CN115600283B

CN115600283B - Multi-layer point type residence parameterization generating method guided by target performance

Info

Publication number: CN115600283B
Application number: CN202211217738.4A
Authority: CN
Inventors: 吴杰; 智旋; 王家兴; 韦俊宇
Original assignee: Guangxi University
Current assignee: Guangxi University
Priority date: 2022-09-30
Filing date: 2022-09-30
Publication date: 2023-11-28
Anticipated expiration: 2042-09-30
Also published as: CN115600283A

Abstract

The invention provides a target performance-oriented multi-layer point type residence parameterization generating method, which comprises the following steps: s1, generating parameters of the multi-layer point type residence: s2, selecting target performance, determining performance indexes, and carrying out simulation calculation on the target performance by combining the multi-layer point type residence parameters; s3, optimizing calculation is carried out on the result of the target performance simulation calculation, optimizing convergence judgment is carried out, and if convergence requirements are met, an optimal solution set is output; if the convergence requirement is not satisfied, parameter adjustment is performed, and the process returns to step S1. The invention can take the target optimization performance as the drive of the generation design, and obtain the multi-layer point type residence proposal parameters which meet the target performance guidance through parameterization generation, performance simulation calculation and intelligent optimization of the target performance.

Description

Multi-layer point type residence parameterization generating method guided by target performance

Technical Field

The invention relates to the technical field of buildings, in particular to a target performance-oriented multi-point type residence parameterization generating method.

Background

Multi-story point type residential building is used as important place for people living and people attach more and more importance to the level and convenience of living, so that related designs are more and more concerned and attach more importance. However, the multi-layer point type house is designed by manual work, and the problems of low design speed, incapability of controlling the design quality, high later correction cost and the like exist.

Along with the development of the building design industry, in the design process of a multi-layer point type residence proposal, a parameterized model with a large number of renaturation works is often required to be constructed, but the key problems of complicated steps, large calculation amount of a plurality of nodes of the existing topology model, errors of the generation of planar bodies by circular multi-agent bodies and the like still exist.

Furthermore, in the face of the various schemes generated, it is often necessary to select according to the target performance as an index, how to select the most suitable scheme among the numerous schemes, and this problem has not yet been proposed as an effective solution.

Disclosure of Invention

The invention aims at least solving the technical problems in the prior art, and particularly creatively provides a target performance-oriented multi-point type residence parameterization generating method.

In order to achieve the above object of the present invention, the present invention provides a target performance oriented multi-layer point type residence parameterization generating method, comprising the steps of:

s1, generating parameters of the multi-layer point type residence:

s2, selecting target performance, determining performance indexes, and carrying out simulation calculation on the target performance by combining the multi-layer point type residence parameters; the target performance comprises carbon emission, energy consumption and lighting, and the performance indexes comprise carbon emission intensity, energy consumption intensity and illumination intensity.

S3, optimizing calculation is carried out on the result of the target performance simulation calculation, optimizing convergence judgment is carried out, and if convergence requirements are met, an optimal solution set is output; if the convergence requirement is not satisfied, parameter adjustment is performed, and the process returns to step S1.

Further, the step S1 includes the steps of:

s1-1, building a building matrix table based on plane information and azimuth constraint information according to design requirements and design rules;

s1-2, generating a floor plan topological relation diagram under azimuth constraint according to functional layout information parameters in a building matrix table;

s1-3, generating a floor plan based on a rectangular splicing method according to plane information parameters in a building matrix table on the basis of a topological model, and generating a three-dimensional model according to elevation information parameters.

Further, the step S1-1 comprises the following steps:

s1-1-1, determining design parameters according to design rules;

s1-1-2, building a building matrix table according to design parameters and design requirements; the design requirements include a rigidity requirement and an elasticity requirement.

The S1-1-2 comprises: respectively encoding basic information parameters and layout information parameters, and initializing plane information parameters and elevation information parameters to obtain a building information matrix table;

The building information matrix table consists of a plurality of two-dimensional arrays, including an adjacent matrix table and building parameter information.

The design parameters include: basic information, layout information, plane information and elevation information;

the basic information includes: building locations, total building area, floor number of houses, stairs, number of entrances and exits and number of floors;

the layout information parameters include: the structure of the sleeve, the space function, the space relation and the space orientation;

the space functions comprise a space in the sleeve and a public transportation core, and the space in the sleeve comprises a main function space and a secondary function space;

the main functional space includes: the main lying, secondary lying, guest lying, living room, restaurant, kitchen, toilet and secondary functional space comprises: balcony and bathroom;

the public transportation core includes: hallways, walkways, traffic cores;

the spatial relationship includes; space unconnected, space connected, space communicated and space contained;

the spatial orientation includes: east, west, south, north, middle;

the plane information parameters include: functional space aspect ratio, functional space area, functional space cation area surface area and main guard configuration;

the parameters of the elevation information include: layer height, sill height, window wall area ratio and window position.

Further, the step S1-2 comprises the following steps:

s1-2-1, a layout information parameter rectangular table in a building information matrix table is called to obtain constraints of space orientations of various sets of functions, so that the solution space of a topological relation diagram is reduced;

s1-2-2, carrying out one-dimensional grid division in different space orientations according to each set of patterns, wherein the grid number of the one-dimensional grid division is the number of functional spaces in the same space orientation;

s1-2-3, fully arranging functional space topological points of each set of unit in a corresponding grid;

s1-2-4, organizing each set of layout information parameters by taking a traffic core as a core, and generating all solutions of the floor plan topological relation diagram.

Further, the step S1-3 comprises the following steps:

s1-3-1, generating a functional space shape: generating a functional space shape according to plane information parameters in the building matrix table, and restricting the rationality of the functional space shape by adopting the functional space area and the functional space length-width ratio;

s1-3-2, splicing functional space shapes: judging whether each functional space is rectangular, and if the functional space is a non-rectangular functional space, unifying the functional space into a rectangular shape through a segmentation method or a complementation method; then splicing according to the shared edges of the functional spaces;

The splicing according to the shared edges of the functional spaces comprises the following steps:

firstly, obtaining connection forms among the functional spaces through the shared edges, wherein the connection forms comprise linear connection, triangular connection, internal connection and annular connection;

then, main functional space splicing is performed: judging whether an annular connection function space with a unique solution exists or not; if the functional space connected in the annular mode is spliced, firstly splicing the walkway and the hall if the functional space connected in the annular mode exists, otherwise, splicing the walkway and the hall; then splicing adjacent functional spaces step by step in a circulating way according to the space relation;

then, after all the functional spaces are spliced, cutting off redundant walkways to obtain a sleeve-type plan after one-time splicing;

finally, performing two-stage functional space splicing: after the one-time splicing of the sleeve type plane is completed, splicing the secondary function space to the attached function space according to the sleeve type matrix table, and finally completing the generation of the sleeve type plane diagram.

S1-3-3, and generating a three-dimensional model.

The three-dimensional model generation comprises the following steps:

firstly, extracting the layer height in the elevation information parameters according to a formulated building information matrix table, endowing a building sleeve type plan, and converting the building sleeve type plan into a three-dimensional model;

Determining a reasonable window wall area ratio range of each functional space as a control parameter of window rationality, calculating each window area, and calculating the window width according to each window area and the window height to finish window generation;

then finishing the generation of the vertical face windowing and sleeve type parameterized three-dimensional model according to the window position;

then window positioning and facade windowing are completed on the wall surface to be windowed according to the window position coordinates;

finally, a parameterized three-dimensional model is finally generated according to the layer number information in the basic information parameters and the design rules.

Further, the optimization calculation is realized by adopting a SPEA2 algorithm, and specifically comprises the following steps:

firstly, an optimization module is established according to target performance, and optimization parameters are set;

inputting the simulation calculation result into an optimization module for optimization;

judging whether the optimization result meets the requirement or not, if so, outputting the optimization result to the solution centralized record of the optimal scheme, otherwise, returning the parameters to be adjusted to the model generation module, regenerating the model and completing the simulation calculation until the optimization is completed to achieve the optimization target;

and finally, selecting and determining an optimal solution.

Further, the method also comprises optimizing and verifying the result of the optimizing calculation, wherein the optimizing and verifying adopts the generation average performance X and the performance average optimizing rate Optimizing the variation amplitude->And number of optimization schemes N _opt One or any combination of the two is used as an evaluation index;

N _opt (G)＝N _opto -N _optr

wherein G represents the number of iterations;

N _opt (G) The number of Pareto optimization scheme solution sets of repeated solutions in the generation G rejection generation;

N _opto representing the number of Pareto optimization solution sets of repeated solutions in the non-eliminated generation;

N _optr representing the number of Pareto optimization solution set repeated solutions in the generation;

the average performance of Pareto optimization scheme solutions of repeated solutions in G generation rejection generation is concentrated;

X _G·i representing the performance of the ith solution in the Pareto optimal solution set of repeated solutions in the G generation rejection generation;

i represents the number of Pareto optimization scheme solution sets of repeated solutions in G generation rejection generation;

R _opt (G, i) the performance optimization rate of the ith solution in the Pareto optimization solution set for the repeated solution in the G generation rejection generation;

X ₀ performance representing the initial solution;

the method comprises the steps of (1) performing average optimization rate of a Pareto optimization scheme solution set for a repeated solution in a G generation rejection generation;

the average optimization rate of the performance of the Pareto optimization scheme solution set of the repeated solutions in the generation G-1 is eliminated;

representing adjacent generations +.>Is a variable amplitude of (a).

If N _opt (G) The adaptability of the optimization is higher as the iteration times increase;the closer to the target performance, the more significant the performance improvement is explained; />The overall trend is upward, so that the optimization performance is better; / >Approaching 0 indicates that the optimization is complete; performance optimization rate R _opt The higher (G, i) indicates the better the solution performance.

In summary, by adopting the technical scheme, the method can take the target optimization performance as the drive of the generation design, and obtain the multi-layer point type residence proposal parameters which meet the target performance guidance through parameterization generation and performance simulation calculation and intelligent optimization of the target performance.

Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention.

Drawings

The foregoing and/or additional aspects and advantages of the invention will become apparent and may be better understood from the following description of embodiments taken in conjunction with the accompanying drawings in which:

FIG. 1 is a schematic flow chart of the method of the present invention.

Fig. 2 is a spatial relationship type of functional space according to the present invention, fig. 2 (a) is a position unconnected, fig. 2 (b) is a position connected (no door), fig. 2 (c) is a spatial communication (door), fig. 2 (d) is an inclusion, fig. 2 (e) is a spatial functional spatial relationship combination schematic, and fig. 2 (f) is a spatial functional spatial relationship combination schematic.

Fig. 3 is a schematic view of the window position coordinate positioning of the present invention.

Fig. 4 is a schematic diagram of the spatial topology within the sleeve of the present invention.

FIG. 5 is a schematic diagram of the topology organization of the traffic core and the jacket of the present invention, FIG. 5 (a) is a two-user one-step, the jacket of two users in FIG. 5 (a) is connected to the traffic core, but the two users are not connected; FIG. 5 (b) is a two-user one-step, and in FIG. 5 (b), the two-user sets are each connected to the traffic core, and the two users are also connected; fig. 5 (c) is a three-user one-step, and fig. 5 (d) is a four-user one-step.

Fig. 6 is a schematic diagram of computer storage diagram rules and topology according to the present invention, fig. 6 (a) is an adjacency matrix, and fig. 6 (b) is a schematic diagram of topology.

Fig. 7 is a comparative schematic diagram of a conventional and improved topology graph generation algorithm and a point distribution model according to the present invention, fig. 7 (a) is a general point distribution model, and fig. 7 (b) is a point distribution model embodying orientation.

Fig. 8 is a schematic diagram of generating a floor plan topology point according to the present invention, fig. 8 (a) is a method of representing a spatial position of a functional space, and fig. 8 (b) is a method of representing a spatial position of an i-th set.

FIG. 9 is a schematic diagram of a functional spatial topological point combination of the spatial orientation constraint of the present invention.

Fig. 10 is a schematic diagram of the generation of a floor plan topology map of the present invention.

Fig. 11 is a schematic diagram of the functional space shape generation of the present invention, fig. 11 (a) is a schematic diagram of the functional space shape generation, and fig. 11 (b) is a schematic diagram corresponding to the functional space shape and the topological relation diagram.

FIG. 12 is a schematic diagram of a basic rectangular stitching relationship of the present invention.

Fig. 13 is a schematic process diagram of the floor plan generating module of the present invention, fig. 13 (a) is a schematic functional space connection form judgment diagram, fig. 13 (b) is a schematic ring connection diagram, fig. 13 (c) is a schematic sequential connection diagram, fig. 13 (d) is a schematic primary splicing diagram, and fig. 13 (e) is a schematic secondary splicing diagram.

Fig. 14 is a schematic view of three-dimensional model generation of a multi-layer point-type house according to the present invention, fig. 14 (a) is a schematic view of floor plan generation, and fig. 14 (b) is a schematic view of three-dimensional model generation.

FIG. 15 is a schematic diagram of a SPEA2 optimization flow.

FIG. 16 is a flow chart of a performance calculation module according to the present invention.

Fig. 17 is a schematic diagram of a performance model building sub-module according to the present invention, fig. 17 (a) is a conversion interface diagram of the performance model building sub-module, and fig. 17 (b) is a schematic diagram of a partition of the performance model building sub-module.

FIG. 18 is a flow chart of the performance calculation module of the optimization design module of the present invention.

Detailed Description

Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to like or similar elements or elements having like or similar functions throughout. The embodiments described below by referring to the drawings are illustrative only and are not to be construed as limiting the invention.

The specific working method of the invention is shown in figure 1, and comprises the following steps:

s1, generating parameters of the multi-layer point type residence:

s1-1, building a building matrix table based on plane functions and azimuth constraint according to design requirements and rules;

s1-1-1, according to the specifications, typical residential plane investigation and related literature data, the design parameters of the design elements are summarized and carded into: basic information, layout information, plane information, elevation information and other parameters.

The generation of the building scheme is controlled through the parameters, the design requirement of the residential scheme is divided into a rigidity requirement and an elasticity requirement, the rigidity requirement is used as a constraint condition, and the elasticity requirement is used as an optimization target. Rigidity requirements are requirements that must be met at the time of design, such as regulatory mandates, etc.; the elastic requirement is not mandatory but is also closely related to the scheme quality, such as sunlight, natural lighting, natural ventilation, carbon emission, sound insulation and the like.

The value range of each typical design parameter is obtained by combining the element selection principle, as shown in table 2, and is used for constructing a design rule parameter library, and related parameters can be selected by reference or can be determined by self according to requirements. The element selection principle is determined by specifying the design elements and organization modes of the multi-layer point type house according to the related specifications.

1. Basic information parameters

The basic information parameters are quantitative parameterized expression of the early basic information and design requirements of the scheme, and the basic information parameters comprise: building locations, total building area, floor number of houses, number of stairs, number of entrances and exits, and number of floors.

According to the building location, the geographical location information of the building scheme is defined to judge the geographical location of the scheme project, and the identification information is provided for scheme generation by calling a corresponding parameter information base in combination with weather characteristics and selection of parameters such as weather. Parameters such as total building area, floor number of houses, stair entrance and exit positions/number of floors and the like are selected and set according to actual requirements.

2. Layout information parameters

The basic description of the layout information of each functional space forming the floor plan is carried out through the layout information parameters, and the layout information parameters consist of four parts of a sleeve structure, spatial functions, spatial relations and spatial orientations.

2.1 cover type structure

The sleeve type structure is a common multi-layer point type house sleeve type space structure, and the following 6 space structure forms can be selected according to requirements: 1 room 1 kitchen 1 defend, 2 room 1 kitchen 1 defend, 3 room 2 room 1 kitchen 2 defend, 4 room 2 room 1 kitchen 2 defend.

2.2 spatial Functions

The space functions are functions of each space of the design elements of the multi-layer point type residence, and comprise each function of the space in the sleeve and a public transportation core, and the specific composition can be selected according to the requirement. The method is characterized in that functions are assigned to each space according to design requirements, other parameter information corresponding to each functional space is matched according to the space functions, and the method is further used for generating a parameterized model of the multi-layer point type residence, and the balcony and the main guard are auxiliary secondary spaces of other functional spaces and are not independently considered as functional spaces.

2.3 spatial relationship

The spatial connection relation (spatial relation) of each functional space of the multi-layer point type residential building can be divided into four basic types: unconnected, spatially connected, inclusive, as in fig. 2 (a) - (d), and other various types of spatial relationships are variations of the underlying spatial relationship type, such as in fig. 2 (e) - (f). The interrelationship of the functional spaces is described herein by way of the four basic spatial relationships described above to define the relative relationship of the functional spaces.

2.4 spatial orientation

The functional space orientation represents the orientation of each functional space, and is divided into: east, west, south, north, middle. And determining the space orientation of the functional space according to the design requirement, and summarizing the general rule of the space orientation of each functional space according to the design specification and related design experience to obtain the suggested orientation of each functional space, and reducing the solution scheme with unreasonable orientation through the constraint of the space orientation to reduce the solution space range.

3. Plane information parameters

The plane information parameters mainly include the functional space aspect ratio (K _ck ) Area of functional space (A _d ) Functional space balcony area (A) _yt ) Master-guard configuration (ZW) _yn ). According to the design specification and practice of the residential buildingThe surface width and depth ranges of the materials can be obtained by grinding, and the surface width and depth ranges are shown in the table 3 and are used as constraint conditions for generating the following functional spaces so as to ensure the rationality of the shape; and the value range of each functional space body can be calculated, and can be used as a design optimization parameter and can be used as a design reference. The calculation method of each plane information parameter is as shown in formulas (3-1) to (3-4):

K _ck ＝L _q /W _q (3-1)

A _d ＝L _q ·W _q (3-2)

K _ckmax ＝D _qmax ² /A _d (3-3)

K _ckmin ＝D _qmin ² /A _d (3-4)

wherein: k (K) _ck And A _d Functional space aspect ratio and functional space area square meter, respectively; l (L) _q 、W _q Is the functional space length (m); k (K) _ckmax 、K _ckmin Representing the maximum and minimum values of the aspect ratio of the functional space, and K _ckmax Not more than 2.00, K _ckmin Not less than 0.50; l (L) _qmax 、L _qmin Maximum and minimum values (m) representing the surface width and depth of the functional space, respectively, where D _qmin <W _q <D _qmax 、D _qmin <L _q <D _qmax 。

4. Parameters of elevation information

The facade information parameter mainly controls the rationality of the facade of each function space of the multi-layer point type residence, and mainly comprises the following components: layer height (H) _q ) Sill height (H) _ct ) Window height (H) _c ) Area ratio of window wall (K) _cq ) Window position (C) _wz )。

The layer height, the windowsill height and the window height are selected and set according to requirements and related specifications; window wall area ratio is according to building daylighting setting Determining the value range by the standard and the related thermal standard, and determining the value range by the minimum window area ratio K _cdmin The minimum window area A required to be met by the functional space is calculated _cmin Further, the minimum window wall area ratio K of the functional space is obtained _cqmin Determining the maximum window wall area ratio K of the functional space when the thermal performance requirement is met according to the related specification _cqmax Finally, the window wall area ratio range K of each functional space is determined _cqmin ～K _cqmax As a control parameter for window size; the window position is described by adopting a coordinate positioning method, a plane rectangular coordinate system is established by taking the lower left corner point of each functional space wall surface as a coordinate origin, and meanwhile, the absolute position (C) of the window is described by taking the central point of the bottom edge of the window as a coordinate point _wzjd ) As shown in FIG. 3, the relative position of the wall surface is mapped to a range of (0-1 ) to represent the relative position (C _wzxd ) The method comprises the steps of carrying out a first treatment on the surface of the Window width W _c Then by window area A _c Height H _c The relevant parameters are determined by formulas (3-5) to (3-8) and table 4:

K _cq ＝A _c /A _q (3-5)

A _cmin ＝A _d ·K _cdmin (3-6)

A _q ＝W _q ·H _q (3-7)

W _c ＝A _c /H _c (3-8)

wherein: k (K) _cq 、A _cmin 、A _q 、W _c The window wall area ratio, the minimum window opening area meeting the specification, the window wall area and the width of the functional space window are respectively. A is that _c 、A _d Is respectively the window opening of the functional space and the ground area (m) ² )；W _q 、H _q Respectively representing the surface width and the layer height (m) of the wall surface of the functional space window; h _c Window for functional spaceHeight (m); k (K) _cdmin 、K _cqmax The minimum window area ratio and the maximum window wall ratio are respectively required by functional space lighting standards, wherein K is as follows _cqmin <K _cq <K _cqmax 。

S1-1-2, building a building matrix table:

the building information matrix table is a storage matrix for representing design requirements by using a two-dimensional array, and is an extension of the traditional adjacent matrix table. Building parameter information is added on the basis of the functional space connection relationship so as to describe and store design requirement information of the multi-layer point type residence, including but not limited to the following parameters: parameters such as functional layout information, plane scale information, elevation scale information and the like. The initial building information matrix table is created according to the required sleeve type combination and structure of the design, and comprises basic information parameters, layout information parameters, plane information parameters and elevation information parameters, as shown in table 5. Building a three-dimensional model of the building information matrix table, a topological relation diagram, a functional space shape, a floor plan diagram and a multi-layer point type residence through related parameters in sequence.

And (3) basic information parameter coding: the basic information parameters are determined by determining the information of building locations, total building areas, floor numbers, stairs, number of entrances and exits and the number of layers, and the information of the building related parameters is determined to provide basic constraint data for building generation, and the related data can refer to table 2.

Layout information parameter coding: the layout information parameter coding is used for coding the sleeve type structure, the space function, the space relation, the space orientation and the like of the functional space and is used for controlling the structure type of the sleeve type, the function, the connection relation and the orientation of each functional space.

1. Sleeve type structure

The housing type structure is expressed by h room i hall j kitchen k guard, a matrix table corresponding to each housing type is created according to the housing type structure, and the housing type structure comprises: 1 room 1 kitchen 1 defend, 2 room 1 kitchen 1 defend, 3 room 2 room 1 kitchen 2 defend, 4 room 2 room 1 kitchen 2 defend.

2. Spatial function coding

After the sleeve structure is determined, each sleeve type building plane is assumed to be W={a ₀ ，a ₁ ，a ₂ ，…a _n The set of } is subjected to set-type plane coding to a _n Simplifying and representing each functional space, firstly determining a certain room position a in the coding process ₀ Then according to a ₀ Determining a ₁ After that, the encoding of each functional space is completed in turn.

3. Spatial relationship coding

The space relation of each functional space of the building is represented by an adjacent matrix, and when the layout information parameters of each functional space are extracted, four space relations are obtained: unconnected, position-connected, spatially-connected, inclusive, are given corresponding index numbers 0, 1, 2, 3, respectively.

4. Spatial orientation coding

Five spatial orientations of each functional space are: the east, west, south, north and middle are respectively indicated by index numbers E, W, S, N, M, and the space orientation of each functional space is encoded according to the standard requirements and design experience.

Plane information parameter initialization: matching functional space aspect ratio (K) in a set according to design requirements and experience _ck ) Area of functional space (A _d ) Functional space balcony area (A) _yt ) Master-guard configuration (ZW) _yn ) The initial parameters can be referred to in tables 2 and 3.

Initializing parameters of the elevation information: the layer height of the functional space (H _q ) Sill height (H) _ct ) Window height (H) _c ) Area ratio of window wall (K) _cq ) Window position (C) _wz ) Setting is carried out to finish giving the elevation information of the multi-layer point type residential building so as to control the rationality and effect of elevation generation.

The initial parameter information base of the multi-layer point type residence is constructed by presetting the floor number of the multi-layer point type residence and basic information of the sleeve structure, and the basic table is generated by calling the database through a program, so that the workload of manually establishing the building matrix table can be reduced.

S1-2, generating a topology model under azimuth constraint according to functional layout information parameters in a building matrix table; converting the topological relation diagram into a topological relation diagram according to design requirements;

The generation of the floor plan is performed starting from the planar topological relation as shown in fig. 4. The organization of multi-layer point residential floor planes is divided into: one-step two-step three-step one-step four-step. With the traffic core as the core, a nested topology is made around the traffic core as shown in fig. 5. When the floor plan design of the residence proposal is carried out, the floor number is determined, the design is carried out by selecting a proper sleeve structure according to the requirement, then the floor plan design is carried out, and the three-dimensional meta-model of the building is constructed.

The connection relation of the functional space is represented by the adjacency matrix, the functional space is represented by the plane vertexes of the topological relation diagram, and the connection lines between the vertexes represent the functional space relation. As shown in fig. 6 (a) and fig. 6 (b) are mutually convertible adjacency matrix and topology, and 0 and 1 in fig. 6 (a) respectively represent that two objects in fig. 6 (b) are not connected and are connected.

The adjacent matrix is used as a conversion carrier, and a topology generation method of a space relation-function layout information parameter (adjacent matrix) -topology relation diagram (undirected diagram) is established. Each set of patterns has own layout information parameters to control the relation of the functional spaces in each set. By adopting the nested matrix table, the solution space of the topological relation diagram can be obviously reduced through the constraint on the functional space orientation.

If the number of shared functional spaces of a certain set is Q, wherein the number of spatial orientations of east (E), west (W), south (S), north (N) and middle (M) is E, W, S, N, M, the functional spatial position distribution model (distribution model) is A=Q-! Seed; defining a topology point generation region of a functional space according to a spatial orientation, the kind of the point distribution model can be reduced to a=e-! W-! S! N! M! . As a general layout model, FIG. 7 (a), its arrangement type is 12-! = 479001600; point model for embodying orientation fig. 7 (b), its arrangement type is 3-! 2! 3! 3! 1! =432.

And the floor plan organizes various sets by taking the traffic core as a core, and the generation of the floor plan topological relation diagram is completed according to the layout information parameters. The relative spatial position of the corresponding topological points of each functional space of the building can be represented by a spatial coordinate system through grid division of the space, namely, each functional room is abstracted into topological points, and the relative spatial position of each functional space is represented by the relative position relation of the topological points. And the topological point position generation area is limited by azimuth constraint, so that the solution space range can be obviously reduced. As shown in fig. 8, each functional space is abstracted into a topological point, the relative spatial position of each functional space is represented by the relative positional relationship of the topological point, and the topological point representing each functional space is inserted into the center where a spatial grid can be generated.

In FIG. 8 (a), the floor plan has a total of i users, the number of i-th user-set-type shared functional spaces is Q _i The number of functional spaces in each space direction corresponding to east (E), west (W), south (S), north (N) and middle (M) is E _i 、W _i 、S _i 、N _i 、M _i . The arrangement and combination of the multi-layer point type residential floor plane functional space can be carried out by the following method: (1) Dividing the relative spatial orientation into east (N), west (W), south (S), north (N) and middle (M) by taking each set of units as a unit according to the spatial orientation coding information; (2) And one-dimensional grid division is carried out according to the number of functional spaces of each set of patterns in different space orientations as shown in fig. 8 (b); (3) Then, the functional space topological points of each set of unit are fully arranged in the corresponding grid; (4) And then, combining the sleeve type combination mode proposed by the S1-2 to obtain all combination possibilities of the floor plan function space under the condition of setting the layout information parameters. The method limits the space orientation to be arranged and combined, and the combination type of the i sets of functional spaces is A _i ＝E _i ！·W _i ！·S _i ！·N _i ！·M _i The following is carried out All combined types of the functional spaces of each set of floor plan are A= pi A _i . It is apparent that the method can greatly reduce the solution space rangeThe solution scheme which is reasonable in mathematics but unreasonable in functional space relative position relationship in actual building design is eliminated.

The space orientation of each set of functional space of the floor plan is restrained through design requirements, specifications and design experience, and compared with the situation that all functional spaces of the floor plan are fully arranged, the solution scheme with unreasonable space orientation can be avoided, and the solution space is obviously reduced. Assuming that the floor plan of a two-user combination of one ladder has 19 functional spaces in total, in the case of space orientation constraint, there are (1 | 3 | 1 |) and (1 | 3 | 1| 1296 combinations, as shown in fig. 9, each space topology point in the figure can represent an arbitrary functional space of the space orientation, and only one of the combination solutions is shown in the figure. Obviously, the method is used for generating the arrangement of the functional space topological points on the premise of carrying out space orientation constraint on the functional space, so that the judgment process can be greatly simplified, the calculation time of a solution scheme is shortened, and the requirement on hardware configuration is reduced.

Generating topological points of each functional space on the basis of space constraint; then defining the relative relation of each functional space through the space relation; comparing the top number of the topological relation graph with the intersection number of the topological connecting lines according to the requirement that the space streamline is not intersected, if the top number of the topological relation graph is not equal, eliminating the solution for the streamline intersection, and if the top number of the topological relation graph is equal, the streamline is not intersected; finally, a solution set of topological relation schemes meeting the azimuth constraint, the spatial relation and the non-crossing topological streamline is generated, as shown in fig. 10.

S1-3-1, functional space shape generation

The generation of the multi-layer point type residential floor plan topological relation diagram is completed in the above step S1-2, and the generation of each functional space shape is completed in this section, and the functional space is considered as a rectangle, as shown in fig. 11 (a); on the basis of the generation of the topological relation diagram and the functional space body, the functional space body and the topological space point are corresponding as shown in fig. 11 (b) so as to perform floor plane splicing generation.

The method for generating the functional space body is described in detail in the above 3. The plane information parameters, the generation of the functional space body is performed according to the plane information parameters in the building matrix table, and the functional space area (a _d ) Aspect ratio of functional space (K) _ck ) The rationality of the functional space shapes is constrained as shown in fig. 11 (a).

S1-3-2, functional space shape splicing

The generation of the functional space in the multi-layer point type residential building model takes a regular rectangle as a main study object, and takes the layout of the regular rectangle as a rectangular layout problem into consideration, so that a design scheme solution which is optimal can be obtained through algorithm evolution. In the process of floor surface splicing generation, the non-rectangular functional space is unified into a rectangular shape through a segmentation method or a complementation method.

Rectangular units are spliced by generating common edges, and can be summarized as follows: the four basic forms of linear connection, triangle connection, internal connection and annular connection are overlapped, as shown in fig. 12, and other node relations and connection forms are formed by overlapping the four basic connection forms.

Based on the rectangular splicing method, judging the functional space connection form of the topological relation diagram generated under the set space relation; and then the functional space shapes are sequentially connected through a rectangular splicing rule, a multi-layer point type residential floor plan is generated by combination, a secondary space balcony and a main guard are connected to the attached functional space according to set requirements, and finally the multi-layer point type residential floor plan is formed, as shown in fig. 14 (a).

As can be seen from fig. 12, the main functional spaces are connected by the shared edge, and the specific method thereof is discussed in detail above, and the connection relationship between the nodes of each functional space is determined by the program in this section, as shown in fig. 13 (a), and the connection relationship between the functional spaces is divided into annular connection (four-point connection), triangular connection (three-point connection), and the like, such as annular connection of halls, kitchens, restaurants and walkways in the case a.

In the multi-layer point type residence floor plane, each entrance hall is connected with a traffic core, the walkways are connected with the entrance hall and are long-strip-shaped, and other functional spaces in the sleeves are organized around the walkways, so that the connection of each entrance hall with the traffic core takes the walkways as cores, each entrance walkway is abstracted into rays connected with the entrance hall, and the other functional spaces in the sleeves gradually shrink around the walkways to finish the splicing of the floor plane. The floor surface splicing items are divided into two parts of main functional space and secondary functional space (balcony and bathroom).

The main function space is spliced, and whether an annular connection function space with a unique solution exists or not is judged first: if there is a functional space with annular connection, firstly splicing the functional space as shown in fig. 13 (b), otherwise, firstly splicing the walkway and the hall as shown in fig. 13 (c); and then splicing adjacent functional spaces step by step in a circulating way according to the spatial relationship. After all the functional spaces are spliced, redundant walkways are cut off to obtain a once spliced sleeve-type plan view, as shown in fig. 13 (d).

And (3) splicing the secondary functional space, after the primary splicing of the sleeve type plane is completed, generating the balcony, the main guard and other shapes according to the configuration requirements of the balcony and the toilet in the sleeve type matrix table, and secondarily splicing the balcony and the main guard and other shapes into the attached functional space, thereby finally completing the generation of the sleeve type plane diagram, as shown in fig. 13 (e).

S1-3-3, three-dimensional model generation

After the pre-step of generating the floor plan of the multi-layer point type residence is completed, the floor plan needs to be converted into a three-dimensional building model, and the generation of building elevation mainly relates to.

When building the building elevation, the control of the elevation windowing is mainly used, and the rationality of the elevation windowing is controlled by the elevation information parameters in the sleeve type matrix table, and the method mainly comprises the following steps: window height (H) _c ) Area ratio of window wall (K) _cq ) Window position (C) _wz ) High (H) _q )。

(1) Firstly, extracting the layer height (H) in the elevation information parameters according to the formulated building information matrix table _q ) And giving a building sleeve-type plan, converting the building sleeve-type plan into a three-dimensional model, and selecting and setting parameter values according to requirements and related specifications. (2) Second, a reasonable window wall area ratio (K) of each functional space is determined _cq ) Range, as a control parameter for window rationality, is recalculatedArea of each window (A) _c ) And according to the area (A) _c ) Window height (H) _c ) Calculate the window width (W) _c ) Completing window generation, wherein related parameters are represented by formulas (3-5) - (3-8); (3) Then, according to the window position (C _wz ) And finishing the generation of the vertical face windowing and sleeve type parameterized three-dimensional model. Window position (C) _wz ) Describing by adopting a coordinate positioning method, taking the left lower corner point of each functional space wall surface as a coordinate origin, establishing a plane rectangular coordinate system, and simultaneously describing the position (C) of the window by taking the relative position of the window center point on the wall surface as a coordinate point _wz ) The method comprises the steps of carrying out a first treatment on the surface of the (4) And then from the window position (C _wz ) The coordinates finish window positioning and facade window opening on the wall surface to be windowed; (5) Finally, a parameterized three-dimensional model is generated according to the layer number information in the basic information parameters and the design rules, and a performance model can be constructed as shown in fig. 14 (b).

S2, taking carbon emission as a target performance, taking carbon emission intensity as a performance index, and then performing simulation calculation.

In addition, the target performance can be energy consumption, lighting and the like, and the performance index can also be energy consumption intensity, illumination intensity and the like.

The following considerations are used in determining the target performance index:

(1) The relationship between design parameters and carbon emission performance is studied in the follow-up text;

(2) The carbon emission of building material production is usually counted in the industrial field, the calculation focus of the carbon emission of the building is the building operation and construction and dismantling stages, but the carbon emission in the construction and dismantling stages is more related to engineering organization design, and the relevance with the scheme design is low;

(3) The carbon emission in the operation stage has the highest proportion in the whole life cycle, and is representative of the key core of low carbonization of the residence;

(4) The international carbon emissions referred to at present are generally referred to as carbon emissions during the operation phase of a building. The target performance index is determined herein as the building run period annual average carbon emission intensity C _ayx Then, as in equation (4-1), in the target performance optimization model herein, the value of l is 1, z= (g) ₁ (y))，g ₁ (y)＝C _ayx Can also root in futureAnd carrying out optimization research on more target performances according to actual demands.

The optimal design of the multi-layer point type house is to adjust the element design parameters thereof by parameterization means so as to realize the scheme optimization of target performance guidance and provide effective optimal scheme reference for the subsequent multi-layer point type house design. The objective performance-oriented optimization design of the multi-layer point type residence is decomposed into 4 mathematical iterative calculation problems, an objective function is firstly constructed to enable the objective performance to be functionalized, the change of a dependent variable along with the change of an independent variable is conveniently described in the final optimization, and the mathematical description of the objective performance optimization is as follows:

The above equations respectively correspond to three processes of scheme generation, scheme evaluation and scheme target performance optimization which meet a certain rule. Where x= (x) ₁ ,…,x _h ) E, X is a design parameter variable (independent variable) in the h dimension, and X is a set (independent variable set) of the design parameter variable in the h dimension; y= (y) ₁ ,…,y _i ) E Y is the design variables (dependent variables) in the i-dimension, Y is the set of design variables (dependent variable set) in the i-dimension; z= (z) ₁ ,…,z _j ) E Z is an evaluation target variable (target performance) in j dimensions, Z is a set of optimization target variables (target performance set) in j dimensions. F (X) is a scheme generating objective function, and comprises k mapping functions (scheme generating rules) from X to Y; g (Y) is a scheme optimization objective function, and comprises l mapping functions (scheme evaluation rules) from Y to Z; when the performance index of the scheme is closer to the design target requirement, the function value of G (y) is closer to 0; r is (r) _m (x) Less than or equal to 0, j=1, 2, …, p defines p inequality constraints, s _n (x) =0, (k=1, 2, …, q) defines q equation constraints. For a certain X ε X _o X, if (3) and (4) of (4-1) are satisfied simultaneously, X is a feasible solution, X _o Is a set of feasible solutions. The target performance optimization model l in the formula (4-1) takes the value of 1 as single-target optimization, and takes the value of more than 1 as multi-target optimization.

Building carbon emissions are an important contributor to current global warming, and the low-carbon development of multi-story point homes is the primary focus. The method has positive practical significance for carrying out low carbonization control on the generated design in the scheme stage, so that the low carbonization realization of the multi-layer point type residence parameterized generated design is taken as a research key point, a calculation method and an evaluation index of carbon emission performance are determined, and a foundation is provided for the research of defining the relation between target performance indexes and the effect of design parameters on the carbon emission performance.

The above method determines that the carbon emission performance is the target optimization performance of the multi-layer point type residence, and defines a calculation method for taking the carbon emission strength as an evaluation index of the carbon emission performance and then selecting the evaluation index. Selected to operate the annual average carbon emission intensity C _ayx As a target to be optimized.

During specific optimization, an objective optimization function with good distribution is required to be constructed for convergence of an optimization solution set so as to ensure the rationality of the result. In practical scheme C _ayx The smaller the carbon black performance is, the better the multi-layer point residential scenario is at run-time, thus at C _ayx And constructing an objective function for the optimization index.

The carbon emission of the building operation stage mainly comprises heating ventilation and air conditioning, domestic hot water, illumination stairs, renewable energy sources and building carbon sinks. The design age is determined based on the design life, with a default of 50 years.

C _ayx ＝C _yx /(A·y) (4-3)

Wherein: c (C) _yx The annual average carbon emission intensity (kgCO 2/(. Square.a)) of the building in the building operation stage is expressed; e (E) _yx,j Represents the annual energy consumption (unit/a) of the j-th class in the construction operation stage; c (C) _p Annual carbon reduction (kgCO 2/a) of building green land carbon sink system; e (E) _yx,j,z Class j energy years representing class z system of building operation stageConsumption (units/a); ER (ER) _yx,j,z Representing the annual consumption of energy of a z-type system in the building operation stage, wherein the j-type energy (unit/a) is provided by a renewable energy system; z building energy system types, including heating air conditioning, lighting, domestic hot water systems, etc.

And S3, optimizing the result of the target performance calculation index.

S3-1, selection of intelligent optimization algorithm

The building performance is optimized through the intelligent optimization algorithm, and the method has remarkable advantages compared with manual adjustment parameter preference. The general intelligent optimization algorithms in the engineering community mainly include genetic Algorithm (Genetic Algorithm, GA), simulated annealing Algorithm (Simulated Annealing, SA), acoustic Search Algorithm (Harmony Search, HS), ant colony Algorithm (Ant Colony Optimization, ACO), immune Algorithm (IA), particle swarm optimization Algorithm (Particle Swarm Optimization, PSO), and the like, and the main characteristics thereof are shown in table 6.

Table 6 Intelligent algorithm features

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The genetic algorithm and the particle swarm optimization algorithm can obtain more stable optimization results in different optimization problems without considering the operation speed, wherein the genetic algorithm is more visual and easy to understand than other algorithms, has higher calculation efficiency and global searching capability, and is widely applied to the field of building engineering. In Holland, j.h. 1975, genetic algorithms were proposed, in scanfer, j.d. 1985, target optimization studies were performed using genetic algorithms, after which Pareto optimization (Pareto Improvement, pareto) was introduced in Goldberg, d.e. 1989, and optimization was more quickly known by analyzing Pareto Front solution (Pareto Front). Pareto optimization is a method suitable for single-objective and multi-objective optimization, and the core is to ensure that at least one optimization objective is better under the condition that other optimization objectives are not degraded, any optimization objective is improved to enable at least one degraded solution of the other optimization objectives to be Pareto Front, and when all Pareto Front is solved, the Pareto optimal state is achieved. Zitzler et al 1999 developed a first generation Pareto genetic evolution algorithm (Strength Pareto Evolutionary Algorithm, SPEA) for searching for multi-objective optimized Pareto Front, and developed an improved version of SPEA2 (Strength Parteto Evolutionary Algorithm 2) in a subsequent study in 2002. After a plurality of improvements, the SPEA2 has better optimization performance compared with the SPEA, overcomes the defect of population degradation caused by the sinking local optimization in the optimizing process, and becomes a classical algorithm commonly used for solving the target optimization problem.

In summary, by comparing the application range of the common intelligent optimization algorithm, selecting a genetic algorithm SPEA2 suitable for global optimization as an algorithm core, and performing target performance-oriented optimization design research on the multi-point residential scheme, the calculation flow of the SPEA2 is shown in fig. 15.

S3-2, target performance optimization calculation

The first step, an optimization module is established through visual programming, and optimization parameters are set;

step two, inputting the simulation calculation result into an optimizing module for optimization;

and thirdly, judging whether the optimization result meets the requirement, outputting the result to the solution centralization record of the optimal scheme, otherwise, returning the parameter to be adjusted to the model generation module, regenerating the model and completing the simulation calculation until the optimization is completed to achieve the optimization target.

And step four, selecting and determining an optimal solution.

And setting the construction sub-items of the optimization target parameters, namely the related parameters (dependent variables) of the target performance, the parameters to be optimized (independent variables) and the optimization tool parameters through the performance optimization module. The optimization plug-in unit is used for carrying out optimization target parameters, parameter construction to be optimized and program parameter setting according to the characteristics of the optimization plug-in unit when the optimization plug-in unit is used by selecting Octopus based on an RH & GH parameterization platform and using SPEA2 as a core algorithm as an optimization tool.

When the optimization target parameters and the parameters to be optimized are constructed, the parameters to be optimized and the parameters to be optimized are both digital Number types, and if the parameters are non-digital parameters, digital processing is needed; and because the objective function value is smaller and better when the Octopus performs optimization solution, all objective parameters to be optimized need to be subjected to corresponding minimization treatment (such as multiplying the result by-1 or taking the reciprocal, etc.) before optimization calculation; the intelligent optimization process has large workload, and the rationality of the trend of the parameter curve is judged in the first generation of optimization so as to ensure the final optimization quality; and the Pareto Front solution (Pareto Front) needs to be analyzed to know the optimization condition more quickly, so that Pareto Front is generally selected for the optimization analysis of the target performance guide design.

S3-3, verifying and evaluating the optimized result

SPEA2 generates a Pareto optimization scheme solution set in the iterative optimization process, and the optimization effect is evaluated conveniently and rapidly, and the average performance is adopted in the processPerformance average optimization rate->Optimizing the variation amplitude->And number of optimization schemes N _oot If the number of Pareto optimization solution sets generated is small as an evaluation index, the solution sets are replaced by all solution sets of the generation (including Pareto optimization solution and non-Pareto optimization solution) to analyze, and related calculations are as shown in formulas (4-10) to (4-14):

Wherein N is _opt (G) The number of Pareto optimization scheme solution sets of repeated solutions in the generation G rejection generation; n (N) _opto Representing the number of Pareto optimization solution sets of repeated solutions in the non-eliminated generation; n (N) _optr Representing the number of Pareto optimization solution set repeated solutions in the generation; />The average performance of Pareto optimization scheme solutions of repeated solutions in G generation rejection generation is concentrated; x is X _G·i Representing the performance of the ith solution in the Pareto optimal solution set of repeated solutions in the G generation rejection generation; i represents the number of Pareto optimization scheme solution sets of repeated solutions in G generation rejection generation; r is R _opt (G, i) the performance optimization rate of the ith solution in the Pareto optimization solution set for the repeated solution in the G generation rejection generation; x is X ₀ Performance representing the initial solution; />The method comprises the steps of (1) performing average optimization rate of a Pareto optimization scheme solution set for a repeated solution in a G generation rejection generation;representing adjacent generations +.>Is a variable amplitude of (a).

Based on the method, parameterized modeling software Rhino & Grasshoper (RH & GH) is adopted as a development platform, and a parameterized generation and optimization program for the residential bushing type scheme is built. The procedure is mainly divided into: and generating a pattern set by parameterization, optimally designing the pattern set, and simulating the pattern set performance. The generation module is compiled by combining a parameterized model generation method of the GH visual programming technology and a Python script language; the optimization module selects Octopus taking SPEA2 as an algorithm core as an intelligent optimization kernel; and the performance simulation module can be used for accessing the performance simulation port in a self-defined mode according to requirements.

The application scene of the method of the invention is as follows: the method is characterized in that data are input through a mobile phone or a computer and the like, then the data are uploaded to a server, and after various schemes are obtained through the method, a user finally selects the scheme.

The invention also provides a target performance-oriented multi-layer point type residence parameterization generating system, as shown in fig. 1, comprising: the system comprises a parameterization generating module, a performance simulation calculating module and an intelligent optimizing module.

The workflow of the performance calculation module is shown in fig. 16 through the automatic conversion processing of the performance model on the parameterized scheme three-dimensional model and other input parameters. The performance calculation module includes: the performance model is constructed, the performance model database and the performance model are calculated by 3 submodules, and the performance model calculation submodule and other submodule data are derived from relevant industry research and specification.

The performance model construction submodule comprises two functions of building partition terms and model conversion terms, and an arithmetic unit of the performance model construction submodule can automatically perform building partition and parameterized performance model automatic generation according to actual conditions of building performance calculation as shown in fig. 17 (a). In the building performance calculation, if the heat convection phenomenon of the interconnected rooms is neglected, the calculation result is greatly influenced, so that the interconnected rooms are combined with hot areas and reasonably partitioned. The building partitions are divided according to whether the functional spaces are communicated or not, if so, the functional spaces are combined into one building partition, if not, the building partitions are used as independent building partitions, and after the building partitions are completed, the three-dimensional model generated above is converted into a parameterized performance model which can be identified by performance simulation software. As in fig. 17 (b), the functional space halls, living rooms, restaurants, and walkways are interconnected, and the program can automatically merge them into one building partition, and the other functional spaces serve as separate building partitions, and after the building partitions are completed, the three-dimensional building model is converted into a parameterized performance model that can be identified by performance simulation software.

The intelligent optimization module is mainly divided into an intelligent optimization submodule and a data recording submodule. The function realized by the optimization module is to optimize the target performance of the generated parameterized scheme, and record scheme solution parameter information generated in the optimizing process according to the requirement, and the main working flow of the module is shown in figure 18. Firstly, determining input parameters, setting solution set recording conditions through parameters of a data recording module, and setting optimization conditions through intelligent optimization parameters; secondly, calling a parameterized generation and performance calculation module to calculate the performance of the solution; thirdly, inputting the solution set of the scheme performance indexes into a data recording submodule for recording, and judging and optimizing the scheme performance through an intelligent optimization submodule; fourth, repeating the above-mentioned processes until the optimization termination condition is satisfied; and finally, outputting an optimization scheme solution set and corresponding information thereof for optimizing effect evaluation.

While embodiments of the present invention have been shown and described, it will be understood by those of ordinary skill in the art that: many changes, modifications, substitutions and variations may be made to the embodiments without departing from the spirit and principles of the invention, the scope of which is defined by the claims and their equivalents.

Claims

1. The target performance-oriented multi-layer point type residence parameterization generating method is characterized by comprising the following steps of:

s1, generating parameters of the multi-layer point type residence:

s1-1-1, determining design parameters according to design rules;

the basic information is used for quantitatively parameterizing and expressing the early basic information and the design requirement of the scheme, and comprises the following steps: building locations, total building area, floor number of houses, number of stairs, number of entrances and exits and number of floors;

the layout information basically describes the layout information of each functional space forming the floor plan, and the layout information parameters comprise a sleeve type structure, a space function, a space relation and a space orientation;

the facade information parameters include: layer height, sill height, window wall area ratio and window position;

the window wall area ratio comprises a maximum window wall area ratio K _cqmax Minimum window to floor area ratio K _cdmin The value is taken out,

for summer hot and winter cold areas, when the space orientation is in the south direction, the maximum window wall area ratio K _cqmax 0.45; when the space orientation is north, the maximum window wall area ratio K _cqmax 0.4; when the space azimuth is east, the maximum window wall area ratio K _cqmax 0.35; when the space orientation is western, the maximum window wall area ratio K _cqmax 0.35;

for summer hot and winter warm areas, when the space orientation is in the south direction, the maximum window wall area ratio K _cqmax 0.4; when the space orientation is north, the maximum window wall area ratio K _cqmax 0.4; when the space azimuth is east, the maximum window wall area ratio K _cqmax 0.3; when the space orientation is western, the maximum window wall area ratio K _cqmax 0.3;

s1-1-2, building a building matrix table according to design parameters and design requirements; the design requirements include a rigidity requirement and an elasticity requirement; taking the rigidity requirement as a constraint condition, taking the elasticity requirement as an optimization target, wherein the rigidity requirement is a requirement which needs to be met in design; the elastic requirement is a non-mandatory requirement but is also a requirement related to the scheme quality, including sunlight, natural lighting, natural ventilation, carbon emission and sound insulation;

S1-3, generating a floor plan based on a rectangular splicing method according to plane information parameters in a building matrix table on the basis of a topological model, and generating a three-dimensional model according to elevation information parameters;

K _ck and A _d Functional space aspect ratio and functional space area, respectively; l (L) _q 、W _q The functional space is long and wide; k (K) _ckmax 、K _ckmin Representative ofMaximum, minimum value of functional space aspect ratio, and K _ckmax Not more than 2.00, K _ckmin Not less than 0.50; l (L) _qmax 、L _qmin Representing the maximum and minimum values of the functional space surface width and depth, respectively, wherein D _qmin ＜W _q ＜D _qmax 、D _qmin ＜L _q ＜D _qmax ；

When the functional space is a hall, K _ckmax 、K _ckmin 0.5 and 2, respectively;

when the functional space is a living room, K _ckmax 、K _ckmin 0.56 and 1.77, respectively;

when the functional space is mainly lying, K _ckmax 、K _ckmin 0.63 and 1.58, respectively;

when the functional space is recumbent, K _ckmax 、K _ckmin 0.7 and 1.43, respectively;

when the functional space is a study room or a guest lying, K _ckmax 、K _ckmin 0.45 and 2, respectively;

when the functional space is a kitchen, K _ckmax 、K _ckmin 0.5 and 2, respectively;

when the functional space is a restaurant, K _ckmax 、K _ckmin 0.62 and 1.62, respectively;

When the functional space is a toilet, K _ckmax 、K _ckmin 0.75 and 1.33, respectively;

the splicing according to the shared edges of the functional spaces comprises the following steps: firstly, obtaining connection forms among the functional spaces through the shared edges, wherein the connection forms comprise linear connection, triangular connection, internal connection and annular connection; then, main functional space splicing is performed: judging whether an annular connection function space with a unique solution exists or not; if the functional space connected in the annular mode is spliced, firstly splicing the walkway and the hall if the functional space connected in the annular mode exists, otherwise, splicing the walkway and the hall; then splicing adjacent functional spaces step by step in a circulating way according to the space relation; then, after all the functional spaces are spliced, cutting off redundant walkways to obtain a sleeve-type plan after one-time splicing; finally, performing two-stage functional space splicing: after the primary splicing of the sleeve type plane is completed, splicing the secondary functional space to the auxiliary functional space according to the sleeve type matrix table, and finally completing the generation of the sleeve type plane diagram;

S1-3-3, generating a three-dimensional model: the three-dimensional model generation comprises the following steps: firstly, extracting the layer height in the elevation information parameters according to a formulated building information matrix table, endowing a building sleeve type plan, and converting the building sleeve type plan into a three-dimensional model; determining a reasonable window wall area ratio range of each functional space as a control parameter of window rationality, calculating each window area, and calculating the window width according to each window area and the window height to finish window generation; then finishing the generation of the vertical face windowing and sleeve type parameterized three-dimensional model according to the window position; then window positioning and facade windowing are completed on the wall surface to be windowed according to the window position coordinates; finally, generating a parameterized three-dimensional model according to the layer number information in the basic information parameters and the design rule;

s2, selecting target performance, determining performance indexes, and carrying out simulation calculation on the target performance by combining the multi-layer point type residence parameters; the target performance comprises carbon emission, energy consumption and lighting, and the performance indexes comprise carbon emission intensity, energy consumption intensity and illumination intensity;

s3, optimizing calculation is carried out on the result of the target performance simulation calculation, optimizing convergence judgment is carried out, and if convergence requirements are met, an optimal solution set is output; if the convergence requirement is not met, carrying out parameter adjustment, and returning to the step S1;

The optimizing calculation of the result of the target performance simulation calculation comprises the following steps:

where x= (x) ₁ ,...,x _h ) E, X is a design parameter variable of h dimension, X is a design of h dimensionA set of parameter variables; y= (y) ₁ ,...,y _i ) E, Y is a design variable in the i dimension, Y is a set of design variables in the i dimension; z= (z) ₁ ,...,z _j ) E Z is an evaluation target variable in j dimensions, Z is a set of optimization target variables in j dimensions; f (X) is a scheme generating objective function, and comprises k mapping functions from X to Y; g (Y) is a scheme optimization objective function, and comprises l mapping functions from Y to Z; when the performance index of the scheme is closer to the design target requirement, the function value of G (y) is closer to 0; r is (r) _m (x) Not more than 0, j=1, 2,..p defines p inequality constraints, s _n (x) =0, (k=1, 2,., q) defines q equality constraints; for a certain X ε X _o X, if (3) and (4) of (4-1) are satisfied simultaneously, X is a feasible solution, X _o Is a feasible solution set; the target performance optimization model l in the formula (4-1) takes the value of 1 as single-target optimization, and takes the value of more than 1 as multi-target optimization.

2. The method for generating target performance oriented multi-story point house parameterization of claim 1, wherein S1-2 comprises the steps of:

S1-2-1, a layout information parameter rectangular table in a building information matrix table is called to obtain constraints of space orientations of various sets of functions, so that the solution space of a topological relation diagram is reduced; s1-2-2, carrying out one-dimensional grid division in different space orientations according to each set of patterns, wherein the grid number of the one-dimensional grid division is the number of functional spaces in the same space orientation;

3. The method for generating target performance oriented multi-point residential parameterization of claim 1, wherein the optimization calculations comprise the steps of:

And finally, selecting and determining an optimal solution.

4. The method for generating target performance oriented multi-story point residential parameterization of claim 1, further comprising performing optimization verification of the results of said optimization calculations, said optimization verification employing a proxy average performancePerformance average optimization rate->Optimizing the variation amplitude->And number of optimization schemes N _opt One or any combination of the two is used as an evaluation index;

N _opt (G)＝N _opto -N _optr

wherein G represents the number of iterations;

N _opto pareto optimization solution set number representing repeated solutions in non-culled generations；

X ₀ performance representing the initial solution;

Representing adjacent generations +.>Is a variable amplitude of (a).