A general layout gravity flow pipe network vertical intelligent design method and system

Abstract

This invention provides a method and system for intelligent vertical design of gravity flow pipe networks, relating to the field of pipe network design technology. The method includes: acquiring a site digital twin model; optimizing the route layout based on the site digital twin model, the upstream pipe segment start point, the downstream pipe segment end point, and avoidance rules to generate an initial pipe network route; dividing the site digital twin model into multiple drainage zones by searching for building location points; performing pipe segment velocity analysis based on drainage data corresponding to each drainage zone and the planar connection angle of adjacent pipe segments at manholes in the initial pipe network route to obtain the actual minimum flow velocity for each pipe segment; and performing vertical correction design on the initial pipe network route based on the actual minimum flow velocity to obtain the overall gravity flow pipe network. The method and system provided by this invention can effectively improve the reliability of vertical design of gravity flow pipe networks.

Description

Technical Field

[0001] This invention relates to the field of pipeline design technology, and in particular to a vertical intelligent design method and system for gravity flow pipeline networks. Background Technology

[0002] Industrial site layouts of underground pipe networks include water supply, drainage, heating, electricity, communications, and process piping. Traditional designs are based on two-dimensional drawings, with cross-sectional views (sections) of the network drawn at key intersections to clearly show the vertical elevation relationships of each pipeline and ensure compliance with code clearance requirements. The layout relies heavily on individual experience, resulting in inconsistent design quality and difficulties in standardization and optimization. Furthermore, the vertical design of underground gravity flow pipe networks is crucial in site layout design, as its rationality directly impacts drainage smoothness, construction costs, and operational safety.

[0003] Therefore, in the existing general layout gravity flow pipeline network design process, since the design can only be carried out on plan drawings, it is difficult to comprehensively consider the vertical conflicts with existing bottom pipelines or structures. Moreover, since the gravity flow pipeline network cannot be dynamically linked with the actual sewage discharge on site during the design, the vertical design of the gravity flow pipeline network is difficult to match the actual discharge flow velocity requirements due to the varying degrees of siltation caused by sewage discharge, which greatly reduces the reliability of the pipeline network in actual use. Summary of the Invention

[0004] This invention provides a vertical intelligent design method and system for general layout gravity flow pipe networks to solve the technical problems in the existing general layout gravity flow pipe network design process. Because the design can only be carried out on planar drawings, it is difficult to comprehensively consider the vertical conflicts with existing bottom pipelines or structures. Moreover, because the gravity flow pipe network cannot be dynamically correlated with the actual sewage discharge on site, the vertical design of the gravity flow pipe network is difficult to match the actual discharge flow rate requirements due to the varying degrees of siltation caused by sewage discharge.

[0005] To achieve the above and other related objectives, this invention provides a vertical intelligent design method for a site gravity flow pipeline network, comprising: acquiring a site digital twin model; optimizing the route layout based on the site digital twin model, the upstream pipe segment start point, the downstream pipe segment end point, and avoidance rules to generate an initial pipeline network route; dividing the site digital twin model into multiple drainage zones by searching for building location points; performing pipe segment velocity analysis based on the drainage data corresponding to each drainage zone and the planar connection angle of adjacent pipe segments at inspection wells in the initial pipeline network route to obtain the actual minimum flow velocity corresponding to each pipe segment; and performing vertical correction design on the initial pipeline network route based on the actual minimum flow velocity to obtain the site gravity flow pipeline network.

[0006] In one embodiment of the present invention, the site digital twin model includes a site elevation model and an existing underground pipeline and structure model. Based on the site digital twin model, the upstream pipe segment start point, the downstream pipe segment end point, and avoidance rules, route optimization is performed to generate an initial pipeline route, including: extracting corresponding avoidance rules based on the coordinate area data of the site elevation model and the existing underground pipeline and structure model to obtain avoidance areas corresponding to each coordinate area; generating allowable areas for pipeline design based on the upstream pipe segment start point, the downstream pipe segment end point, and the site elevation model; isolating conflicts between various avoidance areas based on the allowable areas to obtain corresponding open areas within the allowable areas; creating fixed manhole coordinates based on the corresponding demand drainage areas and open areas in the site digital twin model; creating movable manhole coordinates located between the fixed manhole coordinates based on the vertical elevation data and pipe segment constraints corresponding to each point within the open area; and generating the initial pipeline route based on the fixed manhole coordinates and the movable manhole coordinates.

[0007] In one embodiment of the present invention, the site digital twin model is divided into multiple drainage zones by searching for building location points within the site digital twin model. This includes: searching for building use and scale data corresponding to each building location point within the site digital twin model to obtain multiple building use and scale data corresponding to each building location point; finding the water usage pattern and water consumption corresponding to each building location point based on the building use and scale data; predicting sewage discharge based on the water usage pattern and water consumption to obtain sewage type and sewage volume; and performing clustering processing on adjacent building location points based on sewage type and corresponding sewage volume to divide the site digital twin model into multiple drainage zones.

[0008] In one embodiment of the present invention, based on the drainage data corresponding to each drainage area and the planar connection angle of adjacent pipe segments at the inspection well in the initial pipeline route, pipe segment flow velocity analysis is performed to obtain the minimum flow velocity corresponding to each pipe segment. This includes: generating a cumulative siltation risk value corresponding to each pipe segment in the initial pipeline route based on the drainage data corresponding to each drainage area and the planar connection angle; correcting the Manning roughness coefficient based on the cumulative siltation risk value and the type of pipe laid corresponding to the initial pipeline route to obtain the Manning roughness correction coefficient; and analyzing the actual minimum flow velocity corresponding to each pipe segment based on the Manning roughness correction coefficient.

[0009] In one embodiment of the present invention, based on the drainage data and planar connection angle corresponding to each drainage area, a cumulative siltation risk value corresponding to each pipe segment in the initial pipeline route is generated, including: performing sewage statistics based on the sewage type and corresponding sewage volume corresponding to different building uses and scales at each building location point in each drainage area, to obtain sewage statistics data corresponding to each sewage type; obtaining the downstream pipe segment parameters corresponding to the upstream well coordinates of the nearest inspection well connected to the building location point; obtaining the adjacent siltation risk index of the downstream adjacent pipe segment corresponding to the upstream well coordinate based on the sewage statistics data, downstream pipe segment parameters, and siltation risk factors corresponding to each downstream pipe segment parameter, wherein the downstream pipe segment parameters include pipe segment length, vertical elevation, and pipe segment diameter; optimizing and adjusting the adjacent siltation risk index based on the planar connection angle and siltation conversion factor corresponding to the downstream well coordinates of each downstream adjacent pipe segment, to generate a cumulative siltation risk value corresponding to each pipe segment in the initial pipeline route; the calculation formula for the cumulative siltation risk value is: ;in, This represents the cumulative value of the risk of siltation. This represents the radian value of the angle between two planes. This represents the siltation conversion factor corresponding to different radian values.

[0010] In one embodiment of the present invention, based on wastewater statistics, downstream pipe segment parameters, and the siltation risk factor corresponding to each downstream pipe segment parameter, an adjacent siltation risk index for the downstream adjacent pipe segment corresponding to the upstream well coordinates is obtained. This includes: extracting wastewater combinations based on the wastewater type corresponding to the building location point to obtain wastewater classification categories, which include independent wastewater types and combinations of wastewater types; and obtaining wastewater classification data corresponding to each wastewater classification category based on the wastewater classification categories and wastewater statistics. The wastewater classification data is expressed as follows: , This represents the data for classifying wastewater. This indicates each individual type of wastewater within the wastewater zone. In the The first wastewater statistics data corresponding to each building location point. Indicates the combination of wastewater types within a wastewater zone. Each type of wastewater Corresponding wastewater statistics; based on wastewater classification data, wastewater density, and wastewater viscosity, the sedimentation potential value corresponding to each drainage area is obtained. The formula for calculating the sedimentation potential value is as follows: , This indicates the potential for siltation. This represents the wastewater classification data corresponding to each wastewater classification category. This indicates the wastewater density corresponding to each wastewater category. This indicates the viscosity of the wastewater corresponding to each wastewater category. This represents the first potential conversion factor corresponding to the wastewater classification data. This represents the second potential conversion factor corresponding to the density of wastewater. The third potential conversion coefficient represents the viscosity of the wastewater. Based on the sedimentation potential value, downstream pipe segment parameters, and the weighting coefficients corresponding to each downstream pipe segment parameter, the adjacent sedimentation risk index of the downstream adjacent pipe segment corresponding to the upstream well coordinates is obtained. The formula for calculating the adjacent sedimentation risk index is as follows: ,in, This indicates the proximity siltation risk index for each downstream adjacent pipe section. This represents the first risk coefficient corresponding to the siltation potential value. This represents the second risk coefficient corresponding to the downstream pipe section parameters. This represents the downstream pipe segment parameters for each adjacent downstream pipe segment. This represents the weighting coefficient corresponding to each downstream pipe segment parameter.

[0011] In one embodiment of the present invention, the Manning roughness coefficient is corrected based on the cumulative value of siltation risk and the type of pipe laid corresponding to the initial pipeline route to obtain the Manning roughness correction coefficient. This includes: querying the corresponding Manning theoretical roughness coefficient based on the type of pipe laid corresponding to the initial pipeline route; finding the risk accumulation threshold and coefficient correction factor corresponding to the pipe type based on the cumulative value of siltation risk, pipe type, and correction correspondence table, to calculate the correction coefficient; and correcting the Manning theoretical roughness coefficient using the correction coefficient to obtain the Manning roughness correction coefficient. The calculation formula for the Manning roughness correction coefficient is as follows: ; This represents the Manning roughness correction factor. This represents the roughness coefficient of Manning's theory. This represents the cumulative value of the risk of siltation. Indicates the cumulative risk threshold. This represents the coefficient correction factor.

[0012] In one embodiment of the present invention, the actual minimum flow velocity corresponding to each pipe segment is obtained by analyzing the Manning roughness correction coefficient, including: obtaining an intermediate coefficient based on the theoretical minimum flow velocity of the corresponding pipe segment and the Manning roughness theoretical coefficient corresponding to the pipe segment; and analyzing the actual minimum flow velocity corresponding to each pipe segment based on the Manning roughness correction coefficient and the intermediate coefficient; the calculation formula for the actual minimum flow velocity is as follows: ;in, Indicates the actual minimum flow rate. Indicates the intermediate coefficient. This represents the theoretical minimum flow rate. This represents the roughness coefficient of Manning's theory. Represents the transformation constant. Indicates the hydraulic radius. Indicates the slope of the pipe section. This represents the Manning roughness correction factor.

[0013] In one embodiment of the present invention, the initial pipeline route is vertically corrected based on the actual minimum flow velocity to obtain the overall gravity flow pipeline network. This includes: determining whether the actual minimum flow velocity corresponding to each pipe segment is less than a lower velocity limit; if so, generating vertical correction parameters for the target pipe segment based on the velocity difference between the lower velocity limit and the actual minimum flow velocity, the original pipe segment radius and original pipe segment slope of the target pipe segment in the initial pipeline route. The vertical correction parameters include pipe segment radius correction parameters and pipe segment slope correction parameters; sequentially using each vertical correction parameter to perform operational simulation on the target pipe segment corresponding to the initial pipeline route to obtain the corresponding simulated minimum flow velocity; selecting the minimum value of the simulated minimum flow velocity as the corrected minimum flow velocity for the target pipe segment; and sequentially vertically correcting the initial pipeline route based on the corrected minimum flow velocity to obtain the overall gravity flow pipeline network.

[0014] To achieve the above and other related objectives, the present invention also provides a vertical intelligent design system for a site gravity flow pipeline network, comprising: an acquisition unit for acquiring a site digital twin model; an optimization unit for optimizing the route layout based on the site digital twin model, the upstream pipe segment start point, the downstream pipe segment end point, and avoidance rules to generate an initial pipeline network route; a division unit for dividing the site digital twin model into multiple drainage zones by searching for building location points on the site digital twin model; an analysis unit for analyzing the pipe segment flow velocity based on the drainage data corresponding to each drainage zone and the planar connection angle of adjacent pipe segments at the manhole in the initial pipeline network route to obtain the actual minimum flow velocity corresponding to each pipe segment; and a correction unit for vertically correcting the initial pipeline network route based on the actual minimum flow velocity to obtain the site gravity flow pipeline network.

[0015] The beneficial effects of this invention are as follows: The present invention proposes a vertical intelligent design method and system for gravity flow pipe networks. By performing twin simulation on the design site, it can quickly build the initial pipe network route based on the corresponding site digital twin model and avoidance rules. After the initial pipe network route is established, the water pollution situation in different areas can be determined by further searching the drainage areas. Combined with the planar connection angle of adjacent pipe segments at the inspection well in the initial pipe network route, the actual minimum flow velocity of each pipe segment is analyzed and determined. Based on the actual minimum flow velocity, the initial pipe network route is corrected, thereby ensuring that the final overall gravity flow pipe network is closer to the actual use scenario, reasonably controlling the siltation risk caused by unreasonable vertical design of pipe segments, and improving the reliability of the vertical design of gravity flow pipe networks. Attached Figure Description

[0016] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application. It is obvious that the drawings described below are merely some embodiments of this application, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort.

[0017] In the attached diagram: Figure 1 This is a flowchart illustrating the vertical intelligent design method for gravity flow pipeline networks provided in an embodiment of the present invention.

[0018] Figure 2 The diagram shown is a structural block diagram of a vertical intelligent design system for gravity flow pipe networks provided in an embodiment of the present invention.

[0019] Figure 3 The diagram shown is a structural schematic of an electronic device according to an embodiment of the present invention.

[0020] The attached figures are labeled as follows: Electronic device 1; Vertical intelligent design system for gravity flow pipeline network 11; Memory 12; Processor 13; Acquisition unit 111; Optimization unit 112; Division unit 113; Analysis unit 114; Correction unit 115. Detailed Implementation

[0021] The following specific examples illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments. Various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. In the absence of conflict, the following embodiments and features in the embodiments can be combined with each other.

[0022] It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of the present invention. The drawings only show the components related to the present invention and are not drawn according to the actual number, shape and size of the components in the actual implementation. In the actual implementation, the form, quantity and proportion of each component can be arbitrarily changed, and the layout of the components may also be more complex.

[0023] In the following description, numerous details are explored to provide a more thorough explanation of embodiments of the invention. However, it will be apparent to those skilled in the art that embodiments of the invention may be practiced without these specific details. In other embodiments, well-known structures and devices are shown in block diagram form rather than in detail to avoid obscuring embodiments of the invention.

[0024] This invention provides a vertical intelligent design method for gravity flow pipe networks. By performing a twin simulation of the design site, the initial pipe network route can be quickly constructed based on the corresponding site digital twin model and avoidance rules. After the initial pipe network route is established, further searches of drainage areas can be conducted to determine the water pollution situation in different areas. Combined with the planar connection angle of adjacent pipe segments at inspection wells in the initial pipe network route, the actual minimum flow velocity of each pipe segment can be analyzed and determined. Based on the actual minimum flow velocity, the initial pipe network route can be corrected, thereby ensuring that the final overall gravity flow pipe network is closer to the actual use scenario, reasonably controlling the risk of siltation caused by unreasonable vertical design of pipe segments, and improving the reliability of the vertical design of gravity flow pipe networks.

[0025] Figure 1 A flowchart illustrating an exemplary embodiment of this application shows a vertical intelligent design method for a gravity flow pipe network, applied to a vertical intelligent design system for a gravity flow pipe network, including steps S10-S50. The following will be combined with... Figure 1 The technical solution of this application will be described in detail below.

[0026] First, step S10 is executed to obtain a digital twin model of the site, which includes a site elevation model and a model of existing underground pipelines and structures.

[0027] The site digital twin model in this invention can be a BIM model created based on measured site data such as ground elevations in various areas of the site, existing underground pipeline data, and site structure deployment data. This allows for automatic simulation design based on the BIM model, enabling timely vertical visualization of the gravity flow network design based on the existing underground pipelines and structures. Alternatively, the site digital twin model can be a design model completed by importing site data from other pipeline and building designs, existing underground pipeline data, and site structure deployment data into the site layout gravity flow network vertical intelligent design system of this invention. It can also be a pre-designed finished model obtained directly.

[0028] Next, step S20 is executed, which optimizes the route laying based on the site digital twin model, the upstream pipe segment starting point, the downstream pipe segment ending point, and the avoidance rules, and generates the initial pipeline route.

[0029] After acquiring the site's digital model, the site layout gravity flow pipeline network vertical intelligent design system automatically optimizes the pipeline route based on pre-arranged upstream pipeline start points and downstream pipeline end points to generate a planar route. Then, based on the manhole locations and corresponding avoidance rules in the planar route, it generates a 3D model of the initial pipeline route with vertical elevation and automated conflict avoidance. This conflict can be between the newly constructed initial pipeline route and existing underground pipelines (such as existing water supply, drainage, heating, electricity, communication, and process pipelines), or with structures (such as residential buildings, waterways, and commercial buildings).

[0030] In step S20, based on the site digital twin model, the upstream pipe segment start point, the downstream pipe segment end point, and avoidance rules, route optimization is performed to generate an initial pipeline route, which may further include: Based on the coordinate area data of the site elevation model and the existing underground pipeline and structure model, corresponding avoidance rules are extracted to obtain the avoidance area corresponding to each coordinate area data. Based on the starting point of the upstream pipe segment, the ending point of the downstream pipe segment, and the site elevation model, the allowable area for the pipeline network design is generated. Based on the permitted areas, conflict isolation is carried out in each avoidance area to obtain the corresponding open areas within the permitted areas; Based on the corresponding drainage and open areas in the site's digital twin model, create fixed manhole coordinates; Based on the vertical elevation data and pipe segment constraints of each point in the open area, create the coordinates of the moving manhole located between the coordinates of the fixed manholes; The initial pipeline route is generated based on the coordinates of the fixed manholes and the moving manholes.

[0031] During the initial pipeline route construction process, corresponding avoidance rules can be generated based on the attribute characteristics of existing underground pipelines and structures in the site elevation model and the coordinate area data occupied by their corresponding locations. For example, based on the coordinate area data of existing underground pipelines and the corresponding collision rules, including vertical clearance less than a set distance value, avoidance areas that need to prevent pipeline collisions will be added to the corresponding coordinate area data based on the set distance value. Then, the avoidance areas can be superimposed on the coordinate area data of the corresponding model to serve as the avoidance areas corresponding to the corresponding coordinate area data.

[0032] Then, in the site elevation model, the allowable area for pipeline network design between the upstream pipe segment start point and the downstream pipe segment end point is determined. This allowable area can be the area range of allowable pipe segments from the upstream pipe segment start point to the downstream pipe segment end point, which has been generated in order from high to low vertical elevation. Subsequently, the avoidance area is removed from the allowable area to obtain the open area in the allowable area that avoids various bottom-line pipelines and structures. Thus, the laying restriction range of the gravity flow pipeline network in the site elevation model can be obtained.

[0033] In open areas, based on the pre-defined drainage demand zones in the site's digital twin model and the open area itself, the nearest suitable manhole locations for straight pipe connections are selected as fixed manhole coordinates. Since these fixed manhole coordinates are relatively fixed, they can be used as a basis to find the optimal moving manhole coordinates with decreasing vertical height among the fixed manhole coordinates, utilizing the vertical elevation data and pipe segment constraints of each point within the open area. Based on these fixed and moving manhole coordinates, an initial pipeline route is generated. These pipe segment constraints include maximum pipe length requirements, maximum and minimum slope requirements, maximum and minimum burial depth, minimum and maximum excavation dimensions, etc. It is worth noting that the coordinates of the fixed manhole and the movable manhole can include vertical coordinates at the same time. This allows us to determine the pipe length, pipe radius, pipe slope, pipe type, etc. of each pipe segment arranged between the fixed manhole coordinates and / or the movable manhole coordinates. These can serve as the original data for subsequent vertical correction design, such as the original pipe segment length, original pipe segment radius, original pipe segment slope, and pipe type.

[0034] Next, step S30 is executed, which involves searching for building location points in the site's digital twin model and dividing the site's digital twin model into multiple drainage zones.

[0035] After the initial pipeline network route is established, the site layout gravity flow pipeline vertical intelligent design system can further refine the route by vertically adjusting it to better reflect the actual site drainage conditions. This involves searching for building locations within the site's digital twin model to ensure each location corresponds to a building cluster in the actual environment. Based on the specific drainage situation, the system divides the site's digital twin model into multiple drainage zones. These zones can be differentiated according to the risk of siltation, allowing for optimized design of the gravity flow pipelines within each zone to accommodate varying siltation risks and ensure minimum flow velocity reliability during drainage.

[0036] In step S30, by searching for building location points on the site's digital twin model, the site's digital twin model is divided into multiple drainage zones, which may further include: Search for the building use and scale data corresponding to each building location point in the site digital twin model to obtain multiple building use and scale data corresponding to each building location point; Based on the building's purpose and scale data, find the water usage pattern and water consumption corresponding to each building location; Based on water usage patterns and water consumption, wastewater discharge prediction is conducted to obtain the types and quantities of wastewater. Clustering of adjacent building locations is performed based on the type and volume of wastewater, dividing the site's digital twin model into multiple drainage zones.

[0037] In the process of dividing drainage zones, the first step is to search for the building uses and scale data of the building complexes included in each building location point within the site's digital twin model. This will identify all building uses and scale data corresponding to the respective building location point. These building uses and scale data can include residential buildings, restaurants, bakeries, hospitals, etc., and chemical plants can also be included. However, since chemical plants undergo treatment before discharging wastewater to meet standards, they can be excluded as primary concerns regarding siltation. After identifying the building users, the required water usage patterns and volumes for each building use and scale can be determined based on this data. These water usage patterns and volumes can be obtained directly from the water supply company, or they can be derived from historical water usage patterns and volumes corresponding to historical building uses and scales. After obtaining the water usage patterns and volumes, further wastewater prediction can be performed to determine the type and volume of wastewater corresponding to each building location point. When predicting wastewater discharge based on water usage patterns and volumes, the data can be directly derived from historical building usage and scale data, water usage patterns, and volumes by looking up tables. Alternatively, historical building usage and scale data, water usage patterns, and volumes can be used as a training set, with corresponding wastewater types and volumes as labels. A wastewater prediction model can then be trained to quickly predict the wastewater volume for each type. It's important to note that water usage patterns and wastewater types can have a corresponding relationship; that is, there is a mapping relationship between them. Finally, based on the similarity between wastewater types and corresponding volumes, as well as the distance between adjacent building locations, the site's digital twin model can be divided into multiple drainage zones. For example, if the wastewater types and volumes between adjacent building locations differ significantly, they cannot be considered as a single drainage zone. Similarly, if the distance between adjacent building locations exceeds a distance threshold, they can be excluded from the same drainage zone. Other situations where buildings are not considered as a single drainage zone are also possible.

[0038] Next, step S40 is executed, whereby pipe segment flow velocity analysis is performed based on the drainage data corresponding to each drainage area and the planar connection angle of adjacent pipe segments at the inspection well in the initial pipeline route, to obtain the actual minimum flow velocity corresponding to each pipe segment. The drainage data may include the type and quantity of sewage, and of course, may also include other sewage data.

[0039] After establishing the initial pipeline route and dividing the drainage areas, the vertical intelligent design system for gravity flow pipeline networks can analyze the minimum flow velocity of each pipeline segment by utilizing the drainage data corresponding to each drainage area and the planar connection angle of adjacent pipe segments at the inspection well in the initial pipeline route. This allows for precise determination of whether the actual minimum flow velocity of each pipe segment meets the requirements. If the requirements are not met, the vertical design of the pipe segment can be corrected in a timely manner to ensure the design reliability of each pipe segment.

[0040] In step S40, based on the drainage data corresponding to each drainage area and the planar connection angle of adjacent pipe segments at the inspection well in the initial pipeline route, pipe segment flow velocity analysis is performed to obtain the minimum flow velocity corresponding to each pipe segment, which may further include: Based on the drainage data and plane connection angles corresponding to each drainage area, the cumulative value of siltation risk for each pipe segment in the initial pipeline route is generated. Based on the cumulative value of siltation risk and the type of pipeline laid corresponding to the initial pipeline route, the Manning roughness coefficient is corrected to obtain the Manning roughness correction coefficient. Based on the Manning roughness correction factor, the actual minimum flow velocity corresponding to each pipe section was obtained through analysis.

[0041] When analyzing the minimum flow velocity of each pipe segment within a drainage area, the cumulative value of the siltation risk can be determined based on the main siltation factors corresponding to each drainage area, such as drainage data and planar connection angles. Then, after determining the cumulative siltation risk value, the Manning roughness coefficient corresponding to the pipe type can be corrected according to the initial pipeline route and the corresponding pipe type. This allows for dynamic adjustment of the Manning roughness coefficient based on the actual siltation risk at the site, ensuring the accuracy of the final determination of the actual minimum flow velocity for each pipe segment. This prevents situations where the Manning roughness coefficient fails to accurately reflect the siltation risk of certain pipe segments, leading to difficulties in meeting flow velocity requirements in the vertical design of the pipe segments and significantly reducing the reliability of the gravity flow pipeline design.

[0042] Specifically, based on the drainage data and planar connection angles corresponding to each drainage area, the cumulative siltation risk value for each pipe segment in the initial pipeline route is generated, which may further include: Wastewater statistics are compiled based on the wastewater type and corresponding wastewater volume at each building location point within each drainage zone, corresponding to different building uses and scales, to obtain wastewater statistics data for each wastewater type. Based on the coordinates of the upstream well of the nearest inspection well connected to the building location point, the downstream pipe segment parameters corresponding to the upstream well coordinates are obtained; Based on wastewater statistics, downstream pipe segment parameters, and the siltation risk factor corresponding to each downstream pipe segment parameter, the adjacent siltation risk index of the downstream adjacent pipe segment corresponding to the upstream well coordinates is obtained. The downstream pipe segment parameters include pipe segment length, vertical elevation, and pipe segment diameter. Based on the plane connection angle and siltation conversion factor corresponding to the downstream well coordinates of each downstream adjacent pipe segment, the adjacent siltation risk index is optimized and adjusted to generate the cumulative siltation risk value corresponding to each pipe segment in the initial pipeline route.

[0043] Specifically, when calculating the cumulative value of siltation risk, wastewater statistics can be performed using the wastewater type and volume corresponding to the building use and scale data at each building location point within the drainage area. This yields the total wastewater volume corresponding to the same wastewater type, i.e., the corresponding wastewater statistics. These statistics can correspond to each drainage area, meaning each drainage area can correspond to a nearest inspection well. Then, based on the upstream well coordinates of the nearest inspection well connected to each building location point, the coordinates of the corresponding building location point's sewage inlet well are used to obtain the downstream pipe segment parameters corresponding to those upstream well coordinates. Subsequently, using the wastewater statistics and the downstream pipe segment parameters corresponding to the upstream well coordinates, combined with the corresponding siltation risk factors, the adjacent siltation risk index of the downstream adjacent pipe segment corresponding to the upstream well coordinates can be evaluated and calculated. Then, based on the plane connection angle and siltation conversion factor corresponding to the downstream well coordinates of each downstream adjacent pipe segment, the adjacent siltation risk index is further optimized to determine the cumulative siltation risk value corresponding to each pipe segment in the initial pipeline route.

[0044] Specifically, the cumulative value of sedimentation risk can be calculated using the formula for calculating the cumulative value of sedimentation risk. This formula is as follows: ; in, This represents the cumulative value of the risk of siltation. This represents the radian value of the plane connection angle, in units of . , This represents the siltation conversion factor corresponding to different radian values.

[0045] In the specific calculation, you can first determine the radian value of the plane connection angle. Radius value corresponding to a right angle The difference in curvature between the two sections is used as a siltation factor. Then, based on the pre-set siltation conversion factor that affects siltation risk according to the curvature difference, an incremental siltation risk is generated. Finally, the cumulative siltation risk value is added to the incremental siltation risk value to calculate the cumulative siltation risk value for each pipe segment.

[0046] Based on wastewater statistics, downstream pipe segment parameters, and the siltation risk factor corresponding to each downstream pipe segment parameter, the adjacent siltation risk index of the downstream adjacent pipe segment corresponding to the upstream well coordinates is obtained, which may further include: Based on the wastewater type corresponding to each building location, wastewater is extracted in combination to obtain wastewater classification. Wastewater classification includes independent wastewater types and combinations of wastewater types. Based on the wastewater classification categories and wastewater statistics, the wastewater classification data corresponding to each category is obtained. The wastewater classification data can be represented as follows: , This represents the data for classifying wastewater. This indicates each individual type of wastewater within the wastewater zone. In the The first wastewater statistics corresponding to each building location point Indicates the combination of wastewater types within a wastewater zone. Each type of wastewater Corresponding wastewater statistics; Based on wastewater classification data, wastewater density, and wastewater viscosity, the sedimentation potential value corresponding to each drainage area is obtained. The formula for calculating the sedimentation potential value can be expressed as: , This indicates the potential for siltation. This represents the wastewater classification data corresponding to each wastewater classification category. This indicates the wastewater density corresponding to each wastewater category. This indicates the viscosity of the wastewater corresponding to each wastewater category. This represents the first potential conversion factor corresponding to the wastewater classification data. This represents the second potential conversion factor corresponding to the density of wastewater. This represents the third potential conversion coefficient corresponding to the viscosity of wastewater; Based on the siltation potential value, downstream pipe segment parameters, and the weighting coefficients corresponding to each downstream pipe segment parameter, the adjacent siltation risk index of the downstream adjacent pipe segment corresponding to the upstream well coordinates is obtained. The calculation formula for the adjacent siltation risk index can be expressed as: ,in, This indicates the proximity siltation risk index for each downstream adjacent pipe section. This represents the first risk coefficient corresponding to the siltation potential value. This represents the second risk coefficient corresponding to the downstream pipe section parameters. This represents the downstream pipe segment parameters for each adjacent downstream pipe segment. This represents the weighting coefficient corresponding to each downstream pipe segment parameter.

[0047] In calculating the risk index of adjacent siltation, the wastewater types corresponding to each building location can be used to identify wastewater combinations, thereby further classifying the wastewater types to obtain wastewater classification categories. This distinguishes between independent wastewater types that can independently cause siltation and combinations of wastewater types that can combine to form siltation, ensuring comprehensive wastewater type identification and considering all risks of siltation formation. Then, for different wastewater classification categories, data statistics can be further performed to obtain wastewater classification data corresponding to each wastewater classification category that comprehensively considers the composition of siltation.

[0048] After the wastewater classification data is completed, the sedimentation potential value can be calculated using the wastewater classification data, wastewater density, and wastewater viscosity, according to the sedimentation potential value calculation formula. This is used to calculate the siltation potential value corresponding to each drainage area. In the formula, the first potential conversion coefficient... Second potential conversion coefficient and the third potential conversion coefficient All data are classified based on wastewater. Wastewater density and sewage viscosity The impact index on siltation potential is preset in advance.

[0049] After obtaining the siltation potential value, it can be combined with the downstream pipe segment parameters and the weighting coefficients corresponding to each downstream pipe segment parameter, and the siltation risk index can be calculated using the formula. This further enables the assessment and calculation of adjacent siltation risk indicators. In other words, under the influence of adjacent siltation risk corresponding to the siltation potential value, adjustments to the adjacent siltation risk can be further made through the quantitative configuration of downstream pipeline parameters to derive the adjacent siltation risk indicator corresponding to each divided area. For the first risk coefficient corresponding to the siltation potential value in the calculation formula... The second risk coefficient corresponding to the downstream pipe section parameters The weighting coefficients for each downstream pipe segment parameter are all preset based on empirical values. Downstream pipe segment parameters may include pipe segment length, vertical elevation, and pipe segment diameter, and of course, other parameters may also be included.

[0050] Based on the cumulative value of siltation risk and the type of pipe laid corresponding to the initial pipeline route, the Manning roughness coefficient is corrected to obtain the Manning roughness correction coefficient, which includes: Based on the type of pipe laid according to the initial pipeline route, the corresponding Manning roughness theory coefficient can be obtained. Based on the cumulative value of siltation risk, pipeline type, and correction correspondence table, find the risk cumulative threshold and coefficient correction factor corresponding to the pipeline type, and calculate the correction coefficient. The Manning roughness correction coefficient is obtained by correcting the Manning roughness theory coefficients with a correction factor.

[0051] The formula for calculating the Manning roughness correction factor can be expressed as: ; This represents the Manning roughness correction factor. This represents the roughness coefficient of Manning's theory. This represents the cumulative value of the risk of siltation. Indicates the cumulative risk threshold. This represents the coefficient correction factor, which can be preset in advance based on experience.

[0052] When correcting for the Manning roughness coefficient, one can first use the pipe types corresponding to the initial pipeline route to look up the corresponding theoretical Manning roughness coefficient in a table. Then, using a pre-calibrated correction table, by inputting data such as the cumulative siltation risk value and pipe type, one can find the corresponding risk accumulation threshold and coefficient correction factor for the output pipe type, thus adjusting the correction based on the cumulative siltation risk value. Corresponding risk accumulation threshold and coefficient correction factor Calculate the correction factor Subsequently, the Manning roughness theory coefficients are corrected based on the correction factor to determine the Manning roughness correction factor that accurately reflects the current siltation risk of the corresponding pipeline, thereby ensuring the accuracy of minimum flow velocity analysis based on the Manning roughness correction factor. The correction correspondence table pre-defines the correspondence between the cumulative siltation risk value of the pipe segment, the pipe type, the cumulative risk threshold, and the coefficient correction factor.

[0053] Based on the Manning roughness correction factor, the actual minimum flow velocity corresponding to each pipe section is obtained through analysis, which can further include: The intermediate coefficients are obtained based on the theoretical minimum flow velocity of the corresponding pipe section and the Manning roughness coefficient corresponding to the pipe section. Based on the Manning roughness correction factor and intermediate factor, the actual minimum flow velocity corresponding to each pipe section is obtained through analysis.

[0054] Preferably, the formula for calculating the actual minimum flow velocity can be expressed as: ; in, Indicates the actual minimum flow rate. Indicates the intermediate coefficient. This represents the theoretical minimum flow rate. This represents the roughness coefficient of Manning's theory. Represents the transformation constant. Indicates the hydraulic radius. Indicates the slope of the pipe section. This represents the Manning roughness correction factor.

[0055] The actual minimum flow velocity can be calculated by first simulating the initial pipe network route in a site digital twin model based on parameters such as the slope, length, diameter, type, and Manning roughness coefficient of the current pipe section. Then, the theoretical minimum flow velocity is used. and the corresponding Manning roughness theory coefficient for the pipe section Calculated using intermediate coefficient formula To calculate the intermediate coefficient That is Then, combining the Manning roughness correction factor, the formula is used for calculation. This is used to calculate the actual minimum flow velocity that reflects the true minimum flow velocity. .

[0056] Next, step S50 is executed to vertically correct the initial pipeline route based on the actual minimum flow velocity, so as to obtain the overall gravity flow pipeline network.

[0057] After determining the actual minimum flow velocity, the vertical correction design method for the initial pipeline route can be determined based on its relationship with the lower limit of the flow velocity, thereby completing the design of the overall gravity flow pipeline network.

[0058] Specifically, the initial pipeline route is vertically corrected based on the actual minimum flow velocity to obtain the overall gravity flow pipeline network, which may further include: Determine whether the actual minimum flow velocity for each pipe segment is less than the lower limit of the flow velocity; If so, then based on the velocity difference between the lower limit of the flow velocity and the actual minimum flow velocity, the original pipe segment radius and the original pipe segment slope of the target pipe segment in the initial pipeline route, the vertical correction parameters for the target pipe segment are generated. The vertical correction parameters include pipe segment radius correction parameters and pipe segment slope correction parameters. Each vertical correction parameter was used to perform operational simulation on the target pipe segment corresponding to the initial pipeline route in turn to obtain the corresponding simulated minimum flow velocity; The minimum simulated flow velocity is selected as the corrected minimum flow velocity for the target pipe section. The initial pipeline route is vertically corrected sequentially based on the minimum corrected flow velocity to obtain the overall gravity flow pipeline network.

[0059] When making vertical corrections to each pipe segment of the initial pipeline route, it's first necessary to determine if the corresponding actual minimum flow velocity is less than the lower limit. If not, the vertical parameters of the current pipe segment are acceptable and no further correction is needed. However, if the actual minimum flow velocity is less than the lower limit, the actual minimum flow velocity needs to be corrected by adjusting the pipe segment radius or slope to ensure it meets the requirements. Specifically, based on the velocity difference between the lower limit and the actual minimum flow velocity, vertical correction parameters for the original pipe segment radius or slope of the corresponding target pipe segment in the initial pipeline route can be generated. Then, to better reflect the advantages and disadvantages of the pipe segment radius correction parameters and the pipe segment slope correction parameters, these parameters can be added to the target pipe segment corresponding to the initial pipeline route to perform operational simulation, thereby obtaining the first simulated minimum flow velocity corresponding to the pipe segment radius and the second simulated minimum flow velocity corresponding to the pipe segment slope. By comparing the first and second simulated minimum flow velocities, the minimum value is found as the corrected minimum flow velocity for the target pipe section. Finally, the initial pipe network route is vertically corrected sequentially using the target pipe section with the corrected minimum flow velocity, thereby obtaining a general gravity flow pipe network that matches the actual site, making the design of the general gravity flow pipe network more reliable.

[0060] Preferably, the formula for calculating the pipe section slope correction parameter can be expressed as: ; in, This indicates the correction parameter for the pipe segment radius. Indicates the original pipe section radius. Indicates the actual minimum flow rate. Indicates the lower limit of the flow rate. This represents the radius correction factor, which can be preset using empirical values.

[0061] Similarly, the formula for calculating the pipe section slope correction parameter can be expressed as: ; in, This indicates the slope correction parameters for the pipe section. Indicates the original pipe section slope. Indicates the actual minimum flow rate. Indicates the lower limit of the flow rate. This represents the slope correction factor, which can be preset in advance based on empirical values.

[0062] In addition, the initial pipeline route is vertically corrected sequentially based on the minimum corrected flow velocity to obtain the overall gravity flow pipeline network, which also includes: Based on the pipe segment slope correction parameters corresponding to the minimum flow velocity, the pipe segments in the initial pipeline route are vertically corrected sequentially. After the vertical correction is completed, the burial depth of each pipe segment can be further readjusted based on the corrected pipeline route. The minimum burial depth of each pipe segment from the ground within the required burial depth range corresponding to the gravity flow pipeline network can be re-optimized. Based on the determined executable minimum burial depth, the low workload and economic requirements of the overall gravity flow pipeline network construction can be guaranteed.

[0063] Please see Figure 2 The present invention also provides a vertical intelligent design system 11 for a site gravity flow pipeline network, comprising: an acquisition unit 111 for acquiring a site digital twin model; an optimization unit 112 for optimizing the route laying based on the site digital twin model, the upstream pipe segment start point, the downstream pipe segment end point, and avoidance rules, to generate an initial pipeline network route; a division unit 113 for dividing the site digital twin model into multiple drainage zones by searching for building location points on the site digital twin model; an analysis unit 114 for analyzing the pipe segment flow velocity based on the drainage data corresponding to each drainage zone and the planar connection angle of adjacent pipe segments at the manhole in the initial pipeline network route, to obtain the actual minimum flow velocity corresponding to each pipe segment; and a correction unit 115 for vertically correcting the initial pipeline network route based on the actual minimum flow velocity to obtain the site gravity flow pipeline network.

[0064] It should be noted that the overall layout gravity flow pipeline network vertical intelligent design system 11 provided in the above embodiments and the overall layout gravity flow pipeline network vertical intelligent design method provided in the above embodiments belong to the same concept. The specific operation methods of each module and unit have been described in detail in the method embodiments and will not be repeated here. In practical applications, the overall layout gravity flow pipeline network vertical intelligent design system 11 provided in the above embodiments can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. This is not a limitation here.

[0065] Please see Figure 3 The electronic device 1 may include a memory 12, a processor 13 and a bus, and may also include a computer program stored in the memory 12 and executable on the processor 13, such as a vertical intelligent design program for a gravity flow pipeline network.

[0066] The memory 12 includes at least one type of readable storage medium, such as flash memory, portable hard drive, multimedia card, card-type memory (e.g., SD or DX memory), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the memory 12 can be an internal storage unit of the electronic device 1, such as a portable hard drive. In other embodiments, the memory 12 can be an external storage device of the electronic device 1, such as a plug-in portable hard drive, smart media card (SMC), secure digital (SD) card, flash card, etc., equipped on the electronic device 1. Furthermore, the memory 12 can include both internal and external storage units of the electronic device 1. The memory 12 can be used not only to store application software and various types of data installed on the electronic device 1, such as code for the vertical intelligent design of the overall gravity flow network, but also to temporarily store data that has been output or will be output.

[0067] In some embodiments, the processor 13 may be composed of integrated circuits, such as a single packaged integrated circuit or multiple integrated circuits with the same or different functions, including combinations of one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and various control chips. The processor 13 is the control unit of the electronic device 1, connecting various components of the entire electronic device 1 through various interfaces and lines. It executes programs or modules stored in the memory 12 (e.g., a vertical intelligent design program for a gravity flow pipe network) and calls data stored in the memory 12 to perform various functions of the electronic device 1 and process data.

[0068] The processor 13 executes the operating system of the electronic device 1 and various installed applications. The processor 13 executes the applications to implement the steps in the above-described vertical intelligent design method for gravity flow pipe networks.

[0069] For example, the computer program may be divided into one or more modules, which are stored in the memory 12 and executed by the processor 13 to complete this application. The one or more modules may be a series of computer program instruction segments capable of performing specific functions, which describe the execution process of the computer program in the electronic device 1. For example, the computer program may be divided into units in a vertical intelligent design system for a general gravity flow network.

[0070] The integrated units implemented as software functional modules described above can be stored in a computer-readable storage medium, which can be non-volatile or volatile. These software functional modules, stored in a storage medium, include several instructions to cause a computer device (which may be a personal computer, computer equipment, or network device, etc.) or processor to execute some functions of the vertical intelligent design method for gravity flow pipe networks described in the various embodiments of this application.

[0071] In summary, the present invention discloses a vertical intelligent design method and system for gravity flow pipe networks. By performing a twin simulation of the design site, it enables the rapid construction of the initial pipe network route based on the corresponding site digital twin model and avoidance rules. After the initial pipe network route is established, further searches of drainage areas can determine the water pollution situation in different areas. Combined with the planar connection angle of adjacent pipe segments at inspection wells in the initial pipe network route, the actual minimum flow velocity of each pipe segment is analyzed and determined. Based on this actual minimum flow velocity, the initial pipe network route is corrected, ensuring that the final overall gravity flow pipe network is closer to the actual usage scenario. This effectively controls the risk of siltation caused by unreasonable vertical pipe segment design, improving the reliability of the vertical design of gravity flow pipe networks. Therefore, the present invention effectively overcomes the various shortcomings of the prior art and has high industrial application value.

[0072] The above embodiments are merely illustrative of the principles and effects of the present invention and are not intended to limit the invention. Any person skilled in the art can modify or alter the above embodiments without departing from the spirit and scope of the present invention. Therefore, all equivalent modifications or alterations made by those skilled in the art without departing from the spirit and technical concept disclosed in the present invention should still be covered by the claims of the present invention.

Claims

1. A vertical intelligent design method for a general layout gravity flow pipeline network, characterized in that, include: Obtain a digital twin model of the site; Based on the site digital twin model, the upstream pipe segment starting point, the downstream pipe segment ending point, and the avoidance rules, the route laying is optimized to generate the initial pipeline route. By searching for building location points in the digital twin model of the site, the digital twin model of the site is divided into multiple drainage zones; Based on the drainage data corresponding to each drainage area and the plane connection angle of adjacent pipe segments at the manhole in the initial pipeline route, pipe segment flow velocity analysis is performed to obtain the actual minimum flow velocity corresponding to each pipe segment. The initial pipeline route is vertically modified based on the actual minimum flow velocity to obtain the overall gravity flow pipeline network.

2. The vertical intelligent design method for gravity flow pipeline networks according to claim 1, characterized in that, The site digital twin model includes a site elevation model and a model of existing underground pipelines and structures; Based on the aforementioned site digital twin model, the upstream pipe segment start point, the downstream pipe segment end point, and avoidance rules, route optimization is performed to generate an initial pipeline route, including: Based on the coordinate area data of the site elevation model and the existing underground pipeline and structure model, corresponding avoidance rules are extracted to obtain the avoidance area corresponding to each coordinate area data. Based on the upstream pipe segment start point, the downstream pipe segment end point, and the site elevation model, the allowable area for the pipeline network design is generated. Based on the permitted area, conflict isolation is performed on each of the avoidance areas to obtain the corresponding open area within the permitted area; Based on the required drainage area and the open area in the site digital twin model, create fixed manhole coordinates; Based on the vertical elevation data and pipe segment constraints corresponding to each point within the open area, create the coordinates of the moving inspection well located between the coordinates of the fixed inspection well; The initial pipeline route is generated based on the coordinates of the fixed manhole and the coordinates of the moving manhole.

3. The vertical intelligent design method for gravity flow pipeline networks according to claim 1, characterized in that, By searching for building location points on the site's digital twin model, the site's digital twin model is divided into multiple drainage zones, including: Search for the building use and scale data corresponding to each building location point in the site digital twin model to obtain multiple building use and scale data corresponding to each building location point; Based on the building use and scale data, find the water usage pattern and water consumption corresponding to each building location; Based on the water usage pattern and water usage, sewage discharge prediction is performed to obtain the type and quantity of sewage. Based on the type of wastewater and the corresponding wastewater volume, clustering is performed on adjacent building locations to divide the site digital twin model into multiple drainage zones.

4. The vertical intelligent design method for gravity flow pipeline networks according to claim 1, characterized in that, Based on the drainage data corresponding to each drainage area and the planar connection angle of adjacent pipe segments at the manhole in the initial pipeline route, pipe segment flow velocity analysis is performed to obtain the minimum flow velocity corresponding to each pipe segment, including: Based on the drainage data corresponding to each drainage area and the plane connection angle, a cumulative value of siltation risk is generated for each pipe segment in the initial pipeline route; Based on the cumulative value of siltation risk and the type of pipeline laid corresponding to the initial pipeline route, the Manning roughness coefficient is corrected to obtain the Manning roughness correction coefficient. Based on the Manning roughness correction coefficient, the actual minimum flow velocity corresponding to each pipe section is obtained through analysis.

5. The vertical intelligent design method for gravity flow pipeline networks according to claim 4, characterized in that, Based on the drainage data corresponding to each drainage area and the planar connection angle, a cumulative siltation risk value is generated for each pipe segment in the initial pipeline route, including: Wastewater statistics are conducted based on the wastewater type and corresponding wastewater volume at each building location point within each drainage zone, corresponding to different building uses and scales, to obtain wastewater statistics data for each wastewater type. Based on the coordinates of the upstream well of the nearest inspection well connected to the building location point, the downstream pipe segment parameters corresponding to the upstream well coordinates are obtained; Based on the wastewater statistics, the downstream pipe segment parameters, and the siltation risk factor corresponding to each downstream pipe segment parameter, the adjacent siltation risk index of the downstream adjacent pipe segment corresponding to the upstream well coordinates is obtained. The downstream pipe segment parameters include pipe segment length, vertical elevation, and pipe segment diameter. Based on the plane connection angle and siltation conversion factor corresponding to the downstream well coordinates of each downstream adjacent pipe segment, the adjacent siltation risk index is optimized and adjusted to generate the cumulative siltation risk value corresponding to each pipe segment in the initial pipeline route; The formula for calculating the cumulative value of siltation risk is as follows: ； in, This represents the cumulative value of the risk of siltation. This represents the radian value of the angle between two planes. This represents the siltation conversion factor corresponding to different radian values.

6. The vertical intelligent design method for gravity flow pipeline networks according to claim 5, characterized in that, Based on the wastewater statistics, the downstream pipe segment parameters, and the siltation risk factor corresponding to each downstream pipe segment parameter, the adjacent siltation risk index of the downstream adjacent pipe segment corresponding to the upstream well coordinates is obtained, including: Based on the wastewater type corresponding to the building location, wastewater is extracted in combination to obtain wastewater classification. The wastewater classification includes independent wastewater types and combinations of wastewater types. Based on the wastewater classification categories and the wastewater statistics data, wastewater classification data corresponding to each wastewater classification category is obtained, and the wastewater classification data is represented as follows: , This represents the data for classifying wastewater. Indicates the sewage area Independent wastewater types In the The first wastewater statistics corresponding to each building location point Indicates the combination of wastewater types within a wastewater zone. Each type of wastewater Corresponding wastewater statistics; Based on the wastewater classification data, wastewater density, and wastewater viscosity, the sedimentation potential value corresponding to each drainage area is obtained. The formula for calculating the sedimentation potential value is as follows: , This indicates the potential for siltation. This represents the wastewater classification data corresponding to each wastewater classification category. This indicates the wastewater density corresponding to each wastewater category. This indicates the viscosity of the wastewater corresponding to each wastewater category. This represents the first potential conversion factor corresponding to the wastewater classification data. This represents the second potential conversion factor corresponding to the density of wastewater. This represents the third potential conversion coefficient corresponding to the viscosity of wastewater; Based on the siltation potential value, the downstream pipe segment parameters, and the weighting coefficients corresponding to each downstream pipe segment parameter, the adjacent siltation risk index of the downstream adjacent pipe segment corresponding to the upstream well coordinates is obtained. The calculation formula for the adjacent siltation risk index is as follows: ,in, This indicates the proximity siltation risk index for each downstream adjacent pipe section. This represents the first risk coefficient corresponding to the siltation potential value. This represents the second risk coefficient corresponding to the downstream pipe section parameters. This represents the downstream pipe segment parameters for each adjacent downstream pipe segment. This represents the weighting coefficient corresponding to each downstream pipe segment parameter.

7. The vertical intelligent design method for gravity flow pipeline networks according to claim 4, characterized in that, Based on the cumulative value of siltation risk and the type of pipe laid corresponding to the initial pipeline route, the Manning roughness coefficient is corrected to obtain the Manning roughness correction coefficient, which includes: Based on the type of pipe laid corresponding to the initial pipeline route, the corresponding Manning roughness theory coefficient is obtained by querying. Based on the cumulative value of siltation risk, the pipeline type, and the correction correspondence table, the cumulative risk threshold and coefficient correction factor corresponding to the pipeline type are found, and the correction coefficient is calculated. The Manning roughness theory coefficients are corrected using the aforementioned correction coefficients to obtain the Manning roughness correction coefficients; The formula for calculating the Manning roughness correction factor is as follows: ； This represents the Manning roughness correction factor. This represents the roughness coefficient of Manning's theory. This represents the cumulative value of the risk of siltation. Indicates the cumulative risk threshold. This represents the coefficient correction factor.

8. The vertical intelligent design method for gravity flow pipeline networks according to claim 4, characterized in that, Based on the Manning roughness correction factor, the actual minimum flow velocity corresponding to each pipe section is analyzed and obtained, including: The intermediate coefficient is obtained based on the theoretical minimum flow velocity of the corresponding pipe section and the Manning roughness coefficient corresponding to the pipe section. Based on the Manning roughness correction factor and the intermediate factor, the actual minimum flow velocity corresponding to each pipe section is obtained through analysis; The formula for calculating the actual minimum flow velocity is as follows: ; in, Indicates the actual minimum flow rate. Indicates the intermediate coefficient. This represents the theoretical minimum flow rate. This represents the roughness coefficient of Manning's theory. Represents the transformation constant. Indicates the hydraulic radius. Indicates the slope of the pipe section. This represents the Manning roughness correction factor.

9. The vertical intelligent design method for gravity flow pipeline networks according to claim 1, characterized in that, The initial pipeline route is vertically corrected based on the actual minimum flow velocity to obtain a general gravity flow pipeline network, including: Determine whether the actual minimum flow velocity corresponding to each pipe segment is less than the lower limit of the flow velocity; If so, then based on the velocity difference between the lower velocity limit and the actual minimum velocity, the original pipe segment radius and original pipe segment slope of the target pipe segment in the initial pipeline route, a vertical correction parameter for the target pipe segment is generated. The vertical correction parameter includes a pipe segment radius correction parameter and a pipe segment slope correction parameter. The target pipe segment corresponding to the initial pipeline route is simulated using each of the vertical correction parameters in turn to obtain the corresponding simulated minimum flow velocity; The minimum simulated flow velocity is selected as the minimum corrected flow velocity for the target pipe section. The initial pipeline route is vertically corrected sequentially based on the minimum corrected flow velocity to obtain the overall gravity flow pipeline network.

10. A vertical intelligent design system for a general layout gravity flow pipeline network, characterized in that, include: Acquisition unit, used to acquire a digital twin model of the site; The optimization unit is used to optimize the route laying based on the site digital twin model, the upstream pipe segment starting point, the downstream pipe segment ending point, and the avoidance rules, and generate the initial pipeline route. The division unit is used to divide the site digital twin model into multiple drainage zones by searching for building location points on the site digital twin model; The analysis unit is used to perform pipe segment flow velocity analysis based on the drainage data corresponding to each drainage area and the plane connection angle of adjacent pipe segments at the manhole in the initial pipeline route, so as to obtain the actual minimum flow velocity corresponding to each pipe segment. as well as The correction unit is used to perform vertical correction design on the initial pipeline route based on the actual minimum flow velocity to obtain the overall gravity flow pipeline network.

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