CN109740237A - A kind of building electromechanics point position arranging method based on Monte Carlo - Google Patents
A kind of building electromechanics point position arranging method based on Monte Carlo Download PDFInfo
- Publication number
- CN109740237A CN109740237A CN201811621461.5A CN201811621461A CN109740237A CN 109740237 A CN109740237 A CN 109740237A CN 201811621461 A CN201811621461 A CN 201811621461A CN 109740237 A CN109740237 A CN 109740237A
- Authority
- CN
- China
- Prior art keywords
- building
- point
- monte carlo
- training
- point position
- 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.)
- Granted
Links
Abstract
The invention discloses a kind of building electromechanics point position arranging method based on Monte Carlo, the method includes training process and design generating process, training process is the following steps are included: building input data；With building module gridding space；Monte Carlo stochastical sampling generates original training set；The arrangement and final result evaluation of estimate of training ResNet prediction next step；Monte Carlo carries out Monte Carlo and generating sample set using the prediction of ResNet as knowing；Training obtains generating model.Generating process is designed the following steps are included: building input data；With building module gridding space；Corresponding generation model is selected according to different building types to be designed, generates training result using models for several times is generated；It chooses at least three and generates the best result return of result.The present invention realizes the design for building electromechanical point from scratch by the way of intensified learning, and training process is not necessarily to additional existing design data.
Description
Technical field
The invention belongs to build Electromechanical Design field, and in particular to a kind of building electromechanics point arrangement based on Monte Carlo
Method.
Background technique
The search of Monte Carlo tree is also known as random sampling or statistical test method, belongs to a branch for calculating mathematics, it is
Get up in middle forties in last century in order to adapt to the development of atomic energy cause at that time.Traditional empirical method by
In the physical process for being unable to approaching to reality, hardly result in it is satisfied as a result, and Monte Carlo tree searching method due to can be true
Simulation actual physics process, therefore solve the problems, such as to meet very much with practical, available very satisfactory result.This is also with probability
It is to be solved using random number (or more common pseudo random number) much with a kind of calculation method based on statistical methods
The method of computational problem.Will be solved the problem of, is associated with certain probabilistic model, realizes statistical simulation with electronic computer
Or sampling, to obtain the approximate solution of problem.
Architectural design includes building, structure, plumbing, HVAC, electrical five professions；Plumbing, is electrically referred to as machine at HVAC
Electric profession；Electromechanical speciality in building needs to need matching for design building according to the geometrical arrangements of building and the function of building
Cover the placement of equipment and the link connection of equipment room；For example the illumination in electric specialty is exactly that the daylighting for needing to solve to build needs
It asks, he needs to determine type, the quantity of lamps and lanterns according to the illuminating and lighting demand of not chummery, then needs to design to each lamp
The laying of the power cable of tool.Equipment point arranges the position for as determining some equipment in space, and different equipment has not
Arrangement requirement together, arrangement principle.For example lamps and lanterns are to meet daylighting demand, network interface, socket panel are the function of meeting people
Use demand.In addition some equipment can have certain optimization demand, for example interchanger is needed according to point in comprehensive wiring system
Digit determines different models, such as 48 mouthfuls, 24 mouthfuls, and (it is several layers of that point may share certain when few for the determining position for placing floor
Share an interchanger)；The determining air current composition problem needed to consider in space in position of air conditioner air outlet, return air inlet；Lamps and lanterns
Placement need to combine the natural lighting etc. of building.
Traditional architecture Electromechanical Design teacher needs to carry out hand drawn with design experiences using CAD software in design, draws
Time-consuming and laborious, drawing, there are a large amount of repetitive operations, and manual drawing is easy to appear drawing mistake, needs manually to look into repeatedly
It tests, therefore entire design work is a kind of process of inefficient low-quality.
Related electromechanical Automated Design work is mostly the optimization based on certain design both at home and abroad, is mostly based on stochastic search methods
(such as genetic algorithm, ant group algorithm) optimizes, and electromechanical point is not implemented and designs from scratch, such as
The Hevacomp Electrical Designer software of Bentley company can be used to realize that the grouping to illumination point switch is matched
It sets.
There is similar point arrangement to generate work MANYI LI in furniture installation field, et al. in GRAINS:Generative
It is proposed in Recursive Autoencoders for INdoor Scenes and generates indoor furniture installation using RvNN training
Method.The furniture installation method needs a large amount of training data to carry out the training of implementation model, and model is by training data influence
Larger, generalization is poor.
Summary of the invention
The problem of for background technique, the purpose of the present invention is to provide one kind can be automatically according to space
The building electromechanics point position arranging method based on Monte Carlo of generating device point design drawing, to improve designer's work effect
Rate improves architectural design drawing quality.
To achieve the goals above, the specific technical solution of the present invention is as follows:
A kind of building electromechanics point position arranging method based on Monte Carlo, the method includes training process and design to generate
Process, the specific steps are as follows:
The training process the following steps are included:
(1) for target structures type building (such as office building), designed architectural drawing is structured as BIM number
According to, and the crucial building structure data in building are labeled, the data marked are extracted；
(2) uniform grid processing successively is carried out to each space, and calculates the feasible zone of electromechanical point arrangement,
Remove wall beam sheet-pile, the grid that other existing device points are taken up space, treated, and grid can regard the position layouted as
It sets；
(3) the evaluation index parameter set C that layouts accordingly is selected according to different building types to be designed；
The index parameter collection of office building, residential housing choosing are just directly selected according to preset type, such as office building
With the index parameter collection of residential housing.
(4) Monte Carlo (Monte Carlo Tree Search) is used, according to rule, carries out automatic cloth at random
Point automatically generates cloth point process as original training set S；
(5) it uses the ResNet in convolutional neural networks CNN as heuristic function, is input with S, there are two outputs: (a)
Next layout position and probability of layouting, the evaluation index after the completion of (b) finally layouting；
(6) Monte Carlo is used, is layouted by being changed to use trained ResNet network as guidance at random；According to current
It layouts and calculates the probability of next point position and the value of final evaluation index, take maximum score value as next position layouted；Make
For new sample set Si；
(7) step (5) (6) are repeated, ResNet precision is continuously improved；Training obtains the generation of Monte Carlo deep neural network
Model；
(1)-(7) process is repeated for different building types, training obtains the generation for different building types
Model.Different building types need separately training, also obtain different training patterns, such as the generation mould of office building with regard to training
Type, residential housing generation model.
The design generating process the following steps are included:
(1) designed architectural drawing is structured as BIM data, and to the crucial building structure data in building into
Rower note, extracts the data marked；
(2) uniform grid processing successively is carried out to each space, and calculates the feasible zone of electromechanical point arrangement,
Remove wall beam sheet-pile, the grid that other existing device points are taken up space, treated, and grid can regard the position layouted as
It sets；
(3) the Monte Carlo depth mind for selecting the corresponding training process to obtain according to different building types to be designed
Model is generated through network, models for several times is generated using the Monte Carlo deep neural network and generates training result；
(4) it chooses at least three and generates the best result return of result.
As a preferred solution, in training process step (1), the key building structure includes in building
Wall, beam, plate, column, space, region and existing device point.
As a preferred solution, in training process step (3), it is described layout evaluation index by point coverage rate,
Point number, covering Duplication, point alignment rate, equidistant index, totally six sub- index linear weighted functions with a distance from building element
Gained；
FPoint index=c1fCoverage rate+c2fPoint number+c3fDuplication+c4fAlignment rate+c5fEquidistantly+c6fRange index
There is different weights for the different sub- indexs of space type；
C={ c1,c2,c3,c4,c5,c6}。
As a preferred solution, house class architectural modulus collection are as follows:
CHouse={ 0.25,0.2,0.15,0.15,0.15,0.1 }.
As a preferred solution, office class architectural modulus collection are as follows:
COffice={ 0.35,0.2,0.15,0.1,0.1,0.1 }.
As a preferred solution, underground parking class architectural modulus collection are as follows:
CUnderground parking={ 0.35,0.1,0.2,0.1,0.15,0.1 }.
As a preferred solution, in design generating process step (1), the key building structure includes in building
Wall, beam, plate, column, space, region and existing device point.
As a preferred solution, in design generating process step (4), 3 is chosen and generates the best result of result
It returns.
The beneficial effects of the present invention are:
1, the present invention realizes the design for building electromechanical point from scratch by the way of intensified learning, and trains
The additional existing design data of Cheng Wuxu.The method of the invention is all applicable for various building types, this is a kind of general
Method, every class building type needs to specify different parameter index collection, and needs stand-alone training.
2, the invention proposes a kind of assessment indicator system for covering a variety of point arrangements, it can be used for a variety of point equipment.
Detailed description of the invention
Fig. 1 is the flow chart of the electromechanical point position arranging method of building of the invention；
Fig. 2 is depth residual error network ResNet schematic diagram；
Fig. 3 is that point is aligned schematic diagram, and left figure is unjustified, right figure alignment；
Fig. 4 is that point arranges equidistant schematic diagram, and right figure is smaller than left figure spacing difference；
Fig. 5 is point schematic diagram with a distance from building element, and it is excessively close from wall that right figure is better than left figure left figure point；
Fig. 6 is point and building element collides schematic diagram, and the case where point collides with space boundary has occurred in left figure；
Fig. 7 is that point interdigit collides schematic diagram, and a case where interdigit collides has occurred in left figure；
Fig. 8 is that point exceeds space schematic diagram, has been disposed in border outer there are four point in left figure.
Specific embodiment
With reference to the accompanying drawing and embodiment, detailed elaboration is made to specific embodiments of the present invention.It needs to illustrate
It is that there is no conflict, embodiment and technical characteristic therein can be combined with each other.
This specific embodiment provides a kind of building electromechanics point position arranging method based on Monte Carlo, as shown in Figure 1, institute
The method of stating includes training process and design generating process, the specific steps are as follows:
The training process the following steps are included:
(1) for a certain building type building, such as office building, by the designed architectural drawing structuring of architect
For BIM data, and to the crucial building structure data such as wall, beam, plate, column, space, region, existing device point in building into
Rower note, extracts the data marked；
(2) uniform grid processing successively is carried out to each space, and calculates the feasible zone of electromechanical point arrangement,
Remove wall beam sheet-pile, the grid that other existing device points are taken up space, treated, and grid can regard the position layouted as
It sets；
(3) the evaluation index parameter set C that layouts accordingly is selected according to different building types to be designed；
(4) Monte Carlo (Monte Carlo Tree Search) is used, according to rule, carries out automatic cloth at random
Point automatically generates cloth point process as original training set S；
(5) it uses the ResNet in CNN as heuristic function, is input with S, there are two outputs: (a) next cloth point
It sets and layouts probability, the evaluation index after the completion of (b) finally layouting；Use the data with existing training network；
(6) Monte Carlo is used, is layouted by being changed to use trained ResNet network as guidance at random；According to current
It layouts and calculates the probability of next point position and the value of final evaluation index, comprehensively consider and maximum score value is taken to layout as next
Position；The sample set Si of Cheng Xin；
(7) step (5) (6) are repeated, ResNet precision is continuously improved；
(1)-(7) process is repeated for different building types, training obtains the generation for different building types
Model.
The design generating process the following steps are included:
(1) the designed architectural drawing of architect is structured as BIM data, and in building wall, beam, plate, column,
The key building structure data such as space, region, existing device point are labeled, and are extracted to the data marked；
(2) uniform grid processing successively is carried out to each space, and calculates the feasible zone of electromechanical point arrangement,
Remove wall beam sheet-pile, the grid that other existing device points are taken up space, treated, and grid can regard the position layouted as
It sets；
(3) corresponding previously trained Monte Carlo deep neural network is selected according to different building types to be designed
Generate model；Training result is generated using models for several times is generated；
(4) it chooses and generates the best 3 results return of result.
It can also choose and generate best 4,5, the 6 or 7 results return of result.
Flow chart shown in Fig. 1 includes that training generates model+use generation model generation design result two parts, is generated above
Part is training process.
ResNet: depth residual error network (Deep residual network, ResNet).
Deep neural network is relatively difficult to train because there are gradient disappear and gradient explosion the problems such as, ResNet be by
What He Kaiming team proposed in 2015, ResNet is constructed by residual block (Residual block).As shown in Fig. 2,
The residual block of ResNet consists of two parts, and formula is y=F (x)+x, it provides two kinds of selecting partys known to from formula
Formula, one is Identity Mapping, and what is referred to is exactly curved portion in figure, that is, x itself is inputted, the other is Residual
Mapping, be in addition to curve part, i.e. residual error portion F (x), if network have arrived at it is optimal, continue deepen network,
Residual Mapping will be 0 by push, only remaining Identity Mapping, so theoretically network be constantly in it is optimal
State, the performance of network would not also be reduced as depth increases.
Evaluation index of layouting parameter set C
Evaluation index of layouting by point coverage rate, point number, covering Duplication, point alignment rate, equidistant index, from
Building element distance is obtained by totally six sub- index linear weighted functions.
FPoint index=c1fCoverage rate+c2fPoint number+c3fDuplication+c4fAlignment rate+c5fEquidistantly+c6fRange index
There is different weights for the different sub- indexs of space type.
C={ c1,c2,c3,c4,c5,c6}
Such as house class architectural modulus collection is
CHouse={ 0.25,0.2,0.15,0.15,0.15,0.1 }.
Office class architectural modulus collection be
COffice={ 0.35,0.2,0.15,0.1,0.1,0.1 }.
Underground parking class architectural modulus collection is
CUnderground parking={ 0.35,0.1,0.2,0.1,0.15,0.1 }.
The meaning of each index is described in detail below
1, point coverage rate
Each point has certain functional coverage region, and the pillar or wall in space can hide covering
Gear, such as illuminator.
The functional covering of point is abstracted as two-dimensional areas covering problem, is with each illumination point by taking illumination as an example for circle
The heart, certain radius (public domain such as corridor etc. is 1.8m, other interior spaces are 1.2m) draw circle, this circle is this lamp
The illumination overlay area of tool.
Function coverage height i.e. arrangement point as far as possible be covered to all spaces in area of space can.Coverage rate
It can be indicated with the ratio between area coverage and the construction area gross area:
Wherein the construction area gross area needs to remove the area of building element in space, i.e.,
AThe construction area gross area=ABuild outer edge surround the area-∑ABuilding element area in region-∑ARegion sub-spaces area
2, point number
To meet economic index, under the premise of meeting the coverage rate in room, the usage quantity of point number is reduced to the greatest extent.
3, Duplication is covered
Point has certain overlapping area when being closer, and it is excessively high that point overlapping will lead to partial mulching, therefore is overlapped
The case where rate index limits overlapping generation.
Duplication calculating can be acquired with lap area and overall floorage ratio.
4, point alignment rate
As shown in figure 3, the point arrangement of the identical quantity in left and right two, under the premise of meeting other requirements, right side design
Scheme is better than left side, and because right side arrangement is more neat, left side arrangement is more mixed and disorderly.
5, equidistant index
Point should guarantee equidistant, i.e. spacing between each column and the phase as far as possible of the spacing between every row in arrangement as far as possible
Deng.Spacing index can be indicated with adjacent spacing variance.
As shown in figure 4, the point arrangement of the identical quantity in left and right two, under the premise of meeting other requirements, right side B scheme
It is horizontal and vertical not accomplish equidistantly because point is arranged in the A scheme of left side better than left side A scheme, and right side B scheme does not have
To be equally spaced.
fEquidistantly=σ2 Alignment line adjacent spacing
6, with a distance from building element
To reduce the waste to single point area coverage, single point should be as far as possible far from building element
As shown in figure 5, the point arrangement of the identical quantity in left and right two, under the premise of meeting other requirements, right side B scheme
Better than left side A scheme, because point has appropriate distance (generally to answer away from component distance small away from building element in the B scheme of right side
In covering radius), and left side A scheme does not have.
There are certain constraints in point placement process, as described below:
(1) it cannot be collided with building element
As shown in fig. 6, the point arrangement of the identical quantity in left and right two, under the premise of meeting other requirements, right side B scheme
Better than left side A scheme, because point and building element have collision in the A scheme of left side, and right side B scheme does not have.
(2) it cannot be collided with other point equipment
As shown in fig. 7, the point arrangement of the identical quantity in left and right two, under the premise of meeting other requirements, right side B scheme
Better than left side A scheme, because point equipment has collision each other in the A scheme of left side, and right side B scheme does not have.
(3) in space interior
As shown in figure 8, the point arrangement of the identical quantity in left and right two, under the premise of meeting other requirements, right side B scheme
Better than left side A scheme, because partial dot position equipment is not in space interior in the A scheme of left side, and point equipment is equal in the B scheme of right side
In space interior.
Claims (8)
1. a kind of building electromechanics point position arranging method based on Monte Carlo, which is characterized in that the method includes training process
And design generating process, the specific steps are as follows:
The training process the following steps are included:
(1) it is built for target structures type, designed architectural drawing is structured as BIM data, and in building
Crucial building structure data are labeled, and are extracted to the data marked；
(2) uniform grid processing successively is carried out to each space, and calculates the feasible zone of electromechanical point arrangement；
(3) the evaluation index parameter set C that layouts accordingly is selected according to different building types to be designed；
(4) auto-distribution dot is carried out at random according to rule using Monte Carlo, automatically generate cloth point process as initial sample
This collection S；
(5) it uses the ResNet in convolutional neural networks CNN as heuristic function, is input with S, there are two outputs: (a) next
A layout position and probability of layouting, the evaluation index after the completion of (b) finally layouting；
(6) Monte Carlo is used, is layouted by being changed to use trained ResNet network as guidance at random；According to currently layouting
The probability of next point position and the value of final evaluation index are calculated, takes maximum score value as next position layouted；As new
Sample set Si；
(7) step (5) (6) are repeated, ResNet precision is continuously improved；Training obtains Monte Carlo deep neural network and generates mould
Type；
The design generating process the following steps are included:
(1) designed architectural drawing is structured as BIM data, and the crucial building structure data in building is marked
Note, extracts the data marked；
(2) uniform grid processing successively is carried out to each space, and calculates the feasible zone of electromechanical point arrangement；
(3) the Monte Carlo deep neural network life for selecting corresponding training process to obtain according to different building types to be designed
At model, models for several times is generated using the Monte Carlo deep neural network and generates training result；
(4) it chooses at least three and generates the best result return of result.
2. the building electromechanics point position arranging method according to claim 1 based on Monte Carlo, which is characterized in that in training
In process steps (1), the key building structure includes wall, beam, plate, column, space, region and existing device point in building.
3. the building electromechanics point position arranging method according to claim 1 based on Monte Carlo, which is characterized in that in training
In process steps (3), it is described layout evaluation index by point coverage rate, point number, covering Duplication, point alignment rate, etc.
Spacing index, with a distance from building element obtained by totally six sub- index linear weighted functions；
FPoint index=c1fCoverage rate+c2fPoint number+c3fDuplication+c4fAlignment rate+c5fEquidistantly+c6fRange index
There is different weights for the different sub- indexs of space type；
C={ c1,c2,c3,c4,c5,c6}。
4. the building electromechanics point position arranging method according to claim 3 based on Monte Carlo, which is characterized in that house class
Architectural modulus collection are as follows:
CHouse={ 0.25,0.2,0.15,0.15,0.15,0.1 }.
5. the building electromechanics point position arranging method according to claim 3 based on Monte Carlo, which is characterized in that office class
Architectural modulus collection are as follows:
COffice={ 0.35,0.2,0.15,0.1,0.1,0.1 }.
6. the building electromechanics point position arranging method according to claim 3 based on Monte Carlo, which is characterized in that stop underground
Parking lot class architectural modulus collection are as follows:
CUnderground parking={ 0.35,0.1,0.2,0.1,0.15,0.1 }.
7. the building electromechanics point position arranging method according to claim 1 based on Monte Carlo, which is characterized in that designing
In generating process step (1), the key building structure includes wall, beam, plate, column, space, region and existing device in building
Point.
8. the building electromechanics point position arranging method according to claim 1 based on Monte Carlo, which is characterized in that designing
In generating process step (4), chooses 3 and generate the best result return of result.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811621461.5A CN109740237B (en) | 2018-12-28 | 2018-12-28 | Monte Carlo-based building electromechanical point location arrangement method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811621461.5A CN109740237B (en) | 2018-12-28 | 2018-12-28 | Monte Carlo-based building electromechanical point location arrangement method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN109740237A true CN109740237A (en) | 2019-05-10 |
| CN109740237B CN109740237B (en) | 2020-04-17 |
Family
ID=66361778
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201811621461.5A Expired - Fee Related CN109740237B (en) | 2018-12-28 | 2018-12-28 | Monte Carlo-based building electromechanical point location arrangement method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109740237B (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110570046A (en) * | 2019-09-18 | 2019-12-13 | 谢国宇 | site selection method and system for environment monitoring site |
| CN114491772A (en) * | 2022-03-23 | 2022-05-13 | 清华大学 | Household layout generation method and device |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104134097A (en) * | 2014-07-01 | 2014-11-05 | 哈尔滨工业大学 | Severe cold area office building form energy-saving design method based on GANN-BIM model |
| CN106599417A (en) * | 2016-11-30 | 2017-04-26 | 中国电力科学研究院 | Method for identifying urban power grid feeder load based on artificial neural network |
| WO2018104183A1 (en) * | 2016-12-05 | 2018-06-14 | Commissariat A L'energie Atomique Et Aux Energies Alternatives | Method for constructing a 3d digital model from a 2d plan |
| CN108564120A (en) * | 2018-04-04 | 2018-09-21 | 中山大学 | Feature Points Extraction based on deep neural network |
| CN108681692A (en) * | 2018-04-10 | 2018-10-19 | 华南理工大学 | Increase Building recognition method in a kind of remote sensing images based on deep learning newly |
| CN108734734A (en) * | 2018-05-18 | 2018-11-02 | 中国科学院光电研究院 | Indoor orientation method and system |
-
2018
- 2018-12-28 CN CN201811621461.5A patent/CN109740237B/en not_active Expired - Fee Related
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104134097A (en) * | 2014-07-01 | 2014-11-05 | 哈尔滨工业大学 | Severe cold area office building form energy-saving design method based on GANN-BIM model |
| CN106599417A (en) * | 2016-11-30 | 2017-04-26 | 中国电力科学研究院 | Method for identifying urban power grid feeder load based on artificial neural network |
| WO2018104183A1 (en) * | 2016-12-05 | 2018-06-14 | Commissariat A L'energie Atomique Et Aux Energies Alternatives | Method for constructing a 3d digital model from a 2d plan |
| CN108564120A (en) * | 2018-04-04 | 2018-09-21 | 中山大学 | Feature Points Extraction based on deep neural network |
| CN108681692A (en) * | 2018-04-10 | 2018-10-19 | 华南理工大学 | Increase Building recognition method in a kind of remote sensing images based on deep learning newly |
| CN108734734A (en) * | 2018-05-18 | 2018-11-02 | 中国科学院光电研究院 | Indoor orientation method and system |
Non-Patent Citations (1)
| Title |
|---|
| 罗辉 等: "综合监控系统在楼宇系统集成中的应用", 《都市快轨交通》 * |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110570046A (en) * | 2019-09-18 | 2019-12-13 | 谢国宇 | site selection method and system for environment monitoring site |
| CN110570046B (en) * | 2019-09-18 | 2022-03-01 | 谢国宇 | Site selection method and system for environment monitoring site |
| CN114491772A (en) * | 2022-03-23 | 2022-05-13 | 清华大学 | Household layout generation method and device |
| CN114491772B (en) * | 2022-03-23 | 2022-09-13 | 清华大学 | Household layout generation method and device |
Also Published As
| Publication number | Publication date |
|---|---|
| CN109740237B (en) | 2020-04-17 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN106960410A (en) | Underground pipe gallery approaches to IM based on BIM and GIS technology | |
| CN110532720A (en) | A kind of rapid automatized modeling method of Urban Buried Pipeline Network BIM | |
| CN107423518B (en) | Comprehensive pipe gallery full-line BIM collaborative design method combining Civil3D and Revit | |
| Ferdosi et al. | BIM applications in sustainable construction: scientometric and state-of-the-art review | |
| CN109740237A (en) | A kind of building electromechanics point position arranging method based on Monte Carlo | |
| CN103150614A (en) | Automatic configuration method for land utilization space | |
| CN105205864A (en) | Multi-source-data-based automatic modeling method and system of three-dimension model of geological structural surface | |
| CN105427370A (en) | Method for informationization management and planning design of electric-power underground pipeline | |
| CN105574675A (en) | Rule bank-based method and system for planning and supervising constructional engineering | |
| CN104392064A (en) | Optimal urban form design method for relieving heat island effect on mesoscale | |
| CN113420359B (en) | Method for automatically transmitting parameters to well placement based on REVIT (remote visual inspection) electric power engineering general diagram | |
| CN104537254B (en) | A kind of drafting method that becomes more meticulous based on social statistics data | |
| CN114004000A (en) | System for intelligent pipeline arrangement based on BIM technology | |
| Mostafavi et al. | An interactive assessment framework for residential space layouts using pix2pix predictive model at the early-stage building design | |
| Grobman et al. | Computer-based form generation in architectural design—a critical review | |
| CN104298800A (en) | Three-dimensional integrated model of intelligent substation | |
| Edtmayer et al. | High-resolution, spatial thermal energy demand analysis and workflow for a city district | |
| Zanazzi et al. | Census of abandoned built heritage assets: The importance of defining shared methodologies and ontologies | |
| Gao et al. | Optimization design generation of the rural residential building towards energy saving by using “Digital intelligence” technology | |
| CN103984784B (en) | Diagram-model integrated visualization method and system for nuclear power plant | |
| Chen | Fractal analysis based on hierarchical scaling in complex systems | |
| Yechao et al. | Integrated Strategy of Green Building Model Optimization Based on BIM Technology | |
| Fan et al. | Analysis and Reconstruction Method of Spatial Characteristics of Traditional Chinese Villages Based on Parameterization | |
| Jiang | Design and development of smart campus system based on BIM and GIS | |
| Aksoy et al. | A model for sustainable site layout design with Pareto genetic algorithm: SSPM |
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 | ||
| CF01 | Termination of patent right due to non-payment of annual fee | ||
| CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20200417 Termination date: 20201228 |