A GIS and BIM fusion modeling system for urban intelligent planning

Abstract

The present invention relates to a GIS and BIM fusion modeling system for urban intelligent planning. The system comprises a data processing module, a model storage module, and a model processing module. The data processing module obtains GIS data and BIM data, fuses them, and generates a fusion model. The fusion model is stored in a database via the model storage module and is called by the model processing module. BIM data formats include wexbim and IFC. BIM data in wexbim format is converted to Base64 before storage. BIM data in IFC format is first converted to wexbim format and then to Base64 before storage. Compared with the prior art, the present invention has the advantages of increasing the flexibility of BIM data processing and storage, effectively alleviating the difficulties in processing and storing multivariate data in urban intelligent planning, and improving the modeling integrity of the entire area.

Description

Technical Field

[0001] The present invention relates to the technical field of urban planning, and in particular to a GIS and BIM fusion modeling system for urban intelligent planning. Background Art

[0002] GIS (Geographic Information System) is a technology that collects, converts, stores, displays, and analyzes geographic spatial information. GIS can transform various geographic locations and spatial information into visual graphical displays, allowing for various operations and analyses. GIS is widely used in urban planning, environmental monitoring, and other fields due to its powerful data management, spatial positioning, spatial analysis, and multi-dimensional display capabilities.

[0003] BIM (Building Information Modeling) can be understood as an information model covering the entire lifecycle of a project, from design and construction to operation, management, and even demolition. It is also a vast database containing all the physical and functional information about a building. BIM technology ensures the interoperability of building information over time, providing a reliable basis for management decisions. Many disciplines, such as structure, electrical, and HVAC, can collaborate using BIM models. The emergence of BIM technology has significantly contributed to information integration, shortened construction periods, reduced risks, and environmentally friendly practices.

[0004] GIS's comprehensive control of geographic information, combined with high-precision BIM building information, creates a richer three-dimensional urban model. The integration of GIS and BIM can enable multi-scale urban management, from indoor navigation and safe passage planning to community greening and traffic control, urban and landscape planning, joint prevention and control, and public safety, with immeasurable value. Therefore, the integration of BIM and GIS has become a major research direction in BIM and GIS-related fields both domestically and internationally.

[0005] GIS data and BIM data differ significantly. GIS data primarily consists of geographic information, with a one-to-one mapping relationship where one key corresponds to one value, making it easy to store the required attributes in the database. BIM data, on the other hand, is more complex, containing all the physical and functional information of a building, which doesn't necessarily have a one-to-one mapping relationship. Furthermore, BIM models come in a wide variety of formats, with different types and amounts of information varying. This makes the integrated management of these two types of data very complex. Few researchers have addressed the management of databases for integrated BIM and GIS systems; the more common solution is to manage BIM and GIS datasets independently. Summary of the Invention

[0006] The purpose of the present invention is to overcome the defects of the above-mentioned existing technologies and provide a GIS and BIM fusion modeling system for urban intelligent planning. It imports, displays, stores and analyzes multivariate data based on GIS and BIM, effectively alleviating the difficulties in processing and preserving multivariate data in urban intelligent planning.

[0007] The purpose of the present invention can be achieved by the following technical solutions:

[0008] A GIS and BIM fusion modeling system for urban intelligent planning includes a data processing module, a model storage module, and a model processing module. The data processing module acquires GIS data and BIM data and fuses them to obtain a fusion model. The fusion model is stored in a database through the model storage module and is called by the model processing module. The BIM data formats include wexbim format and IFC format. The BIM data in wexbim format is converted into Base64 code before being stored. The BIM data in IFC format is first converted into wexbim format and then converted into Base64 code before being stored.

[0009] The GIS data comes partly from the ArcGIS API for JavaScript and partly from user input, and the BIM data comes from the BIM model file.

[0010] The system includes an application layer, an interface layer, a business layer, a data access layer and a data layer.

[0011] The application layer, interface layer and business layer are located in the data processing module and the model processing module, and the data access layer and data layer are located in the model storage module.

[0012] The process of converting the IFC format BIM data into Base64 code includes the following steps:

[0013] S101, the application layer obtains BIM data in IFC format input by the user;

[0014] S102, converting the BIM data in IFC format into Base64 code and sending it to the business layer through the interface layer;

[0015] S103, the business layer converts the received Base64 code back to IFC format, and then converts it to wexbim format through the XBIM toolkit;

[0016] S104: Convert the BIM data in the wexbim format into Base64 code and then transmit it back to the application layer.

[0017] Furthermore, in step S102, the Base64 code is transmitted as a parameter. Although the request has only one parameter, for a GET request, sending it as a parameter exceeds the maximum length that can be sent by GET.

[0018] Furthermore, in step S102, the application layer encapsulates the Base64 code into an object, and the interface layer sends the Base64 code to the business layer through a POST request of the axios interface. The business layer is provided with a corresponding class receiving object.

[0019] Furthermore, the business layer is provided with XBIM (eXtensible Building Information Modelling), which is a BIM toolkit developed by .NET open source software and supports data models in IFC format.

[0020] The GIS data in the data processing module is stored in a database in a conventional manner with the required key-value attributes.

[0021] The storage format of the Base64 code is longtext.

[0022] The functions of the model processing module include model display, model management and density analysis.

[0023] Furthermore, the model display methods include point map rendering and heat map rendering.

[0024] Furthermore, the point map rendering process includes obtaining a fusion model from a database, parsing the fusion model to obtain a specific geographic location and corresponding attributes, and generating a point at the corresponding geographic location on the map, wherein the point includes all BIM data and GIS data of the corresponding geographic location in the fusion model.

[0025] Furthermore, the density analysis specifically includes the following steps:

[0026] S201, obtaining a target area selected by a user;

[0027] S202, randomly generating a certain number of points in the target area;

[0028] S203, forming a point set by combining the randomly generated points and the points originally existing in the fusion model;

[0029] S204, adjusting the domain parameters, and clustering the point set using the DBSCAN clustering algorithm based on the neighborhood parameters;

[0030] S205, storing the noise points identified in the clustering process in a set;

[0031] S206: traverse the set, exclude the points originally existing in the fusion model, and obtain a noise point set.

[0032] The noise point set corresponds to the areas in the target area where modeling is sparse, and modeling is performed in these areas as needed to improve the modeling completeness of the entire area.

[0033] Compared with the prior art, the present invention has the following beneficial effects:

[0034] 1. The present invention uses Base64 code as a bridge to send the BIM data in IFC format obtained by the application layer to the business layer through the interface layer for conversion, and finally all of them are converted into BIM data in wexbim format, and then converted into Base64 code for fusion. Base64 is a highly compatible encoding method that can convert any binary data into plain text messages. The Base64 code effectively improves the flexibility of BIM data processing and storage, and effectively alleviates the difficulties in processing and storing multi-dimensional data in urban intelligent planning.

[0035] 2. The present invention performs clustering through the DBSCAN clustering algorithm, identifies noise points in the fusion model, and performs density analysis on the fusion model. By modeling the noise points, the modeling completeness of the entire area can be improved, effectively improving the coverage and integrity of the fusion model.

[0036] 3. This invention integrates GIS data and BIM data, and allows users to view model distribution through point maps and heat maps. Users can observe the model from multiple angles by slicing the model, changing the rendering mode, etc., which optimizes the user experience and facilitates users to view the entire model. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 It is a schematic diagram of the process of the present invention;

[0038] Figure 2 is a system structure diagram in an embodiment of the present invention;

[0039] Figure 3 Schematic diagram of the format conversion process of the present invention;

[0040] Figure 4 Schematic diagram of the density analysis process of the present invention. DETAILED DESCRIPTION

[0041] The present invention is described in detail below with reference to the accompanying drawings and specific embodiments. This embodiment is implemented based on the technical solution of the present invention, and provides a detailed implementation method and specific operation process, but the protection scope of the present invention is not limited to the following embodiments.

[0042] Example

[0043] like Figure 1 As shown, a GIS and BIM fusion modeling system for urban intelligent planning includes a data processing module, a model storage module and a model processing module. The data processing module obtains GIS data and BIM data for fusion to obtain a fusion model. The fusion model is stored in a database through the model storage module and is called by the model processing module. The formats of BIM data include wexbim format and IFC format. BIM data in wexbim format is converted into Base64 code and then stored. BIM data in IFC format is first converted into wexbim format and then converted into Base64 code and then stored.

[0044] Part of the GIS data comes from the ArcGIS API for JavaScript and part comes from user input, and the BIM data comes from the BIM model file.

[0045] like Figure 2 As shown, the system includes application layer, interface layer, business layer, data access layer and data layer.

[0046] In this embodiment, the system adopts a development method with front-end and back-end separation; the application layer (front-end) uses the Vue.js framework, and uses the Vue / cli scaffolding to package and generate a Vue project with its own demo. At the same time, Element-UI is used to assist in the construction of the front-end interface, ArcGIS API for JavaScript is used to generate maps and complete the rendering of GIS data, and Xbim WebUI is used to complete the rendering of the BIM model; the interface layer uses axios to issue HTTP POST / GET requests, and the interface adopts the Restful API architecture; the business layer (back-end) uses the ASP.Net MVC architecture, separates entities, logic, and views, reduces the coupling of the system, and uses XBIM to handle BIM model-related issues; the data access layer uses EntityFramework to connect to the relational database, encapsulates DBcontext to directly interact with the data in the database, and no longer needs to write "pipeline" code to access data; the data layer uses the relational database Mysql.

[0047] The application layer, interface layer and business layer are located in the data processing module and model processing module, and the data access layer and data layer are located in the model storage module.

[0048] like Figure 3 As shown in the figure, the process of converting BIM data in IFC format to Base64 code includes the following steps:

[0049] S101, the application layer obtains BIM data in IFC format input by the user;

[0050] S102, converting the BIM data in IFC format into Base64 code and sending it to the business layer through the interface layer;

[0051] S103, the business layer converts the received Base64 code back to IFC format, and then converts it to wexbim format through the XBIM toolkit;

[0052] S104: Convert the BIM data in the wexbim format into Base64 code and then transmit it back to the application layer.

[0053] In step S102, the Base64 code is transmitted as a parameter. Although the request has only one parameter, for a GET request, sending it as a parameter exceeds the maximum length that can be sent by GET.

[0054] In step S102, the application layer encapsulates the Base64 code into an object, and the interface layer sends the Base64 code to the business layer through a POST request of the axios interface. The business layer is provided with a corresponding class to receive the object.

[0055] The business layer includes XBIM (eXtensible Building Information Modeling), a BIM toolkit developed with .NET open source software that supports IFC format data models.

[0056] In the data processing module, GIS data is stored in the database with the required key-value attributes in a conventional way.

[0057] The storage format of Base64 code is longtext.

[0058] The functions of the model processing module include model display, model management and density analysis.

[0059] Model display methods include point map rendering and heat map rendering.

[0060] The point map rendering process includes obtaining the fusion model from the database, parsing the fusion model to obtain the specific geographical location and corresponding attributes, and generating a point at the corresponding geographical location on the map. The point includes all the BIM data and GIS data of the corresponding geographical location in the fusion model.

[0061] like Figure 4 As shown in Figure 2, density analysis specifically includes the following steps:

[0062] S201, obtaining a target area selected by a user;

[0063] S202, randomly generating a certain number of points in the target area;

[0064] S203, forming a point set by combining the randomly generated points and the points originally existing in the fusion model;

[0065] S204, adjusting the domain parameters, and clustering the point set using the DBSCAN clustering algorithm based on the neighborhood parameters;

[0066] S205, storing the noise points identified in the clustering process in a set;

[0067] S206: traverse the set, exclude the points originally existing in the fusion model, and obtain a noise point set.

[0068] The noise point set corresponds to the sparsely modeled areas in the target area, and modeling is performed in these areas as needed to improve the modeling completeness of the entire area.

[0069] The pseudo code of the DBSCAN adapted algorithm used in the modeling density analysis of this embodiment is shown in Table 1. Using the DBSCAN adapted algorithm, noise points in the point set can be obtained.

[0070] Table 1 DBSCAN adapted algorithm

[0071]

[0072] In addition, it should be noted that the specific embodiments described in this specification may be named differently, and the above content described in this specification is merely an example of the structure of the present invention. Any equivalent changes or simple changes made based on the structure, features and principles of the present invention are included in the protection scope of the present invention. Those skilled in the art of the present invention may make various modifications or supplements to the specific examples described or adopt similar methods, as long as they do not deviate from the structure of the present invention or exceed the scope defined by the claims, they should all fall within the protection scope of the present invention.

Claims

1. A GIS and BIM fusion modeling system for urban intelligent planning, characterized by: It includes a data processing module, a model storage module and a model processing module. The data processing module acquires GIS data and BIM data and fuses them to obtain a fusion model. The fusion model is stored in a database through the model storage module and is called by the model processing module. The formats of the BIM data include wexbim format and IFC format. The BIM data in wexbim format is converted into Base64 code before storage. The BIM data in IFC format is first converted into wexbim format and then converted into Base64 code before storage. The process of converting IFC format BIM data to Base64 code includes the following steps: S101, the application layer obtains BIM data in IFC format input by the user; S102, converting the BIM data in IFC format into Base64 code and sending it to the business layer through the interface layer; S103, the business layer converts the received Base64 code back to IFC format, and then converts it to wexbim format through the XBIM toolkit; S104: Convert the BIM data in the wexbim format into Base64 code and then transmit it back to the application layer.

2. A GIS and BIM fusion modeling system for urban intelligent planning according to claim 1, characterized in that: It includes application layer, interface layer, business layer, data access layer and data layer.

3. The GIS and BIM fusion modeling system for urban intelligent planning according to claim 2 is characterized in that: The application layer, interface layer and business layer are located in the data processing module and the model processing module, and the data access layer and data layer are located in the model storage module.

4. The GIS and BIM fusion modeling system for urban intelligent planning according to claim 1 is characterized in that: In step S102, the interface layer sends the Base64 code to the business layer through the POST request of the axios interface.

5. The GIS and BIM fusion modeling system for urban intelligent planning according to claim 1 is characterized in that: The storage format of the Base64 code is longtext.

6. The GIS and BIM fusion modeling system for urban intelligent planning according to claim 1 is characterized in that: The functions of the model processing module include model display, model management and density analysis.

7. The GIS and BIM fusion modeling system for urban intelligent planning according to claim 6 is characterized in that: The model display methods include point map rendering and heat map rendering.

8. The GIS and BIM fusion modeling system for urban intelligent planning according to claim 7 is characterized in that: The point map rendering process includes obtaining a fusion model from a database, parsing the fusion model to obtain a specific geographic location and corresponding attributes, and generating a point at the corresponding geographic location on the map. The point includes all BIM data and GIS data of the corresponding geographic location in the fusion model.

9. The GIS and BIM fusion modeling system for urban intelligent planning according to claim 6 is characterized in that: The density analysis specifically comprises the following steps: S201, obtaining a target area selected by a user; S202, randomly generating a certain number of points in the target area; S203, forming a point set by combining the randomly generated points and the points originally existing in the fusion model; S204, adjusting the domain parameters, and clustering the point set using the DBSCAN clustering algorithm based on the neighborhood parameters; S205, storing the noise points identified in the clustering process in a set; S206: traverse the set, exclude the points originally existing in the fusion model, and obtain a noise point set.

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