CN115601514A - Automatic association mapping method for digital twin data - Google Patents
Automatic association mapping method for digital twin data Download PDFInfo
- Publication number
- CN115601514A CN115601514A CN202211030079.3A CN202211030079A CN115601514A CN 115601514 A CN115601514 A CN 115601514A CN 202211030079 A CN202211030079 A CN 202211030079A CN 115601514 A CN115601514 A CN 115601514A
- Authority
- CN
- China
- Prior art keywords
- building
- virtual
- model
- spatial
- information
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T17/00—Three dimensional [3D] modelling, e.g. data description of 3D objects
- G06T17/05—Geographic models
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/22—Indexing; Data structures therefor; Storage structures
- G06F16/2228—Indexing structures
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/245—Query processing
- G06F16/2457—Query processing with adaptation to user needs
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/248—Presentation of query results
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/29—Geographical information databases
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- Databases & Information Systems (AREA)
- Data Mining & Analysis (AREA)
- Geometry (AREA)
- Software Systems (AREA)
- Remote Sensing (AREA)
- Computational Linguistics (AREA)
- Evolutionary Computation (AREA)
- Computer Hardware Design (AREA)
- Computer Graphics (AREA)
- Processing Or Creating Images (AREA)
Abstract
The invention provides a digital twin data automatic association mapping method in the technical field of digital twin, which comprises the following steps: s10, obtaining building information of each entity building; s20, carrying out standardized processing on each piece of building information based on the Beidou grid position code and storing the building information to a building information recording table; s30, constructing a virtual space, and generating a virtual space position code of each virtual building model based on the Beidou grid position code; s40, generating a building model information data table based on each virtual space position code and the model ID of the virtual building model; s50, automatically fusing and mapping the building information record table and the building model information data table based on the Beidou grid position code to generate a spatial correlation index table; s60, optimizing the spatial correlation index table; and S70, interacting the physical space and the virtual space based on the space association index table. The invention has the advantages that: the efficiency and the quality of the digital twin data association mapping are greatly improved.
Description
Technical Field
The invention relates to the technical field of digital twinning, in particular to a digital twinning data automatic association mapping method.
Background
The digital twin is to fully utilize data such as a physical model, sensor updating, operation history and the like, integrate a multidisciplinary, multi-physical quantity, multi-scale and multi-probability simulation process and complete mapping in a virtual space so as to reflect the full life cycle process of corresponding entity equipment; digital twinning is an beyond-realistic concept that can be viewed as a digital mapping system of one or more important, interdependent equipment systems.
With the development of the digital twin technology, application scenes are more and more abundant, wherein one application scene is used for the construction of a smart city. The digital twin builds a high fidelity model in a virtual space in a digitized form, which is consistent with the physical world, simulates the behavior of an object in the physical world, monitors the change of the physical world, reflects the operating condition of the physical world, evaluates the state of the physical world, diagnoses occurring problems, predicts future trends, and even optimizes and changes the physical world.
The digital twin has 4 important technical features: accurate mapping, analysis insight, virtual and actual fusion and intelligent intervention; the accurate mapping is used for mapping objects in a virtual space and a physical world in a one-to-one correlation mode, is a basic technology of digital twins, and can perform subsequent analysis insights, virtual-actual fusion and intelligent intervention only on the premise of accurate mapping. At present, the association relationship between business system data and modeling model data needs to be established for accurate mapping, and in order to establish the association relationship, manual configuration is traditionally performed manually, that is, the relevant IDs of the business system data and the modeling model data are associated manually, but the problems of large workload and low efficiency exist, and manual work is easy to distract in the long-time fatigue configuration process, so that misoperation is caused.
Therefore, how to provide an automatic association mapping method for digital twin data to improve the efficiency and quality of association mapping of digital twin data becomes a technical problem to be solved urgently.
Disclosure of Invention
The invention aims to provide a digital twin data automatic association mapping method, which can improve the efficiency and quality of digital twin data association mapping.
The invention is realized in the following way: a digital twin data automatic association mapping method comprises the following steps:
s10, obtaining building information of each entity building in the physical space;
step S20, carrying out standardization processing on each piece of building information based on the Beidou grid position code, and storing each piece of standardized building information into a pre-established building information record table;
s30, constructing virtual spaces containing virtual building models corresponding to the entity buildings one by one based on a geographic coordinate system, and generating virtual space position codes of the virtual building models based on Beidou grid position codes;
step S40, generating a building model information data table based on each virtual space position code and the model ID of the virtual building model;
s50, automatically fusing and mapping the building information record table and the building model information data table based on the Beidou grid position code to generate a spatial correlation index table;
s60, optimizing the spatial correlation index table;
and S70, carrying out interaction between the physical space and the virtual space based on the space association index table.
Further, in step S10, the building information at least includes the following fields: building ID, building name, building year, building structure, building address, longitude and latitude.
Further, in the step S20, the step of performing standardization processing on each piece of building information based on the beidou mesh location code specifically includes:
screening building information lacking the longitude and latitude, and converting a building address in the building information into the longitude and latitude by using a geocoding service;
and converting the longitude and latitude of all the building information into a virtual space position code through a coding rule of the Beidou grid position code.
Further, the precision of the virtual spatial position encoding is 1m × 1m.
Further, the step S30 specifically includes:
constructing a virtual space comprising a plurality of virtual building models by taking a geographic coordinate system as a reference, wherein each virtual building model uniquely corresponds to a model ID;
and coding the virtual space with the precision of 1mI 1m through a coding rule of the Beidou grid position code, and taking the code corresponding to the central point of the virtual building model as the virtual space position code of the virtual building model in the virtual space.
Further, the step S40 specifically includes:
and creating a building model information data table, and storing each virtual space position code and the model ID of the corresponding virtual building model into the building model information data table.
Further, the step S50 specifically includes:
setting a spatial distance threshold, automatically traversing and calculating the spatial distance between the virtual spatial position codes of each entity building in the building information record table and the virtual spatial position codes of each virtual building model in the building model information data table, and when the virtual spatial position codes with the spatial distance smaller than the spatial distance threshold exist, automatically fusing and mapping the building ID of the corresponding entity building and the model ID of the virtual building model to generate corresponding associated data records, and storing the associated data records into a pre-established spatial associated index table.
Further, the spatial distance threshold is 1m.
Further, the step S60 specifically includes:
and highlighting the associated data records of the virtual space position codes with at least two mapping relations in the spatial associated index table, manually checking the highlighted associated data records, checking and optimizing the highlighted associated data records, and resolving the abnormal associated data records.
Further, the step S70 specifically includes:
matching the associated building ID from the spatial association index table by using the model ID to acquire the building information corresponding to the building ID for displaying; and matching the associated model ID from the spatial association index table by using the building ID so as to perform virtual control on the virtual building model corresponding to the model ID.
The invention has the advantages that:
building information of each entity building is subjected to standardization processing through a Beidou grid position code to obtain a corresponding virtual space position code, the virtual space position codes of each virtual building model are set through the Beidou grid position code, then, the space distance of the virtual space position codes of the entity building and the virtual building model is utilized to automatically perform fusion mapping, namely, association binding is performed to obtain an associated data record, a space association index table is further generated, the associated data record of the virtual space position codes with at least two mapping relations in the space association index table is highlighted, the highlighted associated data record is manually checked and resolved, namely, the entity building and the virtual building model can be automatically subjected to association mapping through the virtual space position code generated based on the Beidou grid position code, the associated data record with abnormal mapping only needs to be manually checked, the workload of manual operation is greatly reduced, faults caused by manual operation under long-time fatigue operation are avoided, and finally, the efficiency and quality of digital twin data association mapping are greatly improved.
Drawings
The invention will be further described with reference to the following examples with reference to the accompanying drawings.
FIG. 1 is a flow chart of a digital twin data auto-correlation mapping method according to the present invention.
FIG. 2 is a flow chart of a digital twin data auto-correlation mapping method according to the present invention.
Detailed Description
The technical scheme in the embodiment of the application has the following general idea: the method comprises the steps of setting virtual space position codes of an entity building and a virtual building model through Beidou grid position codes, and carrying out association mapping (fusion mapping) on the entity building and the virtual building model through the space distance of the virtual space position codes, namely, carrying out automatic space position indexing by using the virtual space position codes to replace the traditional longitude and latitude, so that the efficiency of association mapping of digital twin data is improved, the manual intervention is reduced, and the quality of association mapping of the digital twin data is improved.
Referring to fig. 1 to fig. 2, a preferred embodiment of a digital twin data automatic association mapping method according to the present invention includes the following steps:
s10, obtaining building information of each entity building in a physical space;
step S20, carrying out standardization processing on each piece of building information based on the Beidou grid position code, and storing each piece of standardized building information into a pre-established building information record table; through standardization processing, the building address and the longitude and latitude are uniformly converted into a virtual space position code, so that subsequent fusion mapping is facilitated;
s30, constructing virtual spaces containing virtual building models corresponding to the entity buildings one by one based on a geographic coordinate system, and generating virtual space position codes of the virtual building models based on Beidou grid position codes;
step S40, generating a building model information data table based on each virtual space position code and the model ID of the virtual building model; binding the virtual space position code of each virtual building model with a model ID and then storing the binding to a building model information data table;
s50, automatically fusing and mapping the building information record table and the building model information data table based on the Beidou grid position code to generate a spatial correlation index table;
s60, optimizing the spatial correlation index table to remove abnormal mapping data;
and S70, carrying out interaction between the physical space and the virtual space based on the space association index table.
In step S10, the building information at least includes the following fields: building ID, building name, building year, building structure, building address and longitude and latitude.
In the step S20, the standardizing each piece of building information based on the Beidou grid position code specifically includes:
screening the building information lacking the longitude and latitude, and converting the building address in the building information into the longitude and latitude by using a geocoding service;
and converting the longitude and latitude of all the building information into a virtual space position code through a coding rule of the Beidou grid position code.
The Beidou grid position code is a discretization multi-scale area position identification system developed on the basis of a GeoSOT earth space subdivision theory, can endow a globally unique one-dimensional integer number code for any grid with different sizes and highest precision of 1.5 centimeters in earth space from the geocenter to the ground 6 kilometers, and can establish internal correlation with any entity object and various different data in the same district and city range.
The accuracy of the virtual spatial position encoding is 1m × 1m.
The step S30 specifically includes:
constructing a virtual space containing a plurality of virtual building models by taking a geographic coordinate system as a reference, wherein each virtual building model uniquely corresponds to a model ID;
and coding the virtual space with the precision of 1m × 1m by using a coding rule of the Beidou grid position code, and taking a code corresponding to a central point of the virtual building model as a virtual space position code of the virtual building model in the virtual space.
The step S40 is specifically:
and creating a building model information data table, and storing each virtual space position code and the model ID of the corresponding virtual building model into the building model information data table.
The step S50 specifically includes:
setting a spatial distance threshold, constructing a spatial distance between a virtual spatial position code of each entity building in the building information record table and a virtual spatial position code of each virtual building model in the building model information data table by a distributed Spark task in an automatic traversing way, and automatically fusing and mapping a building ID of the corresponding entity building and a model ID of the virtual building model to generate a corresponding associated data record and storing the associated data record into a pre-established spatial associated index table when the virtual spatial position code of which the spatial distance is smaller than the spatial distance threshold exists. The Spark task supports batch processing, so that the efficiency of digital twin data association mapping is greatly improved.
The spatial distance threshold is 1m.
The step S60 specifically includes:
and highlighting the associated data records of the virtual space position codes with at least two mapping relations in the spatial associated index table, manually checking the highlighted associated data records, checking and optimizing the highlighted associated data records, and resolving the abnormal associated data records.
Ideally, only one associated data record exists in the spatial associated index table for one building ID or model ID, and when at least two associated data records exist, it is indicated that abnormal mapping exists, and at this time, manual intervention is required to perform resolution, so as to improve the quality of digital twin data associated mapping.
During specific implementation, intelligent analysis can be performed on the associated data records of the abnormal mapping through a deep learning algorithm, automatic resolution is performed, manual workload is further reduced, and the spatial distance threshold value is continuously optimized based on the intelligent analysis result, so that the number of the abnormal mapping is reduced.
The step S70 specifically includes:
matching the associated building ID from the spatial association index table by using the model ID to obtain the building information corresponding to the building ID for displaying and analyzing insights; and matching the associated model ID from the spatial association index table by using the building ID so as to perform virtual control on the virtual building model corresponding to the model ID, for example, controlling the floor of the virtual building model to be layered.
In summary, the invention has the advantages that:
building information of each entity building is subjected to standardized processing through a Beidou grid position code to obtain a corresponding virtual space position code, the virtual space position codes of each virtual building model are set through the Beidou grid position code, then, the space distance of the virtual space position codes of the entity building and the virtual building model is utilized to automatically perform fusion mapping, namely, association binding is performed to obtain associated data records, a space association index table is further generated, the associated data records of the virtual space position codes with at least two mapping relations in the space association index table are highlighted, the highlighted associated data records are manually checked and resolved, namely, the entity building and the virtual building model can be automatically subjected to association mapping through the virtual space position code generated based on the Beidou grid position code, the associated data records with abnormal mapping only need to be manually checked, the workload of manual operation is greatly reduced, errors caused by manual operation under long-time fatigue operation are avoided, and finally, the efficiency and the quality of digital twin data association mapping are greatly improved.
Although specific embodiments of the invention have been described above, it will be understood by those skilled in the art that the specific embodiments described are illustrative only and are not limiting upon the scope of the invention, and that equivalent modifications and variations can be made by those skilled in the art without departing from the spirit of the invention, which is to be limited only by the appended claims.
Claims (10)
1. A digital twin data automatic association mapping method is characterized in that: the method comprises the following steps:
s10, obtaining building information of each entity building in the physical space;
step S20, carrying out standardization processing on each piece of building information based on the Beidou grid position code, and storing each piece of standardized building information into a pre-established building information record table;
s30, constructing virtual spaces containing virtual building models corresponding to the entity buildings one by one based on a geographic coordinate system, and generating virtual space position codes of the virtual building models based on Beidou grid position codes;
step S40, generating a building model information data table based on each virtual space position code and the model ID of the virtual building model;
s50, automatically fusing and mapping the building information record table and the building model information data table based on the Beidou grid position code to generate a spatial correlation index table;
s60, optimizing the spatial correlation index table;
and S70, carrying out interaction between the physical space and the virtual space based on the space association index table.
2. A digital twin data auto-correlation mapping method as claimed in claim 1, wherein: in step S10, the building information at least includes the following fields: building ID, building name, building year, building structure, building address and longitude and latitude.
3. A digital twin data auto-correlation mapping method as claimed in claim 2, wherein: in the step S20, the step of performing standardized processing on each piece of building information based on the beidou grid location code specifically includes:
screening the building information lacking the longitude and latitude, and converting the building address in the building information into the longitude and latitude by using a geocoding service;
and converting the longitude and latitude of all the building information into a virtual space position code through a coding rule of the Beidou grid position code.
4. A digital twin data auto-correlation mapping method as claimed in claim 3, wherein: the accuracy of the virtual spatial position encoding is 1m × 1m.
5. A digital twin data auto-associative mapping method according to claim 1, wherein: the step S30 specifically includes:
constructing a virtual space comprising a plurality of virtual building models by taking a geographic coordinate system as a reference, wherein each virtual building model uniquely corresponds to a model ID;
and coding the virtual space with the precision of 1mI 1m through a coding rule of the Beidou grid position code, and taking the code corresponding to the central point of the virtual building model as the virtual space position code of the virtual building model in the virtual space.
6. A digital twin data auto-correlation mapping method as claimed in claim 1, wherein: the step S40 is specifically:
and creating a building model information data table, and storing each virtual space position code and the model ID of the corresponding virtual building model into the building model information data table.
7. A digital twin data auto-associative mapping method according to claim 1, wherein: the step S50 is specifically:
setting a spatial distance threshold, automatically traversing and calculating the spatial distance between the virtual spatial position codes of each entity building in the building information record table and the virtual spatial position codes of each virtual building model in the building model information data table, and when the virtual spatial position codes with the spatial distance smaller than the spatial distance threshold exist, automatically fusing and mapping the building ID of the corresponding entity building and the model ID of the virtual building model to generate corresponding associated data records, and storing the associated data records into a pre-established spatial associated index table.
8. A digital twin data auto-associative mapping method according to claim 7, wherein: the spatial distance threshold is 1m.
9. A digital twin data auto-associative mapping method according to claim 1, wherein: the step S60 specifically includes:
and highlighting the associated data records of the virtual space position codes with at least two mapping relations in the spatial associated index table, manually checking the highlighted associated data records, checking and optimizing the highlighted associated data records, and resolving the abnormal associated data records.
10. A digital twin data auto-associative mapping method according to claim 1, wherein: the step S70 is specifically:
matching the associated building ID from the spatial association index table by using the model ID to acquire the building information corresponding to the building ID for displaying; and matching the associated model ID from the spatial association index table by using the building ID so as to perform virtual control on the virtual building model corresponding to the model ID.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211030079.3A CN115601514A (en) | 2022-08-26 | 2022-08-26 | Automatic association mapping method for digital twin data |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211030079.3A CN115601514A (en) | 2022-08-26 | 2022-08-26 | Automatic association mapping method for digital twin data |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN115601514A true CN115601514A (en) | 2023-01-13 |
Family
ID=84843374
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202211030079.3A Pending CN115601514A (en) | 2022-08-26 | 2022-08-26 | Automatic association mapping method for digital twin data |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115601514A (en) |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116137629A (en) * | 2023-02-17 | 2023-05-19 | 软通动力信息技术(集团)股份有限公司 | Method, device, equipment and medium for matching sensing data of Internet of things with space model |
| CN116305420A (en) * | 2023-01-30 | 2023-06-23 | 中国公路工程咨询集团有限公司 | Highway maintenance digital twin body construction method, system, equipment and medium |
| CN116756875A (en) * | 2023-06-21 | 2023-09-15 | 北方工业大学 | Batch blade process model multi-source information association method based on cylindrical coordinate mapping |
| CN117218310A (en) * | 2023-09-22 | 2023-12-12 | 北京三友卓越科技有限公司 | Virtual reconstruction method, device, equipment and medium based on digital twin |
-
2022
- 2022-08-26 CN CN202211030079.3A patent/CN115601514A/en active Pending
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116305420A (en) * | 2023-01-30 | 2023-06-23 | 中国公路工程咨询集团有限公司 | Highway maintenance digital twin body construction method, system, equipment and medium |
| CN116305420B (en) * | 2023-01-30 | 2023-10-17 | 中国公路工程咨询集团有限公司 | Highway maintenance digital twin body construction method, system, equipment and medium |
| CN116137629A (en) * | 2023-02-17 | 2023-05-19 | 软通动力信息技术(集团)股份有限公司 | Method, device, equipment and medium for matching sensing data of Internet of things with space model |
| CN116756875A (en) * | 2023-06-21 | 2023-09-15 | 北方工业大学 | Batch blade process model multi-source information association method based on cylindrical coordinate mapping |
| CN116756875B (en) * | 2023-06-21 | 2024-03-01 | 北方工业大学 | Batch blade process model multi-source information association method based on cylindrical coordinate mapping |
| CN117218310A (en) * | 2023-09-22 | 2023-12-12 | 北京三友卓越科技有限公司 | Virtual reconstruction method, device, equipment and medium based on digital twin |
| CN117218310B (en) * | 2023-09-22 | 2024-04-05 | 北京三友卓越科技有限公司 | Virtual reconstruction method, device, equipment and medium based on digital twin |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN115601514A (en) | Automatic association mapping method for digital twin data | |
| CN110659173A (en) | Operation and maintenance system and method | |
| CN109063178B (en) | Method and device for automatically expanding self-help analysis report | |
| CN116680648B (en) | Service fusion data generation method and system for digital twin city | |
| CN108388640B (en) | Data conversion method and device and data processing system | |
| CN112633822B (en) | Asset management method based on digital twin technology, storage medium and mobile terminal | |
| CN114282752A (en) | Method and device for generating flow task, electronic equipment and storage medium | |
| CN105404472A (en) | Method and apparatus for compressing storage space of log time data | |
| US20210117593A1 (en) | Configurable digital twin | |
| CN111309734A (en) | Method and system for automatically generating table data | |
| CN115657890A (en) | PRA robot customizable method | |
| CN114461600A (en) | Engineering project data multidimensional multiplexing method based on BIM and component identity label | |
| CN110535686A (en) | Anomalous event treating method and apparatus | |
| CN114328452A (en) | Data auditing method, device, platform, electronic equipment and storage medium | |
| CN113495831B (en) | Method, system, equipment and medium for generating test case based on keywords | |
| CN115017141A (en) | Digital twinning method, device and equipment for event full life cycle | |
| CN110334651B (en) | Substation coordinate verification method based on transfer learning | |
| CN114331175A (en) | Centralized statistical evaluation method and system for urban safety performance data | |
| CN114564773A (en) | Component screening and checking method and system based on BIM (building information modeling) | |
| Weinberg | Using geospatial data to inform historical research in R | |
| CN115390853B (en) | Structured analysis method, system, terminal and storage medium for multi-source process file | |
| CN116010349B (en) | Metadata-based data checking method and device, electronic equipment and storage medium | |
| CN117591508B (en) | Data quality guarantee method under big data scene | |
| CN116303342B (en) | Reverse model construction method and system | |
| CN114971255B (en) | Automatic parameter performance detection system and method |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |