Municipal drainage pipeline-based graph modeling processing method

Abstract

The invention relates to the technical field of municipal drainage pipeline one-map modeling processing, in particular to a municipal drainage pipeline one-map modeling processing method. The method comprises the following steps: extracting pipeline structure data based on a municipal drainage pipeline diagram, carrying out rainfall flood drainage main road analysis, and predicting the rainfall flood drainage capability of the main road. And calculating the pressure of the main trunk of the drainage pipeline, and carrying out bearing capacity analysis, including pipeline deformation, fracture probability evaluation and bearing capacity evaluation. Urban rainfall and topographic data are obtained, a runoff drainage pipeline network structure model is constructed, and water flow overload carrying analysis is carried out. According to the water flow overload carrying data and the bearing capacity data, real-time drainage optimization is carried out, and the drainage effect of the drainage pipeline network is improved. According to the method, the real-time drainage technology of the municipal drainage pipe is more efficient and safer through one-map modeling processing optimization of the municipal drainage pipe.

Description

Technical Field

[0001] The present invention relates to the technical field of municipal drainage pipeline modeling processing, and in particular to a municipal drainage pipeline modeling processing method based on a map. Background Art

[0002] As an important part of urban infrastructure, the municipal drainage system is responsible for collecting and draining rainwater, sewage and other waste to prevent urban waterlogging and pollution. With the acceleration of urbanization, the design and maintenance of municipal drainage pipeline systems have become increasingly complex. Traditional municipal drainage pipeline management methods mainly rely on paper drawings and manual records, which are not only inefficient but also prone to data loss and untimely updates. With the rapid development of information technology, especially the widespread application of computer graphics, geographic information systems (GIS), and big data processing technology, new solutions have been provided for the management of municipal drainage pipelines. In recent years, processing methods based on municipal drainage pipeline map models have gradually attracted attention. This method can digitize various types of drainage pipeline information, thereby realizing efficient management, analysis and visualization of pipeline data. However, the traditional municipal drainage pipeline one-map modeling processing method has inaccurate pipeline drainage prediction capabilities and insufficient real-time drainage optimization of drainage pipelines. Summary of the invention

[0003] Based on this, it is necessary to provide a modeling processing method based on a picture of municipal drainage pipelines to solve at least one of the above technical problems.

[0004] To achieve the above purpose, a method for modeling a municipal drainage pipeline is provided, the method comprising the following steps:

[0005] Step S1: extract the pipeline structure based on a map of municipal drainage pipelines to obtain the municipal drainage pipeline structure data; analyze the main rainwater drainage road according to the municipal drainage pipeline structure data to obtain the main road data of the rainwater drainage pipeline; predict the rainwater drainage capacity according to the main road data of the municipal drainage pipeline to obtain the main road rainwater drainage capacity prediction data;

[0006] Step S2: Calculate the pressure of the main drainage pipeline according to the main drainage pipeline data of the municipal drainage pipeline to obtain the pressure data of the main drainage pipeline; analyze the bearing capacity of the main drainage pipeline according to the pressure data of the main drainage pipeline to obtain the bearing capacity data of the main drainage pipeline;

[0007] Step S3: obtaining the maximum rainfall data and urban terrain data of the city; constructing a runoff drainage pipeline network structure model according to the urban rainfall data, the municipal drainage pipeline structure data and the urban terrain data to obtain a runoff drainage pipeline network structure model; performing a water flow overload carrying analysis according to the pipeline rainfall network model to obtain the pipeline trunk water flow overload carrying data;

[0008] Step S4: Perform real-time drainage optimization on the runoff drainage pipeline network structure model according to the pipeline trunk water flow overload carrying data and the drainage pipeline trunk carrying capacity data to obtain the pipeline trunk real-time drainage optimization data.

[0009] The present invention can accurately extract the structural data of the pipeline by analyzing the municipal drainage pipeline map, which provides a basis for subsequent drainage system analysis and optimization. Accurate structural data ensures the reliability of system modeling and the validity of analysis results. The identification of the main rainwater drainage trunk can help determine the key parts of the drainage system, optimize resource allocation and priority management. The trunk usually bears a large drainage load, and understanding its specific data helps to improve the overall drainage capacity of the system. By predicting the rainwater drainage capacity of the trunk, potential drainage bottlenecks can be identified in advance, so that preventive measures can be taken. Effective prediction can help to carry out reasonable system design and improvement, and improve the ability of urban drainage systems to cope with extreme weather conditions. Pressure calculation can monitor the pressure state of the drainage pipeline under different working conditions and help identify pressure overload problems. This is crucial to prevent pipeline rupture or failure. Through bearing capacity analysis, the stability and safety of the drainage pipeline under pressure can be evaluated. This helps to optimize the design of the pipeline and avoid system failures caused by insufficient pipeline load. Accurate pressure and bearing capacity data can guide maintenance and upgrade work to ensure the normal operation of the drainage system under high load conditions. Such preventive measures help reduce the frequency and impact of system failures. By integrating rainfall, pipeline structure and terrain data, the established runoff drainage pipeline network model can fully reflect the actual operation of the drainage system. This comprehensive model improves the understanding and response capabilities of complex drainage environments. Water flow overload analysis can identify the load limit of the system under extreme rainfall conditions. This helps to consider extreme weather factors in the design and management process and take measures in advance to reduce or avoid system overload operation. Through the analysis of water flow overload, early warning of potential problems in the drainage system can be achieved, so that necessary emergency measures can be taken to prevent disasters. Real-time drainage optimization can dynamically adjust the operation strategy of the drainage system based on the latest data to ensure that the system can adapt to changing conditions. This flexibility improves the system's response speed and processing capacity. By optimizing the pipeline network structure, the drainage load can be distributed more effectively to avoid excessive pressure in certain areas. The optimized system can handle rainfall water flow more efficiently and improve overall drainage efficiency. Real-time optimization enables the system to maintain optimal status under various working conditions, thereby reducing the risk of urban waterlogging and other related disasters caused by drainage system failure. Therefore, the present invention is an optimization processing of a traditional method based on a one-picture modeling processing method of municipal drainage pipelines, which solves the problems of inaccurate pipeline drainage prediction ability and insufficient real-time drainage optimization of drainage pipelines in the traditional method based on a one-picture modeling processing method of municipal drainage pipelines, and improves the pipeline drainage prediction ability and real-time drainage optimization of drainage pipelines.

[0010] Preferably, step S1 comprises the following steps:

[0011] Step S11: extracting the pipeline structure based on a map of the municipal drainage pipeline to obtain the municipal drainage pipeline structure data;

[0012] Step S12: Analyze the municipal stormwater drainage pipeline according to the municipal drainage pipeline structure data to obtain stormwater drainage pipeline data;

[0013] Step S13: analyzing the main rainwater drainage channel according to the municipal drainage pipeline structure data and the rainwater drainage pipeline data to obtain the main rainwater drainage pipeline data;

[0014] Step S14: predicting the drainage capacity based on the main road data of the stormwater drainage pipeline to obtain the main road stormwater drainage capacity prediction data.

[0015] The present invention can obtain accurate pipeline layout, size and configuration by extracting pipeline structure data from municipal drainage pipeline map. This provides a solid data foundation for subsequent analysis and modeling, ensuring the accuracy of all calculations and analyses. Structural data extraction is the starting point for the analysis of the entire drainage system. These data include not only the location and direction of the pipeline, but also the material, diameter and other important attributes of the pipeline. Establishing these basic data helps to systematically understand the overall structure of the drainage system. The standardized structural data extraction process ensures the consistency and integrity of the data, helps to avoid analysis errors caused by missing or inconsistent data, and by analyzing the structural data of municipal drainage pipelines, the key drainage pipelines under rain and flood conditions can be identified. This helps to understand the role of these pipelines in the drainage system and their importance in rain and flood management. Municipal rain and flood drainage pipeline analysis can reveal the performance and shortcomings of existing drainage pipelines. This information is of great significance for optimizing drainage system design, increasing drainage capacity and improving drainage efficiency. The obtained rainwater drainage pipeline data provides detailed information on pipeline capacity, flow rate and flow, which helps to further analyze and predict the performance of the drainage system under different rainfall conditions. By analyzing the trunk data obtained, the pipelines responsible for the main drainage function in the drainage system can be identified. These trunks usually carry large flows and pressures, and their operating status is crucial to the effectiveness of the overall drainage system. Identifying trunks helps to focus management and maintenance. The trunks of the drainage system are usually the most prone to problems. Understanding their specific conditions can be used to carry out maintenance and upgrades in advance to prevent system failures. Trunk analysis provides a basis for optimizing the drainage system. By understanding the load capacity and flow distribution of the trunks, the drainage system can be optimized in a targeted manner to improve the overall drainage efficiency. By predicting the drainage capacity of the trunks, the performance of the system under different rainfall intensities can be evaluated. This helps to identify drainage bottlenecks in advance so that preventive measures can be taken before problems occur. Prediction data can provide a basis for system design improvements. If the forecast shows that the trunk is overloaded under certain rainfall conditions, the design of the pipeline can be optimized or other drainage facilities can be added to enhance the drainage capacity of the system. Capacity forecasts can also help develop emergency response plans. In the event of extreme weather events, understanding the drainage capacity of the trunks can help to quickly take emergency measures to reduce the impact of waterlogging or insufficient drainage on the city.

[0016] Preferably, step S14 comprises the following steps:

[0017] Step S141: performing pipeline layout analysis and main road pipeline width calculation according to the rainwater drainage pipeline main road data, and obtaining drainage pipeline main road layout data and main road pipeline width data;

[0018] Step S142: Calculate the inclination of the main drainage pipeline road according to the drainage pipeline main pipeline layout data to obtain the inclination data of the main drainage pipeline road;

[0019] Step S143: Calculate the drainage flow rate according to the drainage pipeline main road inclination data and the main road pipe width data to obtain the drainage flow rate data of the drainage pipeline main road;

[0020] Step S144: predicting the rainwater drainage capacity according to the main road pipeline width data and the main road drainage flow rate data of the drainage pipeline to obtain the main road rainwater drainage capacity prediction data.

[0021] The present invention can evaluate the rationality of the layout of existing drainage pipelines through pipeline layout analysis. This analysis helps to identify potential drainage bottlenecks and unreasonable layout problems, thereby providing a basis for improving pipeline design and optimizing layout. The calculation of the width of the main pipeline ensures an accurate understanding of the pipeline size. The pipeline width directly affects the flow rate and drainage efficiency. Accurate width data helps to design drainage pipelines that meet actual needs and improve the overall performance of the system. Through pipeline layout data and pipeline width data, the capacity and flow distribution of the drainage system can be better understood. This provides a scientific basis for system design and improvement, ensuring that the drainage system can operate effectively under various conditions. The inclination of the pipeline has a direct impact on the drainage flow rate and system operation. By calculating the inclination, it can be evaluated whether the pipeline reaches the optimal angle of drainage flow, ensure smooth drainage, and avoid flow reduction or drainage blockage caused by improper inclination. The inclination data can be used to optimize the pipeline design to ensure that the angle of pipeline installation meets the design standards. This helps to improve the overall efficiency of the drainage system and avoid drainage problems caused by improper design. Improper inclination leads to sediment accumulation and pipeline blockage. Accurate inclination calculation helps prevent such problems and improve the long-term stability and reliability of the system. Flow rate is a key indicator for evaluating the performance of drainage systems. By calculating the flow rate, we can understand the flow capacity of the pipeline under different conditions and ensure that the system can effectively handle the expected flow. Flow rate data can help identify insufficient or excessive flow problems in the pipeline and support the optimization of the drainage system. By adjusting the width or slope of the pipe, the flow rate can be increased, thereby enhancing the drainage capacity. Accurate flow rate calculation helps ensure that the drainage pipeline can maintain an appropriate flow rate under rain and flood conditions, thereby improving the drainage capacity of the system and reducing urban waterlogging and waterlogging problems. By combining pipe width and flow rate data for rain and flood drainage capacity prediction, the performance of the drainage system under different rainfall conditions can be evaluated. This helps to foresee the load capacity and drainage efficiency of the system and ensure that the system can work normally under high load conditions. The prediction data provides the actual performance of drainage capacity, which can help optimize the pipeline design and adjust the pipe size or layout to improve the overall drainage performance and the ability to cope with extreme weather. Accurate drainage capacity prediction can help formulate effective emergency response measures. When the forecast shows that the drainage capacity is insufficient, preparations can be made and measures can be taken in advance to reduce the impact of potential drainage problems on the city.

[0022] Preferably, step S2 comprises the following steps:

[0023] Step S21: Calculate the pressure of the main channel of the drainage pipeline according to the main channel data of the municipal drainage pipeline to obtain the pressure data of the main channel of the drainage pipeline;

[0024] Step S22: performing deformation analysis of the main channel of the drainage pipeline according to the pressure data of the main channel of the drainage pipeline to obtain deformation data of the main channel of the drainage pipeline;

[0025] Step S23: obtaining material data of the main channel of the drainage pipeline; evaluating the probability of rupture of the drainage pipeline according to the deformation data of the main channel of the drainage pipeline, and obtaining the probability data of rupture of the drainage pipeline;

[0026] Step S24: analyzing the bearing capacity of the main drainage pipeline according to the drainage pipeline rupture probability data and the drainage pipeline main pipeline pressure data to obtain the bearing capacity data of the main drainage pipeline.

[0027] The present invention can identify the pressure level of the pipeline under different working conditions by calculating the pressure data of the main road. This helps to find the problem of excessive pressure, so as to take measures to prevent the pipeline from rupturing or leaking. The pressure data can provide a basis for pipeline design and material selection. Understanding the pressure of the pipeline under high load conditions helps to design a more reasonable pipeline system and reduce future maintenance needs and system failure risks. Regular pressure calculation helps to find potential problems of the pipeline early. Through pressure monitoring, maintenance or replacement work can be arranged in advance, extending the service life of the pipeline and reducing maintenance costs. Deformation analysis can detect the deformation of the pipeline after being subjected to pressure and identify pipeline deformation caused by excessive pressure or material fatigue. This helps to understand the actual operating status of the pipeline and its stability. By analyzing the deformation data, the design and material selection of the pipeline can be improved, and the deformation problem of the pipeline during use can be reduced, thereby improving the overall reliability and safety of the system. The deformation data helps to predict potential problems of the pipeline, such as leakage or blockage caused by excessive deformation. Identifying these problems in advance can take preventive maintenance measures to reduce the occurrence of system failures. Obtaining data on pipeline materials can help understand the durability and load-bearing capacity of the pipeline. Different materials react differently to pressure and deformation, and understanding material properties helps to evaluate the overall safety of the pipeline. Rupture probability assessment based on deformation data and material properties can determine the risk of rupture of the pipeline under specific conditions. This helps to identify high-risk areas and take targeted preventive measures. Rupture probability data can guide maintenance and replacement strategies, focusing on those parts of the pipeline with a higher risk of rupture, so as to effectively allocate resources and reduce the impact of pipeline failures. Bearing capacity analysis can evaluate the performance of pipelines under various working conditions and ensure that the pipelines can operate safely in actual applications. This is crucial to the stability of the entire drainage system. Through bearing capacity analysis, deficiencies in pipeline design can be identified and improvement suggestions can be provided. For example, the thickness of the pipe needs to be increased or the material needs to be changed in areas with insufficient bearing capacity. Accurate bearing capacity data can help to formulate reasonable operating parameters and maintenance plans, thereby enhancing the overall safety of the drainage system and reducing urban waterlogging or environmental pollution caused by pipeline failures.

[0028] Preferably, step S21 includes the following steps:

[0029] Step S211: extracting the surrounding environment of the municipal drainage pipeline and measuring the pipeline depth according to the main road data of the municipal drainage pipeline, and obtaining the surrounding environment data of the municipal drainage pipeline and the depth data of the municipal drainage pipeline;

[0030] Step S212: Analyze the surface weight load situation based on the surrounding environment data of the municipal drainage pipeline to obtain the surface weight load data around the pipeline;

[0031] Step S213: Calculating the ground settlement around the pipeline according to the ground weight load data around the pipeline to obtain ground settlement data around the pipeline;

[0032] Step S214: analyzing the soil density around the main road according to the surface settlement data around the pipeline to obtain the soil density data around the main road;

[0033] Step S215: performing soil weight analysis around the main road according to soil density data around the main road and municipal drainage pipeline depth data to obtain soil weight data around the main road;

[0034] Step S216: Calculate the drainage pipeline main road pressure based on the main road soil weight data and the pipeline surrounding surface settlement data to obtain the drainage pipeline main road pressure data.

[0035] The extraction of environmental data around the pipeline of the present invention can provide information about environmental factors such as soil type, groundwater level, and buildings around the pipeline. These data are helpful to understand the environmental conditions of the pipeline, so as to evaluate its potential impact on the pipeline. Measuring the depth of the pipeline is the key to evaluating the stress of the pipeline in the underground environment. Accurate depth data can ensure the effectiveness of pipeline design and maintenance measures, especially in the assessment of the impact of ground load and environmental changes on the pipeline. The analysis of surface heavy object load conditions can evaluate the load conditions of the ground around the pipeline, such as the impact of factors such as buildings and traffic flow on the pipeline. This helps to understand the actual pressure and potential risks of external loads on the pipeline. Identifying surface heavy object loads can help design and implement effective preventive measures, such as strengthening pipeline protection or ground transformation to reduce the impact of external loads on the pipeline, thereby improving the safety and stability of the system. Through load analysis, guidance can be provided for the maintenance and overhaul of the pipeline, especially in high-load areas, where the inspection frequency and maintenance efforts can be increased to prevent failures caused by excessive loads. Surface settlement calculation can help monitor ground settlement caused by external loads, groundwater level changes or other factors. This is crucial to assess whether a pipeline will be affected by subsidence. Understanding the ground subsidence can predict risks to the pipeline, such as deformation or displacement of the pipeline. Early identification of subsidence problems can help develop preventive measures to avoid pipeline rupture or leakage. Ground subsidence data can help adjust pipeline maintenance plans, especially in areas with large subsidence, where special reinforcement measures or adjustments to pipeline directions can be taken to reduce the impact of subsidence on the pipeline. Soil density analysis helps assess the stability and bearing capacity of the soil around the pipeline. Soil density directly affects the soil's ability to support the pipeline, ensuring that the soil can support the pipeline and prevent subsidence. Understanding soil density can help select appropriate materials and technologies in pipeline design and construction to meet the challenges brought by different soil conditions and improve the long-term stability of the pipeline. Soil density data combined with ground subsidence can more accurately predict future subsidence changes and help take measures in advance to reduce the impact of subsidence on the pipeline. Soil weight analysis can assess the actual load of the soil around the pipeline on the pipeline. This is crucial to understanding the stress of the pipeline under different soil conditions, ensuring that the pipeline can withstand the weight of the soil without deformation or damage. Soil weight data can help assess whether the structural design of the pipeline is strong enough to cope with the pressure caused by soil weight. If necessary, the design can be adjusted to enhance the load-bearing capacity of the pipeline. By understanding soil weight, pipeline protection measures can be better planned and implemented to prevent pipeline problems caused by excessive soil weight, thereby improving the safety and reliability of the system. By comprehensively considering the effects of soil weight and surface settlement, the actual pressure of the pipeline can be accurately calculated. This comprehensive pressure assessment helps understand the working load of the pipeline under different environmental conditions.Accurate pressure data can help identify excessive pressure in pipelines under specific conditions and avoid pipeline damage or leakage caused by overload. Based on the pressure calculation results, pipeline design and maintenance measures can be optimized to ensure that the pipeline can operate effectively in actual use and prevent and deal with pressure problems.

[0036] Preferably, step S23 includes the following steps:

[0037] Step S231: Acquire the material data of the main channel of the drainage pipeline;

[0038] Step S232: marking the maximum deformation threshold of the pipeline according to the material data of the main channel of the drainage pipeline to obtain the maximum deformation threshold of the main channel of the drainage pipeline;

[0039] Step S233: performing deformation displacement analysis according to the deformation data of the drainage pipeline main channel and the pressure data of the drainage pipeline main channel to obtain displacement data of the drainage pipeline main channel;

[0040] Step S234: performing deformation overload analysis on the displacement data of the drainage pipeline trunk according to the maximum deformation threshold of the drainage pipeline trunk to obtain deformation overload data of the pipeline trunk;

[0041] Step S235: performing deformation degradation trend analysis according to the pipeline trunk road deformation overload data to obtain pipeline trunk road deformation degradation trend data;

[0042] Step S236: Evaluate the drainage pipeline rupture probability based on the pipeline trunk deformation deterioration trend data and the drainage pipeline trunk pressure data to obtain the drainage pipeline rupture probability data.

[0043] The material data (such as material, thickness, age, etc.) of the pipeline obtained by the present invention is the basis for subsequent analysis. These data are helpful to understand the durability and physical properties of the pipeline. Pipelines of different materials have different deformation and pressure bearing capacities. This step can provide targeted analysis basis and optimize the accuracy of subsequent steps. By marking the maximum deformation threshold, a clear standard can be established to evaluate whether the pipeline is in the normal operating range. This helps to prevent potential problems caused by excessive deformation of the pipeline. The clear maximum deformation threshold makes the subsequent deformation analysis more accurate, and potential risks can be identified and handled in a timely manner. This step can dynamically monitor the actual deformation and displacement of the pipeline and identify the behavior of the pipeline under different pressure conditions. By understanding the actual deformation and displacement, maintenance plans can be better formulated to reduce the occurrence of sudden problems. Deformation overload analysis can help evaluate whether the pipeline exceeds the safety threshold, thereby identifying whether the pipeline is facing an overload problem. Through this analysis, a scientific basis can be provided for subsequent repair and maintenance to avoid serious damage to the pipeline caused by overload. Deterioration trend analysis can reveal the long-term trend of pipeline deformation and help identify the gradual degradation process of the pipeline. Understanding the degradation trend can provide data support for maintenance decisions and optimize pipeline management and repair strategies. By combining deformation degradation trends and pressure data, the probability of pipeline rupture can be evaluated, thus providing a scientific basis for preventing pipeline rupture. This step helps predict potential rupture risks, take preventive measures in advance, and ensure the safety and reliability of the drainage system.

[0044] Preferably, step S3 comprises the following steps:

[0045] Step S31: Acquire urban rainfall data and urban terrain data;

[0046] Step S32: constructing a runoff drainage pipeline network structure model according to urban rainfall data, municipal drainage pipeline structure data and urban terrain data to obtain a runoff drainage pipeline network structure model;

[0047] Step S33: Perform dynamic water flow analysis according to the runoff drainage pipeline network structure model to obtain pipeline dynamic water flow data;

[0048] Step S34: Perform water flow overload analysis based on the main road rainwater drainage capacity prediction data, the drainage pipeline main road carrying capacity data and the pipeline dynamic water flow data to obtain the pipeline main road water flow overload data.

[0049] The present invention uses rainfall data and terrain data as basic information for urban drainage analysis. Rainfall data provides information on rainfall amount and rainfall intensity, while terrain data provides ground undulation and watershed information. Accurate rainfall and terrain data can ensure the accuracy of subsequent model construction and analysis, thereby improving the prediction and response capabilities of the drainage system. Integrating rainfall data, drainage pipeline structure and terrain data to construct a drainage network model can simulate the actual operation of the drainage system. This helps to understand the overall structure and function of the urban drainage system. By establishing a structural model, potential bottlenecks and weaknesses in the existing drainage system can be identified, helping planners optimize pipeline design and drainage capacity. Dynamic water flow analysis can simulate water flow changes under different rainfall scenarios, thereby understanding the performance of the drainage system under actual conditions. This helps to predict the impact of changes in water flow on pipelines. The pressure of future rainfall events on the drainage system can be predicted, thereby providing a scientific basis for preventing waterlogging and flooding. By comprehensively analyzing the rain and flood drainage capacity, pipeline carrying capacity and dynamic water flow data, the overload of the drainage system under different rainfall conditions can be identified. This can help determine whether the pipeline can handle the current or future water flow. Identifying overload situations helps to take preventive measures, such as adjusting drainage strategies, improving pipeline design or strengthening maintenance, thereby effectively reducing the occurrence and impact of flooding disasters.

[0050] Preferably, step S32 includes the following steps:

[0051] Step S321: Analyze the temporal and spatial distribution characteristics of rainfall according to the urban rainfall data to obtain the temporal and spatial distribution characteristic data of rainfall;

[0052] Step S322: Predicting the rainfall change trend according to the rainfall temporal and spatial distribution characteristic data to obtain rainfall change trend data;

[0053] Step S323: performing rainfall runoff intensity analysis according to rainfall change trend data and urban terrain data to obtain rainfall runoff intensity data;

[0054] Step S324: construct a runoff drainage pipeline network model according to the rainfall runoff intensity data and the municipal drainage pipeline structure data to obtain a runoff drainage pipeline network model.

[0055] The present invention can reveal the spatial distribution and temporal variation patterns of rainfall, such as the concentrated areas and frequency of rainfall, by identifying rainfall patterns and analyzing the spatial and temporal distribution characteristics of rainfall. This helps to understand how rainfall affects different areas, so as to carry out targeted management. By analyzing the rainfall distribution characteristics, resources can be allocated and maintenance work can be arranged more accurately, especially in areas with high rainfall. The prediction of rainfall change trends can provide foresight of future rainfall patterns, including the trend of increasing or decreasing rainfall. This helps to plan and prepare in advance and reduce the damage caused by sudden floods. Understanding the changing trends of rainfall can help urban planners and drainage managers formulate long-term flood control and drainage strategies and optimize the design and construction of infrastructure. By analyzing the rainfall change trends and terrain data, the runoff intensity can be accurately calculated, which is crucial to assessing the impact of rainfall on surface runoff. Understanding the runoff intensity can help predict the load of the drainage system. Mastering the runoff intensity data helps to optimize the design of the drainage system to cope with the flow changes under different rainfall intensities, thereby improving the effectiveness of the drainage system. Combining the rainfall runoff intensity and the drainage pipeline structure data, a comprehensive drainage pipeline network model can be constructed to simulate the actual drainage situation. This helps to understand the performance of the pipeline network under different rainfall conditions. By simulating different rainfall scenarios and runoff intensities, bottlenecks and potential problems in the drainage system can be identified, helping to optimize the system and improve drainage capacity and emergency response capabilities.

[0056] Preferably, step S34 includes the following steps:

[0057] Step S341: performing a pipeline main channel water flow peak value analysis and a pipeline main channel baseline flow analysis according to the pipeline dynamic water flow data, and obtaining the pipeline main channel water flow peak value data and the pipeline main channel baseline flow data;

[0058] Step S342: marking the main road flow overload point of the municipal drainage pipeline structure data according to the main road water flow peak data and the main road rain and flood drainage capacity prediction data, and obtaining the main road flow overload point data;

[0059] Step S343: performing a pipeline main road water level overload threshold analysis based on the pipeline main road flow overload point data, the pipeline main road baseline flow data, and the drainage pipeline main road carrying capacity data to obtain the overload point water level overload threshold data;

[0060] Step S344: Perform water flow overload carrying analysis on the pipeline main channel flow overload point data according to the pipeline main channel overload point water level peak data and the overload point water level overload threshold to obtain the pipeline main channel water flow overload carrying data.

[0061] The present invention can help identify the maximum flow of pipeline trunks under extreme rainfall conditions through peak analysis, thereby evaluating the performance of pipelines under high load. This helps to understand the system's ability to operate in extreme situations. Baseline flow analysis provides flow data under normal operating conditions, which helps to monitor the difference between daily use and abnormal conditions. This is crucial for maintenance and operation management. By comparing flow peak data and drainage capacity prediction data, overload points of pipeline trunks can be marked. This helps to identify which pipeline sections are prone to overload under high flow conditions, so as to give priority to these problem areas. Accurately marking overload points can optimize resource allocation, focus maintenance and upgrade work in key areas, and improve the overall efficiency of the drainage system. Water level overload threshold analysis can help determine at what water level the pipeline will be overloaded. This provides specific indicators for pipeline maintenance and upgrades to avoid actual water levels exceeding safety thresholds. Understanding the water level overload threshold can take measures in advance, such as adjusting drainage strategies or infrastructure modifications, to prevent floods. Water flow overload carrying analysis can evaluate the actual carrying capacity of pipeline trunks under different water levels and flow conditions. This helps determine whether the pipeline is operating under overload conditions and the severity of the overload. By understanding the overload conditions, pipeline design and modification work can be guided to improve the carrying capacity of the drainage system and reduce system failures and maintenance costs.

[0062] Preferably, step S34 includes the following steps:

[0063] Step S41: extracting the topological structure based on a map of municipal drainage pipelines to obtain the topological structure data of the municipal drainage pipelines;

[0064] Step S42: analyzing the main roads of the municipal drainage pipeline topology data for easily overloaded pipelines according to the pipeline main road water flow overload carrying data and the drainage pipeline main road carrying capacity data to obtain the main roads of the pipelines that are easily overloaded;

[0065] Step S43: Optimizing the topological drainage path according to the main pipeline prone to overload, and obtaining topological drainage path optimization data;

[0066] Step S44: Perform real-time drainage optimization on the runoff drainage pipeline network structure model according to the topological drainage path optimization data to obtain real-time drainage optimization data for the pipeline trunk.

[0067] By extracting topological structure data, the present invention can clearly identify the actual structure of the drainage pipeline network, including the connection relationship and flow direction of each pipeline. This provides basic data for further analysis and optimization. Topological structure data helps to establish a digital model of the drainage system, making system management and maintenance more efficient and accurate. By analyzing overload carrying data and carrying capacity data, pipeline trunks that are prone to overload under current flow conditions can be identified. This helps to focus on areas where problems occur and give priority. After clarifying the main trunks of pipelines that are prone to overload, targeted maintenance and improvements can be carried out, such as increasing the pipeline diameter or enhancing the pipeline carrying capacity to prevent potential failures and blockages. By optimizing the drainage path, water flow can be effectively distributed and guided, the pressure of pipelines that are prone to overload can be reduced, and the drainage efficiency of the entire drainage system can be improved. Topological drainage path optimization can balance pipeline loads and reduce pressure in high-load areas, thereby improving the reliability of the drainage system under extreme rainfall conditions. Through real-time drainage optimization, the drainage path can be dynamically adjusted according to actual rainfall conditions and flow data to ensure that the drainage system always maintains optimal performance under changing conditions. Through real-time drainage optimization, drainage paths can be dynamically adjusted based on actual rainfall conditions and flow data, ensuring that the drainage system always maintains optimal performance under changing conditions.

[0068] Beneficial effects of the present invention: by analyzing the municipal drainage pipeline map, the present invention can accurately extract the structural data of the pipeline, which provides a basis for subsequent drainage system analysis and optimization. Accurate structural data ensures the reliability of system modeling and the validity of analysis results. The identification of the main rainwater drainage trunk can help determine the key parts of the drainage system, optimize resource allocation and priority management. The trunk usually bears a large drainage load, and understanding its specific data helps to improve the overall drainage capacity of the system. By predicting the rainwater drainage capacity of the trunk, potential drainage bottlenecks can be identified in advance, so that preventive measures can be taken. Effective prediction can help to carry out reasonable system design and improvement, and improve the ability of urban drainage systems to cope with extreme weather conditions. Pressure calculation can monitor the pressure state of the drainage pipeline under different working conditions and help identify pressure overload problems. This is crucial to prevent pipeline rupture or failure. Through load-bearing capacity analysis, the stability and safety of the drainage pipeline when under pressure can be evaluated. This helps to optimize the design of the pipeline and avoid system failures caused by insufficient pipeline load. Accurate pressure and load-bearing capacity data can guide maintenance and upgrade work to ensure the normal operation of the drainage system under high load conditions. This preventive approach helps reduce the frequency and impact of system failures. By integrating rainfall, pipeline structure and terrain data, the established runoff drainage pipeline network model can fully reflect the actual operation of the drainage system. This comprehensive model improves the understanding and response capabilities of complex drainage environments. Water flow overload analysis can identify the load limit of the system under extreme rainfall conditions. This helps to consider extreme weather factors in the design and management process and take measures in advance to reduce or avoid system overload operation. Through the analysis of water flow overload, early warning of potential problems in the drainage system can be achieved, so that necessary emergency measures can be taken to prevent disasters. Real-time drainage optimization can dynamically adjust the operation strategy of the drainage system based on the latest data to ensure that the system can adapt to changing conditions. This flexibility improves the system's responsiveness and processing capabilities. By optimizing the pipeline network structure, the drainage load can be distributed more effectively to avoid excessive pressure in certain areas. The optimized system can handle rainfall water flow more efficiently and improve overall drainage efficiency. Real-time optimization enables the system to maintain optimal conditions under various working conditions, thereby reducing the risk of urban waterlogging and other related disasters caused by drainage system failure. Therefore, the present invention is an optimization processing of a traditional method based on a one-picture modeling processing method of municipal drainage pipelines, which solves the problems of inaccurate pipeline drainage prediction ability and insufficient real-time drainage optimization of drainage pipelines in the traditional method based on a one-picture modeling processing method of municipal drainage pipelines, and improves the pipeline drainage prediction ability and real-time drainage optimization of drainage pipelines. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] Figure 1A schematic diagram of the steps of the method for modeling a municipal drainage pipeline based on a picture;

[0070] Figure 2 for Figure 1 Detailed implementation steps of step S2 in the flowchart;

[0071] Figure 3 for Figure 1 Detailed implementation steps of step S3 in FIG.

[0072] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0073] The technical method of the present invention is described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0074] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0075] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0076] To achieve this, please refer to Figures 1 to 3 , based on a municipal drainage pipeline one-picture modeling processing method, the method comprises the following steps:

[0077] Step S1: extract the pipeline structure based on a map of municipal drainage pipelines to obtain the municipal drainage pipeline structure data; analyze the main rainwater drainage road according to the municipal drainage pipeline structure data to obtain the main road data of the rainwater drainage pipeline; predict the rainwater drainage capacity according to the main road data of the municipal drainage pipeline to obtain the main road rainwater drainage capacity prediction data;

[0078] Step S2: Calculate the pressure of the main drainage pipeline according to the main drainage pipeline data of the municipal drainage pipeline to obtain the pressure data of the main drainage pipeline; analyze the bearing capacity of the main drainage pipeline according to the pressure data of the main drainage pipeline to obtain the bearing capacity data of the main drainage pipeline;

[0079] Step S3: obtaining the maximum rainfall data and urban terrain data of the city; constructing a runoff drainage pipeline network structure model according to the urban rainfall data, the municipal drainage pipeline structure data and the urban terrain data to obtain a runoff drainage pipeline network structure model; performing a water flow overload carrying analysis according to the pipeline rainfall network model to obtain the pipeline trunk water flow overload carrying data;

[0080] Step S4: Perform real-time drainage optimization on the runoff drainage pipeline network structure model according to the pipeline trunk water flow overload carrying data and the drainage pipeline trunk carrying capacity data to obtain the pipeline trunk real-time drainage optimization data.

[0081] In the embodiment of the present invention, reference Figure 1 The above is a schematic diagram of the steps of the method for modeling a municipal drainage pipeline based on a picture of the present invention. In this example, the method for modeling a municipal drainage pipeline based on a picture of the present invention includes the following steps:

[0082] Step S1: extract the pipeline structure based on a map of municipal drainage pipelines to obtain the municipal drainage pipeline structure data; analyze the main rainwater drainage road according to the municipal drainage pipeline structure data to obtain the main road data of the rainwater drainage pipeline; predict the rainwater drainage capacity according to the main road data of the municipal drainage pipeline to obtain the main road rainwater drainage capacity prediction data;

[0083] In an embodiment of the present invention, first, pipeline structure data is extracted from the plan view of the municipal drainage system. By using spatial data analysis tools, the pipeline information on the drawing is converted into vector data to form the geometry and attribute data of the drainage pipeline. Using GIS (Geographic Information System) software, the structural data of the municipal drainage pipeline is established according to the actual location, diameter, material type and other information of the pipeline. Then, the main rainwater drainage trunk is analyzed based on these pipeline structure data. The pipeline is divided into main roads and branch pipes according to flow and flow direction, and the distribution and structure of the main roads are analyzed to determine the main drainage paths and key nodes. The rainwater drainage capacity of the main roads is predicted using flow simulation tools and hydrological algorithms combined with historical rainfall data. These predicted data include the drainage capacity of the main roads under different rainfall intensities, showing under what conditions the main roads can maintain normal drainage.

[0084] Step S2: Calculate the pressure of the main drainage pipeline according to the main drainage pipeline data of the municipal drainage pipeline to obtain the pressure data of the main drainage pipeline; analyze the bearing capacity of the main drainage pipeline according to the pressure data of the main drainage pipeline to obtain the bearing capacity data of the main drainage pipeline;

[0085] In an embodiment of the present invention, the pressure of the drainage pipeline main channel is calculated based on the pipeline main channel data. The pressure data of each main channel is calculated using the fluid mechanics formula in combination with the flow rate, pipe diameter and friction coefficient of the pipeline. Based on the calculated pressure data, a bearing capacity assessment tool is used to analyze the bearing capacity of the drainage pipeline main channel. The calculated pressure is compared with the designed bearing capacity of the pipeline to evaluate the actual bearing condition of the pipeline under different flow and pressure conditions. The tool used in this step can be an engineering calculation software that can provide detailed data on pressure and bearing capacity to help determine whether the pipeline has a potential overload risk.

[0086] Step S3: obtaining the maximum rainfall data and urban terrain data of the city; constructing a runoff drainage pipeline network structure model according to the urban rainfall data, the municipal drainage pipeline structure data and the urban terrain data to obtain a runoff drainage pipeline network structure model; performing a water flow overload carrying analysis according to the pipeline rainfall network model to obtain the pipeline trunk water flow overload carrying data;

[0087] In an embodiment of the present invention, after obtaining the maximum rainfall data and urban terrain data of the city, the meteorological data collection system and terrain data measurement tools (such as digital elevation models) are first used to collect relevant information. These data are input into the GIS system, and the network analysis function and watershed analysis tools in the system are used to construct a runoff drainage pipeline network structure model based on the municipal drainage pipeline structure data and urban terrain data. By combining the rainfall intensity and terrain characteristics, the runoff path and magnitude generated by rainfall are simulated. Then, the water flow overload carrying analysis is performed based on the constructed network structure model. Using the flow distribution and pipeline carrying capacity information, the overload points that appear are identified, and the water flow overload carrying data under different rainfall scenarios are recorded.

[0088] Step S4: Perform real-time drainage optimization on the runoff drainage pipeline network structure model according to the pipeline trunk water flow overload carrying data and the drainage pipeline trunk carrying capacity data to obtain the pipeline trunk real-time drainage optimization data.

[0089] In an embodiment of the present invention, a real-time optimization tool is used to optimize the runoff drainage pipeline network structure based on the water flow overload carrying data of the pipeline trunk and the carrying capacity data of the drainage pipeline trunk. The drainage optimization algorithm is applied to adjust the pipeline flow distribution through dynamic adjustment of flow and pressure data. The optimization process involves adjusting the flow direction of the pipeline, increasing or reducing the flow burden of certain pipelines, and reconfiguring the drainage path to ensure the stability of the drainage system under high flow conditions. The optimization results are verified and adjusted using field monitoring data and real-time calculation tools, and finally real-time drainage optimization data of the pipeline trunk is formed. These data are used to guide actual drainage operations to ensure that the system maintains good drainage capacity under extreme weather conditions.

[0090] Preferably, step S1 comprises the following steps:

[0091] Step S11: extracting the pipeline structure based on a map of the municipal drainage pipeline to obtain the municipal drainage pipeline structure data;

[0092] Step S12: Analyze the municipal stormwater drainage pipeline according to the municipal drainage pipeline structure data to obtain stormwater drainage pipeline data;

[0093] Step S13: analyzing the main rainwater drainage channel according to the municipal drainage pipeline structure data and the rainwater drainage pipeline data to obtain the main rainwater drainage pipeline data;

[0094] Step S14: predicting the drainage capacity based on the main road data of the stormwater drainage pipeline to obtain the main road stormwater drainage capacity prediction data.

[0095] In the embodiment of the present invention, in the operation of extracting the pipeline structure based on the municipal drainage pipeline plan, the drainage pipeline plan is first digitized using a high-precision scanner to convert the paper drawing into an electronic image. Subsequently, the image is vectorized using a vectorization tool to extract the actual position, size, connection relationship and other geometric information of the drainage pipeline. The data editing function in GIS (geographic information system) is applied to correct and improve the vectorized data to ensure that the layout, nodes and intersections of the pipeline are consistent with the actual situation. After completion, the extracted pipeline data is organized into a database with spatial and attribute information to form municipal drainage pipeline structure data, including information such as the length, diameter, and material of the pipeline. When analyzing the municipal rainwater drainage pipeline based on the municipal drainage pipeline structure data, the pipeline structure data is first input into the hydraulic simulation tool to set simulation parameters such as rainfall intensity, flow distribution and pipeline characteristics. The hydraulic calculation method is used to analyze the drainage capacity and flow distribution of each pipeline segment under different rainfall conditions in combination with rainfall data. The performance of each pipeline under different rainwater conditions is determined by analyzing the actual flow and flow direction of the pipeline. The analysis results generate municipal stormwater drainage pipeline data, showing the actual flow load of each pipeline segment and its drainage role in the entire system. When analyzing stormwater drainage trunk roads, all major pipeline information is first extracted from the municipal drainage pipeline structure data and stormwater drainage pipeline data. Then, the main roads in the system are identified using pipeline connection relationships and flow distribution data. These main roads are pipelines that mainly undertake stormwater drainage tasks. Using network analysis tools, the network structure of stormwater drainage trunk roads is drawn based on the flow and pressure data of the pipelines. The analysis results include the flow paths of the trunk roads, the flow carrying capacity of the nodes and key nodes, and the main road data of stormwater drainage pipelines are formed. When predicting drainage capacity, first calculate based on the main road data of stormwater drainage pipelines using the flow prediction model. The model input includes the pipeline characteristics of the main road, rainfall intensity data, and historical flow data. The drainage capacity of the main road is predicted by simulating the flow and pressure under different rainfall intensities. Use these prediction results to evaluate the load capacity of the main road under various rainfall conditions. Finally, the main road stormwater drainage capacity prediction data is generated, including the expected flow and pressure values ​​of each main road under different rainfall conditions. These data are used to evaluate the performance of the drainage system in the face of extreme rainfall events.

[0096] Preferably, step S14 comprises the following steps:

[0097] Step S141: performing pipeline layout analysis and main road pipeline width calculation according to the rainwater drainage pipeline main road data, and obtaining drainage pipeline main road layout data and main road pipeline width data;

[0098] Step S142: Calculate the inclination of the main drainage pipeline road according to the drainage pipeline main pipeline layout data to obtain the inclination data of the main drainage pipeline road;

[0099] Step S143: Calculate the drainage flow rate according to the drainage pipeline main road inclination data and the main road pipe width data to obtain the drainage flow rate data of the drainage pipeline main road;

[0100] Step S144: predicting the rainwater drainage capacity according to the main road pipeline width data and the main road drainage flow rate data of the drainage pipeline to obtain the main road rainwater drainage capacity prediction data.

[0101] In an embodiment of the present invention, firstly, the main road data of the stormwater drainage pipeline is input into the drainage system design tool. The tool converts the main road data into two-dimensional graphic data to display the layout and arrangement of the pipeline. By analyzing the geometric data and connection mode of the pipeline, the arrangement data of the main road pipeline is generated, which includes the specific position, length and arrangement order of each section of the pipeline. Subsequently, the width of the pipeline is calculated according to the design specification using the pipeline diameter and flow data. The calculation method includes calculating the minimum width and maximum width range of the pipeline according to the flow demand and the provisions of the pipeline material. Finally, the arrangement data of the main road of the drainage pipeline and the main road pipeline width data are obtained to ensure that these data meet the design standards and practical application requirements. When calculating the inclination of the main road of the drainage pipeline, the elevation data of the starting point and the end point of each section of the pipeline are first extracted from the structural data of the main road of the drainage pipeline. The elevation change information of the pipeline is obtained using an elevation measurement tool or a digital elevation model (DEM). Next, the inclination of each section of the pipeline is calculated by dividing the height difference of each section of the pipeline by its horizontal distance. In the specific operation, the calculation formula of the inclination is: inclination = (end elevation - start elevation) / horizontal distance. According to this formula, the inclination data of each pipeline segment is obtained. The final inclination data of the main road of the drainage pipeline reflects the slope of each pipeline, which helps to analyze the flow direction and flow velocity of the drainage system. When calculating the drainage flow velocity based on the inclination data of the main road of the drainage pipeline and the width data of the main road pipeline, the flow velocity calculation formula in fluid mechanics is first applied to consider the width and inclination of the pipeline. The flow velocity calculation is usually based on the Manning formula or similar flow formulas, in which the required input data include the width, inclination, roughness coefficient of the inner surface of the pipeline and flow rate. The specific operation is: input the width and inclination of the pipeline into the flow velocity calculation tool to calculate the flow velocity. The flow velocity calculation formula is: flow velocity = (1 / n)*(R^(2 / 3))*(S^(1 / 2)), where n is the Manning roughness coefficient, R is the hydraulic radius, and S is the slope of the pipeline. Finally, the drainage velocity data of the main road of the drainage pipeline is obtained, which is used to evaluate the drainage efficiency of the system. When predicting the stormwater drainage capacity based on the main road pipeline width data and the drainage velocity data of the main road of the drainage pipeline, the pipeline width and velocity data are first input into the drainage capacity prediction model. The model comprehensively considers the width, velocity, material and fluid mechanics of the pipeline to predict the drainage capacity of the main road. The specific operation includes: applying the flow formula Q = A * V, where Q is the flow rate, A is the cross-sectional area of ​​the pipeline (pipeline width multiplied by the effective depth), and V is the drainage velocity. The drainage volume under different rainfall conditions is calculated by this formula to evaluate the drainage capacity of the pipeline under various flow rates. Finally, the stormwater drainage capacity prediction data of the main road is obtained, which provides the expected drainage capacity of the main road under specific rainfall conditions to guide the optimization and design of the system.

[0102] Preferably, step S2 comprises the following steps:

[0103] Step S21: Calculate the pressure of the main channel of the drainage pipeline according to the main channel data of the municipal drainage pipeline to obtain the pressure data of the main channel of the drainage pipeline;

[0104] Step S22: performing deformation analysis of the main channel of the drainage pipeline according to the pressure data of the main channel of the drainage pipeline to obtain deformation data of the main channel of the drainage pipeline;

[0105] Step S23: obtaining material data of the main channel of the drainage pipeline; evaluating the probability of rupture of the drainage pipeline according to the deformation data of the main channel of the drainage pipeline, and obtaining the probability data of rupture of the drainage pipeline;

[0106] Step S24: analyzing the bearing capacity of the main drainage pipeline according to the drainage pipeline rupture probability data and the drainage pipeline main pipeline pressure data to obtain the bearing capacity data of the main drainage pipeline.

[0107] As an example of the present invention, refer to Figure 2 As shown, in this example, step S2 includes:

[0108] Step S21: Calculate the pressure of the main channel of the drainage pipeline according to the main channel data of the municipal drainage pipeline to obtain the pressure data of the main channel of the drainage pipeline;

[0109] In an embodiment of the present invention, when calculating the pressure based on the municipal drainage pipeline trunk data, it is first necessary to obtain the flow rate, pipe diameter, pipe material and pipe length data of the drainage pipeline trunk. Use fluid mechanics formulas, such as the Darcy-Weisbach formula, to calculate the pressure loss inside the pipeline. The formula is: Pressure loss = f*(L / D)*(ρV^2 / 2), where f is the friction factor, L is the pipe length, D is the pipe diameter, ρ is the fluid density, and V is the flow rate. The geometric parameters and fluid properties of the pipeline are input into the calculation tool to obtain the pressure data of each section of the pipeline. The final drainage pipeline trunk pressure data reflects the internal pressure level of the pipeline under different flow conditions, helping to identify pipe sections with too high or too low pressure.

[0110] Step S22: performing deformation analysis of the main channel of the drainage pipeline according to the pressure data of the main channel of the drainage pipeline to obtain deformation data of the main channel of the drainage pipeline;

[0111] In an embodiment of the present invention, when performing deformation analysis on the main channel of the drainage pipeline, the actual pressure data of each section of the pipeline is first extracted from the pressure data of the main channel of the drainage pipeline. The deformation of the pipeline under the applied pressure is calculated using structural mechanics methods, such as elastic deformation theory. The deformation analysis is performed using the following formula: deformation = pressure × length / (elastic modulus of pipeline material × cross-sectional area of ​​pipeline). The material properties, pressure data and geometric dimensions of the pipeline are input into the deformation analysis tool to calculate the deformation of each section of the pipeline. The deformation data of the main channel of the drainage pipeline finally obtained provides the actual deformation of the pipeline under different pressures, helping to determine whether the deformation of the pipeline exceeds the normal range.

[0112] Step S23: obtaining material data of the main channel of the drainage pipeline; evaluating the probability of rupture of the drainage pipeline according to the deformation data of the main channel of the drainage pipeline, and obtaining the probability data of rupture of the drainage pipeline;

[0113] In an embodiment of the present invention, after obtaining the material data of the main channel of the drainage pipeline, the mechanical performance indicators of the material, such as tensile strength, compressive strength and elastic modulus, are used to evaluate the rupture probability of the pipeline. Combined with the deformation data of the main channel of the drainage pipeline, a rupture probability model is used for evaluation. This model is usually based on the material failure theory, such as the Mohr-Coulomb failure criterion, to calculate the rupture probability of the pipeline under the current deformation. The specific operation includes: combining the tensile strength and compressive strength of the material with the deformation data to calculate the probability of the pipeline rupture. The drainage pipeline rupture probability data finally obtained shows the rupture risk of different pipe sections under the current deformation conditions.

[0114] Step S24: analyzing the bearing capacity of the main drainage pipeline according to the drainage pipeline rupture probability data and the drainage pipeline main pipeline pressure data to obtain the bearing capacity data of the main drainage pipeline.

[0115] In an embodiment of the present invention, when performing a bearing capacity analysis on the main channel of a drainage pipeline, the drainage pipeline rupture probability data is first combined with the main channel pressure data to evaluate the bearing capacity of the pipeline in actual use. Using the bearing capacity analysis tool, the bearing capacity is calculated using the following formula based on the material strength and pressure data of the pipeline: bearing capacity = material strength / (pressure / cross-sectional area of ​​the pipeline). Combined with the deformation data of the pipeline, the maximum bearing capacity of the pipeline under existing conditions is evaluated. The final bearing capacity data shows the actual bearing level of each section of the pipeline under current conditions, which helps to determine whether the pipeline needs to be replaced or reinforced.

[0116] Preferably, step S21 includes the following steps:

[0117] Step S211: extracting the surrounding environment of the municipal drainage pipeline and measuring the pipeline depth according to the main road data of the municipal drainage pipeline, and obtaining the surrounding environment data of the municipal drainage pipeline and the depth data of the municipal drainage pipeline;

[0118] Step S212: Analyze the surface weight load situation based on the surrounding environment data of the municipal drainage pipeline to obtain the surface weight load data around the pipeline;

[0119] Step S213: Calculating the ground settlement around the pipeline according to the ground weight load data around the pipeline to obtain ground settlement data around the pipeline;

[0120] Step S214: analyzing the soil density around the main road according to the surface settlement data around the pipeline to obtain the soil density data around the main road;

[0121] Step S215: performing soil weight analysis around the main road according to soil density data around the main road and municipal drainage pipeline depth data to obtain soil weight data around the main road;

[0122] Step S216: Calculate the drainage pipeline main road pressure based on the main road soil weight data and the pipeline surrounding surface settlement data to obtain the drainage pipeline main road pressure data.

[0123] In an embodiment of the present invention, a geographic information system (GIS) is first used to extract environmental data around the municipal drainage pipeline. This process includes obtaining terrain, buildings and road information around the pipeline. High-resolution remote sensing images or laser scanning data are used to extract environmental features around the pipeline, such as the location of buildings, surface facilities and terrain changes. Next, underground detection equipment, such as geological radar or ground penetrating radar (GPR), is used to measure the buried depth of the pipeline. These devices accurately determine the depth of the pipeline by sending electromagnetic waves and receiving reflected waves. Finally, these data are integrated into the GIS system to obtain environmental data around the municipal drainage pipeline and pipeline depth data. Surface load information around the pipeline is extracted from the environmental data around the municipal drainage pipeline. This information includes the distribution and weight of buildings, roads and other heavy objects on the ground. The specific load data of surface heavy objects around the pipeline are recorded using field measurements or building load databases. For example, the road traffic flow and its pressure on the pipeline are determined through field surveys, or the load is calculated using building load design standards. These load data are input into the load analysis tool to analyze the impact of surface heavy objects on the environment around the pipeline, and finally the surface heavy object load data around the pipeline is obtained. Use settlement observation instruments, such as settlement plates or ground laser scanners, to regularly measure the changes in surface height around the pipeline. These instruments can record the settlement of the surface with high accuracy. By comparing the measurement data at different time points, the surface settlement around the pipeline is calculated. These data are sorted and input into the settlement analysis tool, and the surface settlement data around the pipeline is obtained according to the distribution and change of the settlement. These data can help evaluate the potential impact of surface settlement on the pipeline structure. Use a soil sampler to collect soil samples from different depths and send the samples to the soil laboratory for density testing. The laboratory measures the density of the soil through standard soil density testing methods, such as dry density determination. The obtained soil density data is used to analyze the physical properties of the soil around the main road. These density data are input into the soil analysis model to calculate the average density of the soil and its impact on the drainage pipeline. Finally, the soil density data around the main road is obtained, which provides the physical property information of the soil on the main road. The soil density data obtained in step S214 is combined with the municipal drainage pipeline depth data to calculate the weight of the soil. The soil density data and pipeline depth data are input into the soil weight calculation tool, which is usually based on the formula: soil weight = soil density × soil volume. The volume calculation is based on the depth and coverage area of ​​the soil. In actual operation, the volume of each soil layer is calculated, and its weight is calculated based on the density of the soil. Finally, the weight data of the soil around the main road is obtained to help evaluate the degree of soil pressure on the pipeline. Use the pressure calculation formula, which includes the force of soil weight on the pipeline and the effect of settlement on the pipeline. The specific calculation steps are: Pressure = soil weight / pipeline contact area + additional pressure caused by settlement. Input the soil weight and settlement data into the pressure analysis tool to calculate the total pressure on the pipeline.Finally, the pressure data of the main channel of the drainage pipeline is obtained, which shows the actual pressure level that the pipeline is subjected to under the influence of current soil conditions and settlement.

[0124] Preferably, step S23 includes the following steps:

[0125] Step S231: Acquire the material data of the main channel of the drainage pipeline;

[0126] Step S232: marking the maximum deformation threshold of the pipeline according to the material data of the main channel of the drainage pipeline to obtain the maximum deformation threshold of the main channel of the drainage pipeline;

[0127] Step S233: performing deformation displacement analysis according to the deformation data of the drainage pipeline main channel and the pressure data of the drainage pipeline main channel to obtain displacement data of the drainage pipeline main channel;

[0128] Step S234: performing deformation overload analysis on the displacement data of the drainage pipeline trunk according to the maximum deformation threshold of the drainage pipeline trunk to obtain deformation overload data of the pipeline trunk;

[0129] Step S235: performing deformation degradation trend analysis according to the pipeline trunk road deformation overload data to obtain pipeline trunk road deformation degradation trend data;

[0130] Step S236: Evaluate the drainage pipeline rupture probability based on the pipeline trunk deformation deterioration trend data and the drainage pipeline trunk pressure data to obtain the drainage pipeline rupture probability data.

[0131] In the embodiment of the present invention, in the process of obtaining the material data of the main road of the drainage pipeline, the raw material information of the main road is first obtained by using the on-site investigation and engineering archive records. The material type and specifications of the pipeline are determined by using pipeline detection equipment, such as geological radar (GPR) and underground pipeline detectors. The collected data is input into the database, including the material of the pipeline (such as polyethylene, steel or concrete), the wall thickness of the pipeline, the diameter and the installation year of the pipeline. This data provides basic information for subsequent deformation threshold marking and deformation analysis. The acquired material data analyzes the maximum deformation capacity of pipelines of different materials under various pressure conditions. For example, for polyethylene pipelines, refer to its manufacturing specifications and laboratory test data to determine its maximum deformation threshold. Use the material property table in the engineering manual to determine the maximum load-bearing deformation limit of each pipeline material under a specific working environment. These thresholds are input into the data processing system so that they can be used as the reference value of deformation overload in subsequent analysis. The actual deformation data and pressure data of the pipeline are obtained from the on-site monitoring system. Deformation sensors (such as strain gauges) are installed at key positions of the pipeline to record the deformation of the pipeline in real time. Combined with the pressure data during pipeline operation, the deformation displacement data is calculated. The deformation displacement analysis uses mathematical models and data processing algorithms to comprehensively analyze the deformation data and pressure data, calculate the displacement of each monitoring point, and record it in the database. For the displacement of each monitoring point, it is compared with the maximum deformation threshold of the pipeline where it is located to calculate whether there is an overload. Using data processing tools (such as Excel or professional data analysis software), a statistical report on deformation overload is generated to clarify which areas of the pipeline have exceeded their maximum deformation threshold and mark potential overload areas. According to the obtained deformation overload data, it is input into the trend analysis tool to perform deformation degradation trend analysis. Using the time series analysis method, the deformation overload situation of each monitoring point is examined. By analyzing historical data and current data, the trend of deformation degradation is identified. Generate charts and trend curves to show the deformation degradation of the pipeline in different time periods and predict future deformation development trends. Use a probabilistic assessment model to calculate the probability of drainage pipeline rupture. Judge the health status of the pipeline based on the deformation degradation trend, and evaluate the potential risk of rupture in combination with pressure data. Using probability calculation tools and risk assessment algorithms, a rupture probability report for sewer lines is generated, indicating high-risk areas and making recommendations for pipe maintenance or replacement.

[0132] Preferably, step S3 comprises the following steps:

[0133] Step S31: Acquire urban rainfall data and urban terrain data;

[0134] Step S32: constructing a runoff drainage pipeline network structure model according to urban rainfall data, municipal drainage pipeline structure data and urban terrain data to obtain a runoff drainage pipeline network structure model;

[0135] Step S33: Perform dynamic water flow analysis according to the runoff drainage pipeline network structure model to obtain pipeline dynamic water flow data;

[0136] Step S34: Perform water flow overload analysis based on the main road rainwater drainage capacity prediction data, the drainage pipeline main road carrying capacity data and the pipeline dynamic water flow data to obtain the pipeline main road water flow overload data.

[0137] As an example of the present invention, refer to Figure 3 As shown, in this example, step S3 includes:

[0138] Step S31: Acquire urban rainfall data and urban terrain data;

[0139] In an embodiment of the present invention, urban rainfall data can be obtained by downloading historical and real-time rainfall records from the meteorological department. These data include information such as rainfall intensity, rainfall amount, and rainfall period. Data extraction and sorting are performed using the data interface or database of the meteorological station. Urban terrain data can be obtained from a geographic information system (GIS) platform, including information such as the city's elevation, slope, and terrain undulations. Digital elevation model (DEM) data is derived using GIS software to provide detailed information on the terrain. These data are stored in a database to provide the necessary information basis for subsequent runoff and drainage analysis.

[0140] Step S32: constructing a runoff drainage pipeline network structure model according to urban rainfall data, municipal drainage pipeline structure data and urban terrain data to obtain a runoff drainage pipeline network structure model;

[0141] In the embodiment of the present invention, when constructing the runoff drainage pipeline network structure model, firstly integrate the urban rainfall data, drainage pipeline structure data and urban terrain data. Use GIS software to import all relevant data, including the layout and design specifications of the drainage pipeline, such as pipe diameter, material and laying depth. At the same time, the urban terrain data and rainfall data are input into the system to simulate the rainwater runoff situation. By combining the terrain slope and rainfall intensity, the water flow path and flow calculation model of each section in the pipeline network are established. The model includes the flow direction, flow distribution and its relationship with the terrain of the drainage pipeline to form a complete drainage network model.

[0142] Step S33: Perform dynamic water flow analysis according to the runoff drainage pipeline network structure model to obtain pipeline dynamic water flow data;

[0143] In the embodiment of the present invention, the runoff drainage pipeline network structure model constructed in step S32 is used to perform dynamic water flow analysis. Use water flow simulation software (such as HEC-RAS or SWMM) to input dynamic rainfall data into the model. Set the time step and rainfall intensity to simulate the change of water flow under different rainfall conditions. The actual water flow of each section in the pipeline network is obtained by simulation calculation, including peak flow and average flow. These data reflect the dynamic water flow distribution of the pipeline network under different rainfall conditions and are recorded in the data system.

[0144] Step S34: Perform water flow overload analysis based on the main road rainwater drainage capacity prediction data, the drainage pipeline main road carrying capacity data and the pipeline dynamic water flow data to obtain the pipeline main road water flow overload data.

[0145] In the embodiment of the present invention, the obtained pipeline dynamic water flow data is compared with the main road rain and flood drainage capacity prediction data and the main road carrying capacity data of the drainage pipeline. First, the main road drainage capacity data is extracted, including its designed drainage capacity and actual monitoring data, and these data are used to evaluate the pipeline's carrying capacity under different water flow rates. Next, the dynamic water flow data is compared with the main road drainage capacity to identify whether there is a water flow overload situation. Using data analysis tools, a water flow overload carrying analysis report is generated to point out the overload area of ​​the pipeline and recommend improvement measures or adjustment plans.

[0146] Preferably, step S32 includes the following steps:

[0147] Step S321: Analyze the temporal and spatial distribution characteristics of rainfall according to the urban rainfall data to obtain the temporal and spatial distribution characteristic data of rainfall;

[0148] Step S322: Predicting the rainfall change trend according to the rainfall temporal and spatial distribution characteristic data to obtain rainfall change trend data;

[0149] Step S323: performing rainfall runoff intensity analysis according to rainfall change trend data and urban terrain data to obtain rainfall runoff intensity data;

[0150] Step S324: construct a runoff drainage pipeline network model according to the rainfall runoff intensity data and the municipal drainage pipeline structure data to obtain a runoff drainage pipeline network model.

[0151] In the embodiment of the present invention, when analyzing the temporal and spatial distribution characteristics of rainfall, firstly obtain the rainfall data of the city from the meteorological department, including the rainfall, rainfall intensity and spatial distribution thereof in different time periods. Import these data into a data analysis tool (such as Excel or a dedicated statistical analysis software), and draw a time series diagram and a spatial distribution diagram of rainfall and rainfall intensity. Analyze the temporal and spatial variation characteristics of rainfall data, such as the peak period of rainfall, the hotspot area of ​​rainfall distribution, etc. Use statistical analysis methods (such as time series analysis and spatial cluster analysis) to determine the variation law of rainfall and intensity, and finally obtain rainfall temporal and spatial distribution characteristic data, which reveal the timeliness and geographical distribution law of rainfall, and lay the foundation for subsequent analysis. Predict rainfall change trend. Firstly, apply a time series analysis method (such as ARIMA model or linear regression analysis) to perform trend analysis on historical rainfall data, and predict the variation trend of future rainfall and intensity. Combine the spatial data in the geographic information system (GIS), and analyze the variation trend of rainfall distribution. Calculate the rainfall and rainfall intensity within a certain period of time in the future through the prediction model, and generate rainfall change trend data. These data include the expected rainfall intensity change and rainfall time series forecast, providing the expected change information under future rainfall conditions. Perform rainfall runoff intensity analysis. First, obtain the city's digital elevation model (DEM) data from the GIS platform to understand the terrain undulation, slope and watershed distribution. Input the rainfall change trend data into the runoff calculation model (such as the SCS-CN model or the unit map method), and calculate it in combination with the terrain data to obtain the runoff intensity of each area. Analyze the impact of terrain characteristics on runoff intensity, including the aggravation of runoff in high-slope areas and the flood retention in low-lying areas. The rainfall runoff intensity data finally obtained shows the runoff intensity changes under various terrain conditions, providing a basis for the subsequent pipeline network model construction. First, collect the municipal drainage pipeline structure data, including the size, material, layout and connection relationship of the pipeline. These data are obtained from the design drawings and existing databases of the urban drainage system. Combined with the rainfall runoff intensity data obtained in step S323, use GIS or water flow simulation tools to superimpose the pipeline network with the runoff intensity data. According to the design carrying capacity of the pipeline and the expected runoff intensity, a simulation model of the pipeline network is established. The model includes the flow distribution and flow direction of each pipe section, simulating the entire process of rainwater from rainfall to discharge. The resulting runoff drainage pipeline network model describes in detail the working status and drainage capacity of the pipeline, providing detailed basic data for subsequent drainage system analysis. Preferably, step S34 includes the following steps:

[0152] Step S341: performing a pipeline main channel water flow peak value analysis and a pipeline main channel baseline flow analysis according to the pipeline dynamic water flow data, and obtaining the pipeline main channel water flow peak value data and the pipeline main channel baseline flow data;

[0153] Step S342: marking the main road flow overload point of the municipal drainage pipeline structure data according to the main road water flow peak data and the main road rain and flood drainage capacity prediction data, and obtaining the main road flow overload point data;

[0154] Step S343: performing a pipeline main road water level overload threshold analysis based on the pipeline main road flow overload point data, the pipeline main road baseline flow data, and the drainage pipeline main road carrying capacity data to obtain the overload point water level overload threshold data;

[0155] Step S344: Perform water flow overload carrying analysis on the pipeline main channel flow overload point data according to the pipeline main channel overload point water level peak data and the overload point water level overload threshold to obtain the pipeline main channel water flow overload carrying data.

[0156] In an embodiment of the present invention, real-time flow data of pipeline trunks are extracted from a dynamic water flow monitoring system. These data include water flow records in different time periods. The water flow data are processed using a data analysis tool (such as Excel or MATLAB). The data are statistically analyzed to identify the peak value of the water flow, that is, the maximum flow during the peak rainfall period or the period with a large drainage load. The baseline flow of the water flow, that is, the long-term average flow level, is obtained by calculating the mean and standard deviation of the data. Finally, peak water flow data and baseline flow data of the pipeline trunk are generated, which reveal the flow characteristics of the pipeline under different operating conditions and provide a basis for subsequent overload analysis. The design drainage capacity data of the trunk is obtained from the design specifications of the drainage system. These data include the maximum processing capacity of the trunk under different rainfall intensities. The peak water flow data are compared with the predicted drainage capacity to identify areas that exceed the design drainage capacity of the trunk under specific rainfall conditions. The overload points are marked using a data marking tool (such as a symbol marking function in a GIS system) to generate pipeline trunk flow overload point data. These data indicate which pipeline sections are at risk of flow overload under current or predicted rainfall conditions. Conduct water level overload threshold analysis. First, obtain the carrying capacity data from the design specifications of the pipeline, including the maximum water level of the pipeline. Combine the water flow at the overload point with the baseline flow data and calculate the water level change. Use mathematical models (such as fluid mechanics equations) to estimate the water level at the peak flow rate and then compare it with the carrying capacity of the pipeline. Determine the water level overload threshold at the overload point, identify the specific value where the water level exceeds the pipeline carrying capacity, and record it in the data system. These threshold data are used to evaluate whether there is a potential water level overload problem in the current pipeline. Use a relationship model between water flow and water level (such as a flow-water level curve) to calculate the overload situation between the actual water flow and water level at the overload point. Compare the peak water flow with the water level overload threshold to evaluate whether there is a water flow overload phenomenon. Use data analysis tools to generate a water flow overload load analysis report, identify the overload area that appears under actual flow, and provide specific data on overload load. These analysis results help identify and prevent drainage system problems and guide system maintenance and improvement.

[0157] Preferably, step S4 comprises the following steps:

[0158] Step S41: extracting the topological structure based on a map of municipal drainage pipelines to obtain the topological structure data of the municipal drainage pipelines;

[0159] Step S42: analyzing the main roads of the municipal drainage pipeline topology data for easily overloaded pipelines according to the pipeline main road water flow overload carrying data and the drainage pipeline main road carrying capacity data to obtain the main roads of the pipelines that are easily overloaded;

[0160] Step S43: Optimizing the topological drainage path according to the main pipeline prone to overload, and obtaining topological drainage path optimization data;

[0161] Step S44: Perform real-time drainage optimization on the runoff drainage pipeline network structure model according to the topological drainage path optimization data to obtain real-time drainage optimization data for the pipeline trunk.

[0162] In an embodiment of the present invention, when extracting the topological structure of a municipal drainage pipeline, first use the design drawings or maps of the municipal drainage pipeline as a data source. These drawings should indicate in detail the layout, connection relationship and size information of all drainage pipelines. The drainage pipeline drawings are imported into the system through a geographic information system (GIS) tool. The spatial data of the drainage pipeline is processed using the topological analysis function in the GIS software to extract the topological structure data of the pipeline, including pipeline nodes (such as intersections, branch access points) and pipeline connection relationships. The generated topological structure data includes the connection path of each pipeline, the pipeline length and the relationship between the nodes, providing a structural basis for subsequent pipeline analysis. The overload data of each pipeline is compared with its design carrying capacity to determine which pipelines are at risk of overload under a specific flow rate. The carrying capacity and overload data of the pipeline are processed using a data analysis tool (such as the pivot table function of Excel) to identify pipeline trunks that are prone to overload. These pipelines that are prone to overload are marked, and related pipeline trunk data that are prone to overload are generated to indicate pipeline areas that are prone to overload under current or predicted flow conditions. Analyze the pipeline topology and redesign the drainage path of the pipeline network using drainage path optimization tools (such as path analysis functions in GIS). Reduce the load on pipelines that are prone to overload by changing the flow direction of the pipeline, adding diversion or connecting pipelines. Consider factors such as flow distribution, pipeline carrying capacity and economy during the optimization process to ensure that the drainage system is loaded evenly under different rainfall conditions. The generated topological drainage path optimization data shows the adjusted pipeline network structure, including the new pipeline layout and optimized flow path. The optimized pipeline layout data is input into the water flow simulation software (such as SWMM or HEC-RAS) to perform real-time drainage optimization of the drainage pipeline network. Using real-time water flow data and optimized path data, adjust the water flow distribution in the pipeline network to ensure flow balance and maximize drainage capacity in actual operation. Real-time drainage optimization data of pipeline trunks are obtained through model simulation, including optimized flow distribution, reduced overload risk and system improvement effects. These data help improve the performance of the drainage system and ensure its stability and effectiveness under different weather conditions.

[0163] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0164] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A method for modeling a municipal drainage pipeline based on a map, characterized in that: The following steps are involved: Step S1: extracting the pipeline structure based on a map of municipal drainage pipelines to obtain the municipal drainage pipeline structure data; According to the municipal drainage pipeline structure data, the main rainwater drainage road is analyzed to obtain the main road data of the rainwater drainage pipeline; according to the municipal drainage pipeline main road data, the rainwater drainage capacity is predicted to obtain the main road rainwater drainage capacity prediction data; Step S2: Calculate the pressure of the main channel of the drainage pipeline according to the main channel data of the municipal drainage pipeline to obtain the pressure data of the main channel of the drainage pipeline; Carry out bearing capacity analysis of the main drainage pipeline according to the pressure data of the main drainage pipeline to obtain the bearing capacity data of the main drainage pipeline; Step S3: obtaining the maximum rainfall data and urban terrain data of the city; constructing a runoff drainage pipeline network structure model according to the urban rainfall data, the municipal drainage pipeline structure data and the urban terrain data to obtain a runoff drainage pipeline network structure model; performing a water flow overload carrying analysis according to the pipeline rainfall network model to obtain the pipeline trunk water flow overload carrying data; Step S4: Perform real-time drainage optimization on the runoff drainage pipeline network structure model according to the pipeline trunk water flow overload carrying data and the drainage pipeline trunk carrying capacity data to obtain the pipeline trunk real-time drainage optimization data.

2. The method for modeling a municipal drainage pipeline based on a single picture according to claim 1, characterized in that: Step S1 includes the following steps: Step S11: extracting the pipeline structure based on a map of the municipal drainage pipeline to obtain the municipal drainage pipeline structure data; Step S12: Analyze the municipal stormwater drainage pipeline according to the municipal drainage pipeline structure data to obtain stormwater drainage pipeline data; Step S13: analyzing the main rainwater drainage channel according to the municipal drainage pipeline structure data and the rainwater drainage pipeline data to obtain the main rainwater drainage pipeline data; Step S14: predicting the drainage capacity based on the main road data of the stormwater drainage pipeline to obtain the main road stormwater drainage capacity prediction data.

3. The method for modeling a municipal drainage pipeline based on a single picture according to claim 2 is characterized in that: Step S14 includes the following steps: Step S141: performing pipeline layout analysis and main road pipeline width calculation according to the rainwater drainage pipeline main road data, and obtaining drainage pipeline main road layout data and main road pipeline width data; Step S142: Calculate the inclination of the main drainage pipeline road according to the drainage pipeline main pipeline layout data to obtain the inclination data of the main drainage pipeline road; Step S143: Calculate the drainage flow rate according to the drainage pipeline main road inclination data and the main road pipe width data to obtain the drainage flow rate data of the drainage pipeline main road; Step S144: predicting the rainwater drainage capacity according to the main road pipeline width data and the main road drainage flow rate data of the drainage pipeline to obtain the main road rainwater drainage capacity prediction data.

4. The method for modeling a municipal drainage pipeline based on a single picture according to claim 1, characterized in that: Step S2 includes the following steps: Step S21: Calculate the pressure of the main channel of the drainage pipeline according to the main channel data of the municipal drainage pipeline to obtain the pressure data of the main channel of the drainage pipeline; Step S22: performing deformation analysis of the main channel of the drainage pipeline according to the pressure data of the main channel of the drainage pipeline to obtain deformation data of the main channel of the drainage pipeline; Step S23: obtaining material data of the main channel of the drainage pipeline; evaluating the probability of rupture of the drainage pipeline according to the deformation data of the main channel of the drainage pipeline, and obtaining the probability data of rupture of the drainage pipeline; Step S24: analyzing the bearing capacity of the main drainage pipeline according to the drainage pipeline rupture probability data and the drainage pipeline main pipeline pressure data to obtain the bearing capacity data of the main drainage pipeline.

5. The method for modeling a municipal drainage pipeline based on a single picture according to claim 4 is characterized in that: Step S21 includes the following steps: Step S211: extracting the surrounding environment of the municipal drainage pipeline and measuring the pipeline depth according to the main road data of the municipal drainage pipeline, and obtaining the surrounding environment data of the municipal drainage pipeline and the depth data of the municipal drainage pipeline; Step S212: Analyze the surface weight load situation based on the surrounding environment data of the municipal drainage pipeline to obtain the surface weight load data around the pipeline; Step S213: Calculating the ground settlement around the pipeline according to the ground weight load data around the pipeline to obtain ground settlement data around the pipeline; Step S214: analyzing the soil density around the main road according to the surface settlement data around the pipeline to obtain the soil density data around the main road; Step S215: performing soil weight analysis around the main road according to soil density data around the main road and municipal drainage pipeline depth data to obtain soil weight data around the main road; Step S216: Calculate the drainage pipeline main road pressure based on the main road soil weight data and the pipeline surrounding surface settlement data to obtain the drainage pipeline main road pressure data.

6. The method for modeling a municipal drainage pipeline based on a single picture according to claim 4 is characterized in that: Step S23 includes the following steps: Step S231: Acquire the material data of the main channel of the drainage pipeline; Step S232: marking the maximum deformation threshold of the pipeline according to the material data of the main channel of the drainage pipeline to obtain the maximum deformation threshold of the main channel of the drainage pipeline; Step S233: performing deformation displacement analysis according to the deformation data of the drainage pipeline main channel and the pressure data of the drainage pipeline main channel to obtain displacement data of the drainage pipeline main channel; Step S234: performing deformation overload analysis on the displacement data of the drainage pipeline trunk according to the maximum deformation threshold of the drainage pipeline trunk to obtain deformation overload data of the pipeline trunk; Step S235: performing deformation degradation trend analysis according to the pipeline trunk road deformation overload data to obtain pipeline trunk road deformation degradation trend data; Step S236: Evaluate the drainage pipeline rupture probability based on the pipeline trunk deformation deterioration trend data and the drainage pipeline trunk pressure data to obtain the drainage pipeline rupture probability data.

7. The method for modeling a municipal drainage pipeline based on a single picture according to claim 1, characterized in that: Step S3 includes the following steps: Step S31: Acquire urban rainfall data and urban terrain data; Step S32: constructing a runoff drainage pipeline network structure model according to urban rainfall data, municipal drainage pipeline structure data and urban terrain data to obtain a runoff drainage pipeline network structure model; Step S33: Perform dynamic water flow analysis according to the runoff drainage pipeline network structure model to obtain pipeline dynamic water flow data; Step S34: Perform water flow overload analysis based on the main road rainwater drainage capacity prediction data, the drainage pipeline main road carrying capacity data and the pipeline dynamic water flow data to obtain the pipeline main road water flow overload data.

8. The method for modeling a municipal drainage pipeline based on a single picture according to claim 7, characterized in that: Step S32 includes the following steps: Step S321: Analyze the temporal and spatial distribution characteristics of rainfall according to the urban rainfall data to obtain the temporal and spatial distribution characteristic data of rainfall; Step S322: Predicting the rainfall change trend according to the rainfall temporal and spatial distribution characteristic data to obtain rainfall change trend data; Step S323: performing rainfall runoff intensity analysis according to rainfall change trend data and urban terrain data to obtain rainfall runoff intensity data; Step S324: construct a runoff drainage pipeline network model according to the rainfall runoff intensity data and the municipal drainage pipeline structure data to obtain a runoff drainage pipeline network model.

9. The method for modeling a municipal drainage pipeline based on a single picture according to claim 7, characterized in that: Step S34 includes the following steps: Step S341: performing a pipeline main channel water flow peak value analysis and a pipeline main channel baseline flow analysis according to the pipeline dynamic water flow data, and obtaining the pipeline main channel water flow peak value data and the pipeline main channel baseline flow data; Step S342: marking the main road flow overload point of the municipal drainage pipeline structure data according to the main road water flow peak data and the main road rain and flood drainage capacity prediction data, and obtaining the main road flow overload point data; Step S343: performing a pipeline main road water level overload threshold analysis based on the pipeline main road flow overload point data, the pipeline main road baseline flow data, and the drainage pipeline main road carrying capacity data to obtain the overload point water level overload threshold data; Step S344: Perform water flow overload carrying analysis on the pipeline main channel flow overload point data according to the pipeline main channel overload point water level peak data and the overload point water level overload threshold to obtain the pipeline main channel water flow overload carrying data.

10. The method for modeling a municipal drainage pipeline based on a single picture according to claim 7, characterized in that: Step S4 includes the following steps: Step S41: extracting the topological structure based on a map of municipal drainage pipelines to obtain the topological structure data of the municipal drainage pipelines; Step S42: analyzing the main roads of the municipal drainage pipeline topology data for easily overloaded pipelines according to the pipeline main road water flow overload carrying data and the drainage pipeline main road carrying capacity data to obtain the main roads of the pipelines that are easily overloaded; Step S43: Optimizing the topological drainage path according to the main pipeline prone to overload, and obtaining topological drainage path optimization data; Step S44: Perform real-time drainage optimization on the runoff drainage pipeline network structure model according to the topological drainage path optimization data to obtain real-time drainage optimization data for the pipeline trunk.

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