulation, and the method comprises the steps: building a gas pipe network digital twinning model, carrying out online simulation, and automatically triggering safety early warning according to a preset evaluation rule; special simulation is started in the model after early warning, and the monitored pressure and flow data are combined to analyze an abnormal propagation direction and a sequential relationship so as to position a leakage source and dynamically calculate a leakage rate; simulating a leakage gas diffusion process to generate a dynamically updated three-dimensional concentration distribution map, identifying an explosion limit area and generating an explosion risk grade map; a decision support scheme containing valve operation, emergency resource allocation and public evacuation range is generated by integrating related information, and is pushed to a supervision terminal to realize graded alarm and visual display, so that accurate positioning, diffusion simulation and risk assessment of gas pipe network leakage can be realized, and scientific decision support is provided for emergency disposal of gas pipe network leakage.

Description

Technical Field

[0001] This application relates to the field of gas pipeline network simulation technology, and in particular to a method and system for simulating the operating status of gas pipeline networks based on digital twins. Background Technology

[0002] Gas pipeline networks are core urban infrastructure, and their operational status simulation methods are a key technology for ensuring safe gas supply, with broad application prospects in urban gas supervision. This method can monitor the real-time operational status of the pipeline network, proactively mitigate safety hazards, and provide technical support for gas safety management.

[0003] Currently, existing methods for simulating the operational status of gas pipeline networks based on digital twins primarily involve constructing a digital twin model of the pipeline network to achieve online simulation and monitoring of operational data. When abnormal monitoring data occurs, a basic safety warning is triggered. While this method can initially achieve real-time monitoring of the pipeline network's operational status, it does not provide in-depth analysis of the subsequent impacts of anomalies.

[0004] However, the aforementioned existing technologies can only perform simple simulation and early warning, and cannot accurately locate the source of anomalies, analyze the patterns of risk spread, or provide comprehensive decision-making basis for emergency response, resulting in low emergency response efficiency and insufficient targeted risk prevention and control. Therefore, existing technologies suffer from the technical problem that gas pipeline network simulation cannot effectively support safe emergency response. Summary of the Invention

[0005] The purpose of this application is to provide a method and system for simulating the operational status of gas pipeline networks based on digital twins, so as to solve the problem that existing gas pipeline network simulations cannot effectively support safety emergency response.

[0006] To address the aforementioned technical problems, in a first aspect, this application provides a method for simulating the operational status of a gas pipeline network based on digital twins, comprising: A digital twin model of the gas pipeline network is constructed and online simulation is run. The real-time monitoring data of the pipeline network is compared with the simulation expected values, and safety warnings are automatically triggered based on preset evaluation rules. In response to safety warnings, a special simulation is initiated in the digital twin model. Based on the pressure and flow data of the monitoring data, the propagation direction and temporal relationship of abnormal fluctuations in the pipeline network are analyzed to locate the source of potential leaks. The gas leakage rate is dynamically calculated by solving the mass conservation equation based on simulation correction. Based on the source location and leakage rate, combined with current meteorological environmental data and urban three-dimensional geographic data, the diffusion process of leaked gas in the future period is simulated in a digital twin model to generate a dynamically updated three-dimensional concentration distribution map. By analyzing the three-dimensional concentration distribution map, spatial areas with concentrations within the explosive limit range are identified. Combined with the building and population distribution data of the spatial areas, the risk values ​​of different areas are calculated to generate an explosion risk level map. By combining the source location, three-dimensional concentration distribution map, and explosion risk level map, a decision support plan is generated that includes key valve operation, emergency resource allocation path, and public evacuation range. The decision support plan is then pushed to the monitoring terminal for graded alarms and visualization.

[0007] Optionally, by integrating the source location, three-dimensional concentration distribution map, and explosion risk level map, a decision support plan is generated that includes key valve operations, emergency resource allocation routes, and public evacuation areas. This decision support plan is then pushed to the monitoring terminal for tiered alerts and visual display, including: In the digital twin model, the nearest pipeline valve is located based on the source location to simulate the impact of valve closure on gas diffusion and obtain gas concentration change data; Based on concentration change data, key valve combinations that can effectively suppress diffusion are selected, and an operation sequence list is generated based on the key valve combinations. Using an explosion risk level map, high-risk areas and emergency resource sites are identified, the optimal path from emergency resource sites to high-risk areas is calculated, high-risk areas are avoided, and emergency resource allocation paths are generated. Based on the three-dimensional concentration distribution map and population distribution data, the public evacuation area is delineated, and evacuation directions and assembly points are designed to generate a public evacuation plan. Integrate the operational sequence list, emergency resource allocation routes, and public evacuation plans to form a decision support plan, and then convert the decision support plan into a standard instruction format. The decision support solution in the standard instruction format is pushed to the regulatory terminal. Different alarm levels are triggered on the regulatory terminal according to the risk level, and the various elements of the decision support solution are visualized in the geographic interface.

[0008] Optionally, by analyzing the three-dimensional concentration distribution map, spatial areas where the concentration is within the explosive limit range are identified. Combined with building and population distribution data for these spatial areas, risk values ​​for different areas are calculated to generate an explosion risk level map, including: Gas concentration values ​​are extracted from the three-dimensional concentration distribution map. A lower limit and an upper limit of the explosion concentration are set. Grid cells with concentration values ​​between the lower and upper limits of the explosion concentration are selected and marked as hazardous areas. Acquire data on the distribution of buildings within hazardous areas, including building type, structural materials, and personnel capacity, as well as population distribution data, including real-time population density and activity patterns; A multi-factor risk calculation model is constructed based on gas concentration values, building distribution data, and population distribution data. In the multi-factor risk calculation model, the exposure index, vulnerability index, and consequence severity index of each grid cell are calculated, and the comprehensive risk value is obtained by merging the indices. Based on the comprehensive risk value, the hazardous space area is divided into multiple risk levels, and different color codes are used to generate an explosion risk level map.

[0009] Optionally, based on the source location and leakage rate, and combined with current meteorological data and urban 3D geographic data, the diffusion process of the leaked gas in the future is simulated in a digital twin model to generate a dynamically updated 3D concentration distribution map, including: Acquire current meteorological and environmental data, including wind speed, wind direction, temperature distribution, and atmospheric stability parameters, and establish a gas diffusion calculation model based on fluid motion laws; The leakage rate and source location are used as input conditions and injected into the gas diffusion calculation model. The calculation is carried out in time steps in the future period to simulate the migration path of the gas cloud in the three-dimensional scene. Within each time step, the diffusion range and concentration decay of the gas cloud are updated based on meteorological environmental data, and the gas concentration values ​​on the three-dimensional grid nodes are calculated to generate a time-series three-dimensional concentration dataset. The 3D concentration dataset at each time step is mapped onto a 3D scene, and the concentration level is represented by color gradient to synthesize a dynamically updated 3D concentration distribution map.

[0010] Optionally, based on concentration change data, key valve combinations that can effectively suppress diffusion are selected, and an operation sequence list is generated based on these key valve combinations, including: Based on the concentration change data, calculate the concentration decrease rate after each valve node is closed, and select valve nodes with a concentration decrease rate exceeding a preset threshold as candidate valves. Based on the pipeline network topology, the connectivity between candidate valves is analyzed, and interconnected candidate valves are grouped into key valve combinations. Based on the spatial location of each valve in the key valve combination and its upstream and downstream relationship in the pipeline network, the operation priority of the valves is determined, and an operation sequence list is generated.

[0011] Optionally, the gas leakage rate is dynamically calculated by solving the mass conservation equation based on simulation correction, including: In the digital twin model, a virtual control body is set up around the source location, and the first flow data entering the virtual control body and the second flow data leaving the virtual control body are obtained. The flow difference is calculated based on the mass conservation equation. The mass conservation equation is corrected by introducing simulated flow data from a specialized simulation, and the weighting coefficient of the flow difference is adjusted. Based on the corrected flow difference, the gas leakage rate is dynamically output.

[0012] Optionally, in response to a safety warning, a specific simulation is initiated in the digital twin model. Based on the pressure and flow data from the monitoring data, the propagation direction and temporal relationship of abnormal fluctuations in the pipeline network are analyzed to locate the source of potential leaks, including: Activate specialized simulations in the digital twin model, configure simulation parameters to focus on the pipeline section corresponding to the safety warning, obtain the time series of pressure and flow data in the monitoring data, and extract the abnormal fluctuation feature points in the time series; Based on the topological connection of the pipeline network, a wave propagation path network is constructed. The time difference sequence between abnormal wave characteristic points is calculated in the wave propagation path network, and the dominant direction of wave propagation is determined based on the time difference sequence. By tracing back along the dominant direction, the starting node of the fluctuation is identified in the fluctuation propagation path network. Combined with the temporal relationship, the monitoring point where the earliest abnormal fluctuation occurs is selected, and the starting node of the fluctuation is determined as the source of potential leakage.

[0013] Secondly, this application provides a simulation system for the operational status of a gas pipeline network based on digital twins, comprising: The module is used to build a digital twin model of the gas pipeline network and run online simulation. It compares the real-time monitoring data of the pipeline network with the simulation expectation values ​​and automatically triggers safety warnings based on preset evaluation rules. The positioning module is used to respond to safety warnings by initiating a special simulation in the digital twin model. Based on the pressure and flow data of the monitoring data, it analyzes the propagation direction and temporal relationship of abnormal fluctuations in the pipeline network to locate the source of potential leaks. It also dynamically calculates the gas leakage rate by solving the mass conservation equation based on simulation correction. The generation module is used to simulate the diffusion process of leaked gas in the future time period in a digital twin model based on the source location and leakage rate, combined with current meteorological environmental data and the city's three-dimensional geographic data, in order to generate a dynamically updated three-dimensional concentration distribution map. The calculation module is used to identify spatial areas where the concentration is within the explosive limit by analyzing the three-dimensional concentration distribution map, and to calculate the risk value of different areas by combining the building and population distribution data of the spatial area to generate an explosion risk level map. The push module is used to generate a decision support plan that includes key valve operation, emergency resource allocation path and public evacuation range by integrating the source location, three-dimensional concentration distribution map and explosion risk level map. The decision support plan is then pushed to the monitoring terminal for graded alarm and visualization display.

[0014] Thirdly, this application provides an electronic device, comprising: Memory, used to store computer programs; A processor is used to execute computer programs to implement the steps of the digital twin-based gas pipeline network operation state simulation method described in the first aspect above.

[0015] Fourthly, this application provides a computer-readable storage medium storing a computer program, which, when executed by a processor, can implement the steps of the digital twin-based gas pipeline network operation state simulation method described in the first aspect above.

[0016] The digital twin-based gas pipeline network operation status simulation method provided in this application can promptly detect pipeline network operation anomalies and automatically trigger safety warnings by constructing a digital twin model of the gas pipeline network, conducting online simulations, and comparing real-time monitoring data with simulated expected values. By responding to safety warnings and initiating specialized simulations, combining pressure and flow data to analyze the anomaly propagation patterns, locate the leak source, and calculate the leak rate, it can accurately pinpoint the core of the hidden danger and clarify the severity of the leak. By combining meteorological and urban 3D geographic data to simulate the leakage gas diffusion process and generate a dynamic 3D concentration distribution map, it can clearly grasp the gas diffusion situation. By analyzing the 3D concentration distribution map, identifying explosion limit areas, and generating an explosion risk level map, it can accurately determine the risk range and degree of danger. By comprehensively generating decision support solutions based on relevant data and pushing them to the regulatory terminal, it can provide clear guidance for emergency response, achieving visualized risk supervision and tiered alarms.

[0017] Furthermore, in the digital twin model, by locating the nearest valve and simulating its closure to screen key valve combinations and generate operational sequences, the optimal emergency resource allocation path is calculated using an explosion risk level map. Based on three-dimensional concentration and population data, evacuation areas are delineated and plans are designed. All elements are integrated to form a decision support scheme with a standard instruction format, which is then pushed to the monitoring terminal for tiered alarms and visual display. This clearly defines the specific requirements for valve operation, resource allocation, and personnel evacuation in emergency response, ensuring scientific and efficient valve operation, rapid deployment of emergency resources, and safe and orderly public evacuation. The decision support scheme can be directly implemented, further improving the accuracy and efficiency of emergency response to gas pipeline leaks, and ensuring that regulatory personnel can quickly grasp the key points of the response and advance emergency work. Attached Figure Description

[0018] To more clearly illustrate the technical solutions of the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 A flowchart illustrating a method for simulating the operational status of a gas pipeline network based on digital twins, provided in an embodiment of this application; Figure 2 A flowchart illustrating the specific implementation of a gas pipeline network operation status simulation method based on digital twins, provided in this application embodiment; Figure 3 This is a schematic diagram of a gas pipeline network operation status simulation system based on digital twin, provided as an embodiment of this application. Detailed Implementation

[0020] In urban gas safety management, once a gas pipeline leaks, existing simulation methods can only detect anomalies but cannot find the leak point, calculate the leak rate, predict where the gas will spread or where there is an explosion risk, and cannot provide specific emergency response measures. This results in passive and inefficient emergency response when a leak occurs, making it difficult to quickly control risks and protect the safety of people and property.

[0021] To address the aforementioned issues, this application proposes a digital twin-based simulation method for the operational status of gas pipeline networks. The core of this method is to utilize a digital twin model of the gas pipeline network to construct a complete "early warning-location-prediction-decision" process. Specifically, this method first triggers a safety early warning by comparing monitoring data through simulation. Then, it accurately locates the leak point, calculates the leak rate, and combines environmental and urban geographic data to simulate the gas diffusion range and analyze explosion risks. Finally, it generates specific decision-making plans including valve operation, emergency resource allocation, and personnel evacuation, and pushes these plans to the regulatory authorities. This method precisely solves the shortcomings of existing technologies in locating leak sources, predicting risks, and lacking emergency decision-making basis, making gas leak emergency response more scientific, precise, and efficient, effectively preventing explosions and other safety accidents, and ensuring the safe operation of urban gas systems.

[0022] To enable those skilled in the art to better understand the present application, the present application will be further described in detail below with reference to the accompanying drawings and specific embodiments. Obviously, the described embodiments are merely some embodiments of the present application, and not all embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0023] The core of this application is to provide a simulation method for the operational status of gas pipeline networks based on digital twins. A flowchart of one specific implementation is shown below. Figure 1 As shown, the method includes: S101. Construct a digital twin model of the gas pipeline network and run online simulation. Compare the real-time monitoring data of the pipeline network with the simulation expected values, and automatically trigger safety warnings based on preset evaluation rules.

[0024] Optionally, step S101 may specifically include the following steps: Among them, the digital twin model of the gas pipeline network is a digital reconstruction of the actual gas pipeline network, which includes key information such as the layout of the network and pipeline specifications, and is used to simulate the actual operating status of the network; the simulation expected value is the simulation expected value corresponding to the monitoring data of the network operation output by the digital twin model; the preset evaluation rules are pre-set standards used to judge whether the network operation is normal.

[0025] In one specific implementation, a digital reconstruction model matching the actual gas pipeline network is first built by combining the actual construction drawings, pipeline material parameters, and on-site location information. Then, the online simulation mode of the model is started to run continuously in real time. At the same time, pressure and flow monitoring sensors deployed along the pipeline network collect real-time data of the actual operation of the pipeline network and transmit it to the model system. These real-time monitoring data are compared point by point with the simulation expected values ​​corresponding to each monitoring data dynamically output by the model in real time. Then, according to the pre-set evaluation rules, combined with key indicators such as data deviation threshold and abnormal duration, the system automatically determines whether there is an operational abnormality in the pipeline network. If the deviation between the monitoring data and the simulation expected value exceeds the set threshold and continues for a preset duration, a safety warning is automatically triggered and preliminary abnormality information is fed back.

[0026] S102. In response to safety warnings, a special simulation is initiated in the digital twin model. Based on the pressure and flow data of the monitoring data, the propagation direction and temporal relationship of abnormal fluctuations in the pipeline network are analyzed to locate the source of potential leaks. The gas leakage rate is dynamically calculated by solving the mass conservation equation based on simulation correction.

[0027] Among them, the specialized simulation is a targeted simulation conducted on the abnormal pipeline section corresponding to the safety warning. Unlike the global online simulation of S101, the specialized simulation is mainly for the area where the safety warning is sent. Abnormal fluctuations refer to changes in pipeline pressure and flow data that deviate from the normal range. Their propagation direction and time sequence relationship are the core basis for locating the source of leakage. The mass conservation equation for simulation correction is an equation with parameter adjustments to the traditional mass conservation equation based on the specialized simulation data of the digital twin model, which is used to improve the accuracy of leakage rate calculation. The leakage rate refers to the speed at which gas leaks from the leakage source to the outside, directly reflecting the severity of the leakage.

[0028] Optionally, step S102 may specifically include the following steps: S1021. Activate the special simulation in the digital twin model, configure the simulation parameters to focus on the pipeline section corresponding to the safety warning, obtain the time series of pressure data and flow data in the monitoring data, and extract the abnormal fluctuation feature points in the time series.

[0029] The simulation parameters include simulation step size, simulation range, and data sampling frequency. When configuring, only the relevant parameters of the pipeline section corresponding to the safety warning are retained to reduce data interference from irrelevant areas. The time series of pressure and flow data refers to the set of real-time pressure and flow data of each monitoring point in the warning section arranged in chronological order. Abnormal fluctuation characteristic points refer to specific data points in the time series where the pressure and flow data deviate from the normal average and reach the set judgment criteria, which can reflect the start and change of abnormal fluctuations.

[0030] S1022. Based on the topological connection relationship of the pipeline network, construct a wave propagation path network, calculate the time difference sequence between abnormal wave characteristic points in the wave propagation path network, and determine the dominant direction of wave propagation based on the time difference sequence.

[0031] Among them, the topological connection relationship of the pipeline network refers to the connection method and positional relationship between various pipelines, monitoring points and valve groups in the gas pipeline network of the main urban area, which can be obtained through the pipeline construction drawings and point information entered when the digital twin model was built in the early stage; the fluctuation propagation path network is a network model built based on the pipeline topological connection relationship to present the possible propagation path of abnormal fluctuations in the pipeline network, including the connection path and transmission distance between each monitoring point; the time difference sequence refers to the set of time differences between the occurrence of abnormal fluctuation characteristic points at different monitoring points; the dominant direction of fluctuation propagation refers to the main diffusion direction of abnormal fluctuations in the pipeline network, which is the key basis for subsequent reverse tracing of the leak source.

[0032] S1023. Perform reverse tracing along the dominant direction, identify the wave initiation node in the wave propagation path network, and select the monitoring point where the earliest abnormal wave appears by combining the time sequence relationship, and determine the wave initiation node as the source of potential leakage.

[0033] Among them, reverse tracing refers to the process of tracing back from the dominant direction of the abnormal fluctuation to the node where the abnormal fluctuation first occurred; the fluctuation initiation node refers to the earliest pipeline node in the fluctuation propagation path network that generates abnormal fluctuations; the temporal relationship refers to the chronological order in which the abnormal fluctuation characteristic points appear at each monitoring point, which is used to verify the accuracy of the fluctuation initiation node; the potential source location of the leak refers to the actual pipeline location corresponding to the starting point of the abnormal fluctuation, that is, the specific location where gas may leak.

[0034] S1024. In the digital twin model, a virtual control body is set up around the source location, and the first flow data entering the virtual control body and the second flow data leaving the virtual control body are obtained. The flow difference is calculated based on the mass conservation equation.

[0035] The virtual control body is a virtual area defined in the digital twin model, centered on the location of the potential leak source, used to statistically analyze the inflow and outflow of gas within this area. The first flow data refers to the total gas flow entering the virtual control body per unit time; the second flow data refers to the total gas flow leaving the virtual control body per unit time. The mass conservation equation is the fundamental equation used to calculate the change in the mass of matter within the area. Here, its core purpose is to preliminarily determine the magnitude of the leak by using the difference between the inflow and outflow rates. Its core logic is that the change in the mass of gas within the virtual control body is equal to the difference between the inflow and outflow rates.

[0036] S1025. The mass conservation equation is corrected by introducing simulated flow data from a special simulation, the weighting coefficient of the flow difference is adjusted, and the gas leakage rate is dynamically output based on the corrected flow difference.

[0037] Among them, the simulated flow data of the special simulation refers to the simulated gas flow data of the corresponding virtual control body area output by the model after activating the special simulation in S1021; the weighting coefficient is a parameter used to adjust the matching degree between the measured flow difference and the simulated flow data, which can be set according to the historical operation data of the pipeline network and the simulation accuracy requirements; dynamic output refers to continuously adjusting the leakage rate calculation results and outputting them in real time as the monitoring data is updated in real time, so as to ensure that the leakage rate data is consistent with the actual leakage situation.

[0038] In this embodiment, step S102 revolves around the leakage location and rate calculation after the safety warning. It achieves precise handling support through the coordinated action of consecutive sub-steps. First, it focuses on the warning section to activate special simulation and extract abnormal features. Then, it analyzes the wave propagation law to lock the leakage source. Finally, it obtains the accurate leakage rate through flow calculation and correction.

[0039] First, through step S1021, a special simulation is activated in the digital twin model, and simulation parameters such as simulation step size, simulation range, and data sampling frequency are configured to focus on the pipeline section corresponding to the safety warning. At the same time, the pressure and flow data time series of each monitoring point in the warning section are obtained through sensors deployed along the pipeline, and the abnormal fluctuation feature points in the time series are extracted by the threshold judgment method.

[0040] For example, taking the DN300 gas pipeline network on the east side of a commercial street in the main urban area as an example, when S101 triggers a safety warning, the warning section is set as the 500-meter pipeline network surrounding the DN300 pipeline on the east side of the commercial street. A special simulation is activated in the corresponding digital twin model, with a simulation step size of 1 second. The simulation range accurately covers the warning section, and the data sampling frequency is consistent with the monitoring sensors at once every 0.5 seconds. Simultaneously, through sensors deployed along the pipeline, the time series of pressure and flow data from monitoring points A, B, and C within the warning section are obtained. These monitoring points are all located on the DN300 pipeline and are linearly distributed. A threshold judgment method is used to extract abnormal fluctuation feature points in the time series. When the pressure data deviates from the normal average by 0.1 MPa and the flow data deviates from the normal average by 5 m... 3 When the value is / h, it is identified as an abnormal fluctuation feature point. Ultimately, abnormal fluctuation feature points were extracted from monitoring points A, B, and C, and the approximate time of occurrence of the anomalies at each point was initially recorded. The above example is merely one illustration of this application; in practical applications, relevant parameters can be adjusted according to requirements, and this application does not impose any limitations on this.

[0041] Secondly, through step S1022, a wave propagation path network is constructed based on the topological connection relationship of the pipeline network. The monitoring points and valve groups in the early warning section are used as network nodes, and the pipelines are used as connection paths. In this network, the time difference sequence between the abnormal wave characteristic points of each monitoring point is calculated. By analyzing the magnitude and change law of the time difference, the dominant direction of wave propagation is determined, providing a directional basis for reverse tracing of the leak source.

[0042] In practical applications, based on the topological connection of the DN300 gas pipeline network on the east side of the commercial street, monitoring points A, B, and C within the warning section, along with two surrounding valve groups, are used as network nodes, and the DN300 pipeline and its branch lines are used as connection paths to construct a wave propagation path network. Combining the abnormal wave characteristic points extracted in the previous step, the time difference sequence between the abnormal wave characteristic points of each monitoring point is accurately calculated. Statistical analysis shows that the time when monitoring point A experienced an anomaly... The time when the anomaly occurred at monitoring point B was 10:00:00. The time when the anomaly occurred at monitoring point C was 10:00:02. The value is 10:00:03.5, calculated as follows: , Based on this, it can be determined that the dominant direction of wave propagation is from monitoring point A to monitoring point C, that is, the direction of extension along the DN300 pipeline into the commercial district.

[0043] Next, through step S1023, reverse tracing is carried out along the determined dominant direction of wave propagation. Wave initiation nodes are checked and identified one by one in the wave propagation path network. Based on the temporal relationship of the abnormal wave characteristic points of each monitoring point, the monitoring point with the earliest abnormal wave is selected. The wave initiation node corresponding to the monitoring point is determined as the source of potential leakage. The specific location is determined by comparing the digital twin model with the actual pipeline location.

[0044] In this process, the dominant direction determined in the previous step, from monitoring point A to monitoring point C, is traced backwards. Each node in the wave propagation path network is examined one by one, taking into account the temporal relationships. The abnormal fluctuation at monitoring point A was detected earliest, and the fluctuation started from the DN300 pipeline node where monitoring point A is located. Therefore, this pipeline node was identified as the starting point of the fluctuation and determined to be the source of the potential leak. After comparing the digital twin model with the actual pipeline network location, the source node is located on the DN300 gas pipeline 1.2 meters directly below the main road on the east side of the commercial district and directly below monitoring point A.

[0045] Then, in step S1024, a virtual control body is defined around the potential leak source location in the digital twin model. The scope of the virtual control body is reasonably set according to the pipeline specifications and the scope of the leak impact. The model collects the first flow data entering the virtual control body and the second flow data leaving the virtual control body in real time per unit time. The flow difference between the two is calculated based on the mass conservation equation to preliminarily determine the approximate range of the leak flow.

[0046] For example, in the digital twin model, around the potential leak source location of the DN300 pipeline under the main road on the east side of the commercial district determined in the previous step, and considering the DN300 pipeline's 300mm diameter and the initial impact range of the leak, a virtual control body with a radius of 10 meters is delineated. This control body includes the source node, monitoring point A, three surrounding DN300 branch pipelines, and two auxiliary monitoring points. The model collects the first flow rate data entering the virtual control body in real time, which is 20m³ per unit time. 3 / h, the second flow data leaving the virtual control body is 12m 3 / h, the flow difference is calculated based on the mass conservation equation, and the flow difference is 8m³ / h, obtained by subtracting the outflow from the inflow. 3 / h, this difference initially reflects the approximate range of leakage flow.

[0047] The above example is only one example of this application. In practical applications, the range of the virtual control body can be adjusted according to the pipeline specifications. This application does not limit this.

[0048] Finally, in step S1025, the simulated flow data of the virtual control volume region output by the special simulation is introduced to correct the mass conservation equation. The weighting coefficient of the flow difference is adjusted according to the historical operation data of the pipeline network and the simulation accuracy requirements. The measured flow difference is multiplied by the weighting coefficient to obtain the corrected flow difference. The gas leakage rate is dynamically output based on the corrected flow difference, and the calculation results are continuously adjusted as the real-time data of the sensor is updated to ensure the real-time performance and accuracy of the leakage rate data.

[0049] In practical applications, simulated flow data corresponding to the aforementioned virtual control area is introduced from specialized simulation outputs. This simulated flow data is obtained based on parameters such as the current average normal pipeline pressure of 0.4 MPa and a temperature of 25°C, with a corresponding theoretical flow difference of 7.8 m³ / s. 3 / h; Based on the historical operating data of the DN300 pipeline network in this commercial district and the simulation accuracy requirements, the weighting coefficient of the flow difference is adjusted to 0.95, and the measured flow difference of 8m obtained in the previous step is used as the basis for the calculation. 3 Multiplying / h by the weighting factor 0.95 yields a corrected flow rate difference of 7.6m. 3 / h; Based on the corrected flow difference, the gas leakage rate of the DN300 pipeline leakage source is dynamically output, and the leakage rate calculation result is adjusted every 1 second as the sensor data is updated in real time to ensure that the data matches the actual leakage situation.

[0050] This application, by quickly focusing on abnormal sections and accurately locating potential leak sources after a safety warning is triggered, and by dynamically calculating the leakage rate, makes up for the shortcomings of existing simulations that can only provide warnings but cannot quantify leak situations, thus improving the pertinence and scientific nature of abnormal handling of gas pipeline networks.

[0051] S103. Based on the source location and leakage rate, combined with current meteorological environmental data and urban three-dimensional geographic data, simulate the diffusion process of leaked gas in the future time period in a digital twin model to generate a dynamically updated three-dimensional concentration distribution map.

[0052] Meteorological environmental data refers to the set of real-time meteorological parameters that affect gas diffusion, used to accurately simulate the gas migration and concentration change patterns; urban three-dimensional geographic data refers to three-dimensional spatial information such as urban topography, building distribution, and obstacles around pipelines, which can be obtained through urban geographic information systems; future time periods refer to the time periods preset according to the severity of the leak that need to simulate gas diffusion, usually covering the critical periods of emergency response; the leaked gas diffusion process refers to the process by which gas, after leaking from the source, migrates and diffuses to the surrounding space under the influence of meteorological and geographical conditions, and undergoes concentration decay; dynamically updated three-dimensional concentration distribution maps refer to three-dimensional visualization maps that can present the gas concentration at different time points and different spatial locations in real time, which can intuitively reflect the gas diffusion situation.

[0053] Optionally, step S103 may specifically include the following steps: S1031. Obtain current meteorological and environmental data, including wind speed, wind direction, temperature distribution, and atmospheric stability parameters, and establish a gas diffusion calculation model based on fluid motion laws.

[0054] Among these factors, wind speed and direction jointly determine the dominant direction and speed of gas diffusion; temperature distribution refers to the spatial distribution of temperature in the leak area and surrounding space, affecting the movement rate of gas molecules and diffusion efficiency; atmospheric stability parameters refer to the degree to which the atmosphere inhibits or promotes the vertical movement of gas, directly affecting the height and range of gas diffusion; fluid motion laws refer to the physical laws that gas follows when migrating and diffusing in space as a fluid; and the gas diffusion calculation model is a mathematical model built based on fluid motion laws to calculate the gas diffusion range and concentration distribution, which can output dynamic change data of gas diffusion based on input leak parameters and meteorological data.

[0055] S1032. Using the leakage rate and source location as input conditions, inject them into the gas diffusion calculation model, and advance the calculation in time steps within a future period to simulate the migration path of the gas cloud in the three-dimensional scene.

[0056] The input conditions refer to the core parameters required for the gas diffusion calculation model to run. In addition to the leakage rate and source location, auxiliary parameters such as meteorological environmental data and 3D geographic data can also be included. The time step refers to dividing the future period into several equal time intervals. By calculating each time interval, the accuracy of the diffusion simulation is improved. The size of the time step can be set according to the leakage rate and simulation accuracy requirements. The gas cloud refers to the gas accumulation area with a certain concentration and volume formed after the gas leak. Its migration path is affected by wind speed, wind direction and geographical conditions. The 3D scene refers to a realistic spatial scene constructed based on urban 3D geographic data, including surrounding environmental elements such as terrain and buildings.

[0057] S1033. Within each time step, update the diffusion range and concentration decay of the gas cloud based on meteorological environmental data, and calculate the gas concentration values ​​on the three-dimensional grid nodes to generate a time-series three-dimensional concentration dataset.

[0058] Among them, the three-dimensional mesh node refers to dividing the three-dimensional scene in S1032 above into several regular three-dimensional meshes. The vertex of each mesh is a three-dimensional mesh node, which is used to accurately calculate the gas concentration at different spatial locations; the three-dimensional concentration dataset refers to a data set containing the gas concentration value corresponding to each time step and each three-dimensional mesh node.

[0059] S1034. Map the three-dimensional concentration dataset of each time step onto the three-dimensional scene, use color gradient to represent the gas concentration level, and synthesize a dynamically updated three-dimensional concentration distribution map.

[0060] By integrating the concentration distribution images corresponding to each time step in chronological order, a dynamically playable and real-time updated three-dimensional visualization chart is formed, namely, a dynamically updated three-dimensional concentration distribution map.

[0061] In this embodiment, step S103 is based on the leak source location and leak rate determined in S102. Through coherent sub-steps, the leakage gas diffusion simulation is achieved. First, meteorological data is acquired and a diffusion calculation model is established. Then, core parameters are input to simulate the gas migration path. Next, concentration data is calculated step by step over time. Finally, a dynamic three-dimensional concentration distribution map is mapped and synthesized.

[0062] First, through step S1031, current meteorological environmental data of the leak source area and its surroundings are obtained to clarify key parameters such as wind speed, wind direction, temperature distribution and atmospheric stability. Combining the motion law of gas as a fluid, a gas diffusion calculation model that fits the urban gas pipeline network scenario is constructed to provide a model foundation and data support for subsequent diffusion simulation.

[0063] For example, taking the DN300 gas pipeline network on the east side of the commercial district in the main urban area as an example, regarding the location of the leak source determined by S102 and 7.6m... 3 The leakage rate was determined by acquiring current meteorological environmental data of the leakage source area and a 500-meter radius around it through urban meteorological monitoring stations and mobile meteorological monitoring equipment. The current wind speed was determined to be 2 m / s, the wind direction was northeast, the temperature distribution was relatively uniform with an average temperature of 25°C, and the atmospheric stability parameter was neutral and stable. Based on the gas and fluid movement law, a gas diffusion calculation model that fits the terrain and building distribution of the commercial district was constructed. The model includes three-dimensional geographic information of the leakage area and can accurately calculate gas migration and concentration changes.

[0064] The gas diffusion calculation model is based on the physical laws of gas diffusion. It uses the three-dimensional topography and building distribution of the city as spatial constraints, wind speed, wind direction, temperature, and atmospheric stability as environmental factors, and the location and rate of the leak source as initial input conditions. Then, by dividing the entire simulation space into regular spatial units, the model calculates the changes in gas position and concentration unit by unit based on environmental changes and gas movement characteristics at different times, ultimately forming a calculation model that can continuously output the spatiotemporal distribution results of the gas.

[0065] Secondly, through step S1032, the leakage rate and source location determined in S102 are used as core input conditions, and auxiliary parameters such as meteorological environmental data and urban three-dimensional geographic data are simultaneously substituted into the established gas diffusion calculation model. The next hour is preset as the future period of gas diffusion simulation. This period is divided into 360 time steps, each time step is 10 seconds. The model calculation is advanced one time step by one according to the time step order to simulate the migration path of the gas cloud formed by the leaked gas in the three-dimensional scene of the commercial street.

[0066] In practical applications, for the leakage scenario of the DN300 pipeline in this commercial district, the 7.6m determined by S102 will be used. 3 The leakage rate and the source location of the DN300 pipeline under the main road on the east side of the commercial district were used as core input conditions. Simultaneously, meteorological data such as wind speed of 2 m / s and northeast wind, as well as three-dimensional geographic data of the area, were input into the established gas diffusion calculation model. The next hour was preset as the diffusion simulation period, which was divided into 360 time steps, each time step set to 10 seconds. The calculation was carried out step by step according to the time step sequence. The simulation showed that the gas cloud was affected by the northeast wind and mainly migrated to the southwest direction, that is, into the commercial district. The migration speed matched the wind speed and gradually decreased with distance.

[0067] Next, through step S1033, within each set time step, the diffusion range and concentration decay of the gas cloud are adjusted according to the real-time updated meteorological environmental data. The three-dimensional scene of the commercial street is divided into several three-dimensional grids, the gas concentration value on each three-dimensional grid node is calculated, the concentration data is recorded one time period at a time, and finally a time series three-dimensional concentration dataset arranged in the order of time step is generated, which fully presents the dynamic changes in concentration during the gas diffusion process.

[0068] Specifically, for the three-dimensional scene of the commercial district, the area within 500 meters of the leak source was divided into a 100×100×50 three-dimensional grid, with each grid node spaced 5 meters apart. Within each 10-second time step, the diffusion range and concentration decay of the gas cloud were adjusted based on real-time meteorological data. The gas concentration value at each three-dimensional grid node was calculated using a gas diffusion calculation model. For example, in the first time step, i.e., 10 seconds after the leak, the concentration value of the three-dimensional grid nodes within 10 meters of the source was relatively high. As the time step progressed, the concentration value gradually diffused southwestward and gradually decayed. The concentration data of all grid nodes were recorded for each time period, ultimately generating a time-series three-dimensional concentration dataset containing 360 time nodes.

[0069] Finally, in step S1034, the three-dimensional concentration dataset corresponding to each time step is mapped one by one to the three-dimensional scene of the commercial street, matching the concentration value of each three-dimensional grid node. The concentration is represented by a color gradient, with dark red for high concentration areas, orange for medium concentration areas, and yellow and light blue for low concentration areas. The concentration distribution images of all time steps are integrated in sequence to synthesize a three-dimensional concentration distribution map that can be dynamically played and updated in real time, intuitively presenting the gas diffusion situation at different time points.

[0070] For example, for the generated time-series 3D concentration dataset, the concentration data of each time step is mapped one by one to the 3D scene of the commercial district, ensuring that the concentration value of each 3D grid node corresponds precisely to the scene location. A color gradient is used to distinguish between high and low concentrations. The gas concentration ≥10%VOL is set as a high concentration area and marked with dark red, 5%VOL≤concentration<10%VOL is set as a medium concentration area and marked with orange, 1%VOL≤concentration<5%VOL is set as a low concentration area and marked with yellow, and concentration<1%VOL is set as an extremely low concentration area and marked with light blue. The concentration distribution images of 360 time steps are integrated in sequence to synthesize a dynamically updated 3D concentration distribution map. It can be clearly seen that the gas cloud spreads from the source location to the interior of the commercial district in the southwest direction. The concentration gradually decreases with distance and time, and the concentration at any time point and any location can be viewed in real time.

[0071] This application, by taking into account the location and rate of the leak source and combining meteorological and geographical data, accurately simulates the diffusion process of the leaked gas in the future, generating a dynamically updated three-dimensional concentration distribution map. It clearly presents the migration path, range, and concentration distribution differences of the gas diffusion, intuitively reflects the gas concentration in different areas, and improves the pertinence and scientific nature of gas pipeline network leak risk prevention and control.

[0072] S104. By analyzing the three-dimensional concentration distribution map, spatial areas with concentrations within the explosion limit range are identified. Combined with the building and population distribution data of the spatial areas, the risk values ​​of different areas are calculated to generate an explosion risk level map.

[0073] Among them, the explosion limit range refers to the range of gas concentrations that can cause an explosion when mixed with air, and is the core basis for judging the degree of danger of an area; population distribution data is used to assess the risk of casualties that may be caused by an explosion; the explosion risk level map is a map that divides dangerous spaces into risk levels according to risk values ​​and presents them in a visual way.

[0074] Optionally, step S104 may specifically include the following steps: S1041. Extract gas concentration values ​​from the three-dimensional concentration distribution map, set the lower limit and upper limit of the explosion concentration, filter grid cells with concentration values ​​between the lower and upper limits of the explosion concentration, and mark them as hazardous space areas.

[0075] The hazardous space area refers to the spatial region where the gas concentration is within the explosion limit range in the 3D concentration distribution map; building distribution data is used to determine the exposure level of people and property within the area. Gas concentration values ​​are extracted from the generated time-series 3D concentration dataset, corresponding to each 3D grid cell.

[0076] S1042. Obtain building distribution data within the hazardous space area, including building type, structural materials, and personnel capacity, and simultaneously obtain population distribution data, including real-time population density and activity patterns.

[0077] Among these factors, building type affects the degree of population gathering, structural materials determine the building's blast resistance and thus affect the consequences of an explosion, and population capacity assesses the risk of exposure to people inside the building; real-time population density directly reflects the degree of population gathering in the area, and activity patterns affect the probability of people being affected by an explosion.

[0078] S1043. Based on gas concentration values, building distribution data, and population distribution data, construct a multi-factor risk calculation model.

[0079] Among them, the multi-factor risk calculation model is a mathematical model for quantitatively calculating the regional explosion risk value. It comprehensively considers three core factors: gas concentration, building characteristics, and population distribution, which respectively reflect the probability of an explosion, regional vulnerability, and the severity of the consequences. The model parameters can be calibrated and optimized based on historical risk data of the urban gas pipeline network.

[0080] The multi-factor risk calculation model is based on the formation mechanism of explosion risk. It clarifies the weighting principles of three core factors—gas concentration, building characteristics, and population distribution—and combines historical risk data from urban gas pipeline networks and past explosion accident case data to determine the correlation between each factor and explosion risk. Key data such as gas concentration values, building structural characteristics, personnel capacity, and real-time population density are then uniformly converted into quantitative indicators that the model can recognize. Finally, reasonable value ranges for each indicator are set, integrating them to form a complete model calculation framework.

[0081] S1044. In the multi-factor risk calculation model, calculate the exposure index, vulnerability index, and consequence severity index for each grid cell, and integrate the indices to obtain the comprehensive risk value.

[0082] The exposure index is calculated from the gas concentration and population density within the grid cell; the higher the concentration and population density, the higher the index. The vulnerability index is calculated from the building's structural materials and type; the weaker the explosion resistance and the higher the population density, the higher the index. The severity of consequences index is calculated from the personnel capacity and activity patterns; the larger the personnel capacity and the more dynamic personnel, the higher the index. The fusion adopts a weighted summation method, integrating the three indices according to preset weights to obtain a comprehensive risk value. The weights can be adjusted according to the risk assessment focus.

[0083] S1045. Based on the comprehensive risk value, the hazardous space area is divided into multiple risk levels, and different color codes are used to generate an explosion risk level map.

[0084] Among them, risk level refers to the degree of danger of dangerous space areas divided according to the magnitude of comprehensive risk value. It is usually divided into three levels: high risk, medium risk, and low risk. The number of levels can be adjusted according to actual needs. Color coding refers to the use of different hues of color to correspond to different risk levels, which is easy to distinguish intuitively. Usually, high-risk areas are marked with dark red, medium-risk areas with orange, and low-risk areas with yellow. Explosion risk level map refers to the visualization map formed by mapping the risk level of each grid unit onto the three-dimensional geographic scene of the city, which can clearly show the differences in explosion risk in different areas.

[0085] In this embodiment of the application, step S104 is based on the three-dimensional concentration distribution map generated in S103. Through coherent sub-steps, the explosion risk assessment and map generation are achieved. First, dangerous spatial areas are identified from the concentration distribution map. Then, the building and population data of the area are obtained. A multi-factor risk calculation model is constructed to calculate the comprehensive risk value of each grid cell. Finally, the risk level is divided and a visualization map is generated.

[0086] First, in step S1041, the gas concentration value of each three-dimensional grid cell is extracted from the time series three-dimensional concentration dataset. A reasonable lower limit and upper limit of explosion concentration are set in combination with the characteristics of the gas. Grid cells with concentration values ​​between the two are selected one by one and clearly marked to determine the specific range of the dangerous space area, so as to focus on the core area for subsequent risk calculation.

[0087] For example, taking the DN300 gas pipeline network on the east side of a commercial street in the main urban area as an example, for the dynamic three-dimensional concentration distribution map generated by S103, the gas concentration value of each three-dimensional grid cell is extracted from the time-series three-dimensional concentration dataset. Combined with the characteristics of the natural gas used in this scenario, the lower limit of the explosion concentration is set to 5% VOL and the upper limit to 15% VOL. Grid cells with concentration values ​​between 5% VOL and 15% VOL are selected one by one. These grid cells are mainly distributed within the commercial street southwest of the leak source, covering some commercial buildings and the surrounding areas of residential communities. All selected grid cells are uniformly identified as hazardous space areas. The above example is only one example of this application. In practical applications, the upper and lower limits of the explosion concentration can also be adjusted according to the type of gas, and this application does not limit this.

[0088] Secondly, through step S1042, the focus is on the marked hazardous spatial area, and the building distribution data and population distribution data in the area are obtained from the urban geographic information system and population monitoring platform. The types, structural materials and personnel capacity of buildings in the area are identified, and the real-time population density and personnel activity patterns in the area are obtained to provide basic data for building a risk calculation model.

[0089] In practical applications, for the hazardous areas of this commercial district, data on the distribution of buildings in the area were obtained from the city's geographic information system. It was determined that the area contains 3 commercial buildings and 2 residential communities. The commercial buildings are reinforced concrete structures with a capacity of approximately 500 people per building, while the residential communities are brick-concrete structures with a capacity of approximately 800 people per community. Real-time population density in the area was obtained from the city's population monitoring platform. The real-time population density around the commercial buildings was 80 people per square meter, and the real-time population density around the residential communities was 30 people per square meter. The activity pattern of people is mainly dynamic, with shoppers in the commercial buildings and residents in the residential communities.

[0090] Next, through step S1043, the gas concentration value extracted in S1041, the building distribution data and population distribution data obtained in S1042 are used as core input factors. Combined with historical risk data of urban gas pipeline network, a multi-factor risk calculation model is constructed to clarify the correlation between each input factor and risk value in the model, calibrate the model parameters, and ensure that the model calculation results fit the actual scenario.

[0091] Specifically, for the hazardous areas of this commercial district, the gas concentration value of the grid unit, building structural materials, personnel capacity, real-time population density, and personnel activity patterns are used as model input parameters. Combined with the gas pipeline network risk monitoring data of the area over the past 5 years, the model weight parameters are calibrated to construct a multi-factor risk calculation model. The core logic of the model is to reflect the possibility of explosion through gas concentration value, reflect the vulnerability of the area through building data, and reflect the severity of consequences through population data, so as to achieve the organic integration of the three and ensure the accuracy of risk value calculation.

[0092] Then, in step S1044, the gas concentration value, building distribution data, and population distribution data of each grid cell are input into the multi-factor risk calculation model one by one. The exposure index, vulnerability index, and consequence severity index of each grid cell are calculated respectively. According to the preset weights, the three indices are fused by weighted summation to obtain the comprehensive risk value of each grid cell and quantify the explosion risk level of each area.

[0093] For example, for each three-dimensional grid cell within a hazardous space area, the gas concentration value and real-time population density of that cell are input into the model to calculate the exposure index. The higher the concentration and the greater the population density, the higher the exposure index. The building structure material and type are input into the model to calculate the vulnerability index. The vulnerability index of a brick-concrete residential community is higher than that of a reinforced concrete commercial building. The population capacity and activity patterns are input into the model to calculate the consequence severity index. The consequence severity index around a commercial building with a high concentration of dynamic activity population is higher than that around a residential community. The weights of the exposure index, vulnerability index, and consequence severity index are preset to 0.4, 0.3, and 0.3, respectively. The three indices are combined using a weighted summation method to obtain the comprehensive risk value of each grid cell. For example, the comprehensive risk value of a grid cell around a commercial building is 8.5, while the comprehensive risk value of a grid cell around a residential community is 6.2.

[0094] Finally, through step S1045, based on the calculated comprehensive risk value of each grid unit, a reasonable risk level classification standard is set, and the dangerous space area is divided into three levels: high risk, medium risk, and low risk. Different colors are used to correspond to different risk levels, and the risk level of each grid unit is mapped onto the three-dimensional geographic scene of the city to synthesize an explosion risk level map, which intuitively presents the explosion risk level of different areas.

[0095] In practical applications, based on the comprehensive risk value of the hazardous areas in this commercial district, a comprehensive risk value ≥ 8.0 is defined as a high-risk level, 5.0 ≤ comprehensive risk value < 8.0 as a medium-risk level, and a comprehensive risk value < 5.0 as a low-risk level. Color coding is used to distinguish the levels: high-risk areas are marked in dark red, medium-risk areas in orange, and low-risk areas in yellow. The risk level of each grid unit is mapped onto the three-dimensional geographic scene of the commercial district, synthesizing an explosion risk level map. The map clearly shows that the areas around commercial buildings are mostly high- and medium-risk areas, the areas around residential areas are mostly medium- and low-risk areas, and the area near the leakage source has the highest risk level. The above example is only one example of this application. In practical applications, the risk level classification standards and color coding can be adjusted according to needs, and this application does not limit this.

[0096] This application accurately identifies hazardous areas by using three-dimensional concentration distribution maps, integrates building and population distribution data to quantify the explosion risk in different areas, and generates an intuitive explosion risk level map that clearly distinguishes the degree of danger in different areas. This makes up for the shortcomings of traditional risk assessments, such as insufficient accuracy and low visualization, and improves the pertinence and scientific nature of gas pipeline network leak and explosion risk prevention and control.

[0097] Based on S105, the source location, three-dimensional concentration distribution map, and explosion risk level map, a decision support plan is generated that includes key valve operation, emergency resource allocation path, and public evacuation range. The decision support plan is then pushed to the monitoring terminal for graded alarms and visualization.

[0098] Among them, the decision support plan refers to a comprehensive plan for emergency response to gas pipeline leaks, which integrates core information such as the leak source, gas diffusion situation, and explosion risk level. It covers three core aspects: valve operation, resource allocation, and personnel evacuation. For example, it includes the operation of shutting down pipeline valves to cut off the leak source, the optimal transportation route from emergency resource sites to high-risk areas, avoiding dangerous areas, and the delineation of personnel evacuation space based on gas diffusion and risk level.

[0099] Optionally, such as Figure 2 As shown, step S105 may specifically include the following steps: S1051. In the digital twin model, the nearest pipeline valve is located based on the source location to simulate the impact of valve closure on gas diffusion and obtain gas concentration change data.

[0100] S1052. Based on the concentration change data, select key valve combinations that can effectively suppress diffusion, and generate an operation sequence list based on the key valve combinations. Specifically, step S1052 may include the following processes: Calculate the concentration decrease rate after closing each valve node based on the concentration change data, and select valve nodes with a concentration decrease rate exceeding a preset threshold as candidate valves; analyze the connectivity between candidate valves based on the pipeline network topology, and group interconnected candidate valves into key valve combinations; determine the valve operation priority based on the spatial location of each valve in the key valve combination and its upstream and downstream relationships in the pipeline network, and generate an operation sequence list.

[0101] In the above steps, the key valve combination refers to a collection of multiple valves that can work together to suppress gas diffusion to the maximum extent. A single valve may not be able to completely block diffusion, but the coordinated closure of multiple valves can improve the suppression effect. The operation sequence list is a list that clearly defines the order in which the valves in the key valve combination should be closed, used to guide operators to perform valve closure operations in a standardized manner and avoid affecting the suppression effect due to incorrect operation sequence. The concentration reduction rate refers to the percentage decrease in gas concentration in a specified area per unit time after the valve is closed, used to quantify the effect of valve closure on suppressing diffusion. The preset threshold is a pre-set standard for the concentration reduction rate used to judge the effectiveness of valve closure; valves exceeding this threshold are considered effective in suppressing diffusion. Candidate valves are valve nodes whose concentration reduction rate exceeds the preset threshold and can effectively suppress diffusion. The connectivity relationship refers to the connection between candidate valves through pipelines, obtained based on the pipeline topology. The operation priority refers to the order in which the valves in the key valve combination should be closed, determined according to the spatial location of the valves and the upstream and downstream relationships of the pipeline, to ensure efficient and effective operation.

[0102] S1053. Using the explosion risk level map, identify high-risk areas and emergency resource stations, calculate the optimal path from the emergency resource station to the high-risk area, avoid the high-risk area, and generate emergency resource allocation path.

[0103] Among them, emergency resource stations are fixed stations where emergency equipment, personnel, and materials are pre-deployed; the optimal path is the route with the shortest distance, the least time, and avoidance of dangerous areas, calculated through path planning algorithms; and the emergency resource allocation path is the specific route from the station to the high-risk area.

[0104] S1054. Based on the three-dimensional concentration distribution map and population distribution data, delineate the public evacuation area and design evacuation directions and assembly points to generate a public evacuation plan.

[0105] Among them, the public evacuation range is the range of personnel evacuation defined by combining dangerous space areas, risk levels and population distribution; the evacuation direction is the evacuation direction that avoids dangerous areas and points towards safe areas; the assembly point is the pre-set safe area for evacuees to gather; and the public evacuation plan is a plan that includes elements such as evacuation range, direction and assembly point.

[0106] S1055. Integrate the operation sequence list, emergency resource allocation routes, and public evacuation plans to form a decision support plan, and convert the decision support plan into a standard instruction format.

[0107] Integration involves combining valve operation, resource allocation, and public evacuation according to emergency logic to form a complete decision support solution; the standard instruction format is a format that conforms to the receiving and identification standards of the regulatory terminal, and can be directly pushed after conversion without the need for secondary manual processing.

[0108] S1056. Push the decision support solution in the standard instruction format to the regulatory terminal, trigger different alarm levels on the regulatory terminal according to the risk level, and visualize the various elements of the decision support solution in the geographic interface.

[0109] Among them, push notifications send solutions in standard instruction formats to the monitoring terminal; different alarm levels correspond to high, medium, and low risk levels, with different prompting methods and response requirements; the geographic interface is a three-dimensional geographic scene of the city on the monitoring terminal; and the visualization display presents the various elements of the solution intuitively on the geographic interface.

[0110] In this embodiment of the application, step S105 generates, transforms and pushes an emergency decision support solution through a series of sub-steps. First, it simulates valve operation to screen key combinations and operation sequences, then plans resource allocation paths and formulates evacuation plans. After integration and transformation, it is pushed to the terminal to realize hierarchical alarms and visualization. The sub-processes are refined to improve the scientific nature of valve operation and ensure that the solution can be implemented.

[0111] First, through step S1051, in the digital twin model, with the location of the leak source determined in S102 as the center, the nearest pipeline valve node is located, and the valve closure operation is virtually executed. The specific impact of each valve closure operation on gas diffusion is simulated, and the gas concentration data of each three-dimensional grid cell is collected in real time to finally obtain complete gas concentration change data.

[0112] For example, three valves (valve 1, valve 2, and valve 3) around the leak source were located. Simulating single-valve and combined valve closure operations, the concentration changes in each grid cell were monitored over the next 30 minutes. After 10 minutes, the concentration reduction rates were: 45% for valve 1, 38% for valve 2, and 25% for valve 3. When valves 1 and 2 were closed together, the reduction rate was 72%. The above example is merely one illustration of this application; the valve location range can be adjusted according to the pipeline layout, and this application does not limit this.

[0113] Secondly, through step S1052, based on the gas concentration change data obtained in S1051, the concentration decrease rate after each valve node is closed is calculated one by one. Based on the concentration decrease rate, candidate valves that can effectively suppress gas diffusion are selected. The pipeline connectivity relationship between the candidate valves is analyzed and they are grouped into key valve combinations. Then, combined with the spatial position of each valve and its upstream and downstream relationship on the pipeline, the operation priority of each valve is determined, and finally a standardized valve operation sequence list is generated.

[0114] In practical applications, a preset threshold for the concentration reduction rate is set to 30%. The calculated reduction rates are 45% for valve 1, 38% for valve 2, and 25% for valve 3. Valve 1 and valve 2 are selected as candidate valves. The two are connected and grouped into a key valve combination. Since valve 1 is located upstream of the source and valve 2 is located downstream, it is determined that valve 1 should be closed first, and valve 2 should be closed after an interval of 5 minutes. An operation sequence list is generated, which is labeled with valve number, location, and operation time.

[0115] Next, through step S1053, the explosion risk level map generated in S104 is called to accurately identify high-risk areas and surrounding emergency resource stations. The path planning algorithm is used to calculate multiple transportation routes from each emergency resource station to the high-risk area. After strictly avoiding various dangerous areas, the optimal route with the shortest distance and the least time is selected, and finally a standardized emergency resource allocation route is generated.

[0116] The high-risk area is the area around the commercial building, with emergency station A and emergency station B within 2 kilometers. The calculated route from station A to the high-risk area is 1.2 kilometers long and takes 8 minutes, while the route from station B is 1.8 kilometers long and takes 12 minutes. Station A is selected as the optimal route, and the speed of the dispatched vehicles is controlled at 9 kilometers per hour.

[0117] Then, through step S1054, the three-dimensional concentration distribution map generated in S103 and the population distribution data obtained in S104 are called, and the public evacuation range is reasonably delineated in combination with the explosion risk level. The safe evacuation direction is designed in combination with the gas diffusion direction and terrain conditions. Multiple assembly points are preset in the safe area outside the evacuation range. The above elements are integrated to generate a complete public evacuation plan.

[0118] For example, the evacuation area is defined as 800 meters around the leak source, covering 500 people in each of the three commercial buildings and 800 people in each of the two residential communities, with a current real-time population of approximately 2,600 people in the area. Considering the characteristics of the northeast wind speed of 2 m / s and the gas diffusion to the southwest, two evacuation directions are designed in the northeast and northwest, with three pre-set assembly points, each capable of accommodating 800 to 1,000 people. Each planned evacuation route is 4 meters wide, and the evacuation is expected to take 25 minutes to complete.

[0119] Next, in step S1055, the valve operation sequence list generated in S1052, the emergency resource allocation path generated in S1053, and the public evacuation plan generated in S1054 are integrated, supplementing emergency response precautions and the division of responsibilities for each stage to form a complete decision support plan. This decision support plan is then converted into a standard instruction format that conforms to the receiving and recognition standards of the monitoring terminal, ensuring that the plan can be quickly parsed by the monitoring terminal without requiring secondary manual processing. In practical application, the integration of the three core elements clearly indicates the simultaneous advancement of related work: valve operation requires 2 operators, emergency resource allocation requires 3 rescue vehicles, and evacuation requires 10 personnel. After conversion to a standard instruction format, the terminal's parsing response time is no more than 10 seconds.

[0120] Finally, in step S1056, the decision support scheme converted into a standard instruction format is pushed to the gas regulatory department's monitoring terminal. The corresponding alarm level is triggered according to the explosion risk level. At the same time, the core elements of the decision support scheme are visualized in the city's three-dimensional geographic interface of the monitoring terminal, making it convenient for regulatory personnel to quickly view, understand and implement the scheme.

[0121] The solution push response time is no more than 5 seconds. The monitoring terminal triggers alarms according to the risk level: high risk triggers a level 1 alarm, which is a flashing red light accompanied by a voice prompt, once every 3 seconds; medium risk triggers a level 2 alarm, which is a flashing orange light accompanied by a voice prompt, once every 5 seconds; low risk triggers a level 3 alarm, which is a yellow light prompt; the three-dimensional geographic interface can display relevant elements in real time, and the real-time location error of resource allocation does not exceed 10 meters.

[0122] This application builds upon the core data findings presented earlier, integrating three core elements: valve operation, emergency resource allocation, and public evacuation. It generates a scientifically sound and implementable decision support solution. Through format conversion and terminal push, the solution enables tiered alarms and visual displays, overcoming the shortcomings of traditional emergency decision-making, such as a lack of systematic approach and poor implementation. This ensures that regulatory personnel can quickly obtain emergency response guidelines, conduct emergency work in a standardized and efficient manner, minimize gas diffusion, reduce explosion risks, and protect personal safety and urban property.

[0123] Figure 3This application provides a schematic diagram of a specific implementation of a digital twin-based gas pipeline network operation status simulation system, referring to... Figure 3 The system may include: Module 31 is used to build a digital twin model of the gas pipeline network and run online simulation. It compares the real-time monitoring data of the pipeline network with the simulation expectation value and automatically triggers safety warnings based on preset evaluation rules. The positioning module 32 is used to respond to safety warnings by initiating a special simulation in the digital twin model. Based on the pressure and flow data of the monitoring data, it analyzes the propagation direction and temporal relationship of abnormal fluctuations in the pipeline network to locate the source of potential leaks. It also dynamically calculates the gas leakage rate by solving the mass conservation equation based on simulation correction. The generation module 33 is used to simulate the diffusion process of leaked gas in the future period in a digital twin model based on the source location and leakage rate, combined with current meteorological environmental data and three-dimensional geographic data of the city, so as to generate a dynamically updated three-dimensional concentration distribution map. The calculation module 34 is used to identify spatial areas where the concentration is within the explosion limit range by analyzing the three-dimensional concentration distribution map, and to calculate the risk value of different areas by combining the building and population distribution data of the spatial area to generate an explosion risk level map. The push module 35 is used to generate a decision support plan that includes key valve operation, emergency resource allocation path and public evacuation range by integrating the source location, three-dimensional concentration distribution map and explosion risk level map. The decision support plan is then pushed to the monitoring terminal for graded alarm and visualization display.

[0124] The digital twin-based gas pipeline network operation status simulation system of this application embodiment is used to implement the aforementioned digital twin-based gas pipeline network operation status simulation method. Therefore, the specific implementation of the digital twin-based gas pipeline network operation status simulation system can be found in the embodiment section of the digital twin-based gas pipeline network operation status simulation method above. The specific implementation can be referred to the description of the corresponding embodiments, which will not be repeated here.

[0125] This application also provides an electronic device, including: a memory for storing a computer program; and a processor for executing the computer program to implement the steps of the above-described digital twin-based gas pipeline network operation state simulation method.

[0126] This application also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of any of the above-described digital twin-based gas pipeline network operation state simulation methods.

[0127] In one exemplary embodiment, the aforementioned computer-readable storage medium may include, but is not limited to, various media capable of storing computer programs, such as USB flash drives, read-only memory, random access memory, portable hard drives, magnetic disks, or optical disks.

[0128] Embodiments of the present invention also provide a computer program product, which includes a computer program that, when executed by a processor, implements the steps in any of the embodiments of the above-described digital twin-based gas pipeline network operation state simulation method.

[0129] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0130] The above provides a detailed description of the simulation method and system for the operational status of a gas pipeline network based on digital twins, as provided in this application. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the embodiments above are merely for the purpose of helping to understand the method and its core ideas. It should be noted that those skilled in the art can make various improvements and modifications to this application without departing from its principles, and these improvements and modifications also fall within the protection scope of this application.

Claims

1. A method for simulating the operational status of a gas pipeline network based on digital twins, characterized in that, include: A digital twin model of the gas pipeline network is constructed and online simulation is run. The real-time monitoring data of the pipeline network is compared with the simulation expected values, and safety warnings are automatically triggered based on preset evaluation rules. In response to the safety warning, a special simulation is initiated in the digital twin model. Based on the pressure and flow data of the monitoring data, the propagation direction and temporal relationship of abnormal fluctuations in the pipeline network are analyzed to locate the source of potential leaks. The gas leakage rate is dynamically calculated by solving the mass conservation equation based on simulation correction. Based on the source location and leakage rate, combined with current meteorological environmental data and urban three-dimensional geographic data, the diffusion process of the leaked gas in the future time period is simulated in the digital twin model to generate a dynamically updated three-dimensional concentration distribution map. By analyzing the three-dimensional concentration distribution map, spatial areas with concentrations within the explosion limit range are identified. Combined with the building and population distribution data of the spatial areas, the risk values ​​of different areas are calculated to generate an explosion risk level map. Based on the source location, three-dimensional concentration distribution map, and explosion risk level map, a decision support scheme is generated that includes key valve operation, emergency resource allocation path, and public evacuation range. The decision support scheme is then pushed to the monitoring terminal for graded alarms and visualization.

2. The method according to claim 1, characterized in that, Based on the source location, 3D concentration distribution map, and explosion risk level map, a decision support scheme is generated, including key valve operations, emergency resource allocation routes, and public evacuation areas. This decision support scheme is then pushed to the monitoring terminal for tiered alerts and visual display, including: In the digital twin model, the nearest pipeline valve is located based on the source location to simulate the effect of valve closure on gas diffusion and obtain gas concentration change data. Based on the concentration change data, key valve combinations that can effectively suppress diffusion are selected, and an operation sequence list is generated based on the key valve combinations. Using the explosion risk level map, high-risk areas and emergency resource sites are identified, the optimal path from the emergency resource sites to the high-risk areas is calculated, high-risk areas are avoided, and emergency resource allocation paths are generated. Based on the three-dimensional concentration distribution map and population distribution data, the public evacuation area is delineated, and evacuation directions and assembly points are designed to generate a public evacuation plan. The operation sequence list, emergency resource allocation path and public evacuation plan are integrated to form a decision support plan, which is then converted into a standard instruction format. The decision support solution in the standard instruction format is pushed to the regulatory terminal. Different alarm levels are triggered on the regulatory terminal according to the risk level, and the various elements of the decision support solution are visualized in the geographic interface.

3. The method according to claim 1, characterized in that, By analyzing the three-dimensional concentration distribution map, spatial regions with concentrations within the explosive limit range are identified. Combined with building and population distribution data for these spatial regions, risk values ​​for different regions are calculated to generate an explosion risk level map, including: Gas concentration values ​​are extracted from the three-dimensional concentration distribution map. A lower explosion concentration limit and an upper explosion concentration limit are set. Grid cells with concentration values ​​between the lower and upper explosion concentration limits are selected and marked as hazardous space areas. Obtain building distribution data within the hazardous space area, including building type, structural materials, and personnel capacity; and simultaneously obtain population distribution data, including real-time population density and activity patterns. Based on the gas concentration values, building distribution data, and population distribution data, a multi-factor risk calculation model is constructed. In the multi-factor risk calculation model, the exposure index, vulnerability index, and consequence severity index of each grid cell are calculated, and the comprehensive risk value is obtained by merging the indices. Based on the comprehensive risk value, the hazardous space area is divided into multiple risk levels, and an explosion risk level map is generated using different color codes.

4. The method according to claim 1, characterized in that, Based on the source location and leakage rate, combined with current meteorological data and urban 3D geographic data, the diffusion process of the leaked gas in the future time period is simulated in the digital twin model to generate a dynamically updated 3D concentration distribution map, including: Acquire current meteorological and environmental data, including wind speed, wind direction, temperature distribution, and atmospheric stability parameters, and establish a gas diffusion calculation model based on fluid motion laws; The leakage rate and the source location are used as input conditions and injected into the gas diffusion calculation model. The calculation is carried out in time steps in the future period to simulate the migration path of the gas cloud in the three-dimensional scene. Within each time step, the diffusion range and concentration decay of the gas cloud are updated based on the meteorological environmental data, and the gas concentration values ​​on the three-dimensional grid nodes are calculated to generate a time-series three-dimensional concentration dataset. The three-dimensional concentration dataset at each time step is mapped onto a three-dimensional scene, and the concentration level is represented by color gradient to synthesize a dynamically updated three-dimensional concentration distribution map.

5. The method according to claim 2, characterized in that, Based on concentration change data, key valve combinations that can effectively suppress diffusion are selected, and an operation sequence list is generated based on these key valve combinations, including: Based on the concentration change data, calculate the concentration decrease rate after each valve node is closed, and select valve nodes with a concentration decrease rate exceeding a preset threshold as candidate valves. Based on the pipeline network topology, the connectivity between candidate valves is analyzed, and interconnected candidate valves are grouped into key valve combinations. Based on the spatial location of each valve in the key valve assembly and its upstream and downstream relationship in the pipeline network, the operation priority of the valves is determined, and an operation sequence list is generated.

6. The method according to claim 1, characterized in that, And by solving the mass conservation equation based on simulation correction, the gas leakage rate is dynamically calculated, including: In the digital twin model, a virtual control body is set up around the source location, and the first flow data entering the virtual control body and the second flow data leaving the virtual control body are obtained. The flow difference is calculated based on the mass conservation equation. The mass conservation equation is corrected by introducing simulated flow data from a specialized simulation, the weighting coefficient of the flow difference is adjusted, and the gas leakage rate is dynamically output based on the corrected flow difference.

7. The method according to claim 1, characterized in that, In response to the aforementioned safety warning, a specific simulation is initiated in the digital twin model. Based on the pressure and flow data from the monitoring data, the propagation direction and temporal relationship of abnormal fluctuations in the pipeline network are analyzed to locate the source of potential leaks, including: In the digital twin model, a special simulation is activated, and the simulation parameters are configured to focus on the pipeline section corresponding to the safety warning. The time series of pressure data and flow data in the monitoring data are obtained, and the abnormal fluctuation feature points in the time series are extracted. Based on the topological connection of the pipeline network, a wave propagation path network is constructed. The time difference sequence between the abnormal wave characteristic points is calculated in the wave propagation path network, and the dominant direction of wave propagation is determined based on the time difference sequence. By tracing back along the dominant direction, the starting node of the fluctuation is identified in the fluctuation propagation path network. The monitoring point where the earliest abnormal fluctuation occurs is selected by combining the temporal relationship, and the starting node of the fluctuation is determined as the source of the potential leakage.

8. A simulation system for the operational status of a gas pipeline network based on digital twins, characterized in that, include: The module is used to build a digital twin model of the gas pipeline network and run online simulation. It compares the real-time monitoring data of the pipeline network with the simulation expectation values ​​and automatically triggers safety warnings based on preset evaluation rules. The positioning module is used to respond to the safety warning, start a special simulation in the digital twin model, analyze the propagation direction and temporal relationship of abnormal fluctuations in the pipeline network based on the pressure and flow data of the monitoring data, so as to locate the source of potential leakage, and dynamically calculate the gas leakage rate by solving the mass conservation equation based on simulation correction. The generation module is used to simulate the diffusion process of the leaked gas in the future time period in the digital twin model based on the source location and leakage rate, combined with the current meteorological environment data and the city's three-dimensional geographic data, so as to generate a dynamically updated three-dimensional concentration distribution map. The calculation module is used to identify spatial areas where the concentration is within the explosion limit range by analyzing the three-dimensional concentration distribution map, and to calculate the risk value of different areas by combining the building and population distribution data of the spatial areas to generate an explosion risk level map. The push module is used to integrate the source location, three-dimensional concentration distribution map and explosion risk level map to generate a decision support plan that includes key valve operation, emergency resource allocation path and public evacuation range, and push the decision support plan to the monitoring terminal for graded alarm and visualization display.

9. An electronic device, characterized in that, include: Memory, used to store computer programs; A processor, configured to execute the computer program to implement the steps of the digital twin-based gas pipeline network operation status simulation method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, enables the implementation of the digital twin-based gas pipeline network operation status simulation method as described in any one of claims 1 to 7.

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