CN114780798A - Knowledge map system based on BIM - Google Patents
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Abstract
The embodiment of the application discloses a knowledge graph system based on BIM. One embodiment of the method comprises: the data acquisition module is configured to acquire and analyze data of the coal preparation plant informatization system; the data management module is configured to manage the data acquired by the data acquisition module and perform correlation aggregation according to the entity under the BIM service scene of the coal preparation plant to obtain an entity subject of the knowledge map; the knowledge graph construction module is configured to extract entity attributes according to entity topics and combine the relationship construction among the entities to obtain a knowledge graph; and the knowledge graph intelligent application module is configured to provide data intelligent application based on the knowledge graph obtained by the knowledge graph construction module. The implementation mode provides an automatic process from data acquisition, analysis and treatment to knowledge map construction and application, and improves the knowledge utilization efficiency of the coal preparation plant.
Description
Technical Field
The embodiment of the application relates to the technical field of computers, in particular to a knowledge graph system based on BIM.
Background
BIM (Building Information Modeling) is a new tool in architecture, engineering and civil engineering. The technology is a datamation tool applied to engineering design, construction and management, and data modeling of a coal preparation plant can be completed through the three-dimensional modeling technology, namely, the datamation of business is realized so as to construct a three-dimensional visual management platform.
Knowledge map (Knowledge Graph) is a series of different graphs displaying Knowledge development process and structure relationship in the book intelligence field, describing Knowledge resources and carriers thereof by using visualization technology, mining, analyzing, constructing, drawing and displaying Knowledge and mutual relation between Knowledge resources and Knowledge carriers.
At present, the construction and application of the knowledge graph of the coal preparation plant are insufficient, and a process from data acquisition, analysis and treatment to knowledge graph construction and application, which is fit with the service scene of the coal preparation plant, is lacked.
Disclosure of Invention
The embodiment of the application provides a knowledge graph system based on BIM, and the system comprises: the data acquisition module is configured to acquire and analyze data of the coal preparation plant informatization system; the data management module is configured to manage the data acquired by the data acquisition module and perform correlation aggregation according to the entity under the BIM service scene of the coal preparation plant to obtain an entity subject of the knowledge map; the knowledge graph construction module is configured to extract entity attributes according to entity topics and construct a knowledge graph by combining the relationship among the entities; and the knowledge graph intelligent application module is configured to provide data intelligent application based on the knowledge graph obtained by the knowledge graph construction module.
In some embodiments, the system further comprises a retrieval module configured to: and providing a knowledge graph digital archive retrieval service.
In some embodiments, the retrieval module is further configured to: based on the indexing function of the Nebula Graph database modified by the ElasticSearch search engine, multi-index full-text retrieval and fuzzy query matching of the Graph database are achieved.
In some embodiments, the data governance module is further configured to: based on the data acquired by the data acquisition module for layered treatment of the data resource pool, the data resource pool of the coal preparation plant informatization system comprises an original data area, a standard data area, a data subject area and an application subject area.
In some embodiments, the entity topics include: coal entity topics, equipment entity topics, space entity topics, personnel entity topics, time entity topics, event entity topics, process entity topics, and business entity topics.
In some embodiments, the system further comprises an entity and relationship extraction module configured to: and the BIM knowledge map entity and the relation of the coal preparation plant are automatically extracted based on a natural language processing technology.
In some embodiments, a knowledge-graph building module configured to: and realizing the data fusion updating of the cross-knowledge graph based on the knowledge graph fusion technology.
In some embodiments, the data intelligence application includes coal preparation plant equipment health status classification; and a knowledge-graph intelligence application module further configured to: performing data processing on original equipment data in the knowledge graph to obtain a continuous data set; performing instantaneous feature extraction, periodic feature extraction and basic model fitting on a continuous data set, and integrating to obtain a historical baseline model of the equipment; and training based on the historical baseline model of the equipment to obtain a classification model of the health state of the equipment in the coal preparation plant.
In some embodiments, the data is intelligently applied, including coal preparation plant equipment failure early warning monitoring; and a knowledge-graph intelligent application module further configured to: carrying out data classification and aggregation on original equipment data in the knowledge graph to obtain a first target data set; and integrating the fault prediction model constructed based on the association algorithm, the fault time and probability prediction model constructed based on the probability distribution and the fault time and probability prediction model constructed based on the HSMM to obtain the equipment fault prediction model of the coal preparation plant.
In some embodiments, the data is intelligently applied, including coal preparation plant equipment security risk assessment; and a knowledge-graph intelligent application module further configured to: carrying out data classification and aggregation on original equipment data in the knowledge graph to obtain a second target data set; acquiring a feature set of a second target data set through the constructed general evaluation domain and the constructed special evaluation domain; extracting main features from the feature set to obtain a main feature set; and (4) carrying out logistic regression model training optimization based on the main feature set to obtain a coal preparation plant equipment safety risk assessment model.
The knowledge graph system based on the BIM provided by the embodiment of the application provides an automatic process from data acquisition, analysis and treatment to knowledge graph construction and application, and improves the knowledge utilization efficiency of a coal preparation plant.
Drawings
Other features, objects and advantages of the present application will become more apparent upon reading of the detailed description of non-limiting embodiments made with reference to the following drawings:
FIG. 1 is a schematic structural diagram of a BIM-based knowledge mapping system of the present application;
FIG. 2 is a schematic diagram of the entity relationship of the coal preparation plant in the embodiment of the present application;
FIG. 3 is a schematic diagram of a coal preparation plant knowledge map system functional architecture in an embodiment of the present application;
FIG. 4A is a schematic diagram of a classification model of health status of equipment of a coal preparation plant in an embodiment of the present application;
FIG. 4B is a schematic diagram of a coal preparation plant equipment failure prediction model in an embodiment of the present application;
FIG. 4C is a schematic diagram of a safety risk assessment model for coal preparation plant equipment in an embodiment of the present application;
FIG. 5 is a schematic illustration of a coal preparation plant knowledge graph system technology route in an embodiment of the present application;
FIG. 6 is a schematic block diagram of a computer system suitable for use in implementing some embodiments of the present application.
Detailed Description
The present application will be described in further detail with reference to the drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not to be construed as limiting the invention. It should be noted that, for convenience of description, only the portions related to the present invention are shown in the drawings.
It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict. The present application will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.
Referring to FIG. 1, a structure 100 of one embodiment of a BIM-based knowledge graph system according to the present application is shown. The knowledge map system based on the BIM comprises the following modules:
the module 101, a data collection module, is configured to collect and parse data of the coal preparation plant informatization system.
In this embodiment, the data acquisition module may acquire and analyze data of the coal preparation plant informatization system according to the data acquisition task or according to a set data acquisition rule in response to acquiring the data acquisition task. Parsing may be to structure unstructured data or semi-structured data by methods such as protocol or machine learning. In addition, if the situation of crossing systems and databases is encountered, the data acquisition module also needs to carry out protocol adaptation to carry out data acquisition. The data acquisition module can provide data support for BIM full life cycle management of the coal preparation plant.
The module 102 is a data management module configured to manage the data collected by the data collection module and perform correlation aggregation according to the entity under the BIM service scene of the coal preparation plant to obtain an entity topic of the knowledge graph.
In this embodiment, the management data may include operations such as data cleaning, processing, standardization, and the like, and in addition, in consideration of the particularity of the BIM service scene of the coal preparation plant, when planning the equipment entity of the coal preparation plant, the production operation attribute, the online monitoring attribute, the digital archive attribute, the VR maintenance and repair attribute, and the intelligent analysis attribute may be combed around the equipment entity, so that the BIM full life cycle management may be fully performed on the equipment from the links of design, modeling, production, transportation, and the like.
In some optional implementations of this embodiment, the data governance module is further configured to: based on the data acquired by the data resource pool layered treatment data acquisition module, the data resource pool of the coal preparation plant informatization system comprises an original data area, a standard data area, a data subject area and an application subject area. Data in the data source can be directly stored or stored in an original data area after structured processing; cleaning and processing the original data area and standardizing the data according to a preset standard to obtain the data of the standard data area; sorting, dividing and/or performing association aggregation on the data in the standard data area to obtain data in a data subject area; and carrying out field screening and/or association aggregation on the data in the data subject area to obtain the data in the application subject area.
The data resource pool can be a resource pool of a big data platform such as a CPIM (verified in Planning & Inventory Management and certification), and the multi-source heterogeneous data of the coal preparation plant informatization system is divided into functions from an original area, a standard area, a subject area and a thematic area by adopting the idea of data layering treatment, so that the treatment efficiency and the data reusability of the thematic data of the knowledge map can be improved, and the data support constructed by the BIM knowledge map of the coal preparation plant is ensured.
In some optional implementations of this embodiment, the entity topic may include: coal entity topic, equipment entity topic, space entity topic, personnel entity topic, time entity topic, event entity topic, process entity topic, and business entity topic. The specific topic division can be performed according to actual needs, for example, the event entity topic can be further divided into a fault event entity topic and a safety event entity topic.
The module 103, knowledge graph construction module, is configured to extract entity attributes according to entity topics and combine relationship construction between entities to obtain a knowledge graph.
In this embodiment, the knowledge graph constructing module can utilize the functions of the fusion analysis module of the CPIM and other large data platforms to respectively complete the works of coal preparation plant knowledge graph mode (Schema) design, coal preparation plant business entity relationship combing, coal preparation plant equipment entity attribute extraction, coal preparation plant knowledge graph data storage, coal preparation plant domain knowledge expert accumulation and the like. Referring to fig. 2, fig. 2 is a schematic diagram of the entity relationship of the coal preparation plant.
In addition, in some optional implementations of this embodiment, the system further includes an entity and relationship extraction module, where the entity and relationship extraction module is configured to: and (4) realizing automatic extraction of BIM knowledge map entities and relations of the coal preparation plant based on a natural language processing technology.
In some optional implementations of the embodiment, the knowledge-graph building module is configured to: and realizing the cross-knowledge-graph data fusion updating based on the knowledge graph fusion technology.
Referring to fig. 3, fig. 3 is a schematic diagram of a functional architecture of a knowledge graph system of a coal preparation plant, the knowledge graph intelligent application module may finally provide data intelligent applications to an application system of a digital construction center of coal washing engineering and a digital management application system of the coal preparation plant through a business decision engine, an intelligent recommendation engine, a full-text retrieval engine and a visual service engine provided by a fusion analysis module function of a CPIM big data platform, for example, a safety risk management analysis application of the coal preparation plant, a construction digital archive management retrieval application of the coal preparation plant, a state monitoring and fault early warning application of the coal preparation plant, a BIM + production system integrated management application of the coal preparation plant and a BIM + VR device virtual overhaul application of the coal preparation plant, and in addition, graph vectorization analysis mining of the BIM knowledge graph of the coal preparation plant may be realized based on deep learning.
In some optional implementations of this embodiment, the data is intelligently applied, including a coal preparation plant equipment health status classification; and a knowledge-graph intelligent application module further configured to: performing data processing on original equipment data in the knowledge graph to obtain a continuous data set; performing instantaneous feature extraction, periodic feature extraction and basic model fitting on a continuous data set, and integrating to obtain an equipment history baseline model; and training based on the historical baseline model of the equipment to obtain a classification model of the health state of the equipment in the coal preparation plant. Referring to fig. 4A, the data processing may include operations such as data aggregation, data cleaning, pre-fusion, and pre-processing, and the classification model of the health status of the equipment in the coal preparation plant may be obtained by training a support vector machine or other classification models.
In some optional implementation manners of the embodiment, the data is intelligently applied, and the early warning and monitoring of equipment faults of the coal preparation plant are included; and a knowledge-graph intelligent application module further configured to: carrying out data classification and aggregation on original equipment data in the knowledge graph to obtain a first target data set; and integrating a fault prediction model constructed based on a correlation algorithm, a fault time and probability prediction model constructed based on probability distribution and a fault time and probability prediction model constructed based on a Hidden semi-Markov model (HSMM) to obtain the equipment fault prediction model of the coal preparation plant. Referring to fig. 4B, the data classification may be a grouping classification of the devices, and then, based on HSMM parameter estimation model construction and assumption of an improved particle swarm optimization (MPSO), fault rate calculation model construction and conditional reliability calculation model construction are performed, and a fault time and probability prediction model constructed based on HSMM is obtained from the two. A more effective fault prediction model can be obtained through integration of the three models compared with a single model.
In some optional implementations of this embodiment, the data is intelligently applied, including coal preparation plant equipment security risk assessment; and a knowledge-graph intelligent application module further configured to: carrying out data classification and aggregation on original equipment data in the knowledge graph to obtain a second target data set; acquiring a feature set of a second target data set through the constructed general evaluation domain and the constructed special evaluation domain; extracting main features from the feature set to obtain a main feature set; and carrying out logistic regression model training optimization based on the main feature set to obtain a coal preparation plant equipment safety risk assessment model. Referring to fig. 4C, the data classification may be a packet classification of the devices. The three implementation modes provide a coal preparation plant equipment safety risk assessment model, an equipment historical baseline health model and an equipment fault early warning monitoring model by analyzing the particularity of the coal preparation plant service scene, and enrich the intelligent attribute dimensionality constructed by the knowledge graph based on the BIM.
Referring to fig. 5, fig. 5 is a schematic diagram of a technical route of a knowledge map system of a coal preparation plant, a technical route map for data intelligent application construction is divided into four stages, a core task in the first stage is to comb knowledge map entities and relations of the coal preparation plant, a core task in the second stage is to construct a knowledge map subject database of a CPIM big data platform data resource pool, a core task in the third stage is to develop construction work aiming at five intelligent application scenes of the coal preparation plant, and a core task in the last stage is to plan a function module of a digital management application system of the coal preparation plant, so that digital intelligent applications are really planned and implemented, and finally, the application of the coal preparation plant falls to the ground, and closed loops of service datamation, data asset transformation, asset service transformation and service scene transformation are realized.
In some optional implementations of this embodiment, the system further includes a retrieval module configured to: and providing knowledge map digital archive retrieval service. The knowledge graph digital archive retrieval service can be realized through an open source search engine.
In some optional implementations of this embodiment, the retrieval module is further configured to: based on the indexing function of the Nebula Graph database modified by the ElasticSearch search engine, multi-index full-text retrieval and fuzzy query matching of the Graph database are achieved. The Elasticsearch is a Lucene-based search server. It provides a distributed multi-user capable full-text search engine based on RESTful web interface. The Nebula Graph is an open-source distributed Graph database and has the characteristics of horizontal expansion, strong data consistency, high availability, SQL-like query language and the like.
The method provided by the embodiment of the application is configured to acquire and analyze the data of the coal preparation plant informatization system through the data acquisition module; the data management module is configured to manage the data acquired by the data acquisition module and perform correlation aggregation according to the entity under the BIM service scene of the coal preparation plant to obtain an entity subject of the knowledge map; the knowledge graph construction module is configured to extract entity attributes according to entity topics and combine the relationship construction among the entities to obtain a knowledge graph; the knowledge graph intelligent application module is configured to provide data intelligent application based on the knowledge graph obtained by the knowledge graph construction module, provides an automatic process from data acquisition, analysis and treatment to knowledge graph construction and application, and improves the knowledge utilization efficiency of the coal preparation plant.
Referring now to FIG. 6, shown is a block diagram of a computer system 600 suitable for use in implementing embodiments of the present application. The computer system illustrated in FIG. 6 is only an example and should not impose any limitations on the functionality or scope of use of embodiments of the present application.
As shown in fig. 6, the computer system 600 includes a Central Processing Unit (CPU)601 that can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM)602 or a program loaded from a storage section 608 into a Random Access Memory (RAM) 603. In the RAM 603, various programs and data necessary for the operation of the system 600 are also stored. The CPU 601, ROM 602, and RAM 603 are connected to each other via a bus 604. An input/output (I/O) interface 605 is also connected to bus 604.
The following components may be connected to the I/O interface 605: an input portion 606 such as a keyboard, mouse, or the like; an output portion 607 including a display such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker; a storage section 608 including a hard disk and the like; and a communication section 609 including a network interface card such as a LAN card, a modem, or the like. The communication section 609 performs communication processing via a network such as the internet. A driver 610 is also connected to the I/O interface 605 as needed. A removable medium 611 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 610 as necessary, so that a computer program read out therefrom is mounted in the storage section 608 as necessary.
In particular, the processes described above with reference to the flow diagrams may be implemented as computer software programs, according to embodiments of the present disclosure. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer-readable medium, the computer program comprising program code for performing the method illustrated by the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network through the communication section 609, and/or installed from the removable medium 611. The computer program performs the above-described functions defined in the method of the present application when executed by a Central Processing Unit (CPU) 601. It should be noted that the computer readable medium described herein can be a computer readable signal medium or a computer readable medium or any combination of the two. A computer readable medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this application, a computer readable medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In this application, however, a computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.
Computer program code for carrying out operations for aspects of the present application may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + +, and conventional procedural programming languages, such as the C language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The modules described in the embodiments of the present application may be implemented by software or hardware. The described modules may also be provided in a processor, which may be described as: a processor comprises a data acquisition module, a data management module, a knowledge graph construction module and a knowledge graph intelligent application module. Where the names of these modules do not in some cases constitute a limitation on the modules themselves, for example, a data collection module may also be described as a "module configured to collect and parse data for a coal preparation plant informatization system".
The foregoing description is only exemplary of the preferred embodiments of the application and is illustrative of the principles of the technology employed. It will be appreciated by those skilled in the art that the scope of the invention herein disclosed is not limited to the particular combination of features described above, but also encompasses other arrangements in which any combination of the features described above or their equivalents does not depart from the spirit of the invention disclosed above. For example, the above features may be replaced with (but not limited to) features having similar functions disclosed in the present application.
Claims (10)
1. A BIM-based knowledge graph system, comprising:
the data acquisition module is configured to acquire and analyze data of the coal preparation plant informatization system;
the data management module is configured to manage the data acquired by the data acquisition module and perform correlation aggregation according to the entity under the BIM service scene of the coal preparation plant to obtain an entity theme of the knowledge map;
the knowledge graph construction module is configured to extract entity attributes according to the entity topics and combine the entity attributes with the relationship construction among the entities to obtain a knowledge graph;
a knowledge graph intelligent application module configured to provide data intelligent application based on the knowledge graph obtained by the knowledge graph construction module.
2. The system of claim 1, wherein the system further comprises a retrieval module configured to:
and providing a knowledge graph digital archive retrieval service.
3. The system of claim 1, wherein the retrieval module is further configured to:
based on the indexing function of the Nebula Graph database modified by the ElasticSearch search engine, multi-index full-text retrieval and fuzzy query matching of the Graph database are achieved.
4. The system of claim 1, wherein the data governance module is further configured to:
and processing the data acquired by the data acquisition module based on a data resource pool in a layering manner, wherein the data resource pool of the coal preparation plant informatization system comprises an original data area, a standard data area, a data subject area and an application subject area.
5. The system of claim 1, wherein the entity topics comprise: coal entity topic, equipment entity topic, space entity topic, personnel entity topic, time entity topic, event entity topic, process entity topic, and business entity topic.
6. The system of claim 1, wherein the system further comprises an entity and relationship extraction module configured to:
and the BIM knowledge map entity and the relation of the coal preparation plant are automatically extracted based on a natural language processing technology.
7. The system of claim 1, wherein the knowledge-graph building module is configured to:
and realizing the data fusion updating of the cross-knowledge graph based on the knowledge graph fusion technology.
8. The system of claim 1, wherein the data intelligence application comprises a coal preparation plant equipment health status classification; and the knowledge-graph intelligent application module further configured to:
performing data processing on original equipment data in the knowledge graph to obtain a continuous data set;
performing instantaneous feature extraction, periodic feature extraction and basic model fitting on the continuous data set, and integrating to obtain a historical baseline model of the equipment;
and training based on the historical baseline model of the equipment to obtain a classification model of the health state of the equipment of the coal preparation plant.
9. The system of claim 1, wherein the data intelligence application comprises coal preparation plant equipment failure early warning monitoring; and the knowledge-graph intelligent application module further configured to:
carrying out data classification and aggregation on original equipment data in the knowledge graph to obtain a first target data set;
and integrating the fault prediction model constructed based on the association algorithm, the fault time and probability prediction model constructed based on the probability distribution and the fault time and probability prediction model constructed based on the HSMM to obtain the equipment fault prediction model of the coal preparation plant.
10. The system of any one of claims 1-9, wherein the data intelligence application comprises a coal preparation plant equipment security risk assessment; and the knowledge-graph intelligent application module further configured to:
carrying out data classification and aggregation on original equipment data in the knowledge graph to obtain a second target data set;
acquiring a feature set of the second target data set through the constructed general evaluation domain and the constructed special evaluation domain;
extracting main features from the feature set to obtain a main feature set;
and carrying out logistic regression model training optimization based on the main feature set to obtain a coal preparation plant equipment safety risk assessment model.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114969383A (en) * | 2022-08-02 | 2022-08-30 | 深圳易伙科技有限责任公司 | Application processing method and device based on zero code development |
| CN116882032A (en) * | 2023-09-04 | 2023-10-13 | 中国建筑西南设计研究院有限公司 | Building design atlas digitizing and visualizing method and device for applying same |
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114969383A (en) * | 2022-08-02 | 2022-08-30 | 深圳易伙科技有限责任公司 | Application processing method and device based on zero code development |
| CN116882032A (en) * | 2023-09-04 | 2023-10-13 | 中国建筑西南设计研究院有限公司 | Building design atlas digitizing and visualizing method and device for applying same |
| CN116882032B (en) * | 2023-09-04 | 2023-11-17 | 中国建筑西南设计研究院有限公司 | Building design atlas digitizing and visualizing method and device for applying same |
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