CN115758522A - Digital twin city management system and method - Google Patents

Digital twin city management system and method Download PDF

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Publication number
CN115758522A
CN115758522A CN202211417932.7A CN202211417932A CN115758522A CN 115758522 A CN115758522 A CN 115758522A CN 202211417932 A CN202211417932 A CN 202211417932A CN 115758522 A CN115758522 A CN 115758522A
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data
spatial
fusion
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rendering
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许天会
高云峰
李永斌
吴东平
邹霓
王雅婷
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Cccc Urban Rural Construction Planning And Design Research Institute Co ltd
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Cccc Urban Rural Construction Planning And Design Research Institute Co ltd
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Abstract

The embodiment of the application provides a digital twin city management system and a method, wherein the system comprises a data center module, an application center module and a service center module, wherein the data center module is used for integrating city data resources, establishing a spatio-temporal data system which takes city spatio-temporal data as an index, basic geographic data and natural resource planning data as a basis, social resource data as a backbone and internet-of-things perception data as supplement; the application center module is used for carrying out BIM modeling according to the requirements of the application field on the fineness degree of the entity object and the association fusion relation covered in the spatio-temporal data system aiming at each target subject in the urban planning, and carrying out space path planning based on the obtained BIM model; and the service center module is used for classifying and storing various data generated in the data integration, fusion, three-dimensional entity modeling and spatial data correlation calculation processes according to the data rule characteristics, the interaction characteristics and the data types.

Description

Digital twin city management system and method
Technical Field
The application relates to the technical field of city management, in particular to a digital twin city management system and a digital twin city management method.
Background
With the continuous revolution of urban development concept and the continuous development of digital twin related technologies in the future, the concept of urban digital twin is inoculated. The urban digital twin agrees with the strategic requirements of the current industry transformation upgrading, and is a basic technical means for the fusion development of cities in physical space, social space and digital space. The urban digital twin is a new innovation source and an initial point for urban digital and visual construction and becomes a core base of a new stage of urban development in the future.
In recent years, relevant organizations at home and abroad begin to explore and construct new paths for future city construction from the view point of city digital twin, such as a singapore digital twin collaboration platform, a Japanese city-level digital twin open platform, a China digital peace CIM platform and the like. However, at present, the operation of each part in the city still depends on manpower, and the stable and safe operation of the city is realized through the respective employment of each industry. Even if individual industries rely on modern information technology to improve management efficiency, the problem of low management efficiency still exists.
Disclosure of Invention
The embodiment of the application aims to provide a digital twin city management system and method, so that digital management and control of cities can be realized.
The embodiment of the application provides a digital twin city management system, which comprises a data center module, an application center module and a service center module, wherein:
the data center module is used for integrating urban data resources, and establishing a spatio-temporal data system which takes urban spatio-temporal data as an index, basic geographic data and natural resource planning data as a basis, social resource data as a main frame and Internet of things perception data as supplement through relevant data fusion on the spatial, temporal and semantic levels;
the application pivot module is used for carrying out BIM modeling on a three-dimensional entity aiming at each target subject in the urban planning by combining the requirement of the application field on the fineness degree of the entity object and the association fusion relation covered in the spatio-temporal data system;
the application center module is also used for carrying out spatial data correlation calculation based on the obtained BIM model and combined with path planning requirements, and carrying out spatial path planning based on the obtained correlation calculation result;
the service center module is used for performing classified storage on various data generated in the processes of data integration, fusion, three-dimensional entity modeling and spatial data correlation calculation according to data rule features, interaction features and data types, and the classified storage comprises online storage, near-line storage and filing storage.
The embodiment of the application provides a digital twin city management method, which comprises the following steps:
integrating urban data resources, and establishing a spatio-temporal data system which takes urban spatio-temporal data as an index, takes basic geographic data and natural resource planning data as a basis, takes social resource data as a backbone and takes Internet of things perception data as supplement through relevant data fusion on the spatial, temporal and semantic levels;
aiming at each target subject in the urban planning, BIM modeling of a three-dimensional entity is carried out by combining the requirement of the application field on the fineness degree of the entity object and the association fusion relation covered in the spatio-temporal data system;
based on the obtained BIM model, combining with the path planning requirement, performing spatial data correlation calculation, and based on the obtained correlation calculation result, performing spatial path planning;
and according to the data rule characteristics, the interaction characteristics and the data types, performing classified storage on various data generated in the processes of data integration, fusion, three-dimensional entity modeling and spatial data correlation calculation, wherein the classified storage comprises online storage, near-line storage and filing storage.
From the above, the digital twin city management system and method provided in the embodiments of the present application can accelerate the transition from the traditional "pyramid" linear management to the new management mode based on the network management organization architecture in the internet era, gradually implement the replication and fission of the advanced management system and mode, determine the division of duties by the digitization and informatization application technology, avoid the repetition of functions, break data islands, open data sharing, improve the programming, automation and integration levels of the process management, and implement the digital management and control of the city.
Additional features and advantages of the present application will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by the practice of the embodiments of the present application. The objectives and other advantages of the application may be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
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To more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required to be used in the embodiments of the present application will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and that those skilled in the art can also obtain other related drawings based on the drawings without inventive efforts.
Fig. 1 is a schematic structural diagram of a digital twin city management system according to an embodiment of the present disclosure;
FIG. 2 is a schematic view of a visualization rendering;
FIG. 3 is a diagram illustrating the structure of a dynamic database cluster;
fig. 4 is a schematic flowchart of a digital twin city management method according to an embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. The components of the embodiments of the present application, generally described and illustrated in the figures herein, can be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present application, presented in the accompanying drawings, is not intended to limit the scope of the claimed application, but is merely representative of selected embodiments of the application. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present application without making any creative effort, shall fall within the protection scope of the present application.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined or explained in subsequent figures. Meanwhile, in the description of the present application, the terms "first", "second", and the like are used only for distinguishing the description, and are not construed as indicating or implying relative importance.
Referring to fig. 1, fig. 1 is a schematic structural diagram of a digital twin city management system in some embodiments of the present application. The present application discloses a digital twin city management system 100, the system 100 includes a data hub module 101, an application hub module 102 and a service hub module 103, wherein:
the data hub module 101 is configured to integrate urban data resources, and establish a spatio-temporal data system using urban spatio-temporal data as an index, basic geographic data and natural resource planning data as a basis, social resource data as a backbone, and internet of things perception data as a supplement through relevant data fusion on the spatial, temporal and semantic levels.
It should be noted that data integration is the basis of data fusion, wherein the data integration includes unification of spatial reference, unification of data model, and unification of semantic coding. Specifically, in the implementation process, the data integration mainly realizes the organic integration of multi-source data through four modes of data exchange, direct access, data interoperation and body integration.
Specifically, the data fusion technology is an information processing technology that automatically analyzes and synthesizes a plurality of observation information obtained by a computer according to a time sequence under a certain criterion to complete a required decision and evaluation task. The data fusion technology comprises the steps of collecting, transmitting, integrating, filtering, correlating and synthesizing useful information given by various information sources so as to assist people in situation/environment judgment, planning, detection, verification and diagnosis.
In one embodiment, when performing data fusion, it may be considered to perform data fusion directly on the original data layer (i.e., the original information without preprocessing). In addition, it is also possible to perform feature extraction on the acquired original information (the feature may be an edge, a direction, a speed, or the like of the extraction target), and then perform comprehensive analysis and processing based on the feature information. In addition, fusion judgment of a decision layer can also be carried out through association processing.
The application pivot module 102 is configured to perform BIM modeling of a three-dimensional entity for each target subject in the city plan by combining the requirement of the application field for the fineness of the entity object and the association fusion relationship included in the spatio-temporal data system.
Specifically, when performing BIM modeling, the covered operation steps include:
1. and operating the BIM software by a user, and establishing a grid and a floor line based on the BIM software.
It should be noted that, when the architect draws the architectural design drawing and the construction drawing, the grid and the floor are important bases, and the site constructors can find the correct position on the foundation only by relying on the grid for lofting and column position judgment. The floor line is the basis for expressing the floor height, and the positions of the beams and the columns, the height of the wall and the position of the floor are described, and most architects design the floor and the beam at the position below the floor, and the wall is positioned below the beam or the floor.
2. A user leads the pre-processed CAD document into BIM software, so that the user can conveniently and directly click and select a drawing or draw the drawing according to the drawing when establishing the column beam plate wall in the next step.
3. A user operates BIM software to place components such as columns, beams, plates and walls on the model according to the drawing, and the components are drawn in a corresponding mode according to different types of the components, so that the design and the presentation of the three-dimensional model are finally completed.
The application center module is further used for carrying out spatial data correlation calculation based on the obtained BIM and combined with path planning requirements, and carrying out spatial path planning based on the obtained correlation calculation results.
Specifically, the spatial analysis and calculation capability covered currently refers to the implementation of measurement capabilities such as distance measurement, area measurement and volume measurement, the implementation of space-time analysis capabilities such as superposition analysis, sequence analysis and prediction analysis, the implementation of scene analysis capabilities such as path planning, roaming customization, visual field analysis and the like, and the implementation of marking capabilities such as panorama customization and scene marking, based on a digital twin city three-dimensional model (i.e., a BIM model), in combination with the beidou space-time grid technology and beidou positioning service, and aiming at specific business application requirements, the relevant calculation and analysis of spatial data are carried out.
The Beidou space-time grid technology can accurately encode and express information of global universe space, can endow a computable and easily-retrievable global unique space identifier for each inch of space in land, sea, air and ground, can also endow space-time data attributes for earth space everything by combining time coding based on a large data organization frame of space, and realizes the data interconnection of everything with the space-time coding as a main index.
On the other hand, the Beidou positioning service is suitable for providing positioning and navigation services for users through a Beidou navigation system.
The service hub module 103 is configured to perform classified storage on various data generated in the data integration, fusion, three-dimensional entity modeling, and spatial data correlation calculation processes according to data rule features, interaction features, and data types, where the classified storage includes online storage, near-line storage, and archive storage.
Specifically, before classification, the identification of the keyword, the key field and the key identifier may be performed, and then the cluster analysis may be performed on the generated data to determine the classification type of the data. Wherein, (1) online storage refers to storing data on a network in a networking state. From the perspective of the user, the user can not only perform online manual file management in a web mode, but also realize offline editing and online automatic synchronous uploading in a client mode. That is, the user can edit the files in the local folder whether the client is online or offline, and once the client is online, the system automatically synchronizes the files to the network. From the perspective of the server, the system shields the bottom storage details and the storage process, and the purpose of convenience and easy use for users is achieved. The user can conveniently read data at any time and any place by connecting to the network through any computer equipment capable of being connected to the network. (2) Near-line storage (Nearline storage) refers to a concept proposed with the refinement of the client storage environment, so-called near-line storage, which is relatively extensive and is mainly targeted at applications between client online storage and offline storage. This means that data that is not frequently used or has a small access amount is stored on a storage device with low performance. But at the same time the requirements for these devices are fast addressing and high transfer rates. (3) Archival storage is the safe, low cost, long lasting preservation of historical data that can be easily searched when needed.
Based on this, in the process of performing data classification storage, it may be determined whether data has been cached previously, and the frequency of being called and accessed, and for data with high frequency, an online storage manner may be adopted, which is convenient for a user to read and call. In addition, for data with relatively low calling frequency, a data caching mode can be adopted for archiving and storing so as to ensure the storage reliability. Other types of data can be selectively cached by using a near line storage mode. Of course, in the current embodiment, the data caching mode is not limited to be determined by the called and accessed frequency, for example, the current network state, the storage resource occupation information, and the service requirement information may also be used, which is not limited in the embodiment of the present application.
According to the embodiment, data caching is carried out through various storage modes, the resource utilization rate is improved under the condition that the diversified storage requirements of users are met, and follow-up further development and mining of data are facilitated.
From the above, the digital twin city management system disclosed in the application can accelerate the transition from the traditional pyramid linear management to a novel management mode based on a network type management organization structure in the internet era, gradually realize the copy and fission of an advanced management system and a mode, determine responsibility division by using a digital and information application technology, avoid function repetition, break a data island, open data sharing, improve the programming, automation and integration level of process management, and realize the digital management and control of a city.
In one embodiment, the data hub module 101 is further configured to perform spatial fusion based on spatial features included in entity objects in a spatial dimension, and determine a spatial fusion degree between corresponding fusion targets based on the obtained spatial fusion value, where the entity objects include at least one of a building entity, a traffic entity, a facility entity, and an underlying pipe network, and the spatial features include spatial location information and spatial local information.
Specifically, the traffic entity may be, for example, a traffic light, a street lamp, a forest land, a road, etc., the building entity may be, for example, an independent building (e.g., a residential building, a shopping mall, an office building, etc.), the interior of the building entity may contain at least one unit entity, and the facility entity may be a municipal infrastructure, such as an urban road, a public transportation, etc. In the process of spatial fusion, when matching of entities with the same name is required, corresponding feature information may be extracted based on predetermined spectral features (e.g., a mean value, a gray scale ratio, a standard deviation, and the like), shape features (e.g., an area, an aspect ratio, and the like), and texture features (e.g., a gray scale co-occurrence matrix, and the like), and the matching is completed based on the feature information.
The data center module 101 is further configured to perform time fusion on observed values of the targets at different times in time sequence based on an adaptive weighted average method with a minimum total variance as a target for a plurality of first target objects that have completed space fusion, and determine a time fusion degree between corresponding fusion targets based on the obtained time fusion value;
in the current embodiment, time fusion is performed on historical data, current data covered by a target object and dynamic detection data monitored based on an internet of things sensing device. In the fusion process, the time sequence characteristics of the objects can be determined based on the time sequence global information covered by the objects, and then fusion processing is carried out based on the time sequence characteristics.
The data center module 101 is further configured to, for a plurality of second target objects that have been subjected to space-time fusion, fuse, from a semantic level, basic attributes, entity relationships, and other socioeconomic information of geographic entities based on semantic information covered by the objects, and obtain space-time integration digital twins using the geographic entities as basic units.
In one embodiment, the application hub module 102 is further configured to obtain an ontology attribute of the target subject and an environment attribute of the target subject, where the ontology attribute includes at least one of geometry, color, texture, and material, and the environment attribute includes at least one of light, temperature, and humidity.
The application center module 102 is further configured to determine a visualization style according to the determined body attribute and the determined environment attribute, perform visualization rendering according to the visualization style, and display a three-dimensional space scene integrating buildings, roads, rail crossings, bridges, water systems, greenery, terrains, and underground spaces according to a virtual simulation mode.
Specifically, the final rendering effect schematic diagram can refer to fig. 2, and a three-dimensional space scene integrating buildings, roads, rail crossings, bridges, water systems, greening, terrains, and underground spaces can be constructed in the scene modeling process based on a mode of "BIM + GIS + oblique photography", so that the spatial geometric modeling of the physical world is completed. In the rendering process, multi-level and multi-scale rendering display from a natural geographic pattern to a natural resource reality condition and local characteristics is covered, spatial relations and attribute relations of various types of natural resources on the ground, on landmarks and underground are expressed visually, and a three-dimensional digital scene is provided for natural resource management and application.
In the current embodiment, the change conditions of data, indexes and the like are dynamically presented in the form of a data view, and a single trend, change and state presentation can be performed on multiple types of data, so that system maintenance personnel can more conveniently and more intuitively know the rendering state of the current system.
In one embodiment, the visualization rendering process further includes the following steps:
step S1, carrying out layering processing on an object to be rendered by combining the requirement of the application field on the fineness degree of an entity object to obtain a layered object of at least one level.
And S2, determining rendering rules and rendering priority levels according to the number of the rendering patches and the size of the rendering area covered by each layered object.
Specifically, rendering is the final process of CG (except post-production, of course), and is also the stage of the 3D scene that the image finally conforms to. There are various kinds of software for rendering, such as: each CG software has its own rendering engine, also such as RenderMan. Building design, animation production and the like, after models and animation frames are produced by using software such as 3DS MAX and MAYA, the designed contents are made into final effect diagrams or animations by using the software itself or auxiliary software (such as lightscape and vray). The number of the contained rendering patches and the size of the rendering area are combined to determine the rendering rule, so that the rendering range, which objects can be rendered and presented in the range, and which objects need to be shielded can be further determined, the visual rendering processing can be performed in a proper mode at present, and the purpose of rendering as required is achieved.
When each layered object is loaded to a corresponding renderer, the layered objects can be sequenced according to the determined rendering priority level, so that the rendering sequence is clear, and the corresponding entity object can be presented in a digital space from a multi-scale and hierarchical level.
And S3, performing layered rendering according to the determined rendering rule and the rendering priority level in sequence, and integrating rendering effects according to a predetermined layered sequence when rendering is finished.
Specifically, the rendered scene images may be transmitted to the data integration server in advance, and the data integration server may combine different scene images into a set of complete scene images with rendering effects according to a predetermined hierarchical order.
According to the embodiment, a series of problems that a large-scale three-dimensional model is low in rendering speed, large system resources such as memory and video memory are occupied, the number of rendered surface slices, rendering is not smooth, visual conflict exists, efficiency is low and the like can be avoided, and rendering efficiency is improved.
In one embodiment, the spatial path planning process further includes performing spatial data correlation calculation between the physical objects through superposition analysis and sequence analysis in combination with a space-time grid technology and a beidou positioning service, wherein the spatial data correlation calculation includes at least one of spatial distance measurement, covered area measurement and volume measurement.
In one embodiment, the system further comprises a situation assessment module, wherein:
and the situation evaluation module is used for determining resource information covered by the natural resource object in the area to be evaluated based on the BIM, wherein the resource information comprises resource attribute information, resource type information, the belonged space range and associated information of other resource objects.
And the situation evaluation module is also used for carrying out space geometric analysis, three-dimensional measurement subdivision and distribution statistical analysis based on the resource information so as to determine the space distribution and the change trend of the natural resource objects.
And the situation evaluation module is also used for predicting the growth trend based on the spatial distribution and the change trend.
Specifically, in the current embodiment, measures such as spatial geometric analysis, three-dimensional measurement and subdivision, query statistics and the like are mainly considered to perform accurate measurement and efficient analysis on spatial distribution and change trend of natural resources, so that dynamic observation and accurate grasp on entity positions, quantity, quality and growth trend of natural resources such as mountain and water forest fields, lakes and grasses are realized, and the current situation of the natural resources and the future prediction situation are presented.
In one embodiment, the system further comprises a data center setup module, wherein:
the data center establishing module is used for establishing a strongly-associated dynamic database group by combining data attribute characteristic association, support degree and confidence degree, wherein the dynamic database group comprises at least one of a space geographic basic database, a planning management and control database, an engineering economic database, a social economic database and a dynamic perception database.
Specifically, referring to fig. 3, in the process of building the database, since one map of the parcel data is the primary component of the system, all data association, linkage and fusion must be performed around the CIM map, including two-dimensional space data and three-dimensional space data. The two-dimensional space data comprises basic geographic information data, planning data of all departments, multi-rule-in-one result data, engineering project space, cost data and the like. The three-dimensional space data includes three-dimensional ground model data, three-dimensional underground space model data and the like. In the current embodiment, the various data generated by establishing IoT mainly includes traffic network, environmental monitoring, noise detection, and the like.
As shown in fig. 4, an embodiment of the present invention further provides a digital twin city management method, including the following steps:
s1: integrating urban data resources, and establishing a spatio-temporal data system which takes urban spatio-temporal data as an index, takes basic geographic data and natural resource planning data as a basis, takes social resource data as a backbone and takes Internet of things perception data as supplement through relevant data fusion on the spatial, temporal and semantic levels;
s2: aiming at each target subject in the urban planning, BIM modeling of a three-dimensional entity is carried out by combining the requirement of the application field on the fineness degree of the entity object and the association fusion relation covered in the spatio-temporal data system;
s3: based on the obtained BIM model, combining with the path planning requirement, performing spatial data correlation calculation, and based on the obtained correlation calculation result, performing spatial path planning;
s4: and according to the data rule characteristics, the interaction characteristics and the data types, performing classified storage on various data generated in the data integration, fusion, three-dimensional entity modeling and spatial data correlation calculation processes, wherein the classified storage comprises online storage, near-line storage and filing storage.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units is only one logical division, and there may be other divisions when actually implemented, and for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed coupling or direct coupling or communication connection between each other may be through some communication interfaces, indirect coupling or communication connection between devices or units, and may be in an electrical, mechanical or other form.
In addition, units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
Furthermore, the functional modules in the embodiments of the present application may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.
In this document, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions.
The above description is only an example of the present application and is not intended to limit the scope of the present application, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims (8)

1. A digital twin city management system, the system comprising a data hub module, an application hub module, and a service hub module, wherein:
the data center module is used for integrating urban data resources, and establishing a spatio-temporal data system which takes urban spatio-temporal data as an index, basic geographic data and natural resource planning data as a basis, social resource data as a main frame and Internet of things perception data as supplement through relevant data fusion on the spatial, temporal and semantic levels;
the application pivot module is used for carrying out BIM modeling on a three-dimensional entity aiming at each target subject in the urban planning by combining the requirement of the application field on the fineness degree of the entity object and the association fusion relation covered in the spatio-temporal data system;
the application center module is also used for carrying out spatial data correlation calculation based on the obtained BIM model and combined with path planning requirements, and carrying out spatial path planning based on the obtained correlation calculation result;
the service center module is used for classifying and storing various data generated in the processes of data integration, fusion, three-dimensional entity modeling and spatial data correlation calculation according to data rule characteristics, interaction characteristics and data types, and the classified storage comprises online storage, near-line storage and filing storage.
2. The system according to claim 1, wherein the data hub module is further configured to perform spatial fusion based on spatial features included in entity objects in spatial dimensions, and determine a spatial fusion degree between corresponding fusion targets based on the obtained spatial fusion value, the entity objects include at least one of building entities, traffic entities, facility entities, and underlying pipe networks, and the spatial features include spatial location information and spatial local information;
the data center module is also used for carrying out time fusion on observed values of the targets at different times according to the time sequence by taking the minimum total variance as the target based on an adaptive weighted average method and aiming at a plurality of first target objects which are subjected to space fusion, and judging the time fusion degree between the corresponding fusion targets based on the obtained time fusion values;
the data center module is also used for fusing the basic attributes, entity relationships and other social and economic information of geographic entities from a semantic level based on semantic information covered by objects aiming at a plurality of second target objects which are subjected to space-time fusion to obtain space-time integration digital twin bodies taking the geographic entities as basic units.
3. The system of claim 1, wherein the application hub module is further configured to obtain an ontology attribute of the target subject and an environment attribute of the target subject, the ontology attribute comprises at least one of geometry, color, texture, and material, and the environment attribute comprises at least one of light, temperature, and humidity;
the application pivot module is further used for determining a visualization style by combining the determined body attribute and the determined environment attribute, performing visualization rendering according to the visualization style, and displaying the corresponding three-dimensional space scene according to a virtual simulation mode.
4. The system of claim 3, wherein the visualization rendering process further comprises the steps of:
carrying out layering processing on an object to be rendered by combining the requirement of an application field on the fineness degree of an entity object to obtain a layered object of at least one level;
determining rendering rules and rendering priority levels according to the number of the rendering patches covered and the size of a rendering area for each layered object;
and performing layered rendering according to the determined rendering rule and the rendering priority level in sequence, and integrating rendering effects according to a predetermined layered sequence when rendering is finished.
5. The system of claim 1, wherein the spatial path planning process further comprises performing spatial data correlation calculation between the physical objects by stack analysis and sequence analysis in combination with spatio-temporal meshing technology and beidou positioning service, wherein the spatial data correlation calculation comprises at least one of spatial distance measurement, covered area measurement and volume measurement.
6. The system of claim 1, further comprising a situation assessment module, wherein:
the situation evaluation module is used for determining resource information covered by natural resource objects in the area to be evaluated based on the BIM, wherein the resource information comprises resource attribute information, resource type information, the affiliated space range and associated information of other resource objects;
the situation evaluation module is also used for carrying out space geometric analysis, three-dimensional measurement subdivision and distribution statistical analysis based on the resource information so as to determine the space distribution and the change trend of the natural resource object;
and the situation evaluation module is also used for predicting the growth trend based on the spatial distribution and the change trend.
7. The system of claim 1, further comprising a data center setup module, wherein:
the data center establishing module is used for establishing a strongly-associated dynamic database group by combining data attribute characteristic association, support degree and confidence degree, wherein the dynamic database group comprises at least one of a space geographic basic database, a planning management and control database, an engineering economy database, a social economy database and a dynamic perception database.
8. A digital twin city management method is characterized by comprising the following steps:
integrating urban data resources, and establishing a spatio-temporal data system which takes urban spatio-temporal data as an index, basic geographic data and natural resource planning data as a basis, social resource data as a backbone and Internet of things perception data as supplement through related data fusion on the spatial, temporal and semantic levels;
aiming at each target subject in the urban planning, BIM modeling of a three-dimensional entity is carried out by combining the requirement of the application field on the fineness degree of the entity object and the association fusion relation covered in the spatio-temporal data system;
based on the obtained BIM model, combining with the path planning requirement, performing spatial data correlation calculation, and based on the obtained correlation calculation result, performing spatial path planning;
and according to the data rule characteristics, the interaction characteristics and the data types, performing classified storage on various data generated in the data integration, fusion, three-dimensional entity modeling and spatial data correlation calculation processes, wherein the classified storage comprises online storage, near-line storage and filing storage.
CN202211417932.7A 2022-11-14 2022-11-14 Digital twin city management system and method Pending CN115758522A (en)

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CN116362573A (en) * 2023-06-02 2023-06-30 金田产业发展(山东)集团有限公司 Method and system for evaluating current situation of homeland space planning
CN116597680A (en) * 2023-03-28 2023-08-15 北京知藏云道科技有限公司 Line feasibility prediction system based on data analysis
CN116595813A (en) * 2023-07-18 2023-08-15 中交一公局第五工程有限公司 Digital twin construction method and device for intelligent network connection automobile test
CN116680648A (en) * 2023-03-24 2023-09-01 中乾思创(北京)科技有限公司 Service fusion data generation method and system for digital twin city
CN117150631A (en) * 2023-10-27 2023-12-01 北京祝融视觉科技股份有限公司 Digital twinning-based city infrastructure construction simulation method and system
CN117744498A (en) * 2023-12-29 2024-03-22 南京林业大学 Urban green illumination space data modeling analysis and visual rendering system and method
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CN116680648A (en) * 2023-03-24 2023-09-01 中乾思创(北京)科技有限公司 Service fusion data generation method and system for digital twin city
CN116680648B (en) * 2023-03-24 2024-01-16 中乾思创(北京)科技有限公司 Service fusion data generation method and system for digital twin city
CN116597680A (en) * 2023-03-28 2023-08-15 北京知藏云道科技有限公司 Line feasibility prediction system based on data analysis
CN116362573A (en) * 2023-06-02 2023-06-30 金田产业发展(山东)集团有限公司 Method and system for evaluating current situation of homeland space planning
CN116362573B (en) * 2023-06-02 2023-08-25 金田产业发展(山东)集团有限公司 Method and system for evaluating current situation of homeland space planning
CN116595813A (en) * 2023-07-18 2023-08-15 中交一公局第五工程有限公司 Digital twin construction method and device for intelligent network connection automobile test
CN117150631A (en) * 2023-10-27 2023-12-01 北京祝融视觉科技股份有限公司 Digital twinning-based city infrastructure construction simulation method and system
CN117150631B (en) * 2023-10-27 2024-01-12 北京祝融视觉科技股份有限公司 Digital twinning-based city infrastructure construction simulation method and system
CN117744498A (en) * 2023-12-29 2024-03-22 南京林业大学 Urban green illumination space data modeling analysis and visual rendering system and method
CN117744498B (en) * 2023-12-29 2024-05-31 南京林业大学 Urban green illumination space data modeling analysis and visual rendering system and method

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