A method and system for detecting harmful gases based on smart manhole covers

Abstract

The application discloses a harmful gas detection method and system based on a smart well lid, and relates to the field of environmental monitoring.The method is applied to a gas monitoring system, and the method comprises the following steps: acquiring pipeline monitoring data of a region governed by a target well lid, wherein the pipeline monitoring data comprises a first underground pipeline network diagram, a plurality of diffusion nodes and gas distribution data; generating a plurality of feasible diffusion paths according to the pipeline monitoring data; predicting harmful gas prediction data of the plurality of feasible diffusion paths by adopting a three-dimensional convection diffusion model; correcting the first underground pipeline network diagram according to the gas distribution data and the harmful gas prediction data of the plurality of feasible diffusion paths, so as to obtain a second underground pipeline network diagram; and performing gas diffusion analysis on the second underground pipeline network, so as to obtain a real harmful gas distribution diagram of the region governed by the target well lid. By implementing the technical scheme provided in the application, the problem that early warning of harmful gas is not timely due to frequent blockage and leakage of underground pipelines is solved.

Description

Technical Field

[0001] This application relates to the technical field of environmental monitoring, specifically to a method and system for detecting harmful gases based on smart manhole covers. Background Technology

[0002] With the rapid development of cities, smart manhole covers, as a key node in the intelligent management of urban underground pipe networks, are crucial for the effective detection of harmful gases in manholes.

[0003] Currently, the detection of harmful gases in underground pipelines mainly includes two aspects: concentration detection and distribution area detection. Concentration detection involves installing sensors inside the pipeline to detect the concentration data of harmful gases, and then sending the concentration data to the gas monitoring system of the smart manhole cover. Distribution area detection involves performing gas diffusion analysis on the concentration data of the area covered by the manhole cover to obtain the distribution of harmful gases in that area. If the smart manhole cover detects that the concentration of harmful gases exceeds the standard or the distribution area of ​​harmful gases is too large, the smart manhole cover sends an early warning information to the municipal facilities management system to prevent the further spread of harmful gases and avoid casualties.

[0004] However, underground pipelines often experience blockages and leaks, making it impossible to accurately determine the flow and distribution of harmful gases within the pipelines, thus resulting in untimely warnings of harmful gases. Summary of the Invention

[0005] To address the problem of untimely warnings of harmful gases caused by frequent blockages and leaks in underground pipelines, this application provides a method and system for detecting harmful gases based on smart manhole covers.

[0006] In a first aspect, this application provides a method for detecting harmful gases based on smart manhole covers, applied to a gas monitoring system, the method comprising:

[0007] Acquire pipeline monitoring data for the area covered by the target manhole cover, including a first underground pipeline network map, multiple diffusion nodes, and gas distribution data;

[0008] Based on the pipeline monitoring data, multiple feasible diffusion paths are generated;

[0009] A three-dimensional convection-diffusion model was used to predict the hazardous gas prediction data for multiple feasible diffusion paths.

[0010] Based on the gas distribution data and the hazardous gas prediction data of multiple feasible diffusion paths, the first underground pipeline map is corrected to obtain the second underground pipeline map.

[0011] Gas diffusion analysis was performed on the second underground pipeline network to obtain the actual distribution map of harmful gases in the area governed by the target manhole cover.

[0012] Optionally, based on the pipeline monitoring data, multiple feasible diffusion paths are generated, specifically:

[0013] Based on the gas distribution data of the first underground pipeline network map, the total concentration of harmful gases at multiple diffusion nodes within their respective preset areas is calculated.

[0014] Multiple initial diffusion nodes are determined based on the total concentration of harmful gases at the multiple diffusion nodes;

[0015] Acquire pipeline environmental data, including temperature, humidity, and wind speed;

[0016] Based on the pipeline environment data, calculate the diffusion range of the multiple initial diffusion nodes;

[0017] Based on the multiple initial diffusion nodes and their corresponding diffusion ranges, multiple feasible diffusion paths are generated.

[0018] Optionally, the step of using a three-dimensional convection-diffusion model to predict hazardous gas prediction data for multiple feasible diffusion paths further includes:

[0019] Obtain pipeline data for a first feasible diffusion path, the pipeline data including the pipeline inner diameter and the pipeline inner wall roughness, and the first feasible diffusion path is any one of the multiple feasible diffusion paths;

[0020] Based on the pipeline data, calculate the turbulent diffusion coefficient of the first feasible diffusion path;

[0021] Based on the turbulent diffusion coefficient, the diffusion coefficient of the three-dimensional convection diffusion model is adjusted, and the adjusted three-dimensional convection diffusion model is used to predict the harmful gas prediction data of the first feasible diffusion path.

[0022] Optionally, based on the gas distribution data and the hazardous gas prediction data of multiple feasible diffusion paths, the first underground pipeline map is corrected to obtain a second underground pipeline map, specifically as follows:

[0023] Harmful gas monitoring data of multiple diffusion nodes on a second feasible diffusion path are extracted from the gas distribution data, wherein the second feasible diffusion path is any one of the multiple feasible diffusion paths;

[0024] Based on the hazardous gas monitoring data of multiple diffusion nodes along the second feasible diffusion path, a monitoring data slope curve is constructed.

[0025] Based on the hazardous gas prediction data of multiple diffusion nodes on the second feasible diffusion path, a prediction data slope curve is constructed;

[0026] Calculate the similarity between the slope curve of the monitored data and the slope curve of the predicted data;

[0027] If the similarity is less than a preset similarity threshold, then the second feasible diffusion path is determined to be an abnormal diffusion path;

[0028] The abnormal diffusion path is marked on the first underground pipeline map to obtain the second underground pipeline map.

[0029] Optionally, calculating the similarity between the slope curve of the monitored data and the slope curve of the predicted data specifically includes:

[0030] Calculate the fluctuation coefficient of the slope curve of the monitored data;

[0031] Based on the fluctuation coefficient, determine the initial fractal size of the monitoring data slope curve and the predicted data slope curve;

[0032] Based on the initial fractal size, calculate the first fractal dimension of the monitoring data slope curve and the second fractal dimension of the predicted data slope curve;

[0033] The similarity between the slope curve of the monitored data and the slope curve of the predicted data is obtained by subtracting the first fractal dimension from the second fractal dimension.

[0034] Optionally, gas diffusion analysis is performed on the second underground pipeline network to obtain a true distribution map of harmful gases in the area covered by the target manhole cover, specifically including:

[0035] Calculate the variance of the slope curve of the monitoring data of the abnormal diffusion path;

[0036] Count the number of diffusion nodes and the path length of the abnormal diffusion path;

[0037] Based on the number of diffusion nodes, path length, and curve variance of the abnormal diffusion path, adjust the gas diffusion parameters of the preset diffusion gas simulation model in the abnormal diffusion path.

[0038] The monitoring data of harmful gases from multiple feasible diffusion paths are input into the preset diffusion gas simulation model to obtain the real distribution map of harmful gases in the area governed by the target manhole cover.

[0039] Secondly, this application provides a hazardous gas detection system based on a smart manhole cover. The system is a gas monitoring system, comprising an acquisition module, a processing module, and a transmission module, wherein:

[0040] The acquisition module is used to acquire pipeline monitoring data of the area under the jurisdiction of the target manhole cover. The pipeline monitoring data includes a first underground pipeline network map, multiple diffusion nodes, and gas distribution data.

[0041] The processing module is used to generate multiple feasible diffusion paths based on the pipeline monitoring data; use a three-dimensional convection diffusion model to predict the hazardous gas prediction data of the multiple feasible diffusion paths; and correct the first underground pipeline network map based on the gas distribution data and the hazardous gas prediction data of the multiple feasible diffusion paths to obtain a second underground pipeline network map.

[0042] The sending module is used to perform gas diffusion analysis on the second underground pipeline network to obtain a true distribution map of harmful gases in the area governed by the target manhole cover.

[0043] Thirdly, this application provides an electronic device including a processor, a memory, a user interface, and a network interface. The memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory to cause the electronic device to perform the method as described in any one of the first aspects.

[0044] Fourthly, this application provides a computer-readable storage medium storing instructions that, when executed, perform the method described in any one of the first aspects.

[0045] In summary, one or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages:

[0046] This application generates multiple feasible diffusion paths for harmful gases within the pipeline based on pipeline monitoring data within the area covered by the target manhole cover. Then, a three-dimensional convection-diffusion model is used to predict harmful gas data for these paths. It should be noted that this predicted data assumes no blockage or leakage in the pipeline. Theoretically, the trend of the predicted harmful gas data along a feasible diffusion path should be consistent with the trend of the original harmful gas monitoring data along that path. Therefore, based on this principle, abnormal diffusion paths can be identified among the multiple feasible diffusion paths, thus determining the abnormal pipeline. The original underground pipeline network map is then modified, and the diffusion parameters of harmful gases at the abnormal pipeline are adjusted to simulate the abnormal pipeline situation. Finally, the original gas distribution data is input into the pre-set diffusion gas simulation model after adjusting the diffusion parameters for gas diffusion analysis, resulting in a realistic harmful gas distribution map. This effectively solves the problem of untimely early warnings caused by pipeline blockage or leakage, significantly improving the accuracy and real-time performance of harmful gas diffusion prediction. Attached Figure Description

[0047] Figure 1 This is a schematic flowchart of a hazardous gas detection method based on a smart manhole cover provided in an embodiment of this application.

[0048] Figure 2 This is a schematic diagram of a hazardous gas detection system based on a smart manhole cover, provided in an embodiment of this application.

[0049] Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application.

[0050] Explanation of reference numerals in the attached drawings: 1. Acquisition module; 2. Processing module; 3. Transmission module; 300. Electronic device; 301. Processor; 302. Communication bus; 303. User interface; 304. Network interface; 305. Memory. Detailed Implementation

[0051] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments.

[0052] In the description of the embodiments of this application, the words "for example" or "for instance" are used to indicate examples, illustrations, or explanations. Any embodiment or design that is described as "for example" or "for instance" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design options. Rather, the use of the words "for example" or "for instance" is intended to present the relevant concepts in a specific manner.

[0053] In the description of the embodiments of this application, the term "multiple" means two or more. For example, multiple systems means two or more systems, and multiple screen terminals means two or more screen terminals. Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the indicated technical features. Thus, a feature defined with "first" or "second" may explicitly or implicitly include one or more of that feature. The terms "comprising," "including," "having," and variations thereof all mean "including but not limited to," unless otherwise specifically emphasized.

[0054] In the process of rapid urban development, underground pipe networks are like the "lifeline" of a city, and smart manhole covers, as a key node on this lifeline, are particularly important for effectively detecting harmful gases underground. They are not only related to the normal operation of urban infrastructure, but also closely related to the safety of citizens.

[0055] Currently, the detection of hazardous gases in underground pipelines mainly focuses on two core aspects: concentration detection and distribution area detection. For concentration detection, various sensors are typically installed at key nodes within the pipeline. These sensors can capture hazardous gas concentration data in real time and quickly transmit the data to the smart manhole cover's gas monitoring system. For example, in sewage pipes in some older urban areas, hydrogen sulfide sensors can accurately detect the concentration of hydrogen sulfide gas, providing timely feedback once the concentration changes. Distribution area detection, on the other hand, involves in-depth analysis based on concentration data. By analyzing the gas diffusion of concentration data within the area covered by the manhole cover, the distribution of hazardous gases in that area can be mapped. For instance, in an underground pipeline network beneath a bustling commercial street, when a hazardous gas leak occurs in one pipe, distribution area detection technology can quickly determine the extent of the hazardous gas's spread—whether it's limited to a few blocks or has spread to a larger area. When the smart manhole cover detects that the hazardous gas concentration exceeds the standard, or that the distribution area of ​​the hazardous gas exceeds the safe range, it will immediately send an early warning to the municipal infrastructure management system to prevent further spread of the hazardous gas and potential casualties.

[0056] However, underground pipelines frequently experience blockages and leaks, making gas flow unpredictable. If a pipeline is blocked, harmful gases accumulate upstream of the blockage point, unable to flow and diffuse along their normal paths. This renders gas flow models based on normal conditions ineffective, making it difficult to accurately determine flow patterns and distribution areas. If a pipeline leaks, harmful gases escape from the leak point into the surrounding environment, making gas distribution even more complex and unpredictable. Both situations severely interfere with the detection and analysis of harmful gases, leading to delayed warnings and posing a threat to urban safety.

[0057] To address the aforementioned problems, this application provides a method for detecting harmful gases based on smart manhole covers. This method is applied to gas monitoring systems, such as... Figure 1 As shown, the method includes steps S101 to S105, which are as follows:

[0058] S101. Obtain pipeline monitoring data for the area covered by the target manhole cover. The pipeline monitoring data includes the first underground pipeline network map, multiple diffusion nodes, and gas distribution data.

[0059] In the above steps, various sensors for gas concentration, pressure, and temperature are installed at appropriate intervals within the underground pipelines in the area covered by the target manhole cover to obtain gas distribution data. Each sensor then transmits the data to the gas monitoring system in real time via a wireless communication module. Finally, the gas monitoring system performs preprocessing on the raw data, including noise reduction and signal format conversion. The preprocessed data is then integrated and matched with a pre-stored first underground pipeline network map and its diffusion node information to form complete pipeline monitoring data. The first underground pipeline network map is a pre-planned route map of the pipeline's normal operation during construction, and the diffusion nodes are the branch nodes of the pipeline.

[0060] S102. Based on pipeline monitoring data, generate multiple feasible diffusion paths.

[0061] In the above steps, the boundary conditions of the feasible diffusion path are determined by the first underground pipeline network map. Then, multiple diffusion nodes are arranged and combined to obtain multiple feasible diffusion paths. During the arrangement and combination process, it is necessary to ensure that each feasible diffusion path is connected.

[0062] In one possible implementation, since the gas diffuses from a high-concentration area to a low-concentration area, some of the pre-obtained multiple feasible diffusion paths may be unreasonable (i.e., diffusion paths from low concentration to high concentration) or too long or too short. These diffusion paths will consume a lot of analytical resources, thereby reducing the prediction efficiency of subsequent hazardous gas diffusion data. Therefore, this application needs to screen multiple feasible diffusion paths to obtain multiple feasible diffusion paths that conform to the actual situation of the first underground pipeline network map.

[0063] Specifically, the process involves: First, analyzing the gas distribution data of the first underground pipeline network map, and statistically determining the total concentration of harmful gases at multiple diffusion nodes within their respective predetermined areas. The predetermined area can be understood as the effective detection range of the concentration sensors installed at the diffusion nodes. Then, performing cluster analysis on the total concentration of harmful gases at multiple diffusion nodes yields multiple concentration clusters. Next, calculating the average concentration of these clusters, and finally selecting multiple diffusion nodes from the cluster with the highest average concentration as the initial diffusion nodes. Considering that the diffusion of harmful gases is related to pipeline environmental data, including temperature, humidity, and wind speed, this application calculates the diffusion range of multiple initial diffusion nodes based on the pipeline environmental data. This application proposes the following formula for calculation:

[0064]

[0065] Where L is the diffusion radius, D is the diffusion coefficient of harmful gases, t is the preset diffusion time, H is the humidity, T is the temperature, and V is the wind speed.

[0066] In the above formula, humidity T, temperature H, and wind speed V are all data divided by their respective standard parameters, i.e., T = T0 / T 标 H = H0 / H 标 V=V0 / V 标 T0, H0, and V0 represent the current ambient temperature, humidity, and wind speed, respectively. 标 H 标 V 标 Given the ambient temperature, humidity, and wind speed under standard conditions; then based on Fick's Law... The diffusion radius of harmful gases under ideal conditions can be calculated. However, the pipeline environment is complex and variable, and the diffusion of harmful gases does not always conform to ideal conditions. For example, as the temperature rises, the thermal motion of gas molecules intensifies, increasing the diffusion coefficient; as humidity rises, water molecules hinder gas diffusion, decreasing the diffusion coefficient; and as wind speed increases, the mobility of gas molecules accelerates, increasing the diffusion coefficient. Based on these characteristics, the initial diffusion coefficient of harmful gases is adjusted to make the diffusion pattern of harmful gases more consistent with the current diffusion environment of the pipeline. The preset diffusion time is related to the location of the manhole cover. If the manhole cover is located in a densely populated area, the preset diffusion time is set to be shorter, generally 8 hours; if the manhole cover is located in a non-residential area, the preset diffusion time can be set to be longer, generally 3 days.

[0067] After determining the diffusion range of the initial diffusion node using the above formula, the multiple feasible diffusion paths obtained in advance are sequentially screened to obtain multiple feasible diffusion paths with high analytical value, providing reliable data support for subsequent diffusion analysis of harmful gases.

[0068] S103. A three-dimensional convection-diffusion model is used to predict the data of harmful gases along multiple feasible diffusion paths.

[0069] In the above steps, the flow of harmful gases in underground pipelines is usually the result of the combined effects of convection and diffusion. The three-dimensional convection-diffusion equation integrates both convection and diffusion terms, simultaneously considering the transport process caused by overall gas flow (convection) and concentration gradient (diffusion). When a certain flow velocity exists within the pipeline, gas diffusion is not merely molecular diffusion; it also involves convective transport along with the airflow. The three-dimensional convection-diffusion equation can well describe this complex process, better reflecting the actual situation of harmful gas flow in underground pipelines and facilitating accurate analysis of gas propagation and distribution within the pipeline. Therefore, this application, based on current gas distribution data within the pipeline, uses a three-dimensional convection-diffusion model to predict harmful gas prediction data for multiple feasible diffusion paths. The gas distribution data consists of data uploaded by all sensors in the first underground pipeline network diagram, and the harmful gas prediction data is the harmful gas distribution data on feasible diffusion paths after a preset diffusion time t. The three-dimensional convection-diffusion equation is as follows:

[0070]

[0071] Where C represents the spatial concentration of the harmful gas, and t represents the preset diffusion time. Let be the velocity vector of the harmful gas. is the diffusion coefficient of harmful gases, and S is the source-sink term, representing the source of the substance's generation or consumption.

[0072] In the above formula, C can also be expressed as C(x, y, z, t), which describes the distribution of the concentration of harmful gases at different times and locations. It is the partial derivative of concentration C with respect to t, representing the rate of change of concentration over time, reflecting how fast the concentration of a substance changes with time at a fixed location; It can also be expressed as (x, y, z, t) represents the flow velocity of the fluid in various directions in space, describing the fluid's motion state. The convection term represents the mass transport caused by the macroscopic flow (convection) of the fluid, i.e., the movement of matter from one location to another by the fluid, resulting in changes in the concentration distribution. The gradient operator is specifically represented as follows: ; The Laplace operator for C describes the second-order rate of change of concentration in space, reflecting the diffusion trend of substances in space caused by concentration gradients, i.e., substances diffuse from high-concentration areas to low-concentration areas, making the concentration distribution tend to be uniform. The left side of the formula represents the total rate of change of concentration, including concentration changes due to time variations and fluid convection. The right side represents the causes of concentration changes, i.e., diffusion and the influence of sources and sinks. According to the law of conservation of energy, the increase or decrease in concentration at a point per unit time is equal to the change in concentration at that point caused by diffusion plus the change in concentration caused by source and sink terms. Therefore, based on this formula, multiple feasible diffusion paths can be predicted for hazardous gas data after a preset diffusion time from current gas distribution data.

[0073] In one possible implementation, the aforementioned three-dimensional convection-diffusion model can predict the ideal diffusion state of harmful gases. However, in reality, the flow of harmful gases within a pipe is turbulent, which means that the parameters of the pipe itself will affect the flow state of the harmful gases. For example, if the inner wall of the pipe is relatively rough, it will slow down the diffusion rate of the harmful gases; the larger the inner diameter of the pipe, the faster the diffusion rate. Therefore, in the three-dimensional convection-diffusion model, in addition to considering the diffusion coefficient of the harmful gases themselves, the turbulent diffusion coefficient also needs to be considered. Based on this characteristic, this application obtains pipe data on feasible diffusion paths, including the pipe inner wall roughness and the pipe inner diameter. Then, based on the pipe data, the turbulent diffusion coefficient of the feasible diffusion path is calculated. The specific calculation method is as follows:

[0074]

[0075] in, The turbulent diffusion coefficient is... The flow rate of harmful gases. For the roughness of the inner wall of the pipe, The inner diameter of the pipe. This is a correction factor.

[0076] This formula provides a method for calculating the turbulent diffusion coefficient, characterizing its variation under the combined effects of harmful gas velocity, pipe wall roughness, and pipe diameter. However, this calculation is not accurate and requires further adjustment using a correction factor k. This correction factor can be determined by directly solving the Navier-Stokes equations, which accurately calculate turbulent motion at all scales in the flow field, thereby allowing the calculation of the correction factor k. After correction by the correction factor k, the turbulent environment of the current pipeline is better matched, thus obtaining a more accurate turbulent diffusion coefficient.

[0077] After obtaining the turbulent diffusion coefficient, the diffusion coefficient of harmful gases in the three-dimensional convection diffusion equation is added to the turbulent diffusion coefficient to obtain a new diffusion coefficient. Then, the predicted data of harmful gases for multiple feasible diffusion paths are calculated.

[0078] S104. Based on the gas distribution data and the hazardous gas prediction data of multiple feasible diffusion paths, the first underground pipeline map is corrected to obtain the second underground pipeline map.

[0079] In the above steps, hazardous gas monitoring data of multiple diffusion nodes along multiple feasible diffusion paths are extracted from gas distribution data. Then, the hazardous gas monitoring data of multiple diffusion nodes along multiple feasible diffusion paths are used to construct a monitoring data slope curve. Then, the hazardous gas prediction data of multiple diffusion nodes along multiple feasible diffusion paths are used to construct a prediction data slope curve. It should be noted that both the monitoring data slope curve and the prediction data slope curve reflect the concentration change law of hazardous gases along the feasible diffusion path. If there is no blockage or leakage on the feasible diffusion path, the similarity between the monitoring data slope curve and the prediction data slope curve is high. Based on this characteristic, this application calculates the similarity between the monitoring data slope curve and the prediction data slope curve. If the similarity is less than a preset similarity threshold, the feasible diffusion path can be determined to be an abnormal diffusion path. At this time, the feasible diffusion path is marked on the first underground pipeline map to obtain the second underground pipeline map.

[0080] In one possible implementation, when constructing the monitoring data slope curve, the diffusion of harmful gases in underground pipe networks is affected by various complex factors, and the monitoring data slope curve and the predicted data slope curve may exhibit highly irregular and self-similar characteristics. Therefore, it is impossible to directly use curve similarity algorithms to determine the similarity between the monitoring data slope curve and the predicted data slope curve. Therefore, this application determines the similarity between the two based on fractal theory and box counting. This method does not directly calculate the similarity between the two curves, but rather represents the characteristics of the two curves by the number of boxes of the same size. For example, the monitoring data slope curve can be represented by 3000 square boxes of size r, and the predicted data slope curve can be represented by 3100 square boxes of size d. Then, the number of boxes for each curve is logarithmically fitted to the box size to obtain the fractal dimension of each curve. Finally, the similarity between the two curves is obtained by calculating the absolute difference between their fractal dimensions. The smaller the absolute difference, the higher the similarity; the larger the absolute difference, the lower the similarity.

[0081] Specifically, the process involves: First, calculating the fluctuation coefficient of the monitoring data slope curve. The fluctuation coefficient can be any one of the standard deviation, variance, or mean. The fluctuation coefficient reflects the irregularity of the monitoring data slope curve. Then, based on the fluctuation coefficient, determining the initial fractal size of the monitoring data slope curve and the predicted data slope curve. If the irregularity is high, the initial fractal size is small; if the irregularity is low, the initial fractal size is large. The initial fractal size is the size of the box. The purpose of this step is to improve the curve fractal efficiency. Then, based on the initial fractal size, performing fractal processing on the monitoring data slope curve and the predicted data slope curve respectively, obtaining the corresponding number of boxes and box sizes. It should be explained that fractal processing represents the curve using multiple boxes of the same size, each box containing a part of the curve. The process involves gradually splitting from the box of the initial fractal size. With each split, the size of the box is reduced by half until the last split box can no longer contain the curve, at which point the splitting stops.

[0082] Finally, the number of boxes and box sizes corresponding to the slope curves of the monitored data and the predicted data are calculated. Then, a logarithmic fit is performed on the number of boxes and box sizes of the monitored data slope curve to obtain the first fractal dimension. Similarly, a logarithmic fit is performed on the number of boxes and box sizes of the predicted data slope curve to obtain the second fractal dimension. The logarithmic fit uses the following formula:

[0083]

[0084] Where R is the fractal dimension. () represents the number of boxes. This refers to the box dimensions.

[0085] The above formula indicates that the number of boxes required to cover a fractal object changes as the box size approaches 0, thus quantitatively describing the complexity of the fractal object.

[0086] Finally, the similarity between the slope curve of the monitored data and the slope curve of the predicted data is obtained by calculating the absolute difference between the first fractal dimension and the second fractal dimension.

[0087] S105. Perform gas diffusion analysis on the second underground pipeline network to obtain the actual distribution map of harmful gases in the area covered by the target manhole cover.

[0088] In the above steps, when performing gas diffusion analysis on underground pipeline networks, a pre-established diffusion gas simulation model is generally used. However, this model is based on a normally operating underground pipeline network. When blockages or leaks occur in the underground pipeline network, the analysis results will be inaccurate. Re-establishing the diffusion gas simulation model would be time-consuming. Therefore, this application aims to improve the robustness of the diffusion gas simulation model to save the time cost of re-establishing it. It identifies abnormal diffusion paths in the second underground pipeline network diagram, calculates the variance of the slope curve of the abnormal diffusion path's monitoring data, and determines the degree of fluctuation of the abnormal diffusion path. Then, it counts the number of diffusion nodes and the path length of the abnormal diffusion path. Based on the number of diffusion nodes, path length, and curve variance of the abnormal diffusion path, it adjusts the gas diffusion parameters of the pre-established diffusion gas simulation model for the abnormal diffusion path. The specific calculation formula is as follows:

[0089]

[0090] Where S is the diffusion parameter for adjusting the abnormal diffusion path. These are the initial diffusion parameters along the abnormal diffusion path. Let L be the number of diffusion nodes in the abnormal diffusion path, and L be the path length of the abnormal diffusion path. The variance of the slope curve for monitoring data of abnormal diffusion paths. This is the curve average equation of the slope curves for monitoring data of multiple feasible diffusion paths.

[0091] In the above formula, This represents the node density per unit length of an abnormal diffusion path. A higher node density per unit length indicates a more complex gas diffusion situation, and a greater impact of any anomaly on gas diffusion. In such cases, a larger diffusion coefficient is needed to ensure timely warnings under complex conditions. However, relying solely on node density per unit length may lead to misjudgments. Therefore, the diffusion coefficient needs to be further adjusted by considering the gas concentration distribution within the pipeline. This is achieved by comparing the variance of the slope curve of the abnormal diffusion path with the average equation of the slope curves of multiple feasible diffusion paths. Specifically, when… > This means that the fluctuations in the monitoring data of the abnormal diffusion path are more drastic than the average, indicating that the gas diffusion process on that path is more complex, multiplied by the density of nodes per unit length. This increases the density of nodes per unit length after adjustment, highlighting the enhanced influence of node density on the path due to complex diffusion conditions; when At this time, it means that the fluctuation of the abnormal diffusion path monitoring data is relatively small and the diffusion process is relatively simple. At this time, multiplying by This reduces the density of nodes per unit length after adjustment, thus appropriately mitigating the impact of node density on the path.

[0092] Finally, the initial diffusion parameters in the preset diffusion gas simulation model are modified to adjust the diffusion parameters, so that the preset diffusion gas simulation model can improve its attention to abnormal diffusion paths. Then, the hazardous gas monitoring data of multiple feasible diffusion paths are input into the preset diffusion gas simulation model to obtain the real hazardous gas distribution map of the area covered by the target manhole cover. At this time, by performing regional distribution analysis and concentration analysis on the real hazardous gas distribution map, timely warning can be given before large-scale hazardous gas leakage.

[0093] Reference Figure 2 This application also provides a hazardous gas detection system based on a smart manhole cover. The system is a gas monitoring system, comprising an acquisition module 1, a processing module 2, and a transmission module 3, wherein:

[0094] Module 1 is used to acquire pipeline monitoring data of the area covered by the target manhole cover. The pipeline monitoring data includes a first underground pipeline network map, multiple diffusion nodes, and gas distribution data.

[0095] Processing module 2 is used to generate multiple feasible diffusion paths based on pipeline monitoring data; use a three-dimensional convection diffusion model to predict the hazardous gas prediction data of multiple feasible diffusion paths; and correct the first underground pipeline network map based on the gas distribution data and the hazardous gas prediction data of multiple feasible diffusion paths to obtain the second underground pipeline network map.

[0096] The sending module 3 is used to perform gas diffusion analysis on the second underground pipeline network to obtain the actual distribution map of harmful gases in the area covered by the target manhole cover.

[0097] In one possible implementation, multiple feasible diffusion paths are generated based on pipeline monitoring data, specifically:

[0098] Based on the gas distribution data of the first underground pipeline network map, the total concentration of harmful gases at multiple diffusion nodes within their respective preset areas is calculated.

[0099] Multiple initial diffusion nodes are determined based on the total concentration of harmful gases at multiple diffusion nodes;

[0100] Acquire pipeline environmental data, including temperature, humidity, and wind speed;

[0101] Based on pipeline environmental data, calculate the diffusion range of multiple initial diffusion nodes;

[0102] Based on multiple initial diffusion nodes and their corresponding diffusion ranges, multiple feasible diffusion paths are generated.

[0103] In one possible implementation, a three-dimensional convection-diffusion model is used to predict hazardous gas prediction data for multiple feasible diffusion paths, specifically including:

[0104] Obtain pipeline data for the first feasible diffusion path, including pipeline inner diameter and pipeline inner wall roughness. The first feasible diffusion path is any one of multiple feasible diffusion paths.

[0105] Based on the pipeline data, calculate the turbulent diffusion coefficient of the first feasible diffusion path;

[0106] Based on the turbulent diffusion coefficient, the diffusion coefficient of the three-dimensional convection diffusion model is adjusted, and the adjusted three-dimensional convection diffusion model is used to predict the harmful gas prediction data of the first feasible diffusion path.

[0107] In one possible implementation, the first underground pipeline map is corrected based on gas distribution data and hazardous gas prediction data for multiple feasible diffusion paths to obtain a second underground pipeline map, specifically:

[0108] Harmful gas monitoring data of multiple diffusion nodes on a second feasible diffusion path are extracted from the gas distribution data. The second feasible diffusion path is any one of multiple feasible diffusion paths.

[0109] Based on the hazardous gas monitoring data of multiple diffusion nodes along the second feasible diffusion path, a monitoring data slope curve is constructed.

[0110] Based on the hazardous gas prediction data of multiple diffusion nodes on the second feasible diffusion path, a slope curve of the prediction data is constructed.

[0111] Calculate the similarity between the slope curve of the monitoring data and the slope curve of the predicted data;

[0112] If the similarity is less than the preset similarity threshold, the second feasible diffusion path is determined to be an abnormal diffusion path.

[0113] The abnormal diffusion path is marked on the first underground pipeline map to obtain the second underground pipeline map.

[0114] In one possible implementation, calculating the similarity between the slope curve of the monitored data and the slope curve of the predicted data specifically includes:

[0115] Calculate the fluctuation coefficient of the slope curve of the monitoring data;

[0116] Based on the fluctuation coefficient, determine the initial fractal size of the slope curve of the monitoring data and the slope curve of the predicted data;

[0117] Based on the initial fractal size, the first fractal dimension of the slope curve of the monitored data and the second fractal dimension of the slope curve of the predicted data are calculated.

[0118] The similarity between the slope curve of the monitored data and the slope curve of the predicted data is obtained by subtracting the first fractal dimension from the second fractal dimension.

[0119] In one possible implementation, gas diffusion analysis is performed on the second underground pipeline network to obtain a true distribution map of harmful gases in the area covered by the target manhole cover, specifically including:

[0120] Calculate the variance of the slope curve of the monitoring data for abnormal diffusion paths;

[0121] Count the number of diffusion nodes and path length of abnormal diffusion paths;

[0122] Based on the number of diffusion nodes, path length, and curve variance of the abnormal diffusion path, adjust the gas diffusion parameters of the preset diffusion gas simulation model in the abnormal diffusion path.

[0123] Harmful gas monitoring data from multiple feasible diffusion paths are input into a preset diffusion gas simulation model to obtain a real harmful gas distribution map of the area covered by the target manhole cover.

[0124] It should be noted that the above embodiments of the apparatus are only illustrated by the division of the above functional modules. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the apparatus and method embodiments provided in the above embodiments belong to the same concept, and the specific implementation process can be found in the method embodiments, which will not be repeated here.

[0125] This application also discloses an electronic device. (See reference...) Figure 3 , Figure 3 This is a schematic diagram of the structure of an electronic device disclosed in an embodiment of this application. The electronic device 300 may include: at least one processor 301, at least one network interface 304, a user interface 303, a memory 305, and at least one communication bus 302.

[0126] The communication bus 302 is used to enable communication between these components.

[0127] The user interface 303 may include a display screen and a camera. Optionally, the user interface 303 may also include a standard wired interface and a wireless interface.

[0128] The network interface 304 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface).

[0129] The processor 301 may include one or more processing cores. The processor 301 connects to various parts of the server using various interfaces and lines, and performs various server functions and processes data by running or executing instructions, programs, code sets, or instruction sets stored in memory 305, and by calling data stored in memory 305. Optionally, the processor 301 may be implemented using at least one hardware form of Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), or Programmable Logic Array (PLA). The processor 301 may integrate one or a combination of several of the following: Central Processing Unit (CPU), Graphics Processing Unit (GPU), and modem. The CPU primarily handles the operating system, user interface, and applications; the GPU is responsible for rendering and drawing the content required for display; and the modem handles wireless communication. It is understood that the modem may also not be integrated into the processor 301 and may be implemented as a separate chip.

[0130] The memory 305 may include random access memory (RAM) or read-only memory. Optionally, the memory 305 may include a non-transitory computer-readable storage medium. The memory 305 may be used to store instructions, programs, code, code sets, or instruction sets. The memory 305 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 (such as touch function, sound playback function, image playback function, etc.), instructions for implementing the above-described method embodiments, etc.; the data storage area may store data involved in the above-described method embodiments, etc. Optionally, the memory 305 may also be at least one storage device located remotely from the aforementioned processor 301. (Refer to...) Figure 3 The memory 305, which serves as a computer storage medium, may include an operating system, a network communication module, a user interface module, and an application program for a method of detecting harmful gases based on smart manhole covers.

[0131] exist Figure 3 In the illustrated electronic device 300, the user interface 303 is mainly used to provide an input interface for the user and acquire user input data; while the processor 301 can be used to call an application program stored in the memory 305 for detecting harmful gases based on smart manhole covers. When executed by one or more processors 301, the electronic device 300 performs one or more of the methods described in the above embodiments. It should be noted that, for the foregoing method embodiments, for the sake of simplicity, they are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, because according to this application, some steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also understand that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0132] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0133] In the various embodiments provided in this application, it should be understood that the disclosed apparatus can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some service interface; the indirect coupling or communication connection between apparatuses or units may be electrical or other forms.

[0134] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0135] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0136] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage device (CMD). Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned memory includes various media capable of storing program code, such as USB flash drives, portable hard drives, magnetic disks, or optical disks.

[0137] The above description is merely an exemplary embodiment of this disclosure and should not be construed as limiting the scope of this disclosure. Any equivalent changes and modifications made in accordance with the teachings of this disclosure shall still fall within the scope of this disclosure. Other embodiments of this disclosure will be readily apparent to those skilled in the art upon consideration of the specification and the disclosure of practical truths.

[0138] This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not described in this disclosure. The specification and embodiments are to be considered exemplary only, and the scope and spirit of this disclosure are defined by the claims.

Claims

1. A method for detecting harmful gases based on smart manhole covers, characterized in that, The method, applied to a gas monitoring system, includes: Acquire pipeline monitoring data for the area covered by the target manhole cover, including a first underground pipeline network map, multiple diffusion nodes, and gas distribution data; Based on the pipeline monitoring data, multiple feasible diffusion paths are generated. A three-dimensional convection-diffusion model was used to predict the hazardous gas prediction data for multiple feasible diffusion paths. Based on the gas distribution data and the hazardous gas prediction data for multiple feasible diffusion paths, the first underground pipeline map is corrected to obtain a second underground pipeline map, which is as follows: Harmful gas monitoring data of multiple diffusion nodes on a second feasible diffusion path are extracted from the gas distribution data, wherein the second feasible diffusion path is any one of the multiple feasible diffusion paths; Based on the hazardous gas monitoring data of multiple diffusion nodes along the second feasible diffusion path, a monitoring data slope curve is constructed. Based on the hazardous gas prediction data of multiple diffusion nodes on the second feasible diffusion path, a prediction data slope curve is constructed; The slope curves of both the monitoring data and the predicted data reflect the concentration variation patterns of harmful gases along feasible diffusion paths. Calculating the similarity between the slope curve of the monitored data and the slope curve of the predicted data specifically includes: Calculate the fluctuation coefficient of the slope curve of the monitored data; Based on the fluctuation coefficient, determine the initial fractal size of the monitoring data slope curve and the predicted data slope curve; Based on the initial fractal size, calculate the first fractal dimension of the monitoring data slope curve and the second fractal dimension of the predicted data slope curve; The similarity between the slope curve of the monitored data and the slope curve of the predicted data is obtained by subtracting the first fractal dimension from the second fractal dimension. If the similarity is less than a preset similarity threshold, then the second feasible diffusion path is determined to be an abnormal diffusion path; The abnormal diffusion path is marked on the first underground pipeline map to obtain the second underground pipeline map; Gas diffusion analysis was performed on the second underground pipeline network to obtain the actual distribution map of harmful gases in the area governed by the target manhole cover.

2. The method according to claim 1, characterized in that, Based on the pipeline monitoring data, multiple feasible diffusion paths are generated, specifically: Based on the gas distribution data of the first underground pipeline network map, the total concentration of harmful gases at multiple diffusion nodes within their respective preset areas is calculated. Multiple initial diffusion nodes are determined based on the total concentration of harmful gases at the multiple diffusion nodes; Acquire pipeline environmental data, including temperature, humidity, and wind speed; Based on the pipeline environment data, calculate the diffusion range of the multiple initial diffusion nodes; Based on the multiple initial diffusion nodes and their corresponding diffusion ranges, multiple feasible diffusion paths are generated.

3. The method according to claim 1, characterized in that, The method of using a three-dimensional convection-diffusion model to predict hazardous gas data for multiple feasible diffusion paths specifically includes: Obtain pipeline data for a first feasible diffusion path, the pipeline data including the pipeline inner diameter and the pipeline inner wall roughness, and the first feasible diffusion path is any one of the multiple feasible diffusion paths; Based on the pipeline data, calculate the turbulent diffusion coefficient of the first feasible diffusion path; Based on the turbulent diffusion coefficient, the diffusion coefficient of the three-dimensional convection diffusion model is adjusted, and the adjusted three-dimensional convection diffusion model is used to predict the harmful gas prediction data of the first feasible diffusion path.

4. The method according to claim 1, characterized in that, Gas diffusion analysis was performed on the second underground pipeline network to obtain a true distribution map of harmful gases in the area covered by the target manhole cover, specifically including: Calculate the variance of the slope curve of the monitoring data of the abnormal diffusion path; Count the number of diffusion nodes and the path length of the abnormal diffusion path; Based on the number of diffusion nodes, path length, and curve variance of the abnormal diffusion path, adjust the gas diffusion parameters of the preset diffusion gas simulation model in the abnormal diffusion path. The monitoring data of harmful gases from multiple feasible diffusion paths are input into the preset diffusion gas simulation model to obtain the real distribution map of harmful gases in the area governed by the target manhole cover.

5. A hazardous gas detection system based on smart manhole covers, characterized in that, A method for performing a hazardous gas detection method based on a smart manhole cover as described in any one of claims 1-4, comprising an acquisition module (1), a processing module (2), and a transmission module (3), wherein: The acquisition module (1) is used to acquire pipeline monitoring data of the area under the jurisdiction of the target manhole cover. The pipeline monitoring data includes a first underground pipeline network map, multiple diffusion nodes, and gas distribution data. The processing module (2) is used to generate multiple feasible diffusion paths based on the pipeline monitoring data; to predict the hazardous gas prediction data of the multiple feasible diffusion paths using a three-dimensional convection diffusion model; and to correct the first underground pipeline network map based on the gas distribution data and the hazardous gas prediction data of the multiple feasible diffusion paths to obtain a second underground pipeline network map. The sending module (3) is used to perform gas diffusion analysis on the second underground pipeline network to obtain a real distribution map of harmful gases in the area governed by the target manhole cover.

6. An electronic device, characterized in that, The device includes a processor (301), a memory (305), a user interface (303), and a network interface (304). The memory (305) is used to store instructions. The user interface (303) and the network interface (304) are used to communicate with other devices. The processor (301) is used to execute the instructions stored in the memory (305) to cause the electronic device (300) to perform the method as described in any one of claims 1 to 4.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores instructions that, when executed, perform the method as described in any one of claims 1 to 4.

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