CN115481084B - BIM model resource management system - Google Patents
BIM model resource management system Download PDFInfo
- Publication number
- CN115481084B CN115481084B CN202211084301.8A CN202211084301A CN115481084B CN 115481084 B CN115481084 B CN 115481084B CN 202211084301 A CN202211084301 A CN 202211084301A CN 115481084 B CN115481084 B CN 115481084B
- Authority
- CN
- China
- Prior art keywords
- bim model
- data
- classification
- data packet
- bim
- 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.)
- Active
Links
- 238000000034 method Methods 0.000 claims description 42
- 238000007726 management method Methods 0.000 claims description 30
- 239000013598 vector Substances 0.000 claims description 19
- 238000006243 chemical reaction Methods 0.000 claims description 14
- 238000004088 simulation Methods 0.000 claims description 12
- 238000012550 audit Methods 0.000 claims description 5
- 230000009469 supplementation Effects 0.000 claims description 4
- 238000012163 sequencing technique Methods 0.000 claims description 3
- 230000009286 beneficial effect Effects 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 238000004891 communication Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/10—File systems; File servers
- G06F16/11—File system administration, e.g. details of archiving or snapshots
- G06F16/113—Details of archiving
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/30—Computing systems specially adapted for manufacturing
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Data Mining & Analysis (AREA)
- General Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- General Health & Medical Sciences (AREA)
- Artificial Intelligence (AREA)
- Biophysics (AREA)
- Evolutionary Computation (AREA)
- Biomedical Technology (AREA)
- Molecular Biology (AREA)
- Computing Systems (AREA)
- Computational Linguistics (AREA)
- Life Sciences & Earth Sciences (AREA)
- Mathematical Physics (AREA)
- Software Systems (AREA)
- Health & Medical Sciences (AREA)
- Databases & Information Systems (AREA)
- Data Exchanges In Wide-Area Networks (AREA)
Abstract
The invention discloses a BIM model resource management system, which belongs to the technical field of BIM model resource management and comprises a model library, a storage management module, a retrieval module and a server; the model library is used for storing BIM model data packets and comprises a first storage node and a second storage node; the storage management module is used for managing the storage data in the model library, setting building feature data corresponding to each BIM model data packet in the first storage node, and converting the obtained building feature data into corresponding feature points; establishing a classification chart, inputting the feature points into the classification chart, identifying the positions of the feature points in the classification chart, matching the corresponding BIM model classification according to the identified positions, marking the corresponding BIM model data packet with the corresponding BIM model classification label, and cutting the BIM model data packet with the BIM model classification label into a second storage node; the retrieval module is used for retrieving the needed BIM model resources.
Description
Technical Field
The invention belongs to the technical field of BIM model resource management, and particularly relates to a BIM model resource management system.
Background
Along with the wider and wider application of BIM technology, building industry parties gradually accumulate huge BIM model resources through the large-scale establishment of project models in the process of applying the BIM technology, and the BIM resources form digital assets of enterprises.
The existing BIM model resource management mode of many enterprises is mainly stored in a personal computer in a file form, and a series of problems such as incapability of carrying out system management on the BIM model resource, poor safety (loss and leakage), low use efficiency, single sharing mode and the like exist.
Therefore, it is necessary to develop an effective BIM model library management method, standardize BIM model resource management modes, and improve BIM model resource management, use and sharing efficiency.
Disclosure of Invention
In order to solve the problems of the scheme, the invention provides a BIM model resource management system.
The aim of the invention can be achieved by the following technical scheme:
a BIM model resource management system comprises a model library, a storage management module, a retrieval module and a server;
the model library is used for storing BIM model data packets and comprises a first storage node and a second storage node;
the storage management module is used for managing the storage data in the model library, setting building feature data corresponding to each BIM model data packet in the first storage node, and converting the obtained building feature data into corresponding feature points; establishing a classification chart, inputting the feature points into the classification chart, identifying the positions of the feature points in the classification chart, matching the corresponding BIM model classification according to the identified positions, marking the corresponding BIM model data packet with the corresponding BIM model classification label, and cutting the BIM model data packet with the BIM model classification label into a second storage node;
the retrieval module is used for retrieving the needed BIM model resources.
Further, the method for establishing the model library comprises the following steps:
an auditing unit is arranged to audit the uploaded BIM model data packet through the auditing unit, a first database is established on the private cloud platform, the first database comprises a first storage node and a second storage node, the BIM model data packet which is audited by the auditing unit is sent to the first storage node in the first database to be stored, and the current first database is marked as a model library.
Further, the working method of the auditing unit comprises the following steps:
setting a data auditing list, identifying the data name and the data format contained in the uploaded BIM model data packet, comparing the identified data name and the data format with the data auditing list, identifying whether the BIM model data packet has missing item data or multiple item data, generating a corresponding auditing data list when judging that the BIM model data packet has missing item data or multiple item data, sending the corresponding auditing data list to a corresponding uploading person, and uploading the corresponding BIM model data packet again after the supplementation is completed by the uploading person according to the auditing data list; and when judging that the missing item data or the multiple items of data are not available, checking and passing.
Further, the method for converting the obtained building characteristic data into the corresponding characteristic points comprises the following steps:
the method comprises the steps of obtaining feature single data, setting a conversion assignment for each feature single data, establishing an assignment matching table after summarizing, inputting the feature single data into the assignment matching table for matching, obtaining conversion assignments corresponding to each feature single data, and integrating the obtained conversion assignments into feature points.
Further, the method for establishing the classification chart comprises the following steps:
setting the BIM model classification, setting the region range corresponding to each BIM model classification, and drawing a classification chart according to the set region range.
Further, the method for setting the region range corresponding to each BIM model classification comprises the following steps:
the method comprises the steps of obtaining historical building feature data corresponding to historical BIM model data packets, converting the historical building feature data into corresponding simulation points, distributing the simulation points into a coordinate system, classifying the simulation points according to the number of BIM model classification, obtaining a corresponding number of classification areas, determining the boundary of each classification area, marking BIM model classification corresponding to each classification area, and marking the current classification area as an area range.
Further, the method for determining the boundaries of each classification region comprises the following steps:
step SA1: establishing a point location model, analyzing the classifying area through the point location model, generating a plurality of expansion point locations, converting the obtained expansion point locations into corresponding building feature data, judging whether the obtained building feature data belongs to the BIM model classification, and carrying out boundary correction of the classifying area according to a judging result;
step SA2: and repeating the step SA1 until the boundary of the classifying area cannot be corrected, and finishing the boundary determination of the classifying area.
Further, the working method of the search module comprises the following steps:
obtaining search content, identifying search feature data in the search content, converting the search feature data into corresponding search feature vectors, calculating the similarity between the search feature vectors and each BIM model data packet in a second storage node, marking the obtained similarity as XSi, wherein i=1, 2, … …, N is a positive integer, i represents the ith BIM model data packet, obtaining uploading personnel of each BIM model data packet, setting the completion score of each BIM model data packet according to the obtained uploading personnel, marking the set completion score as PFi, calculating a priority value according to a formula Qi=b1×W×XSi+b2× PFi, wherein b1 and b2 are all proportionality coefficients, the value range is 0< b1 < 1,0< b2 < 1, W represents similarity adjustment values, arranging the calculated priority values according to the order from large to small, obtaining a first sequence, recommending the M model data packets corresponding to the priority value of N before sequencing in the first sequence, and N is a positive integer.
Further, the method for calculating the similarity between the search feature vector and each BIM model data packet in the second storage node comprises the following steps:
and converting the feature points corresponding to each BIM model data packet into corresponding BIM characteristic vectors, and calculating the similarity between the retrieval feature vectors and each BIM characteristic vector.
Compared with the prior art, the invention has the beneficial effects that:
by setting up the model library on the private cloud platform, confidentiality and security of BIM model resources are improved; compared with the management mode of storing BIM model resources in a personal computer, the method realizes unified management of the BIM model resources and improves management efficiency of the BIM model resources; and overall management of BIM model resources is comprehensively realized, so that the BIM model resources are more beneficial to modeling and use of the BIM model, and further, the management efficiency and the use efficiency of the BIM model resources are improved.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the following description will briefly explain the drawings used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings can be obtained according to these drawings without inventive effort to a person skilled in the art.
Fig. 1 is a functional block diagram of the present invention.
Description of the embodiments
The technical solutions of the present invention will be clearly and completely described in connection with the embodiments, and it is obvious that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
As shown in FIG. 1, a BIM model resource management system comprises a model library, a storage management module, a retrieval module and a server;
the model library, the storage management module, the encryption module and the retrieval module are all in communication connection with the server.
The model library is used for storing BIM model data packets, and the specific method comprises the following steps:
an auditing unit is arranged and used for auditing the uploaded BIM model data, the auditing unit is used for auditing the uploaded BIM model data package, a first database is established on the private cloud platform and comprises a first storage node and a second storage node, the BIM model data package which is audited by the auditing unit is sent to the first storage node in the first database for storage, and the current first database is marked as a model library.
The working method of the auditing unit comprises the following steps:
setting a data auditing list in a manual mode, namely setting data to be contained in a corresponding BIM model data packet and a corresponding data format in a manual mode according to the subsequent identification requirement, and further summarizing to establish the data auditing list; the data auditing list comprises auditing items, data formats and other contents, such as project introduction, building introduction, BIM model and the like; identifying the data name and the data format contained in the uploaded BIM model data packet, comparing the identified data name and data format with a data auditing list, and identifying whether the BIM model data packet has missing item data or multiple items of data, wherein the multiple items of data refer to other redundant data in the BIM model data packet;
when judging that the data has the missing item data or the multiple items of data, generating a corresponding audit data list, namely, which missing item data or the multiple items of data are provided, sending the corresponding audit data list to corresponding uploading personnel, carrying out corresponding data supplementation by the uploading personnel according to the audit data list, and uploading the corresponding BIM model data package again after supplementation is completed; and when judging that the missing item data or the multiple items of data are not available, checking and passing.
The storage management module is used for managing the storage data in the model library, and the specific method comprises the following steps:
setting building feature data corresponding to each BIM model data packet in a first storage node, and converting the obtained building feature data into corresponding feature points; establishing a classification chart, inputting the feature points into the classification chart, identifying the positions of the feature points in the classification chart, matching the corresponding BIM model classification according to the identified positions, marking the corresponding BIM model data packet with the corresponding BIM model classification label, and cutting the BIM model data packet with the BIM model classification label into a second storage node.
The method for setting the building feature data corresponding to each BIM model data packet in the first storage node comprises the following steps: the method is characterized in that a corresponding feature recognition model is built based on a CNN network or a DNN network, a corresponding training set is built through a manual mode to train, corresponding building features, such as building types, building styles and other building features, are recognized in corresponding files in BIM model data packages, and the specific building and training processes are common knowledge in the art.
The method for converting the obtained building characteristic data into the corresponding characteristic points comprises the following steps:
the method comprises the steps of obtaining possibly-possessed feature single data, wherein building feature data is formed by combining a plurality of feature single data; setting a conversion assignment for each feature single data by adopting a manual mode, establishing an assignment matching table after summarizing, inputting the feature single data into the assignment matching table for matching, obtaining conversion assignments corresponding to each feature single data, and integrating the obtained conversion assignments as feature points, wherein if the conversion assignments are 1,2,3,4 and 5, the feature points are (1, 2,3,4 and 5).
The method for establishing the classification chart comprises the following steps:
setting BIM model classification, setting in an artificial mode, specifically setting according to the subsequent use requirement, setting the region range corresponding to each BIM model classification, and drawing a classification chart according to the set region range.
The method for setting the region range corresponding to each BIM model classification comprises the following steps:
the method comprises the steps of obtaining historical building feature data corresponding to a historical BIM data packet, converting the historical building feature data into corresponding simulation points, namely feature points, distributing the simulation points into a coordinate system, classifying the simulation points according to the number of the BIM classification, obtaining a corresponding number of classification areas, determining the boundary of each classification area, marking BIM classification corresponding to each classification area, and marking the current classification area as an area range.
The method for classifying the simulation points according to the classified quantity of the BIM models is based on the current existing clustering algorithm, such as a K-means algorithm, the specific clustering process is common knowledge in the field, and the corresponding clustering limiting conditions are set manually.
The method for determining the boundaries of each classification area comprises the following steps: because the boundary of the classification area obtained directly through the simulation points is inaccurate, the boundary range may be smaller;
step SA1: establishing a point location model, analyzing the classifying area through the point location model, generating a plurality of expansion point locations, converting the obtained expansion point locations into corresponding building feature data, judging whether the obtained building feature data belongs to the BIM model classification, and carrying out boundary correction of the classifying area according to a judging result;
step SA2: and repeating the step SA1 until the boundary of the classifying area cannot be corrected, and finishing the boundary determination of the classifying area.
The point location model is established based on a CNN network or a DNN network, and is trained by setting a corresponding training set in a manual mode and is used for expanding a plurality of expansion points outwards based on boundary simulation points of the classification area.
The search module is used for searching the needed BIM model resources, because a large number of BIM model resources are arranged in the model library, the search is troublesome one by one in a manual mode, and the efficiency is low, so that a search function is needed to be provided for quickly searching the needed BIM model resources. The specific method comprises the following steps:
acquiring search content, identifying search feature data in the search content, wherein the search feature data is feature data defining BIM model data, specifically, a corresponding search identification model can be established based on a CNN network or a DNN network, training is performed by setting a corresponding training set in a manual mode, and the search feature data in the search content is identified by the search identification model after successful training; converting the search feature data into corresponding search feature vectors, calculating the similarity between the search feature vectors and each BIM model data packet in a second storage node, marking the obtained similarity as XSi, wherein i=1, 2, … …, N and N are positive integers, i represents the ith BIM model data packet, acquiring uploading personnel of each BIM model data packet, setting the completion score of each BIM model data packet according to the acquired uploading personnel, marking the set completion score as PFi, calculating a priority value according to a formula of Qi=b1×W×XSi+b2× PFi, wherein b1 and b2 are all proportional coefficients, the value range is 0< b1.ltoreq.1, 0< b2.ltoreq.1, W represents the similarity adjustment value, setting by an expert group as a fixed value, arranging the calculated priority values in the order from big to small, acquiring a first sequence, recommending the BIM model data packet corresponding to the priority value of N before sequencing in the first sequence, wherein N is the positive integer.
The method for converting the retrieval feature data into the corresponding retrieval feature vector is based on the assignment matching table for conversion, specifically, a corresponding retrieval feature conversion model is established based on a CNN network or a DNN network, a corresponding training set is established based on the assignment matching table for training in a manual mode, and corresponding conversion is carried out through the retrieval feature conversion model after successful training.
The method for calculating the similarity between the search feature vector and each BIM model data packet in the second storage node comprises the following steps:
and converting the feature points corresponding to each BIM model data packet into corresponding BIM characteristic vectors, and calculating the similarity between the retrieval feature vectors and each BIM characteristic vector.
The method for setting the completion score of each BIM model data packet according to the acquired uploading personnel comprises the following steps:
the capacity level of each BIM modeling personnel is set in a manual mode, because corresponding setting can be carried out according to the past modeling results in the same group company, personnel level tables are built in a summarizing mode, corresponding BIM model screenshot is obtained, the obtained BIM model screenshot and the capacity level of a corresponding uploading personnel are integrated into scoring input data, a corresponding scoring model is built based on a CNN network or a DNN network, a corresponding training set is set in a manual mode for training, the scoring input data is analyzed through the scoring model after successful training, and corresponding completion scoring is obtained.
The above formulas are all formulas with dimensions removed and numerical values calculated, the formulas are formulas which are obtained by acquiring a large amount of data and performing software simulation to obtain the closest actual situation, and preset parameters and preset thresholds in the formulas are set by a person skilled in the art according to the actual situation or are obtained by simulating a large amount of data.
The working principle of the invention is as follows: storing BIM models through a model library, wherein a storage management module is used for managing storage data in the model library, setting building feature data corresponding to each BIM model data packet in a first storage node, and converting the obtained building feature data into corresponding feature points; establishing a classification chart, inputting the feature points into the classification chart, identifying the positions of the feature points in the classification chart, matching the corresponding BIM model classification according to the identified positions, marking the corresponding BIM model data packet with the corresponding BIM model classification label, and cutting the BIM model data packet with the BIM model classification label into a second storage node; the required BIM model resources are retrieved by the retrieval module.
The above embodiments are only for illustrating the technical method of the present invention and not for limiting the same, and it should be understood by those skilled in the art that the technical method of the present invention may be modified or substituted without departing from the spirit and scope of the technical method of the present invention.
Claims (6)
1. The BIM model resource management system is characterized by comprising a model library, a storage management module, a retrieval module and a server;
the model library is used for storing BIM model data packets and comprises a first storage node and a second storage node;
the storage management module is used for managing the storage data in the model library, setting building feature data corresponding to each BIM model data packet in the first storage node, and converting the obtained building feature data into corresponding feature points; establishing a classification chart, inputting the feature points into the classification chart, identifying the positions of the feature points in the classification chart, matching the corresponding BIM model classification according to the identified positions, marking the corresponding BIM model data packet with the corresponding BIM model classification label, and cutting the BIM model data packet with the BIM model classification label into a second storage node;
the retrieval module is used for retrieving the needed BIM model resources;
the method for converting the obtained building characteristic data into the corresponding characteristic points comprises the following steps:
acquiring characteristic single data, setting a conversion assignment for each characteristic single data, establishing an assignment matching table after summarizing, inputting the characteristic single data into the assignment matching table for matching, acquiring conversion assignments corresponding to each characteristic single data, and integrating the acquired conversion assignments into characteristic points;
the method for establishing the classification chart comprises the following steps:
setting the BIM model classification, setting the region range corresponding to each BIM model classification, and drawing a classification chart according to the set region range;
the method for setting the region range corresponding to each BIM model classification comprises the following steps:
the method comprises the steps of obtaining historical building feature data corresponding to historical BIM model data packets, converting the historical building feature data into corresponding simulation points, distributing the simulation points into a coordinate system, classifying the simulation points according to the number of BIM model classification, obtaining a corresponding number of classification areas, determining the boundary of each classification area, marking BIM model classification corresponding to each classification area, and marking the current classification area as an area range.
2. The BIM model resource management system of claim 1, wherein the method of creating the model base includes:
an auditing unit is arranged to audit the uploaded BIM model data packet through the auditing unit, a first database is established on the private cloud platform, the first database comprises a first storage node and a second storage node, the BIM model data packet which is audited by the auditing unit is sent to the first storage node in the first database to be stored, and the current first database is marked as a model library.
3. The BIM model resource management system of claim 2, wherein the method for operating the auditing unit includes:
setting a data auditing list, identifying the data name and the data format contained in the uploaded BIM model data packet, comparing the identified data name and the data format with the data auditing list, identifying whether the BIM model data packet has missing item data or multiple item data, generating a corresponding auditing data list when judging that the BIM model data packet has missing item data or multiple item data, sending the corresponding auditing data list to a corresponding uploading person, and uploading the corresponding BIM model data packet again after the supplementation is completed by the uploading person according to the auditing data list; when judging that the missing item data or the multiple items of data are not available, checking and passing, wherein the multiple items of data refer to redundant data in the BIM model data packet.
4. The BIM model resource management system of claim 1, wherein the method of determining the boundaries of each categorization section comprises:
step SA1: establishing a point location model, analyzing the classifying area through the point location model, generating a plurality of expansion point locations, converting the obtained expansion point locations into corresponding building feature data, judging whether the obtained building feature data belongs to the BIM model classification, and carrying out boundary correction of the classifying area according to a judging result;
step SA2: and repeating the step SA1 until the boundary of the classifying area cannot be corrected, and finishing the boundary determination of the classifying area.
5. The BIM model resource management system of claim 1, wherein the operation method of the search module includes:
obtaining search content, identifying search feature data in the search content, converting the search feature data into corresponding search feature vectors, calculating the similarity between the search feature vectors and each BIM model data packet in a second storage node, marking the obtained similarity as XSi, wherein i=1, 2, … …, N is a positive integer, i represents the ith BIM model data packet, obtaining uploading personnel of each BIM model data packet, setting the completion score of each BIM model data packet according to the obtained uploading personnel, marking the set completion score as PFi, calculating a priority value according to a formula Qi=b1×W×XSi+b2× PFi, wherein b1 and b2 are all proportionality coefficients, the value range is 0< b1 < 1,0< b2 < 1, W represents similarity adjustment values, arranging the calculated priority values according to the order from large to small, obtaining a first sequence, recommending the M model data packets corresponding to the priority value of N before sequencing in the first sequence, and N is a positive integer.
6. The BIM model resource management system of claim 5, wherein the method for calculating the similarity between the search feature vector and each BIM model data packet in the second storage node includes:
and converting the feature points corresponding to each BIM model data packet into corresponding BIM characteristic vectors, and calculating the similarity between the retrieval feature vectors and each BIM characteristic vector.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211084301.8A CN115481084B (en) | 2022-09-06 | 2022-09-06 | BIM model resource management system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211084301.8A CN115481084B (en) | 2022-09-06 | 2022-09-06 | BIM model resource management system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN115481084A CN115481084A (en) | 2022-12-16 |
| CN115481084B true CN115481084B (en) | 2023-07-11 |
Family
ID=84392450
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202211084301.8A Active CN115481084B (en) | 2022-09-06 | 2022-09-06 | BIM model resource management system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115481084B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116069834B (en) * | 2023-03-07 | 2023-06-23 | 武汉东方赛思软件股份有限公司 | BIM technology-based enterprise data visual management method |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109684701A (en) * | 2018-12-17 | 2019-04-26 | 中铁四局集团有限公司 | BIM model resource management system and BIM model creation method |
| CN110750824A (en) * | 2019-09-27 | 2020-02-04 | 贵州百胜工程建设咨询有限公司 | BIM family management method |
| CN111177811A (en) * | 2019-12-24 | 2020-05-19 | 武汉理工光科股份有限公司 | Automatic fire point location layout method applied to cloud platform |
| CN114331031A (en) * | 2021-12-08 | 2022-04-12 | 北京华清安地建筑设计有限公司 | Building traditional feature recognition and evaluation method and system |
| KR20220115326A (en) * | 2021-02-10 | 2022-08-17 | 주식회사 케이티 | Server, method and computer program for generating building information model |
-
2022
- 2022-09-06 CN CN202211084301.8A patent/CN115481084B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109684701A (en) * | 2018-12-17 | 2019-04-26 | 中铁四局集团有限公司 | BIM model resource management system and BIM model creation method |
| CN110750824A (en) * | 2019-09-27 | 2020-02-04 | 贵州百胜工程建设咨询有限公司 | BIM family management method |
| CN111177811A (en) * | 2019-12-24 | 2020-05-19 | 武汉理工光科股份有限公司 | Automatic fire point location layout method applied to cloud platform |
| KR20220115326A (en) * | 2021-02-10 | 2022-08-17 | 주식회사 케이티 | Server, method and computer program for generating building information model |
| CN114331031A (en) * | 2021-12-08 | 2022-04-12 | 北京华清安地建筑设计有限公司 | Building traditional feature recognition and evaluation method and system |
Non-Patent Citations (1)
| Title |
|---|
| 基于风险界面的绿色建筑脆弱性评价;陈为公等;《工程管理学报》;第33卷(第5期);第[045]-[050]页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN115481084A (en) | 2022-12-16 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN113590698B (en) | Artificial intelligence technology-based data asset classification modeling and hierarchical protection method | |
| CN111274301B (en) | Intelligent management method and system based on data assets | |
| CN110310012B (en) | Data analysis method, device, equipment and computer readable storage medium | |
| CN115481084B (en) | BIM model resource management system | |
| CN112651218A (en) | Automatic generation method and management method of bidding document, medium and computer | |
| CN117271767A (en) | Operation and maintenance knowledge base establishing method based on multiple intelligent agents | |
| CN113706263A (en) | Electronic commerce system based on cloud platform | |
| CN114491034B (en) | Text classification method and intelligent device | |
| CN110765276A (en) | Entity alignment method and device in knowledge graph | |
| WO2024065776A1 (en) | Method for data processing, apparatus for data processing, electronic device, and storage medium | |
| CN117093556A (en) | Log classification method, device, computer equipment and computer readable storage medium | |
| CN108108444B (en) | Enterprise business unit self-adaptive system and implementation method thereof | |
| CN116739408A (en) | Power grid dispatching safety monitoring method and system based on data tag and electronic equipment | |
| CN111428821A (en) | Asset classification method based on decision tree | |
| CN114817754B (en) | VR learning system | |
| CN115147020A (en) | Decoration data processing method, device, equipment and storage medium | |
| CN115239066A (en) | Communication informationization data management and control platform | |
| KR100809751B1 (en) | System and method for making analysis of document | |
| CN112528662A (en) | Entity category identification method, device, equipment and storage medium based on meta-learning | |
| KR20200129132A (en) | Data preparation method and data utilization system for data utilization | |
| JP2019185582A5 (en) | ||
| CN113434760B (en) | Construction method recommendation method, device, equipment and storage medium | |
| CN115062676B (en) | Data processing method, device and computer readable storage medium | |
| CN111311329B (en) | Tag data acquisition method, device, equipment and readable storage medium | |
| CN110766037B (en) | Processing method for reserve project relevance cluster |
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 | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |