Indoor gas leakage risk management and control system and method based on coupling analysis

Abstract

The invention relates to the technical field of gas safety, and discloses an indoor gas leakage risk management and control system and method based on coupling analysis. According to the system, instantaneous gas flow time sequence data, pipeline completion and design data and environment monitoring data are fused, a gas pipeline is divided into continuous equal-length gas pipe sections, and related data are extracted; analyzing and extracting internal damage risk characteristics and external damage risk characteristics of the gas pipe section based on the acquired data, performing coupling analysis to obtain a coupling damage risk value of the gas pipe section, and evaluating a fire risk level of the gas pipe section based on the gas pipeline data; comprehensively coupling the damage risk and the fire risk level, and generating a risk management and control scheme of all the gas pipe sections; according to the invention, quantitative evaluation and precise management and control of the risk of the indoor gas pipeline are realized, traditional passive response is converted into active prevention, and the intelligent level of gas safety management is significantly improved.

Description

Technical Field

[0001] This application relates to the field of gas safety technology, and in particular to an indoor gas leak risk management system and method based on coupling analysis. Background Technology

[0002] Currently, the safety management of indoor gas pipelines mainly relies on regular manual inspections and passive responses from indoor gas alarms. Manual inspections are inefficient, have limited coverage, and heavily depend on personal experience, making it difficult to detect potential risks such as early corrosion and stress damage inside the pipeline. Ordinary gas alarms only sound an alarm when the leakage reaches a certain concentration, which is a reactive measure and cannot predict or prevent leakage risks. In addition, existing technologies usually assess gas pipelines as a whole and only consider single risk factors when assessing gas pipeline risks, lacking detailed and quantitative analysis of various parts of the gas pipeline. This leads to inadequate risk control measures and an inability to achieve precise point-to-point prevention, resulting in a long-term passive response situation for indoor gas safety.

[0003] Therefore, there is an urgent need for an intelligent risk management solution that can deeply integrate multi-source heterogeneous data, perform internal and external risk coupling analysis on various parts of the pipeline, and dynamically generate precise risk management strategies based on the risk analysis results. Summary of the Invention

[0004] To overcome the above-mentioned drawbacks, this application provides an indoor gas leak risk management system and method based on coupling analysis. It aims to construct a complete technical closed loop from risk quantification and identification to precise control decision-making by integrating instantaneous gas flow time series data, gas pipeline spatial status data and internal and external environmental data, so as to achieve proactive, intelligent and precise prevention and control of coupled risks of indoor gas pipelines.

[0005] To achieve the above objectives, this application adopts the following technical solution: Firstly, this application provides an indoor gas leak risk management system based on coupling analysis, including: The raw data acquisition module is used to acquire instantaneous gas flow time-series data, gas pipeline external microenvironment data, and gas pipeline spatial state data; The internal risk extraction module is used to extract the internal damage risk characteristics of various locations in the gas pipeline based on instantaneous gas flow time-series data and gas pipeline spatial state data. The external risk extraction module is used to extract the external damage risk characteristics of various locations of the gas pipeline based on the external microenvironment data and spatial state data of the gas pipeline. The coupled risk analysis module is used to analyze the coupled damage risk at each location of the gas pipeline based on the internal damage risk characteristics and external damage risk characteristics at each location of the gas pipeline. The hazard assessment module is used to assess the fire hazard level of various locations along the gas pipeline based on spatial status data. The control scheme generation module is used to generate indoor gas leak risk control schemes based on the coupled damage risk and fire hazard level at various locations of the gas pipeline.

[0006] According to the above technical solution, the steps for obtaining instantaneous gas flow time-series data, gas pipeline external microenvironment data, and gas pipeline spatial state data include: Step S11: Obtain instantaneous gas flow time series data, specifically including: collecting cumulative gas consumption data recorded at fixed sampling intervals from smart gas meters, performing time series difference calculation on the cumulative gas consumption data, and generating instantaneous gas volume flow time series data; Step S12: Obtain spatial status data of the gas pipeline, specifically including: obtaining the topological structure information of the gas pipeline system, the spatial coordinates of the inner diameter and centerline of the gas pipeline from the as-built drawings of the gas pipeline of the target building; dividing the gas pipeline into continuous equal-length gas pipe segments with the minimum physical length of all straight pipe segments in the pipeline system as the step size, generating a unique identifier for each gas pipe segment and associating it with its spatial geometric parameters; at the same time, reading and recording the minimum allowable operating flow rate, maximum allowable operating flow rate, critical ambient temperature for easy corrosion of the gas pipeline material, relative humidity, spatial coordinates of all fixed ignition sources, and the internal net volume of the minimum enclosed structure where each gas pipe segment is located from the design documents of the gas pipeline system and the building. Step S13: Obtain external microenvironment data of the gas pipeline. Specifically, this includes: deploying multiple temperature and humidity sensors within the space where the gas pipeline is located, collecting raw time-series monitoring data of ambient temperature and humidity at the same sampling frequency and synchronized start and end times as the gas flow data in step S11; associating the monitoring data of each sensor with spatial coordinates to form a microenvironment monitoring dataset with spatial labels; and using an inverse distance weighted spatial interpolation algorithm to calculate the local ambient temperature time-series data and local ambient humidity time-series data corresponding to each gas pipeline segment based on the centerline coordinates of each gas pipeline segment and the monitoring dataset with spatial labels, thus forming a microenvironment dataset of the gas pipeline.

[0007] According to the above technical solution, the steps for extracting the internal damage risk characteristics of various locations in a gas pipeline based on instantaneous gas flow time-series data and gas pipeline spatial state data include: Step S21: Based on the instantaneous gas flow time series data and the gas pipeline spatial state data, extract the gas flow velocity time series data in each gas pipeline segment; Step S22: Based on the gas flow velocity time series data and gas pipeline spatial state data in each gas pipeline segment, a coupling method based on the hyperbolic tangent function is used to extract the internal damage risk characteristics of each location in the gas pipeline.

[0008] Based on the above technical solution, the steps for extracting external damage risk characteristics at various locations of the gas pipeline, based on external microenvironment data and spatial state data of the gas pipeline, are as follows: Step S31: Based on the external microenvironment data and spatial state data of the gas pipeline, extract the high temperature and high humidity co-exposure factor and temperature and humidity co-fluctuation factor for each gas pipeline segment. Step S32: Based on the high temperature and high humidity co-exposure factor and temperature and humidity co-fluctuation factor of each gas pipeline segment, an exponentially modulated co-risk model is used to extract the external damage risk characteristics of each location of the gas pipeline.

[0009] According to the above technical solution, the steps for analyzing the coupled damage risk at various locations of a gas pipeline, based on the internal damage risk characteristics and external damage risk characteristics at various locations of the gas pipeline, include: Step S41: For each gas pipe segment in the gas pipeline, obtain the corresponding internal damage risk characteristic value and external damage risk characteristic value; Step S42: Based on the obtained internal damage risk characteristic value and external damage risk characteristic value, calculate the coupled damage risk value of each gas pipeline segment.

[0010] According to the above technical solution, the steps for assessing the fire hazard level of various locations along a gas pipeline based on spatial status data include: Step S51: Based on the spatial status data of the gas pipeline, extract the ignition source proximity factor and ventilation limitation factor for each gas pipeline segment; Step S52: Based on the ignition source proximity factor and ventilation restriction factor of each gas pipeline segment, extract the fire hazard level value of each gas pipeline segment.

[0011] Based on the above technical solution, the steps for generating an indoor gas leak risk management plan, based on the coupled damage risk and fire hazard level at various locations of the gas pipeline, include: Step S61: Based on the coupled damage risk value and fire hazard level value at each location of the gas pipeline, determine the risk level of all gas pipeline sections. Step S62: Based on the risk level assessment results of all gas pipeline sections, generate an indoor gas leak risk management plan.

[0012] Secondly, this application also provides a method for managing indoor gas leak risks based on coupling analysis, including the following steps: S1. Acquire instantaneous gas flow time-series data, gas pipeline external microenvironment data, and gas pipeline spatial status data; S2. Based on instantaneous gas flow time series data and gas pipeline spatial state data, extract the internal damage risk characteristics of each location in the gas pipeline; S3. Based on the external microenvironment data and spatial state data of the gas pipeline, extract the external damage risk characteristics of each location of the gas pipeline. S4. Based on the internal damage risk characteristics and external damage risk characteristics of each location in the gas pipeline, analyze the coupled damage risk at each location in the gas pipeline. S5. Based on the spatial status data of the gas pipeline, assess the fire hazard level at various locations along the gas pipeline. S6. Based on the coupled damage risk and fire hazard level at various locations of the gas pipeline, generate an indoor gas leak risk management plan.

[0013] Thirdly, this application provides an electronic device including a processor and a memory, wherein the memory stores a computer program that can be called by the processor, and the processor executes an indoor gas leak risk management method based on coupling analysis by calling the computer program stored in the memory.

[0014] Fourthly, this application provides a computer-readable storage medium storing instructions that, when executed on a computer, cause the computer to perform an indoor gas leak risk management method based on coupling analysis.

[0015] Compared with the prior art, this application has the following advantages and beneficial effects: This application transforms traditional passive safety management into data-driven proactive preventative control, achieving coupled quantitative assessment of internal damage risks and external corrosion risks in indoor gas pipelines. It also generates tiered risk control instructions based on the spatial topology of the gas pipeline, enabling precise location of high-risk gas pipeline sections and outputting differentiated prevention and control solutions ranging from structural maintenance to key monitoring. This not only significantly improves the accuracy of risk identification and early warning capabilities but also transforms abstract risk assessment results into actionable guidelines that efficiently utilize maintenance resources, providing solid technical support for building an intelligent and preventative modern indoor gas safety system. Attached Figure Description

[0016] Other features, objects, and advantages of this application will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings: Figure 1 This is an overall flowchart of the indoor gas leak risk management system based on coupling analysis provided in the embodiments of this application; Figure 2 This is a data acquisition flowchart provided in an embodiment of this application; Figure 3 This is a flowchart of the internal damage risk feature extraction provided in the embodiments of this application; Figure 4 This is a flowchart of the external damage risk feature generation process provided in the embodiments of this application; Figure 5 This is a flowchart of the coupling damage risk analysis provided in the embodiments of this application; Figure 6 This is a flowchart of the fire hazard level assessment provided in the embodiments of this application; Figure 7 This is a flowchart illustrating the generation process of the indoor gas leak risk management solution provided in this application embodiment. Detailed Implementation

[0017] The technical solution of this application will be further described in detail below with reference to the accompanying drawings and specific embodiments, so as to facilitate understanding and implementation by those skilled in the art. It should be understood that the specific embodiments described herein are only for explaining this application and are not intended to limit this application.

[0018] Please see Figure 1 , Figure 1 This is an overall flowchart of the indoor gas leak risk management system based on coupling analysis provided in the embodiments of this application, which specifically includes the following modules: The raw data acquisition module is used to acquire instantaneous gas flow time-series data, gas pipeline external microenvironment data, and gas pipeline spatial state data.

[0019] Please see Figure 2 , Figure 2 The complete technical process for data acquisition in the embodiments of this application is illustrated, and the specific steps are as follows: Step S11: Obtain instantaneous gas flow time series data, specifically including: collecting cumulative gas consumption data recorded at fixed sampling intervals from smart gas meters, performing time series difference calculation on the cumulative gas consumption data, and generating instantaneous gas volume flow time series data; Step S12: Obtain spatial status data of the gas pipeline, specifically including: obtaining the topological structure information of the gas pipeline system, the spatial coordinates of the inner diameter and centerline of the gas pipeline from the as-built drawings of the gas pipeline of the target building; dividing the gas pipeline into continuous equal-length gas pipe segments with the minimum physical length of all straight pipe segments in the pipeline system as the step size, generating a unique identifier for each gas pipe segment and associating it with its spatial geometric parameters; at the same time, reading and recording the minimum allowable operating flow rate, maximum allowable operating flow rate, critical ambient temperature for easy corrosion of the gas pipeline material, relative humidity, spatial coordinates of all fixed ignition sources, and the internal net volume of the minimum enclosed structure where each gas pipe segment is located from the design documents of the gas pipeline system and the building. Step S13: Obtain external microenvironment data of the gas pipeline, specifically including: deploying multiple temperature and humidity sensors in the space where the gas pipeline is located, collecting raw time-series monitoring data of ambient temperature and humidity at the same sampling frequency and synchronized start and end time as the gas flow data in step S11, associating the monitoring data of each sensor with spatial coordinates to form a microenvironment monitoring dataset with spatial labels; using an inverse distance weighted spatial interpolation algorithm, calculating the local ambient temperature time-series data and local ambient humidity time-series data corresponding to each gas pipeline segment based on the centerline coordinates of each gas pipeline segment and the monitoring dataset with spatial labels, forming a microenvironment dataset of the gas pipeline. The data acquisition process in this embodiment resolves the core contradiction of inconsistent data dimensions and computational scales. Furthermore, it transforms scattered data into a standardized data model that is spatiotemporally aligned and unitized, thereby providing a reliable and feasible input for the subsequent accurate extraction of internal, external, and coupled risk features at each location. This fundamentally supports the overall logical consistency and technical feasibility of the method.

[0020] The internal risk extraction module is used to extract the internal damage risk characteristics of various locations in the gas pipeline based on instantaneous gas flow time-series data and gas pipeline spatial state data.

[0021] Please see Figure 3 , Figure 3 This is a flowchart of the internal damage risk feature extraction provided in an embodiment of this application. The specific steps are as follows: Step S21: Based on the instantaneous gas flow time series data and the gas pipeline spatial state data, extract the gas flow velocity time series data in each gas pipeline segment; the specific steps are as follows: First, the cross-sectional area of ​​each gas pipe segment is calculated based on the inner diameter of each gas pipe segment in the spatial state data of the gas pipeline. Secondly, for each gas pipeline segment, based on the principle of fluid continuity, the instantaneous gas volume flow rate at each sampling time point in the instantaneous gas flow time series data is divided by the cross-sectional area of ​​the gas pipeline segment to obtain the gas flow velocity of the gas pipeline segment at each sampling time point; this calculation is repeated for all sampling time points to form the gas flow velocity time series data in each gas pipeline segment. Step S22: Based on the gas flow velocity time-series data and gas pipeline spatial state data within each gas pipeline segment, a coupling method based on the hyperbolic tangent function is used to extract the internal damage risk characteristics at each location of the gas pipeline; the specific steps are as follows: First, based on the spatial state data of the gas pipeline, the minimum allowable operating flow rate of the pipeline to prevent condensate accumulation is obtained from the gas pipeline system design documents and used as the safe flow rate threshold; the maximum allowable operating flow rate of the pipeline to ensure system safety and equipment life is obtained from the gas pipeline system design documents and used as the peak flow rate threshold. Secondly, based on the gas flow velocity time series data in each gas pipeline segment, the number of sampling points where the gas flow velocity is lower than the safe flow velocity threshold is counted and divided by the total number of sampling points to obtain the low flow velocity stagnation risk factor for each gas pipeline segment; the number of sampling points where the gas flow velocity exceeds the peak flow velocity threshold is counted and divided by the total number of sampling points to obtain the high flow velocity impact risk factor for each gas pipeline segment. Finally, based on the low-velocity retention risk factor and high-velocity impact risk factor of each gas pipe segment, a coupling method based on the hyperbolic tangent function is used to calculate the internal damage risk characteristic value of each gas pipe segment:

[0022] In the formula, The internal damage risk characteristic value is a non-negative dimensionless scalar that represents the level of internal damage risk of the gas pipeline segment. The higher the value, the higher the comprehensive internal damage risk of the gas pipeline segment. The low-flow-velocity stagnation risk factor for gas pipeline sections is a value range of The dimensionless scalar represents the proportion of time that the gas flow velocity inside the gas pipeline is below the safety lower limit. The larger the value, the higher the risk of gas stagnation. The high-velocity impact risk factor for gas pipeline sections is a value range of The dimensionless scalar represents the proportion of time during which the gas flow velocity inside a gas pipeline exceeds the safe limit; the larger the value, the higher the risk of gas surge. It is the hyperbolic tangent function; The formula for calculating the internal damage risk characteristic value in this embodiment is a coupled quantitative model specifically constructed for the risk mechanism of internal damage induced by abnormal flow velocity in indoor gas pipelines; specifically: core coupling term This embodies the core logic that both stagnation and impact flow patterns must coexist and interact to constitute a significant risk of internal damage. The product operation ensures that when either risk factor is zero, the coupling term is zero. This precisely corresponds to the principle in the physical mechanism of pipeline failure that without stagnation, impact cannot accumulate corrosive media, and without impact, stagnation cannot accelerate damage propagation. (Outer function) It is a nonlinear mapping that standardizes and saturates the coupling strength. The hyperbolic tangent function maps the product result to a value range between 0 and 1, simulating the saturation effect of indoor gas pipeline risk. That is, when the synergistic effect of retention and impact reaches a certain level, the resulting risk level will tend to level off rather than increase infinitely linearly. The internal damage risk feature extraction process in this embodiment transforms macroscopic instantaneous gas flow time-series data and microscopic pipeline space data into internal damage risk feature values ​​for each gas pipe segment through two closely linked steps. This achieves stable and objective quantification of complex flow-induced damage risks, providing a unique and reliable basis for subsequent risk classification and precise control.

[0023] The external risk extraction module is used to extract the external damage risk characteristics of various locations of the gas pipeline based on the external microenvironment data and spatial state data of the gas pipeline.

[0024] Please see Figure 4 , Figure 4 This is a flowchart of the external damage risk feature generation process provided in this application embodiment. The specific steps are as follows: Step S31: Based on the external microenvironment data and spatial state data of the gas pipeline, extract the high temperature and high humidity co-exposure factor and the temperature and humidity co-fluctuation factor for each gas pipeline segment; the specific steps are as follows: First, based on the external microenvironment data of the gas pipeline, the local ambient temperature time series data and local ambient humidity time series data corresponding to each gas pipeline segment are extracted; based on the spatial state data of the gas pipeline, the critical ambient temperature and relative humidity that are prone to corrosion as defined in the gas pipeline system design documents are extracted. Secondly, the high temperature and high humidity synergistic exposure factor for each gas pipeline segment was calculated:

[0025] In the formula, The high temperature and high humidity synergistic exposure factor is a value range of The dimensionless scalar represents the proportion of the cumulative time that the gas pipeline section is in a corrosive and humid environment. For the gas pipeline section in the sampling period Ambient temperature values ​​at each sampling point; For the gas pipeline section within the sampling period The ambient humidity values ​​at each sampling point; The critical ambient temperature at which corrosion is easily initiated, as defined in the gas pipeline system design documents; The relative humidity that is prone to corrosion, as defined in the gas pipeline system design documents; This represents the total number of sampling points within the sampling period. ; This is an indicator function that takes the value 1 when the condition is true, i.e., the formula within the parentheses is true, and 0 otherwise; The high-temperature and high-humidity synergistic exposure factor calculation formula in this embodiment quantifies the risk exposure intensity of periodic humid and hot steam to the outer wall of the indoor gas pipeline system within the enclosed space; where, the summation sign and denominator... The design purpose is to convert the count of high temperature and high humidity exceeding the standard into a time percentage based on a fixed evaluation period. This makes the evaluation results unaffected by fluctuations in the duration of a single sampling, and is suitable for stable evaluation of discontinuous damp and heat loads generated by daily indoor life; indicator function and This is to transform continuous environmental data collected by sensors into discrete events that characterize whether the indoor pipe corrosion triggering conditions have been met; multiplication operation This rigorously simulates the synergistic nature of indoor humid heat steam corrosion, ensuring that only when the temperature and humidity data at the same sampling time both exceed the corrosion threshold of the pipeline material are they counted as an effective high temperature and high humidity risk exposure. This precisely excludes working conditions where only high temperature and high humidity play a role but do not work synergistically, thereby extracting the most threatening humid heat synergistic exposure events for indoor gas pipelines. Finally, for each gas pipeline segment, the sample covariance of the local ambient temperature time series data and the local ambient humidity time series data is calculated, and the absolute value is taken to obtain the initial temperature and humidity co-fluctuation intensity value of each gas pipeline segment. In order to convert the absolute intensity into a relative indicator that can be used for risk comparison, the maximum value normalization method is used to process each gas pipeline segment. The specific process is as follows: find the maximum value among the initial temperature and humidity co-fluctuation intensity values ​​of all gas pipeline segments, divide the initial temperature and humidity co-fluctuation intensity value of each gas pipeline segment by the maximum value, and obtain the temperature and humidity co-fluctuation factor of each gas pipeline segment. Step S32: Based on the high temperature and high humidity co-exposure factor and temperature and humidity co-fluctuation factor of each gas pipeline segment, an exponentially modulated co-risk model is used to extract the external damage risk characteristics at each location of the gas pipeline.

[0026] In the formula, The external damage risk characteristic value is a non-negative dimensionless scalar that characterizes the level of damage risk to the gas pipeline section caused by its external microenvironment. The temperature and humidity symmetric fluctuation factor is a value range of The dimensionless scalar represents the intensity of the unidirectional drastic change in temperature and humidity in the environment where the gas pipeline section is located due to activity and ventilation. The external damage risk characteristic calculation formula in this embodiment quantifies the dual risk coupling effect of steady-state humid heat exposure and dynamic temperature and humidity synergistic fluctuations in the external corrosion of indoor gas pipelines; specifically, it uses the high temperature and high humidity synergistic exposure factor. As the risk base, it directly characterizes the fundamental source of risk: the cumulative time that gas pipelines spend in corrosive environments; the index term It is a specially designed risk amplification modulator, with a value that is always greater than 0 and varies with... Increase and decrease; when the temperature and humidity fluctuations are extremely weak, that is When the value approaches zero, the exponent approaches 1, representing the characteristic value of external damage risk. Approaching This indicates that in a highly stable environment, the corrosion risk is mainly determined linearly by the duration of combined exposure to high temperature and high humidity; conversely, when the combined exposure to humidity is abnormally drastic, i.e. As the value approaches 1, the exponent decreases, making it easier to apply the same high temperature and high humidity synergistic exposure factor. External damage risk characteristic value The increase precisely describes the physical nature of how drastic temperature and humidity fluctuations act as a catalyst, amplifying and accelerating the corrosion potential inherent in steady-state exposure, thus leading to a nonlinear and rapid increase in the risk of external damage to indoor gas pipelines. The external damage risk feature generation process in this embodiment constructs a quantitative model based on corrosion mechanism with clear physical meaning, thereby stably and reliably mapping seemingly random environmental fluctuations to comparable and interpretable deterministic risk outputs on each pipeline space unit, providing an indispensable and solid external risk dimension for subsequent precise spatial coupling with internal flow risks.

[0027] The coupled risk analysis module is used to analyze the coupled damage risk at each location of the gas pipeline based on the internal damage risk characteristics and external damage risk characteristics at each location of the gas pipeline.

[0028] Please see Figure 5 , Figure 5 This is a flowchart of the coupling damage risk analysis provided in an embodiment of this application. The specific steps are as follows: Step S41: For each gas pipe segment in the gas pipeline, obtain the corresponding internal damage risk characteristic value and external damage risk characteristic value; Step S42: Based on the obtained internal damage risk characteristic values ​​and external damage risk characteristic values, calculate the coupled damage risk value for each gas pipeline segment:

[0029] In the formula, The coupled damage risk value is a non-negative dimensionless scalar that characterizes the overall risk level of a gas pipeline segment caused by the coupling effect of internal flow damage risk and external environmental corrosion risk. This indicates taking the larger value within the parentheses; This indicates taking the smaller value within the parentheses; The coupled damage risk value calculation formula in this embodiment quantifies the failure physical mechanism of indoor gas pipeline leakage: the first term The most significant threats faced by each gas pipeline segment were identified and established, and this was used as the baseline level for assessing coupled damage risk, reflecting the weakest link effect—that is, the shortest link in a system determines the lower limit of risk; the second item The catalytic and amplifying effects of non-dominant risks on dominant risks were quantified. In the actual operation of gas pipelines, internal and external damage mechanisms are not independent but interconnected and mutually reinforcing, thus secondary damage risks... The presence of this significantly increases the likelihood and severity of gas pipeline leaks caused by the dominant damage risk. Specifically, external corrosion damage to the pipe wall greatly reduces the pipeline's tolerance to internal fluid impact, making rupture possible even under normally safe internal conditions. Simultaneously, continuous internal pressure fluctuations create alternating stress in areas with existing corrosion defects, accelerating the transformation and propagation of corrosion pits into cracks. The synergistic effect of internal and external damage risks is represented as a dynamic amplification factor, and compared with the baseline dominant risk. Multiplication ultimately manifests as the following mechanism: when there is a risk of damage from either the internal or external source, A value greater than 1 increases the final calculated risk value of coupled damage; when the secondary damage risk... The larger the value, the stronger the amplification effect, and the higher the risk of coupling damage; while when When the risk of coupled damage remains stable or relatively low, the magnitude of the risk value depends on the dominant risk at the baseline. This accurately depicts the risk evolution path of the dominant risk triggering and the synergistic risk aggravation in the gas pipeline system; The coupled damage risk analysis process in this embodiment not only avoids the limitations of simple arithmetic operations, but also shows that coupled risk is determined by both the internal structure and the external total amount of risk. It provides a novel and solid mathematical tool for understanding the complex coupled risks of indoor gas pipelines, and the output coupled damage risk value is a direct basis for subsequent risk classification and precise control.

[0030] The hazard assessment module is used to assess the fire hazard level of various locations along the gas pipeline based on spatial status data.

[0031] Please see Figure 6 ,Figure 6 This is a flowchart of the fire hazard level assessment provided in the embodiments of this application. The specific steps are as follows: Step S51: Based on the spatial state data of the gas pipeline, extract the ignition source proximity factor and ventilation limitation factor for each gas pipeline segment; the specific steps are as follows: First, extract the spatial coordinates of each gas pipeline segment from the gas pipeline spatial status data, and calculate the straight-line distance from each gas pipeline segment to all known fixed ignition sources. Take the minimum value as the original distance value of each gas pipeline segment, and extract the maximum value among all the original distance values ​​of the gas pipeline segments. Divide the original distance value of each gas pipeline segment by the maximum value to obtain a quotient, and subtract the quotient from one to obtain the ignition source proximity factor of each gas pipeline segment. Secondly, obtain the internal net volume value of the minimum enclosed structure where the center point of each gas pipeline segment is located from the gas pipeline spatial status data, and find the minimum value among all the internal net volume values ​​of the gas pipeline segments; divide the minimum value by the internal net volume value of each gas pipeline segment to obtain the ventilation limitation factor of each gas pipeline segment; if the gas pipeline segment is in an open space, directly set the ventilation limitation factor of the gas pipeline segment to zero. Step S52: Based on the ignition source proximity factor and ventilation restriction factor of each gas pipeline segment, extract the fire hazard level value of each gas pipeline segment; the specific steps are as follows:

[0032] In the formula, Fire hazard rating is a non-negative dimensionless scalar value that represents the fire risk level of a gas pipeline section in an indoor environment. The higher the value, the greater the risk. The ignition source proximity factor for the gas pipeline section is a value with a range of [value range missing]. The dimensionless scalar value represents the degree of proximity of a gas pipe section to the ignition source relative to the pipe section furthest from the ignition source in the household piping system. The larger the value, the closer the gas pipe section is to the high heat source in space. The ventilation limiting factor for the gas pipeline section is a value with a range of [value range missing]. The dimensionless scalar value represents the relative degree of airtightness of the local space where the gas pipeline is located. The larger the value, the more enclosed the space where the gas pipeline is located is relative to the most enclosed space in the house. This is an operation to retrieve the maximum value within the parentheses; The fire hazard rating calculation formula in this embodiment describes the coupling effect of two core factors: proximity to a fire source and poor ventilation; molecule The basic risk item reflects the contribution of proximity to the ignition source and poor ventilation to the fire hazard when these two factors exist independently, which is consistent with the basic principle of risk accumulation; the denominator Designing based on the principle of risk interaction in gas safety engineering is key to achieving coupled amplification and saturation constraints; when and When both values ​​are high, it means that the gas pipeline is simultaneously in an extremely unfavorable environment where it is easily ignited and gas easily accumulates. These two factors create a strong synergistic effect, leading to a non-linear and rapid increase in fire risk; the denominator contains... This item is precisely the quantitative manifestation of this synergistic effect. Increasing the value of the denominator will decrease the value of the fire hazard rating, thereby amplifying the fire hazard rating. ; The fire hazard assessment process in this embodiment outputs a definitive fire hazard level value for each gas pipe section. It can not only distinguish the risks that are visible near the stove, but also accurately reveal those fire risks that are hidden in enclosed cabinets or ceilings, but are actually higher. This provides a direct and accurate spatial positioning basis for the subsequent development of differentiated risk control measures.

[0033] The control scheme generation module is used to generate indoor gas leak risk control schemes based on the coupled damage risk and fire hazard level at various locations of the gas pipeline.

[0034] Please see Figure 7 , Figure 7 This is a flowchart illustrating the generation process of the indoor gas leak risk management solution provided in this application embodiment. The specific steps are as follows: Step S61: Based on the coupled damage risk value and fire hazard level value at each location of the gas pipeline, determine the risk level of all gas pipeline sections; the specific steps are as follows: First, the square of the coupled damage risk value of each gas pipeline segment is added to the square of the fire hazard level value, and the square root is taken to obtain the comprehensive risk index of each gas pipeline segment. ; Secondly, calculate the comprehensive risk index for all gas pipeline sections. average with standard deviation And assess the risk level of all gas pipeline sections: if If so, the corresponding gas pipeline section is determined to be of a high-risk level; if If so, the corresponding gas pipeline section is classified as medium risk; if If so, the corresponding gas pipeline section is determined to be of low risk level; Step S62: Based on the risk level assessment results of all gas pipeline sections, generate an indoor gas leak risk management plan; the specific steps are as follows: First, based on the risk level assessment results of all gas pipeline segments, the high-risk gas pipeline segments are clustered according to their topological connection relationship in the gas pipeline spatial status data to identify spatially adjacent high-risk pipeline segments. Secondly, based on different risk levels and spatial clustering characteristics, a hierarchical control instruction set with clear spatial orientation is generated: For a set of high-risk gas pipeline segments that are spatially continuous and longer than one meter, an indoor gas leak risk control instruction is generated and output: "Shut down the gas supply to this continuous gas pipeline segment and conduct a comprehensive structural overhaul." For isolated, short high-risk gas pipeline segments, an indoor gas leak risk control instruction is generated and output: "Add a real-time combustible gas concentration sensor monitoring probe at the corresponding spatial location of this gas pipeline segment and increase the monitoring frequency to once per minute." For all medium-risk gas pipeline segments, an indoor gas leak risk control instruction is generated and output: "Shorten the next planned periodic inspection cycle of the pipeline to which this gas pipeline segment belongs by 50%." For all low-risk gas pipeline segments, an indoor gas leak risk control instruction is generated and output: "Maintain the original routine inspection and maintenance plan for the pipeline to which this gas pipeline segment belongs." By combining the risk level judgment results of all gas pipeline segments, the comprehensive risk index of all gas pipeline segments, and all indoor gas leak risk control instructions, an indoor gas leak risk control plan is generated. The indoor gas leak risk management solution generation process in this embodiment directly transforms risk quantification data into executable engineering instructions, achieving seamless integration of risk assessment and control actions. Specifically, it uses an adaptive statistical method for risk classification, ensuring the objectivity and scenario relevance of the classification. More innovatively, it combines pipeline spatial topology to distinguish between continuous high-risk sections and isolated high-risk points, and generates targeted control instructions such as structural maintenance and enhanced monitoring, which are distinctly different and spatially oriented. This results in a spatialized maintenance work order that prioritizes risks, clarifies action locations and specific measures, rather than a general risk report. This significantly improves the accuracy, operability, and resource utilization efficiency of indoor gas safety management.

[0035] This application provides a method for managing indoor gas leak risks based on coupling analysis, including the following steps: S1. Acquire instantaneous gas flow time-series data, gas pipeline external microenvironment data, and gas pipeline spatial status data; S2. Based on instantaneous gas flow time series data and gas pipeline spatial state data, extract the internal damage risk characteristics of each location in the gas pipeline; S3. Based on the external microenvironment data and spatial state data of the gas pipeline, extract the external damage risk characteristics of each location of the gas pipeline. S4. Based on the internal damage risk characteristics and external damage risk characteristics of each location in the gas pipeline, analyze the coupled damage risk at each location in the gas pipeline. S5. Based on the spatial status data of the gas pipeline, assess the fire hazard level at various locations along the gas pipeline. S6. Based on the coupled damage risk and fire hazard level at various locations of the gas pipeline, generate an indoor gas leak risk management plan.

[0036] This application provides an electronic device, including a memory, a processor, and a communication bus; the memory and the processor are connected via the communication bus; the memory stores an indoor gas leak risk management method based on coupling analysis that can be loaded and executed by the processor as provided in the above embodiments.

[0037] The memory can be used to store instructions, programs, code, code sets, or instruction sets; the memory may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function, and instructions for implementing the indoor gas leak risk control method based on coupling analysis provided in the above embodiments, etc.; the data storage area may store data involved in the indoor gas leak risk control method based on coupling analysis provided in the above embodiments, etc.

[0038] The processor may include one or more processing cores; the processor executes or runs instructions, programs, code sets or instruction sets stored in memory, calls data stored in memory, and performs various functions and processes data in this application; the processor may be at least one of the following: Application-Specific Integrated Circuit (ASIC), Digital Signal Processor (DSP), Digital Signal Processing Device (DSPD), Programmable Logic Device (PLD), Field Programmable Gate Array (FPGA), Central Processing Unit (CPU), controller, microcontroller and microprocessor; it is understood that for different devices, the electronic device used to implement the above processor functions may also be other, and the embodiments of this application do not specifically limit it.

[0039] A communication bus may include a path for transmitting information between the aforementioned components; the communication bus may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc.; the communication bus may be divided into address bus, data bus, control bus, etc.

[0040] This application provides a computer-readable storage medium storing a computer program that can be loaded by a processor and executed as described in the above embodiments, which is a method for managing indoor gas leak risks based on coupling analysis.

[0041] In this embodiment, a computer-readable storage medium can be a tangible device that holds and stores instructions used by an instruction execution device; a computer-readable storage medium can be, but is not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any combination thereof; specifically, a computer-readable storage medium can be a portable computer disk, a hard disk, a USB flash drive, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital multifunction disc (DVD), a memory stick, a floppy disk, an optical disk, a magnetic disk, a mechanical encoding device, or any combination thereof.

[0042] The terms “comprising,” “including,” or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0043] The above description is merely a preferred embodiment of this application and an explanation of the technical principles used. Those skilled in the art should understand that the scope of this application is not limited to the technical solutions formed by a specific combination of the above-mentioned technical features, but should also cover other technical solutions formed by any combination of the above-mentioned technical features or their equivalent features without departing from the foregoing application concept; for example, technical solutions formed by replacing the above-mentioned features with (but not limited to) technical features with similar functions applied in this application.

Claims

1. An indoor gas leak risk management system based on coupling analysis, characterized in that, The system includes: The raw data acquisition module is used to acquire instantaneous gas flow time-series data, gas pipeline external microenvironment data, and gas pipeline spatial state data; The internal risk extraction module is used to extract the internal damage risk characteristics of various locations in the gas pipeline based on instantaneous gas flow time-series data and gas pipeline spatial state data. The external risk extraction module is used to extract the external damage risk characteristics of various locations of the gas pipeline based on the external microenvironment data and spatial state data of the gas pipeline. The coupled risk analysis module is used to analyze the coupled damage risk at each location of the gas pipeline based on the internal damage risk characteristics and external damage risk characteristics at each location of the gas pipeline. The hazard assessment module is used to assess the fire hazard level of various locations along the gas pipeline based on spatial status data. The control scheme generation module is used to generate indoor gas leak risk control schemes based on the coupled damage risk and fire hazard level at various locations of the gas pipeline.

2. The indoor gas leak risk management system based on coupling analysis according to claim 1, characterized in that, The acquisition of instantaneous gas flow time-series data, gas pipeline external microenvironment data, and gas pipeline spatial state data includes the following specific contents: Acquiring instantaneous gas flow time-series data specifically includes: collecting cumulative gas consumption data recorded at fixed sampling intervals from smart gas meters, performing time-series difference calculation on the cumulative gas consumption data, and generating instantaneous gas volume flow time-series data; Acquiring spatial status data of gas pipelines specifically includes: obtaining the topological structure information of the gas pipeline system, the spatial coordinates of the inner diameter and centerline of the gas pipeline from the as-built drawings of the gas pipeline of the target building; dividing the gas pipeline into continuous equal-length gas pipe segments with the minimum physical length of all straight pipe segments in the pipeline system as the step size, generating a unique identifier for each gas pipe segment and associating it with its spatial geometric parameters; and simultaneously reading and recording the minimum allowable operating flow rate, maximum allowable operating flow rate, critical ambient temperature for corrosion of the gas pipeline material, relative humidity, spatial coordinates of all fixed ignition sources, and the internal net volume of the minimum enclosed structure of each gas pipe segment from the design documents of the gas pipeline system and the building in which it is located. Acquiring external microenvironmental data for gas pipelines specifically includes: deploying multiple temperature and humidity sensors within the space where the gas pipeline is located, collecting raw time-series monitoring data of ambient temperature and humidity at the same sampling frequency and synchronized start and end times as the gas flow data in step S11; associating the monitoring data of each sensor with spatial coordinates to form a spatially labeled microenvironmental monitoring dataset; and using an inverse distance weighted spatial interpolation algorithm to calculate the local ambient temperature time-series data and local ambient humidity time-series data corresponding to each gas pipeline segment based on the centerline coordinates of each gas pipeline segment and the spatially labeled monitoring dataset, thus forming an external microenvironmental dataset for the gas pipeline.

3. The indoor gas leak risk management system based on coupling analysis according to claim 2, characterized in that, The method for extracting internal damage risk characteristics at various locations within a gas pipeline based on instantaneous gas flow time-series data and spatial state data of the gas pipeline includes the following specific details: Based on instantaneous gas flow time series data and gas pipeline spatial state data, extract gas flow velocity time series data in each gas pipeline segment; Based on the gas flow velocity time series data and gas pipeline spatial state data in each gas pipeline segment, a coupling method based on the hyperbolic tangent function is used to extract the internal damage risk characteristics of each location in the gas pipeline.

4. The indoor gas leak risk management system based on coupling analysis according to claim 3, characterized in that, The method for extracting external damage risk characteristics at various locations of the gas pipeline based on external micro-environment data and spatial state data of the gas pipeline includes the following specific content: Based on the external microenvironment data and spatial state data of the gas pipeline, the high temperature and high humidity co-exposure factor and temperature and humidity co-fluctuation factor of each gas pipeline segment are extracted. Based on the high temperature and high humidity co-exposure factor and temperature and humidity co-fluctuation factor of each gas pipeline segment, an exponentially modulated co-risk model is used to extract the external damage risk characteristics of each location in the gas pipeline.

5. The indoor gas leak risk management system based on coupling analysis according to claim 4, characterized in that, The analysis of coupled damage risk at various locations along a gas pipeline, based on the internal and external damage risk characteristics at each location, includes the following specific aspects: For each gas pipeline segment, obtain the corresponding internal damage risk characteristic value and external damage risk characteristic value; Based on the obtained internal damage risk characteristic values ​​and external damage risk characteristic values, the coupled damage risk value of each gas pipeline segment is calculated.

6. The indoor gas leak risk management system based on coupling analysis according to claim 5, characterized in that, The assessment of the fire hazard level at various locations along the gas pipeline, based on spatial status data, includes the following specific aspects: Based on the spatial status data of gas pipelines, the ignition source proximity factor and ventilation limitation factor of each gas pipeline segment are extracted. Based on the ignition source proximity factor and ventilation limitation factor of each gas pipeline segment, the fire hazard level value of each gas pipeline segment is extracted.

7. The indoor gas leak risk management system based on coupling analysis according to claim 6, characterized in that, The indoor gas leak risk management plan, based on the coupled damage risk and fire hazard level at various locations of the gas pipeline, includes the following steps: Based on the coupled damage risk value and fire hazard level value at each location of the gas pipeline, the risk level of all gas pipeline sections is determined. Based on the risk level assessment results of all gas pipeline sections, an indoor gas leak risk management plan is generated.

8. A method for indoor gas leak risk management based on coupling analysis, applied to the indoor gas leak risk management system based on coupling analysis as described in any one of claims 1-6, characterized in that, Includes the following steps: S1. Acquire instantaneous gas flow time-series data, gas pipeline external microenvironment data, and gas pipeline spatial status data; S2. Based on instantaneous gas flow time series data and gas pipeline spatial state data, extract the internal damage risk characteristics of each location in the gas pipeline; S3. Based on the external microenvironment data and spatial state data of the gas pipeline, extract the external damage risk characteristics of each location of the gas pipeline. S4. Based on the internal damage risk characteristics and external damage risk characteristics of each location in the gas pipeline, analyze the coupled damage risk at each location in the gas pipeline. S5. Based on the spatial status data of the gas pipeline, assess the fire hazard level at various locations along the gas pipeline. S6. Based on the coupled damage risk and fire hazard level at various locations of the gas pipeline, generate an indoor gas leak risk management plan.

9. An electronic device comprising a processor and a memory, characterized in that, The memory stores a computer program that can be called by a processor; the processor executes the indoor gas leak risk control method based on coupling analysis as described in claim 8 by calling the computer program stored in the memory.

10. A computer-readable storage medium storing instructions, characterized in that, When the instructions are executed on a computer, the computer performs the indoor gas leak risk management method based on coupling analysis as described in claim 8.

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