An intelligent site selection system for drainage connection in a GIS- and CIM-based metaverse virtual space

Abstract

This invention provides a drainage connection intelligent site selection system based on GIS and the CIM metaverse virtual space. This system aims to achieve intelligent site selection and optimized management of urban drainage network connection points through advanced geographic information system and urban information modeling technologies. The system comprises a model construction module, a drainage network generation module, a model optimization module, a model adjustment module, an address confirmation module, a governance module, and an interaction module. By optimizing and dynamically adjusting city and drainage network models based on actual urban development, drainage network connection address information is aligned with current urban development. This information is automatically generated using an algorithm based on the city and drainage network models, resulting in a highly scientific approach. The system can dynamically adjust the city and drainage network models based on real-time urban development data, addressing the lack of scientific and systematic methods in existing drainage network connection site selection and providing strong technical support for the planning and management of urban drainage systems.

Description

Technical Field

[0001] The present invention relates to the technical field of smart city construction, and more specifically, to an intelligent site selection system for drainage connection based on GIS and CIM metaverse virtual space. Background Art

[0002] With the acceleration of urbanization, urban drainage systems are facing increasing challenges. Traditionally, the site selection method for drainage connection points involves submitting a connection application to a park, community, or other entity, and then submitting a planning and construction CAD drawing to the drainage management department. The approval process relies on manual experience and the existing drainage network to determine a preliminary site selection for the connection. The final connection point is then confirmed through on-site verification. This traditional approach relies heavily on empirical judgment and lacks scientific and systematic analysis.

[0003] In addition, the existing method for selecting the site for drainage connection points does not integrate the hydrodynamics of the pipeline network, nor does it take into account the overall topological structure of the large-scale drainage pipeline network in the city. The structural survey cycle of the pipeline network is once every 5 to 10 years, resulting in an unclear status of the drainage pipeline network and no way to verify whether the currently confirmed connection point is the optimal connection point. Coupled with the serious problems of illegal discharge, leakage and wrong connection, the site selection of drainage connection is like a black box project, which leads to problems such as sewage overflow and urban waterlogging, and the phenomenon of repeated treatment is serious.

[0004] Therefore, in order to improve the efficiency and adaptability of urban drainage systems, an intelligent drainage connection point site selection system is urgently needed. Summary of the Invention

[0005] The purpose of the present invention is to provide an intelligent site selection system for drainage connection based on GIS and CIM metaverse virtual space, so as to at least solve the problem that the existing drainage network connection site selection lacks scientificity and systematicness.

[0006] To solve the above technical problems, the present invention provides an intelligent site selection system for drainage connection based on GIS and CIM metaverse virtual space, including:

[0007] Model building module, used to build city models;

[0008] Drainage network generation module, used to build drainage network model;

[0009] Model optimization module, used to analyze and confirm the site selection factors, and use the confirmed site selection factors to optimize the urban model and drainage network model;

[0010] Model adjustment module, used to obtain real-time drainage network connection operation information to dynamically adjust the city model and drainage network model;

[0011] The address confirmation module is used to confirm the drainage network connection address information based on the city model and the drainage network model;

[0012] The management module is used to manage existing drainage facilities and pipe network connection points;

[0013] Site selection module, used for site selection, address editing, model editing, model demonstration and simulation.

[0014] The present invention provides an intelligent drainage connection site selection system based on GIS and CIM metaverse virtual space, comprising: constructing a city model and a drainage network model; analyzing and confirming site selection factors; optimizing the city model and drainage network model using the confirmed site selection factors; acquiring drainage network connection operation information in real time to dynamically adjust the city model and drainage network model; and confirming drainage network connection address information based on the city model and drainage network model. By constructing, optimizing, and dynamically adjusting the city model and drainage network model based on actual urban construction, the drainage network connection address information confirmed based on the city model and drainage network model is consistent with current urban construction and is systematic. Furthermore, because the drainage network connection address information is automatically generated using the city model and drainage network model according to an algorithm, the city model and drainage network model can be dynamically adjusted based on real-time urban construction data, independent of manual experience, and highly scientific. This system addresses the lack of scientificity and systematicity in existing drainage network connection site selection and provides strong technical support for the planning and management of urban drainage systems.

[0015] Optionally, in the intelligent site selection system for drainage connection based on GIS and CIM metaverse virtual space, the method for constructing the city model and the drainage network model includes:

[0016] Obtain basic data on the city, including population density, urban topography, and infrastructure;

[0017] Obtain basic data of the drainage network, including existing drainage facilities and network connection points;

[0018] Use GIS technology to integrate the city's basic data, and use metaverse technology to build a virtual space model of the city and the drainage network;

[0019] A pipe network health evaluation model is constructed based on the virtual space model of the city and the drainage network to obtain the city model and drainage network model. Through advanced geographic information system and urban information model technology, intelligent site selection and optimized management of urban drainage network connection points are achieved.

[0020] Optionally, in the intelligent site selection system for drainage connection based on GIS and CIM metaverse virtual space, the method for analyzing and confirming site selection factors includes:

[0021] Conduct a population density-weighted analysis to identify service needs at drainage connection points;

[0022] Analyze urban topography to confirm the impact of urban topography on drainage performance;

[0023] Conduct infrastructure compatibility assessments to confirm the compatibility of pipeline connection points with infrastructure;

[0024] Analyze the potential impact of existing drainage facilities and pipe network connection points on the surrounding environment.

[0025] Optionally, in the intelligent site selection system for drainage connection based on GIS and CIM metaverse virtual space, based on the weighted analysis of population density to confirm the service demand of drainage connection points, a method for calculating the drainage flow prediction value is provided:

[0026]

[0027] Among them, i represents the current city area; Indicates the predicted drainage flow value of the current urban area; Indicates the current population of the urban area, which can be obtained through census data; Indicates the area of the current urban area; Indicates the drainage demand coefficient of the current urban area; Indicates the rainfall in the current urban area. represents the relationship function between rainfall and drainage flow prediction value; ML represents the machine learning model, are model parameters.

[0028] Optionally, in the intelligent site selection system for drainage connection based on GIS and CIM metaverse virtual space, based on the analysis of the potential impact of existing drainage facilities and pipe network connection points on the surrounding environment, a comprehensive fitness function is provided to consider the suitability of various evaluation factors:

[0029]

[0030]

[0031]

[0032]

[0033] Among them, x represents the current location, Represents the terrain analysis of the current location, Represents the compatibility analysis of the current location with existing infrastructure, Indicates the environmental impact analysis of the current location, represents the overall suitability analysis of the current location, is an adjustable weight; is the weight of the terrain factor, Represents the terrain model parameters, controlling the influence of terrain on suitability, Indicates the minimum altitude, Indicates the altitude of the current location. Indicates the terrain slope at the current location; is the compatibility factor weight, A score indicating the compatibility of the current location with existing infrastructure; is the environmental factor weight, Represents the environmental model parameters, controlling the degree of influence of the environment on suitability, Indicates the environmental impact score of the current location.

[0034] Optionally, in the intelligent site selection system for drainage connection based on GIS and CIM metaverse virtual space, the method for optimizing the city model and the drainage network model using the confirmed site selection factors includes:

[0035] Define the objective function and constraints;

[0036] According to the objective function and constraints, the urban model and drainage network model are optimized using the confirmed site selection factors.

[0037] Optionally, in the intelligent site selection system for drainage connection based on GIS and CIM metaverse virtual space, the objective function includes drainage efficiency, cost, service coverage, and environmental impact; the constraint conditions include the service scope, drainage capacity, and safety standards of the pipeline connection point; the method for optimizing the urban model and the drainage network model includes one or more of genetic algorithms, particle swarm optimization algorithms, and machine learning methods.

[0038] Optionally, in the intelligent site selection system for drainage connection based on GIS and CIM metaverse virtual space, the method for obtaining drainage network connection operation information in real time to dynamically adjust the city model and drainage network model includes:

[0039] Based on the Internet of Things technology, sensors are used to obtain real-time information on the connection and operation of the drainage network;

[0040] Using the preset dynamic adjustment algorithm, the connection network operation load and the layout of the network connection points in the urban model and the drainage network model are dynamically adjusted according to the real-time drainage network connection operation information.

[0041] Optionally, in the intelligent site selection system for drainage connection based on GIS and CIM metaverse virtual space, confirming drainage based on the city model and the drainage network model includes:

[0042] According to planning requirements, identify multiple site selection options in the urban model and drainage network model;

[0043] According to the interactive results, the final plan is confirmed from multiple site selection plans;

[0044] Based on the final plan, pipe network connection points are automatically deployed, and the city model and drainage network model are updated.

[0045] Optionally, in the intelligent site selection system for drainage connection based on GIS and CIM metaverse virtual space, the method for confirming multiple site selection schemes in the city model and the drainage network model according to planning requirements includes:

[0046] Set up a real-time updated urban drainage knowledge base in the city model and drainage network model;

[0047] Using the knowledge in the urban drainage knowledge base, multiple site selection schemes are confirmed according to planning requirements, including the address of the pipe network connection point, expected effects and potential risks.

[0048] Compared with existing technologies, this invention offers the advantage of intelligent site selection and optimized management of urban drainage network connection points through advanced geographic information systems and urban information modeling technologies. Furthermore, drainage network connection address information is automatically generated using an algorithm based on a city model. This algorithm can dynamically adjust the city and drainage network models based on real-time urban development data, independent of manual experience. This approach is highly scientific and addresses the lack of scientific and systematic approach to existing drainage network connection site selection, providing strong technical support for the planning and management of urban drainage systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 This is a structural schematic diagram of an intelligent site selection system for drainage connection based on GIS and CIM metaverse virtual space of the present invention.

[0050] Figure 2 This is a schematic diagram of a management solution based on GIS and CIM metaverse virtual space drainage connection intelligent site selection system of the present invention.

[0051] Figure 3 This is a model demonstration diagram of the intelligent site selection system for drainage connection based on GIS and CIM metaverse virtual space of the present invention.

[0052] Figure 4The present invention is a flow chart of a method for an intelligent site selection system for drainage connection based on GIS and CIM metaverse virtual space. DETAILED DESCRIPTION

[0053] The following is a detailed description of the method and system for selecting a site for a drainage network connection proposed by the present invention, with reference to the accompanying drawings and specific embodiments. It should be noted that the drawings are all simplified and not precisely scaled, and are intended solely to facilitate and clearly illustrate the embodiments of the present invention. Furthermore, the structures shown in the drawings are often portions of the actual structures. In particular, different drawings may require different emphases and may employ different scales.

[0054] It should be noted that the terms "first", "second", etc. in the specification, claims, and accompanying drawings of the present invention are used to distinguish similar objects in order to describe the embodiments of the present invention, and are not used to describe a specific order or sequence. It should be understood that the structures used in this way can be interchanged under appropriate circumstances. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units that are not explicitly listed or are inherent to these processes, methods, products, or apparatuses.

[0055] Example

[0056] like Figure 1 As shown, this embodiment provides an intelligent site selection system for drainage connection based on GIS and CIM metaverse virtual space, including:

[0057] Model building module, used to build city models;

[0058] Drainage network generation module, used to build drainage network model;

[0059] Model optimization module, used to analyze and confirm the site selection factors, and use the confirmed site selection factors to optimize the urban model and drainage network model;

[0060] Model adjustment module, used to obtain real-time drainage network connection operation information to dynamically adjust the city model and drainage network model;

[0061] The address confirmation module is used to confirm the drainage network connection address information based on the city model and the drainage network model;

[0062] The management module is used to manage existing drainage facilities and pipe network connection points;

[0063] Site selection module, used for site selection, address editing, model editing, model demonstration and simulation.

[0064] This embodiment provides a system for intelligent site selection for drainage connections based on GIS and the CIM Metaverse virtual space. This system can quickly understand and evaluate data from various dimensions of candidate plots, significantly improving the efficiency, scientificity, and accuracy of site selection. This embodiment provides a system for intelligent site selection for drainage connections based on GIS and the CIM Metaverse virtual space. This system uses a standardized evaluation system to automatically score sites based on expert experience, pipe network structure, defects, and drainage capacity, helping to eliminate bias caused by individual expert judgment and resulting in more objective and scientific evaluation results. The present invention provides a system for intelligent site selection for drainage connections based on GIS and the CIM Metaverse virtual space. This system aims to achieve intelligent site selection and optimized management of urban drainage pipe network connection points through advanced geographic information system and urban information modeling technologies. This embodiment provides a system for intelligent site selection for drainage connections based on GIS and the CIM Metaverse virtual space. This intelligent site selection system considers multiple factors, such as terrain, infrastructure compatibility, and environmental impact, making site selection more comprehensive and reasonable. Real-time monitoring and dynamic adjustments improve the quality of decision-making. Continuous optimization ensures more accurate evaluation results.

[0065] Specifically, such as Figure 2 As shown, in this embodiment, step S1, the method of constructing a city model and a drainage network model includes:

[0066] S11, obtain basic data of the city, including population density, urban topography, and infrastructure;

[0067] S12, obtain basic data of the drainage network, including existing drainage facilities and network connection points.

[0068] In practical applications, in addition to the aforementioned information, basic urban data can also include information on the city's transportation network, landforms, buildings, roads, green spaces, and even meteorological information such as rainfall cycles and rainfall amounts. The more comprehensive the basic data obtained, the more refined the city model constructed later, and the more accurate, effective, and realistic the confirmed drainage network connection address information will be.

[0069] S13, use GIS technology to integrate the city's basic data, and use metaverse technology to build a virtual space model of the city and a virtual space model of the drainage network.

[0070] GIS (Geographic Information System) is a very important spatial information system. Supported by computer hardware and software, GIS is a technical system that collects, stores, manages, calculates, analyzes, displays, and describes geographic distribution data on the entire or partial Earth surface (including the atmosphere).

[0071] With the help of GIS technology, the obtained basic data of the city and the basic data of the drainage network can be organically processed, so as to construct a virtual space model of the city and the virtual space model of the drainage network that are proportional to the actual city.

[0072] In practical applications, CIM (City Information Model) can also be used to organically process acquired building and infrastructure data, historical drainage connection usage data, population distribution, climate data, etc., and together with GIS technology, construct a virtual urban space model and a virtual drainage network space model.

[0073] In addition, metaverse technologies, such as virtual reality (VR-AR), can be used to visualize the constructed urban virtual space models and drainage network virtual space models.

[0074] S14, constructing a pipe network health evaluation model based on the urban virtual space model and the drainage pipe network virtual space model to obtain a city model and a drainage pipe network model.

[0075] In order to better manage the various types of data required for the urban virtual space model and the drainage network virtual space model, as well as to effectively evaluate the urban virtual space model and the drainage network virtual space model, a network health evaluation model is also constructed in this embodiment. The network health evaluation model is used to implement data governance of the network, including metadata management of the network, master data management of the network, standard management of the network data, lineage management of the network data, fusion processing of the network data, quality management of the network data, audit management of the network data, security management of the network data, and asset management of the network data. In other words, the urban model and drainage network model described in this embodiment not only include the urban virtual space model and the drainage network virtual space model to display the current urban construction situation, but also include a network health evaluation model to evaluate the drainage network situation in the city.

[0076] Furthermore, in this embodiment, the method of analyzing and confirming the site selection factors in step S2 includes:

[0077] S21, perform a weighted analysis of population density to determine the service demand at the drainage connection point.

[0078] Specifically, in this embodiment, machine learning models, such as random forests and neural networks, can be used to predict drainage flows at different locations under specific conditions (such as specific population densities and specific rainfall events).

[0079] This embodiment provides a method for calculating the drainage flow prediction value:

[0080]

[0081] Among them, i represents the current city area; Indicates the predicted drainage flow value of the current urban area; Indicates the current population of the urban area, which can be obtained through census data; Indicates the area of the current urban area; Indicates the drainage demand coefficient of the current urban area; Indicates the rainfall in the current urban area. represents the relationship function between rainfall and drainage flow prediction value; ML represents the machine learning model, are model parameters.

[0082] S22, analyze the urban topography to confirm the impact of urban topography on drainage performance;

[0083] S23, conduct an infrastructure compatibility assessment to confirm the compatibility of the pipeline network connection points with the infrastructure;

[0084] S24, analyze the potential impact of existing drainage facilities and pipe network connection points on the surrounding environment.

[0085] Specifically, in this embodiment, factors such as terrain analysis, infrastructure compatibility, and environmental impact assessment are comprehensively considered to help the algorithm assess the suitability of each potential site. For example, terrain can affect the direction and speed of water flow, thereby affecting drainage effectiveness. Therefore, factors such as elevation and slope need to be considered when selecting a site.

[0086] This embodiment provides a comprehensive fitness function to consider the suitability of each evaluation factor:

[0087]

[0088]

[0089]

[0090]

[0091] Among them, x represents the current location, Represents the terrain analysis of the current location, Represents the compatibility analysis of the current location with existing infrastructure, Indicates the environmental impact analysis of the current location, represents the overall suitability analysis of the current location, is an adjustable weight; is the weight of the terrain factor, Represents the terrain model parameters, controlling the influence of terrain on suitability, Indicates the minimum altitude, Indicates the altitude of the current location. Indicates the terrain slope at the current location; is the compatibility factor weight, A score indicating the compatibility of the current location with existing infrastructure; is the environmental factor weight, Represents the environmental model parameters, controlling the degree of influence of the environment on suitability, Indicates the environmental impact score of the current location.

[0092] In practical applications, 、 、 and The factors can be adjusted according to the actual situation and policy requirements to reflect the importance of different factors. By maximizing the fitness function, the optimal connection point location can be selected.

[0093] When using a machine learning model, you can first try to train the machine learning model using historical data to ensure the generalization performance and robustness of the machine learning model.

[0094] In addition, in practical applications, in addition to the above-mentioned population density, rainfall, drainage flow, topography, compatibility with existing drainage facilities, and environmental impact assessment, the site selection factors to be considered may also include other relevant indicators. Through a comprehensive evaluation of the site selection factors, areas with high population density, large weighted flow demand, high infrastructure compatibility, and small environmental impact factors, which can both efficiently drain water and coexist harmoniously with the surrounding environment, can be selected as potential connection point locations, thereby ensuring that the site selection of drainage connection points is more scientific and reasonable.

[0095] Furthermore, in this embodiment, in step S3, the method of optimizing the city model using the confirmed location factors includes:

[0096] S31, define the objective function and constraints;

[0097] Specifically, in this embodiment, the objective function includes drainage efficiency, cost, service coverage, and environmental impact. Drainage efficiency can be achieved by minimizing drainage time or maximizing drainage rate; cost can be achieved by minimizing the total cost of connecting point construction and maintenance; service coverage can be achieved by maximizing the proportion of the population served by the connecting point; and environmental impact can be achieved by minimizing the negative impact of the connecting point on the surrounding environment.

[0098] Furthermore, in this embodiment, the constraints include the service range, drainage capacity, and safety standards of the pipe network connection points. The service range must ensure that each connection point's service range meets minimum requirements; the drainage capacity must ensure that the connection point can handle the maximum expected flow within its service range; and the safety standards require that the design and location of the connection point must comply with established safety standards.

[0099] Of course, in practical applications, other objective functions and constraints can be set according to actual needs, and this application does not impose any restrictions on this.

[0100] S32, based on the objective function and constraints, the confirmed site selection factors are used to optimize the urban model and the drainage network model.

[0101] Specifically, in this embodiment, the method for optimizing the city model includes one or more of a genetic algorithm, a particle swarm optimization algorithm, and a machine learning method.

[0102] For example, a genetic algorithm (multi-objective genetic algorithm) can be used to calculate multiple objective function values for each connection point position (individual) and convert them into fitness scores; select individuals for reproduction based on the fitness scores, giving priority to those that perform well on multiple objectives; apply crossover operations (such as sequential crossover, partial mapping crossover) to produce new offspring; mutate the offspring individuals to introduce new genetic diversity; retain a certain proportion of the most adaptable individuals to directly enter the next generation to ensure that excellent solutions are not lost; generate a new population through selection, crossover and mutation operations; stop the algorithm when the predetermined number of iterations is reached or the fitness score no longer increases significantly; output the optimal solution set in the non-dominated sorting, which achieves a balance between multiple objectives.

[0103] In this embodiment, the mathematical expression of the multi-objective genetic algorithm can be:

[0104] Assume X is the set of all possible connection point locations, is the i-th objective function, is the jth constraint, and the multi-objective optimization problem can be expressed as:

[0105] Maximize / Minimize , ,……, ;

[0106] constraint , ,……, ;

[0107] Where k is the number of objective functions and m is the number of constraints.

[0108] Objective function:

[0109]

[0110] in, represents costs, including construction and maintenance costs; represents the service coverage, i.e. the proportion of the population served by the connection point; Indicates environmental impact, such as the degree of interference with ecological and environmental protection systems; Indicates the total length of the path; Indicates the drainage time under current rainfall conditions; Indicates the redundancy of the path, that is, the availability of the backup path; 、 、 、 and is the weight coefficient, which can be adjusted according to actual conditions.

[0111] Constraints:

[0112] g1(location): The connection point must be located in an area where construction is allowed.

[0113] g2 (capacity): The drainage capacity of the connection point must meet the maximum expected flow.

[0114] g3(path): The connection path must meet the engineering requirements of structure and materials.

[0115] g4(demand): The connecting path must be able to handle the drainage demand under real-time rainfall conditions.

[0116] Multi-objective genetic algorithms are particularly well-suited for solving complex problems with multiple conflicting objectives, such as the intelligent site selection problem for drainage connections involved in this application. This approach can generate a set of solutions that balance different objectives, allowing decision-makers to ultimately select the most appropriate solution based on the specific circumstances.

[0117] Of course, when other optimization algorithms are selected, such as particle swarm optimization algorithm and machine learning method, those skilled in the art can know the corresponding algorithm implementation scheme through the above-mentioned optimization direction, and this application will not go into details.

[0118] Furthermore, in this embodiment, in step S4, the method of obtaining the drainage network connection operation information in real time to dynamically adjust the city model and the drainage network model includes:

[0119] S41, based on the Internet of Things technology, uses sensors to obtain real-time information on the connection and operation of the drainage network;

[0120] S42: Using a preset dynamic adjustment algorithm, dynamically adjust the connection pipe network operation load and the layout of the pipe network connection points in the city model and the drainage pipe network model according to the drainage pipe network connection operation information obtained in real time.

[0121] Specifically, the operating load of the connecting pipeline network and the layout of the connecting points can be dynamically adjusted according to real-time data, and the two best connecting points can be selected.

[0122] In this way, the actual city data information can be synchronized with the virtual city model and drainage network model, thereby ensuring the validity of the drainage network connection address information obtained using the city model and drainage network model.

[0123] Furthermore, in this embodiment, in step S5, the method of confirming the drainage network connection address information based on the city model and the drainage network model includes:

[0124] S51: Based on planning requirements, identify multiple site selection options in the urban model and drainage network model.

[0125] Specifically, in this embodiment, a real-time updated urban drainage knowledge base can be set up in the city model. The urban drainage knowledge base includes the latest urban construction data and drainage technology. Then, the knowledge in the urban drainage knowledge base is used to confirm multiple site selection plans based on planning requirements. The site selection plans include the addresses of pipeline connection points, expected effects and potential risks, etc.

[0126] S52: Confirm the final solution from multiple site selection solutions based on the interaction results.

[0127] Specifically, in this embodiment, the technical staff can use the interactive interface to select the best solution from multiple alternative site selection options to be the final solution. Of course, an approval process can also be added during the selection process, so that the final solution can be approved by the superior who decides the content of the subordinate.

[0128] S53, based on the final plan, automatically deploys pipe network connection points and updates the city model and drainage network model.

[0129] Specifically, in this embodiment, the final plan is provided in the form of a detailed decision support report, including recommended connection points, expected effects and potential risks, etc. Once the connection points are confirmed, the city model and drainage network model will be automatically updated with the content of the final plan.

[0130] In addition, considering the differences between actual urban construction and the city model, the governance module in this embodiment includes:

[0131] Specifically for existing drainage systems, in this embodiment, data governance, data quality inspection and analysis, and data anomaly repair and update are primarily required for drainage infrastructure nodes (manholes, stormwater inlets, outfalls, gates, valves, pumping stations, storage tanks, and household sewage connection points), pipelines (drain pipes, drainage channels), and surfaces (household connection areas, water areas, and storage facilities). Static drainage data includes: pipe diameter, age, material, length, bottom elevation, slope, burial depth, rainwater zoning, sewage zoning, horizontal clearance, sediment thickness, cover depth, soil type, land use type, building distribution, topography, permeability coefficient, and runoff coefficient; dynamic basic drainage data includes: rainfall, river water level, pipe network level, pipe network flow, outlet flow, and water level at waterlogging points.

[0132] When managing drainage networks, it is necessary to conduct data quality inspection, analysis, and management of the integrity, topology, connectivity, scope, elevation, flow direction, pipe diameter, and other aspects of the drainage infrastructure's raw data. For example, this involves integrating and organizing drainage facility CAD data, as well as facility-related tables, documents, and images.

[0133] like Figure 3 As shown in the figure, the model demonstration and simulation of the interactive module of this embodiment is shown. The model demonstration combines urban locations with the urban drainage network. Green lines represent existing drainage pipes, yellow lines represent urban roads, and red lines represent the fullness of the drainage pipes. The darker the color, the higher the fullness. This demonstration can also monitor sewage treatment and rainwater collection, and provide real-time and forecast feedback. Furthermore, the interactive module of this embodiment also includes address selection, address editing, and model editing (not shown).

[0134] like Figure 4 As shown, when conducting data integrity management, you should check whether each data in each data table is complete, and mark any problems such as non-unique identification codes.

[0135] In addition, data topology governance includes: for pipe point overlap, pipeline overlap, isolated points, pipeline misconnection, no outflow, the number of downstream is greater than 1, the existence of a downstream outlet, the existence of multiple upstream outlets, the existence of an upstream stormwater inlet, node spatial position offset, pipeline reversal, pipeline connection loss, pipeline reverse slope, ring network or broken network, pipeline duplication, pipeline disconnection in the middle, etc., combined with the comparison of relevant indicator parameters in the pipeline network design standard for verification and processing. For example: for pipeline topology anomalies, it is necessary to check whether the start and end codes of the drainage channel are included in the attribute table of the corresponding facility type; for upstream analysis of key points such as outlets and pumping stations, it is necessary to check whether the nodes and pipelines upstream are connected, and it is also necessary to check whether the receiving water body number of the outlet is included in the data table of receiving water bodies such as rivers and lakes. Topological anomalies can be verified and processed through the topological rules of various pipeline data inspection item parameters integrated in the topology toolbox, and the standard values are matched to automatically repair the topological anomalies.

[0136] Furthermore, data anomaly management includes the management of topological structure anomalies, elevation anomalies, attribute item anomalies, pipe diameter anomalies, flow direction anomalies, pipeline overlength, pipeline reverse slope, and pipe network misconnection. For example, the system sets invalid pipe diameters with obvious anomalies in the pipe-node topology relationship table found during the topology check to zero, and then fills all zero / empty pipe diameters using a rule-based upstream and downstream pipe diameter value filling method to detect and repair abnormal pipe diameter data. The pipe diameter filling scheme can be controlled by input parameters, and single isolated empty pipe sections can also be identified and empty pipe channels at the beginning can be deleted. The pipe top elevation data of the branch pipe sections in the pipe section branch / multi-pass node set found in the topology check are processed based on the least squares method, and the elevation points with a large deviation from the fitting curve are detected as abnormal pipe top elevation data to form pipe section branch elevation data; the upstream pipe section elevation data is modified based on the rules, and the reference value of the correction elevation is the corresponding function value on the curve to form multi-pass node elevation data; then, the elevation data of the multi-pass node is based on the connection relationship between the pipeline and the inspection well elevation, and the elevation anomalies at the multi-pass node and the elevation anomalies at the bottom of the inspection well are detected and repaired through judgment rules.

[0137] Furthermore, data connectivity analysis is used to analyze the collection of drainage facilities connected to the pipeline. Connectivity analysis can visually determine the integrity of the regional topology and identify errors such as network islands and disconnections. It can also identify all upstream pipelines, determine drainage zoning, and visually identify errors such as disconnections and misconnections. It can also visually identify issues such as pipelines with no downstream outlets or those discharging directly into rivers and streams.

[0138] Furthermore, pipeline network profile analysis, including both cross-section and longitudinal sections, can be performed. This analysis identifies anomalies such as pipelines exposed above ground or inserted into wells, based on information such as pipeline length, slope, and burial depth. Cross-section analysis reveals the horizontal relative positions of pipelines and their underground burial conditions, effectively identifying redundant pipelines.

[0139] In this way, by managing the existing drainage facilities and pipeline connection points, we can not only sort out the existing urban drainage facilities and pipeline connection points and make unified improvements to the problems therein, but also ensure that the established urban model and drainage pipeline model are consistent with the current status of the urban entity, thereby ensuring that the new connection point is the best connection point.

[0140] Obviously, the above embodiments of the present invention are merely examples for the purpose of clearly illustrating the technical solutions of the present invention, and are not intended to limit the specific implementation methods of the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the claims of the present invention shall be included within the scope of protection of the claims of the present invention.

Claims

1. An intelligent site selection system for drainage connection based on GIS and CIM metaverse virtual space, including: Model building module, used to build city models; Drainage network generation module, used to build drainage network model; Model optimization module, used to analyze and confirm the site selection factors, and use the confirmed site selection factors to optimize the urban model and drainage network model; Model adjustment module, used to obtain real-time drainage network connection operation information to dynamically adjust the city model and drainage network model; The address confirmation module is used to confirm the drainage network connection address information based on the city model and the drainage network model; The management module is used to manage existing drainage facilities and pipe network connection points; Interactive modules for site selection, site editing, model editing, model demonstration and simulation The method for analyzing and confirming the site selection factors includes: Conduct a population density-weighted analysis to identify service needs at drainage connection points; Analyze urban topography to confirm the impact of urban topography on drainage performance; Conduct infrastructure compatibility assessments to confirm the compatibility of pipeline connection points with infrastructure; Analyze the potential impact of existing drainage facilities and pipe network connection points on the surrounding environment; Based on the analysis of the potential impact of existing drainage facilities and pipe network connection points on the surrounding environment, a comprehensive fitness function is proposed to consider the suitability of each evaluation factor: Among them, x represents the current location, Represents the terrain analysis of the current location, Represents the compatibility analysis of the current location with existing infrastructure, Indicates the environmental impact analysis of the current location, represents the overall suitability analysis of the current location, is an adjustable weight; is the weight of the terrain factor, Represents the terrain model parameters, controlling the influence of terrain on suitability, Indicates the minimum altitude, Indicates the altitude of the current location. Indicates the terrain slope at the current location; is the compatibility factor weight, A score indicating the compatibility of the current location with existing infrastructure; is the environmental factor weight, Represents the environmental model parameters, controlling the degree of influence of the environment on suitability, Indicates the environmental impact score of the current location.

2. The intelligent site selection system for drainage connection based on GIS and CIM metaverse virtual space according to claim 1 is characterized in that: The method for constructing the city model and the drainage network model includes: Obtain basic data on the city, including population density, urban topography, and infrastructure; Obtain basic data of the drainage network, including existing drainage facilities and network connection points; Use GIS technology to integrate the city's basic data, and use metaverse technology to build a virtual space model of the city and the drainage network; A pipe network health evaluation model is constructed based on the urban virtual space model and the drainage pipe network virtual space model to obtain the urban model and the drainage pipe network model.

3. The intelligent site selection system for drainage connection based on GIS and CIM metaverse virtual space according to claim 1 is characterized in that: Based on the weighted analysis of population density to determine the service demand of drainage connection points, a method for calculating the drainage flow forecast value is given: Among them, i represents the current city area; Indicates the predicted drainage flow value of the current urban area; Indicates the current population of the urban area, which can be obtained through census data; Indicates the area of the current urban area; Indicates the drainage demand coefficient of the current urban area; Indicates the rainfall in the current urban area. represents the relationship function between rainfall and drainage flow prediction value; ML represents the machine learning model, are model parameters.

4. The intelligent site selection system for drainage connection based on GIS and CIM metaverse virtual space according to claim 1 is characterized in that: The method for optimizing the city model and the drainage network model using the confirmed location factors includes: Define the objective function and constraints; According to the objective function and constraints, the urban model and drainage network model are optimized using the confirmed site selection factors.

5. The intelligent site selection system for drainage connection based on GIS and CIM metaverse virtual space according to claim 4 is characterized in that: The objective function includes drainage efficiency, cost, service coverage, and environmental impact; the constraint conditions include the service scope, drainage capacity, and safety standards of the pipeline connection point; the method for optimizing the urban model and the drainage network model includes one or more of genetic algorithms, particle swarm optimization algorithms, and machine learning methods.

6. The intelligent site selection system for drainage connection based on GIS and CIM metaverse virtual space according to claim 1 is characterized in that: The method for obtaining the drainage network connection operation information in real time to dynamically adjust the city model and the drainage network model includes: Based on the Internet of Things technology, sensors are used to obtain real-time information on the connection and operation of the drainage network; Using the preset dynamic adjustment algorithm, the connection network operation load and the layout of the network connection points in the urban model and the drainage network model are dynamically adjusted according to the real-time drainage network connection operation information.

7. The intelligent site selection system for drainage connection based on GIS and CIM metaverse virtual space according to claim 1 is characterized in that: The confirmation of the drainage network connection address information based on the city model and the drainage network model includes: According to planning requirements, identify multiple site selection options in the urban model and drainage network model; According to the interactive results, the final plan is confirmed from multiple site selection plans; Based on the final plan, pipe network connection points are automatically deployed, and the city model and drainage network model are updated.

8. The intelligent site selection system for drainage connection based on GIS and CIM metaverse virtual space according to claim 7 is characterized in that: The method for confirming multiple site selection options in the city model and the drainage network model according to planning requirements includes: Set up a real-time updated urban drainage knowledge base in the city model and drainage network model; Using the knowledge in the urban drainage knowledge base, multiple site selection schemes are confirmed according to planning requirements, including the address of the pipe network connection point, expected effects and potential risks.

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