Three-dimensional building function layout automatic generation and energy-saving optimization method

Abstract

The invention provides a three-dimensional building function layout automatic generation and energy-saving optimization method, and belongs to the field of building model automatic generation and optimization in computational design. Comprising the following steps: acquiring basic modeling data of a target building, inputting the basic modeling data into model generation software, determining fixed parameters of a layout scheme and generating rule parameters, and performing three-dimensional space layout on a grid model by using a three-dimensional space layout algorithm to obtain a three-dimensional space layout model; calculating the annual load per unit building area and the building function layout applicability score of the basic building model, setting the operation parameters of a multi-objective evolutionary algorithm, and carrying out the optimization, clustering analysis and visualization processing of the basic building model through the multi-objective evolutionary algorithm. According to the automatic three-dimensional modeling method for the energy-saving building, the modeling and optimizing efficiency is improved by using the three-dimensional space layout algorithm and the multi-objective evolutionary algorithm at the same time, and the obtained model is better in applicability.

Description

Technical Field

[0001] The present invention relates to the field of automatic generation and optimization of building models in computational design, and in particular to a method for automatic generation and energy-saving optimization of three-dimensional building functional layout. Background Art

[0002] Studies have shown that optimizing functional layout can effectively reduce the heating, cooling and lighting needs of buildings. Therefore, energy-saving oriented building functional layout design is crucial in early design. As a part of early design, how to carry out energy-saving oriented building functional layout design is a problem worth studying. The process of automatic generation of energy-saving oriented building space layout can be summarized into five parts: input of scheme information, automatic generation, performance evaluation, scheme optimization and automatic process setting.

[0003] There are relatively few studies on the combination of automatic spatial layout planning and building energy conservation, most of which are only applicable to residential buildings and have different limitations. The main problem involved is the contradiction between the tool and the applicable object, that is, most studies are based on single-layer planes, but the two-dimensional spatial layout generation method has limitations, and the three-dimensional spatial layout generation method also means more complex and difficult building layout generation program design. In addition, traditional methods mainly rely on subjective experience and lack an objective scoring system, which makes the established model less applicable. Summary of the invention

[0004] In order to overcome the shortcomings of the prior art, the purpose of the present invention is to provide a method for automatic generation and energy-saving optimization of three-dimensional building functional layout, which uses a three-dimensional space layout algorithm and a multi-objective evolutionary algorithm to increase the efficiency of modeling and optimization and improve the practicality of the model.

[0005] To achieve the above object, the present invention provides the following solutions:

[0006] A method for automatic generation and energy-saving optimization of three-dimensional building functional layout, comprising:

[0007] Obtain basic modeling data of the target building;

[0008] Inputting the basic modeling data into model generation software to obtain a grid model;

[0009] Determine fixed parameters of the layout scheme and generate rule parameters;

[0010] Based on the fixed parameters and the rule parameters, the grid model is laid out in three dimensions using a three-dimensional space layout algorithm to obtain a basic building model;

[0011] Calculating the annual load per unit building area and the applicability score of the building function layout of the basic building model to obtain load and score data;

[0012] According to the load and scoring data, setting the operating parameters of the multi-objective evolutionary algorithm;

[0013] The optimization target of the basic building model is optimized by using a multi-objective evolutionary algorithm to obtain the building model of each iteration and the annual load data per unit building area of ​​the building model of each iteration and the building function layout suitability score result;

[0014] The k-means algorithm is used to perform cluster analysis on the building function layout suitability scoring results to obtain analysis results, and grasshopper and python are used to visualize the analysis results to obtain target visualization optimization results.

[0015] Preferably, the basic modeling data includes the building form model, grid auxiliary surface, column grid arrangement and number of floors of the target building.

[0016] Preferably, the fixed parameters include: the name of each functional area, the area of ​​each functional area, the area tolerance of each functional area, the common attributes of each functional area, the specified floors of each functional area, the number of vertical traffic cores and the proportion of vertical traffic area.

[0017] Preferably, the rule parameters include: the starting unit of each functional area, the growth step in each direction of each functional area, the growth order in each direction of each functional area, the starting unit of the first floor plane of the vertical traffic core, the growth step in each direction of the first floor plane of the vertical traffic core, and the growth order in the direction of the first floor plane of the vertical traffic core.

[0018] Preferably, the step of performing three-dimensional spatial layout on the grid model using a three-dimensional spatial layout algorithm comprises:

[0019] After the percentage of the vertical traffic area to the total area and the number of vertical traffic cores are input into the grid model, vertical body growth is performed on the vertical traffic plane of the grid model;

[0020] After the vertical body growth is completed, the starting unit of each functional space of the grid model and the attribute parameters of the starting unit are determined according to the rule parameters, and functional area growth is performed to obtain a basic building model.

[0021] Preferably, the annual load per unit building area is the ratio of the total amount of energy consumed by the building in one year to the total building area of ​​the building; the total amount of energy consumed includes: building heating energy consumption, cooling energy consumption, lighting energy consumption and equipment energy consumption.

[0022] Preferably, the building function layout suitability score includes a floor score and a privacy score.

[0023] Preferably, the optimization objectives include: the annual load per unit building area, the floor score and the privacy score.

[0024] The present invention discloses the following technical effects:

[0025] The present invention provides a method for automatic generation and energy-saving optimization of three-dimensional building functional layout. Through a three-dimensional space layout algorithm and a multi-objective evolutionary algorithm, the defect of conventional building layout methods being limited to two-dimensional space is solved, and the growth and optimization of the three-dimensional structure of the building model is realized; by constructing a scoring system, the defect of conventional layouts relying on manual evaluation is solved, and objective analysis of the model structure is realized. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0027] Figure 1 A flow chart of the automatic generation and optimization method provided by an embodiment of the present invention;

[0028] Figure 2 A schematic diagram of an ideal grid model and its adjacent spatial units provided by an embodiment of the present invention;

[0029] Figure 3 A schematic diagram of the projection of an ideal grid model provided by an embodiment of the present invention in a three-dimensional building model;

[0030] Figure 4 A generation logic schematic diagram provided by an embodiment of the present invention;

[0031] Figure 5 A schematic diagram of some results of automatic arrangement of random generation parameters of functional space layout provided by an embodiment of the present invention;

[0032] Figure 6 An axonometric view and a ground floor plan of a building block of an urban complex provided in an embodiment of the present invention;

[0033] Figure 7 A statistical diagram of optimization results provided by an embodiment of the present invention;

[0034] Figure 8 A schematic diagram of changes in the annual load per unit area of ​​a building during 80 evolutionary iterative calculations provided in an embodiment of the present invention;

[0035] Fig. 9 A schematic diagram of changes in privacy scores during 80 evolutionary iterative calculations provided in an embodiment of the present invention;

[0036] Fig.10 A schematic diagram of the change of floor scores in 80 evolutionary iterative calculations provided in an embodiment of the present invention;

[0037] Fig.11 A schematic diagram of cluster analysis results of non-dominated solutions obtained after optimization provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0038] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0039] The purpose of the present invention is to provide a method for automatic generation and energy-saving optimization of three-dimensional building functional layout, which uses a three-dimensional space layout algorithm and a multi-objective evolutionary algorithm to increase the efficiency of modeling and optimization and improve the practicality of the model.

[0040] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0041] Figure 1 The automatic generation and optimization method flow chart provided by the embodiment of the present invention is as follows: Figure 1 As shown, the present invention provides a method for automatic generation and energy-saving optimization of three-dimensional building functional layout, comprising:

[0042] Step 100: Obtain basic modeling data of the target building;

[0043] Step 200: inputting the basic modeling data into the model generation software to obtain a grid model;

[0044] Step 300: Determine fixed parameters of the layout scheme and generate rule parameters;

[0045] Step 400: Based on the fixed parameters and the rule parameters, a three-dimensional space layout algorithm is used to perform a three-dimensional space layout on the grid model to obtain a basic building model;

[0046] Step 500: Calculate the annual load per unit building area and the applicability score of the building function layout of the basic building model to obtain load and score data;

[0047] Step 600: setting the operating parameters of the multi-objective evolutionary algorithm according to the load and scoring data;

[0048] Step 700: Optimizing the optimization target of the basic building model using a multi-objective evolutionary algorithm to obtain the building model of each iteration and the annual load data per unit building area of ​​the building model of each iteration and the building function layout applicability score result;

[0049] Step 800: Perform cluster analysis on the building function layout suitability scoring results using the k-means algorithm to obtain analysis results, and visualize the analysis results using grasshopper and python to obtain target visualization optimization results.

[0050] Specifically, the basic modeling data includes the building form model, grid auxiliary surface, column grid arrangement and number of floors of the target building.

[0051] Optionally, the fixed parameters include: the name of each functional area, the area of ​​each functional area, the area tolerance of each functional area, the common attributes of each functional area, the specified floors of each functional area, the number of vertical traffic cores and the proportion of vertical traffic area.

[0052] Preferably, the rule parameters include: the starting unit of each functional area, the growth step in each direction of each functional area, the growth order in each direction of each functional area, the starting unit of the first floor plane of the vertical traffic core, the growth step in each direction of the first floor plane of the vertical traffic core, and the growth order in the direction of the first floor plane of the vertical traffic core.

[0053] Specifically, the step of performing three-dimensional spatial layout on the grid model using a three-dimensional spatial layout algorithm includes:

[0054] After the percentage of the vertical traffic area to the total area and the number of vertical traffic cores are input into the grid model, vertical body growth is performed on the vertical traffic plane of the grid model;

[0055] After the vertical body growth is completed, the starting unit of each functional space of the grid model and the attribute parameters of the starting unit are determined according to the rule parameters, and functional area growth is performed to obtain a basic building model.

[0056] Optionally, the annual load per unit building area is the ratio of the total amount of energy consumed by the building in one year to the total building area of ​​the building; the total amount of energy consumed includes: building heating energy consumption, cooling energy consumption, lighting energy consumption and equipment energy consumption.

[0057] Preferably, the building function layout suitability score includes a floor score and a privacy score.

[0058] Specifically, the optimization objectives include: the annual load per unit building area, the floor score, and the privacy score.

[0059] Furthermore, this embodiment develops a three-dimensional space allocation algorithm tool suitable for building functional layout based on the reverse workflow in automated building design. Using a multi-objective evolutionary algorithm, with the best building functional applicability and the lowest building energy consumption as the optimization goals, an automated process framework for automatic generation of three-dimensional space layout and energy consumption simulation evaluation is established, and the feasibility is verified using a high-rise comprehensive office building as an example.

[0060] Furthermore, this embodiment constructs a set of energy-saving oriented three-dimensional building layout optimization framework based on the reverse workflow, aiming to screen out energy-saving and overall functional layout solutions that meet the use requirements in the huge search space. The framework mainly consists of two parts. The first part is an automatic generation algorithm of building functional space based on discrete grid space layout mode. This part realizes the automatic layout of building functional space and the creation of building model through rhino-grasshopper and python self-programming program linkage. The second part is the scheme optimization combined with the building layout applicability scoring system and the annual load simulation calculation per unit area of ​​the building. This part uses the honeybee energy consumption simulation tool of the grasshopper platform to establish an energy consumption model and simulate the annual load of the building unit, and uses the Octopus tool in the grasshopper platform for multi-objective optimization. Finally, combining grasshopper and python, the k-means algorithm is used to cluster and visualize the optimization results. By integrating computational design and energy simulation, the framework provides architects with a user-friendly and scientifically guided energy-saving building functional layout design method.

[0061] Specifically, the spatial layout of this embodiment adopts the representation method of the grid system. In the early design of the building, the column grid arrangement plays a key role in the spatial planning and structural layout of the building. The spatial functional layout is usually interrelated with the position of the column grid and can be regarded as a three-dimensional grid system. However, many spatial layouts represented by the grid system must comply with the rules of the orthogonal grid, which will cause the generated scheme to be too low in versatility and only applicable to some ideal simple geometric architectural schemes. In this embodiment, by establishing an ideal three-dimensional grid model and then projecting it onto an irregular three-dimensional building model, the method can be applied to irregular building forms within a certain complexity, thereby improving its versatility and applicability.

[0062] refer to Figure 2The ideal grid model is composed of several spatial units. The information of six adjacent spatial units in the east, west, south, north, up and down directions can be obtained through discrete coordinate search between these spatial units. If the states of adjacent spatial units are consistent, the two spatial units are connected and together with other connected units, they form a connected domain. When the states of adjacent spatial units located in the same horizontal layer are the same, they are regarded as the same connected domain, that is, a functional partition. In addition, through this model, the distribution and specific attributes of the area can be defined to help meet the needs of architectural planning. This model is designed to represent the distribution and attributes of different areas in an ideal grid form in architectural design and spatial planning, and to describe the architectural space layout for automatic generation and optimization.

[0063] refer to Figure 3 For the actual building model, the actual operation steps in Grasshopper are as follows: input the outline of the building, then input the floor height, grid auxiliary surface and column grid axis. By inputting these parameters, the building shape can be divided into a three-dimensional irregular grid model by the program, and the area of ​​each space unit is calculated. The area data of each space unit will be transmitted to the core layout generation module, and the area attribute of the ideal model space unit will be updated. This method takes each space across the column grid as the smallest unit, corresponding to the space unit in the ideal grid model, and achieves the goal of identifying the building form model of irregular blocks and non-orthogonal grids.

[0064] refer to Figure 4 ,The 3D space layout generation module needs to input two sets of parameters. One set is the fixed parameter info_data (see Table 1) containing the specific scheme information of each partition required by the user, which is used to define the requirements and end conditions of scheme generation. The other set is the rule parameter gene_data (see Table 2) about the generation process, which is a variable parameter used to generate different scheme results.

[0065] Table 1

[0066]

[0067]

[0068] Table 2

[0069]

[0070]

[0071] refer to Figure 4The design of the three-dimensional spatial layout algorithm takes into account the area requirements of each functional space and the problem of regional connectivity. In this embodiment, the so-called "growth" refers to changing the vacant adjacent space unit to the same state as the space unit to increase the area of ​​a certain entity area. The three-dimensional spatial layout algorithm can be divided into three steps in total, and its pseudo code is shown in Algorithm 1 and Algorithm 2. The first step is to generate vertical traffic cores: This is an optional step. Enter the percentage of vertical traffic area to total area and the number of vertical traffic cores. This step will generate a specified number of vertical forms that can lead to all floors in the building form. The growth area of ​​the vertical traffic plane is limited to the space unit where the minimum projection plane is located, so that the vertical traffic area will not be interrupted in the vertical direction. The second step is to generate each functional space area: first, according to the generation rule parameters of each functional space, select a starting unit for each functional space and set its attributes to the corresponding state. Then, each area grows in turn according to the growth direction specified in the generation rule parameters. The number of units grown each time is determined by the step size in the generation rule parameters. The growth process continues until the area requirement is met or there are no more vacant adjacent space units available for growth. When there are no more free space units nearby for growth, but the area that has grown does not meet the demand, the space unit with the smallest linear number among the available space units will be selected as the new starting unit, and growth will continue until the demand is met or it can no longer grow. The third step is to fill the remaining vacant units: when all areas have completed growth in the second step and meet their minimum area requirements, if there are still unassigned space units, each area will continue to grow according to the growth rules until all remaining vacant units are filled.

[0072] Algorithm 1, three-dimensional space layout generation algorithm:

[0073] Input: program, gene_data, info_data

[0074] Output: result_program

[0075] Plane generation for vertical transportation

[0076] vt_plan=creat_transport_plan(program,gene_data,info_data) performs vertical growth of vertical transportation

[0077] program=creat_vertical_mass(program,vt_plan)

[0078] Synchronous growth of multi-connected domains

[0079] result_program=grow_program(program,gene_data,info_data)If there are any space units left, continue to fill and grow until all are filled iflen(program.get_attri_unit_seq(0))>0doresult_program=fill_program(result_program,gene_data,info_data)

[0080] end

[0081] Algorithm 2, grow_program (growth program):

[0082] Input: program, gene_data, info_data

[0083] Output: program

[0084] Set the starting point according to the genome

[0085] program=set_start_point(program,gene_data.start_unit)

[0086] Grow according to the genome until all regions are grown

[0087] while bool(finish_check)=True do

[0088] stop_check=[True]*info.zone_count

[0089] Grows sequentially according to the orientation in the genome

[0090] for dir in gene_data.direction do

[0091] for zone in info_data.zone do

[0092] Grow according to the direction of the genome

[0093] for step in gene_data.step_len[zone][dir]do

[0094] Traverse the spatial units of the area

[0095] forunit inprogram[zone]do

[0096] If the spatial unit in the growth direction is vacant, then grow as part of that region grow_unit = get_neighbor_unit(program, unit, dir)

[0097] ifgrow_unit.attri == 0 do

[0098] program[grow_unit].attri=zone

[0099] stop_check[zone]=False

[0100] Check whether the area meets the required area. If it does, the area is grown.

[0101] if

[0102] cal_area(zone)>=info_data.area_demand[zone]*info_data.area_tolerancedo

[0103] finish_check[zone] = True

[0104] If an area cannot continue to grow and does not meet the area requirements, select a vacant unit as a new growth point

[0105] for zone in info_data.zone do

[0106] iffinish_check[zone]==False and stop_check[zone]==Ture do

[0107] new_start_unit=get_vacant_unit(program)

[0108] program[new_start_unit].attri=zone

[0109] end

[0110] Furthermore, the specific implementation process is written in Python. The implementation of this layout generation method relies on the state changes of adjacent spatial units under the guidance of different generation rule parameters. This method can quickly and effectively generate a three-dimensional spatial layout with specified area and layout characteristics through reasonable starting unit allocation and continuous growth and filling processes. Figure 5Two sets of partial solution results generated by random generation rule parameters are shown, and an average of 50 sets of solutions can be generated per second. The robustness of this method is proved by testing cases with different plane shapes and height features.

[0111] Optionally, in the selection of tool platforms, this embodiment uses the plug-in Octopus on the grasshopper platform. It is a multi-objective optimization plug-in based on SPEA-2 and HypE algorithms, which can optimize multiple objectives at the same time and generate a series of trade-off solutions between each objective. It is widely used in the fields of architectural parametric design and building energy-saving design.

[0112] Specifically, the decision variables are the spatial layout modes under the same environment, function and specified area. There are three groups of genetic parameters used for the evolutionary algorithm, which are the linear index of the starting spatial unit of each region, the order of growth direction and the step length of each growth. Each group of parameters corresponds to a spatial layout scheme. The spatial layout scheme image and the generation rule parameters correspond to the expressivity and genotype in the genetic algorithm. The advantage of using the generation rule parameters rather than the spatial layout image itself as the genotype is that it can solve the continuity problem between spatial layout units while optimizing with a smaller number of genetic parameters. On the other hand, since the original parameters are discontinuous and the value ranges are not the same, in this link, all genetic variables are linearly mapped and evenly mapped to the interval [0, 1], so that the value range of each gene position is consistent, avoiding the problem of mismatched value ranges.

[0113] This example adopts the annual load per unit area of ​​the building E annualLoad Used to evaluate the energy efficiency of building solutions, the unit is kW·h / (m 2 The annual load per unit area of ​​a building refers to the ratio of the total energy consumed by a building in one year to the total floor area of ​​the building, including the building's heating energy consumption, cooling energy consumption, lighting energy consumption and equipment energy consumption. The formula is as follows:

[0114] E annualLoad =Q heating +Q cooling +Q lighting +Q equipment

[0115] Optionally, in order to simulate the energy consumption of a building, this embodiment uses the Honeybee plug-in to simulate the annual load per unit building area. Honeybee is an energy consumption simulation plug-in developed on the Grasshopper platform, and its energy consumption calculation core is EnergyPlus. The plug-in provides a series of components and tools for simulating the energy performance of a building and can perform visual analysis. Through the Honeybee plug-in, we can simulate the energy consumption of a building under different conditions and evaluate the energy consumption performance of different design schemes.

[0116] Furthermore, the floor score is intended to evaluate the rationality of the floor arrangement of the functional layout scheme of multi-story buildings. In multi-story or high-rise buildings, different floor heights have different properties. For example, public functions such as commercial catering and reception halls are often arranged on the lower floors to facilitate more people to reach and use. Functions such as offices and residences arranged on high floors can obtain better views and privacy. Users can set the specified functions to be arranged on high floors or low floors according to their needs. The three-dimensional functional layout generation module will calculate the floor score according to the needs for evaluation and optimization. The closer the score is to 1, the less it meets the set floor requirements, and the closer it is to 0, the more reasonable the floor arrangement. Finally, the weighted average score calculation method is used to assign greater weights to values ​​with larger scores, thereby reducing overly unreasonable arrangement results. The floor score calculation formula is as follows:

[0117]

[0118] The weight distribution formula is as follows:

[0119]

[0120] Among them, S f : Floor score of the functional plane area; F: Total number of floors above ground of the building; F z : The floor number where the functional area is located; D f :The required height setting of the functional area. If it needs to be arranged on a high floor, it is h; if it needs to be arranged on a low floor, it is l.

[0121] Furthermore, the privacy score refers to the concept of connectivity in space syntax, which reflects the rationality of the arrangement of functional areas adjacent to other areas in the overall building, thereby evaluating the degree of privacy of the functional area. Connectivity refers to the sum of the number of nodes connected to a node and other nodes, which is used to describe and analyze the connection, relationship and accessibility between different spatial elements. In this scoring, each functional plane area is regarded as an independent spatial element, and its connectivity is the number of functional types adjacent to it. The more adjacent functional types, the higher the connectivity. When a function is set as a private area, the three-dimensional function generation module will calculate its privacy score according to its corresponding matching function. For each functional plane area with privacy requirements, this embodiment will count the number of adjacent functions that do not match, so as to calculate its privacy score. The closer it is to 0, the better the privacy of the functional area. Similarly, the overall privacy score of the final solution is processed by weighted average score calculation to reduce overly unreasonable solutions in the optimization. The privacy score calculation formula is as follows:

[0122]

[0123] Among them, S p : Privacy score of the functional plane area; Fu i : Function of adjacent plane area; Fu m : Functions that match this function; C a : The number of other plane areas adjacent to the functional plane area; C f : The number of all functional categories; C m : The number of function types that are compatible with this function.

[0124] Specifically, after the multi-objective optimization is completed, a series of Pareto front solutions can be obtained, and all solutions together constitute the Pareto front surface. The Pareto front solution represents the best balance point in the multi-objective optimization problem, and it is no longer possible to make it better by improving a certain goal alone. This embodiment uses the k-means clustering algorithm to perform cluster analysis on the generated Pareto front solution. The basis for clustering is the similarity of the optimization objectives of these solutions. This functional algorithm is written in python. K-means is an unsupervised learning technology, and its core goal is to assign data points to a pre-set number of clusters. This process aims to maximize the similarity of data points within the cluster and minimize the similarity of data points between different clusters, so as to achieve effective grouping of data. The number of clusters is determined by the user. By performing k-means clustering analysis on the generated Pareto front solution, the decision-making process can be significantly simplified, allowing users to quickly find representative optimization solutions. After the clustering process is completed, the solution with the lowest annual load per unit area of ​​the building in each cluster is visualized in detail in the visualization module of Grasshopper. The visual display includes floor plans of each plan, building energy consumption related data, building zoning model, and building energy consumption model schematic diagram, etc. These intuitive images and data provide architects with direct and easy-to-understand design references.

[0125] Specifically, this embodiment takes a comprehensive high-rise office building as an example to conduct a three-dimensional layout generation test, evaluate the energy-saving and practical effects of the generated building layout plan, and verify the effectiveness of the framework of this embodiment. The case building has 19 floors and a total construction area of ​​19,400 square meters. The building shape and functional areas to be arranged are as follows: Figure 5 As shown. The building has a total of seven functional areas, including unit offices, open offices, electronic R&D rooms, conference rooms, commercial facilities, canteens and employee dormitories. The basic parameters and basic information of each functional area are shown in Table 3. The time and method of personnel activities in each functional area are different, and their lighting usage habits and heating and cooling settings will also be different. These differences combined with the arrangement of different functions will affect the energy performance of the overall building. This embodiment adopts traditional lighting design, and the lighting load is only related to the building area.

[0126] Table 3

[0127]

[0128]

[0129] Optionally, this embodiment uses the classic meteorological annual data of a certain city for simulation and analysis. The building envelope materials and related parameters are shown in Table 4. The window-to-wall ratio of the front and rear facades of the building is set to 30%, and the window-to-wall ratio of the side facade is 50%. Through the linkage of the three-dimensional function arrangement module and the honeybee module, the program automatically establishes the building envelope according to the preset window-to-wall ratio parameters, thereby realizing the automatic construction of the building energy consumption model. All energy consumption simulation parameter information is input into the corresponding module of honeybee-energy of Rhino-grasshopper, and combined with the energy consumption simulation model to prepare for the simulation and evaluation of building energy consumption.

[0130] Table 4

[0131]

[0132] Specifically, the business and canteen functions are set to be arranged close to the lower floors for better traffic accessibility, and the dormitories are set to be arranged close to the higher floors to reduce interference from other functions. Business and canteens are set as public areas, which means that the relevant scores of these two areas will not be calculated in the privacy score. Dormitories are set as independent private areas and have no associated functions, that is, dormitory functions are expected to be arranged in completely independent areas. Unit offices, open offices, R&D rooms, and conference rooms are set as interrelated functional groups. When they are arranged adjacent to each other and have no other adjacent functions, the corresponding scheme will obtain a higher privacy score. The corresponding module will calculate and analyze the floor scores and privacy scores of different functional layout schemes based on these settings to evaluate the rationality of the scheme.

[0133] Furthermore, the population size set in the evolutionary algorithm is 30. The elite strategy helps to retain the best solution by retaining individuals with high fitness and passing them directly to the next generation, thereby improving the performance of the algorithm. Taking into account that too many elite individuals will lead to too little diversity and fall into the local optimal solution, the proportion of elite individuals is set to 10% in this embodiment. The mutation operation is to increase the diversity of the search space and avoid falling into the local optimal solution. The mutation rate and mutation probability are selected to be 30%, which means that a small number of individuals in each generation will perform Gaussian mutation operations to increase diversity. This embodiment uses the maximum number of iterations of 30 as the termination condition for optimization to avoid unnecessary calculations. After 80 iterative calculations, this embodiment generated a total of 7368 historical solutions and 28 non-dominated solutions. All historical solutions from generation 1 to generation 80 and the Pareto front surface are shown as follows. Figure 6 Table 5 shows the target parameter range and maximum reduction of all historical solutions. The annual load per unit area of ​​all historical solutions ranges from 253.3kW·h / (㎡) to 268.08kW·h / (m 2 ), the difference is 14.78kW·h / (m2 ), with the largest decrease being 5.51%, of which the reduction in cooling load played a major role.

[0134] Table 5

[0135]

[0136]

[0137] Furthermore, although the optimization target parameters change differently during the iteration process, the average value of the shortest distance of all solutions decreases continuously during the iteration calculation until it reaches a stable value, such as Figure 8 , 9, 10. This embodiment sets three clusters, and divides the 28 Pareto front solutions of the experimental results into three categories. The solution with the lowest annual load per unit area of ​​the building in each cluster is selected for visualization (refer to Fig.11 and Table 6). The three schemes all performed well in terms of the rationality of functional arrangement. The canteen and supporting commercial facilities are arranged on the lower floors, while the dormitory area is concentrated on the top floor, and their floor scores are all close to 0. The matching of adjacent functions is also considered in the optimization process, and the privacy scores of the three schemes also show good scores among all historical solutions. Among them, Scheme A is the scheme with the lowest annual load per unit area of ​​all historical solutions. Similar to Schemes B and C, the commercial function is mainly arranged on the northwest side, while the canteen function is arranged on the southeast side. The open office and R&D room are arranged adjacent to each other, and the R&D room tends to be arranged on the south side. Scheme clustering and visualization analysis not only provides an intuitive way to display the optimization results, but also enables architects to obtain more convenient thinking and decision-making methods for the optimization of functional layout energy performance during the design process.

[0138] Table 6

[0139]

[0140]

[0141] The beneficial effects of the present invention are as follows:

[0142] The present invention provides an automatic three-dimensional modeling method for energy-saving buildings, which simultaneously utilizes a three-dimensional space layout algorithm and a multi-objective evolutionary algorithm to increase the efficiency of modeling and optimization, and the obtained model has better applicability.

[0143] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0144] The principles and implementation methods of the present invention are described in this article using specific examples. The description of the above embodiments is only used to help understand the method and core idea of ​​the present invention. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. A method for automatic generation and energy-saving optimization of three-dimensional building functional layout, characterized in that: include: Obtain basic modeling data of the target building; Inputting the basic modeling data into model generation software to obtain a grid model; Determine fixed parameters of the layout scheme and generate rule parameters; Based on the fixed parameters and the rule parameters, the grid model is laid out in three dimensions using a three-dimensional space layout algorithm to obtain a basic building model; Calculating the annual load per unit building area and the applicability score of the building function layout of the basic building model to obtain load and score data; According to the load and scoring data, setting the operating parameters of the multi-objective evolutionary algorithm; The optimization target of the basic building model is optimized by using a multi-objective evolutionary algorithm to obtain the building model of each iteration and the annual load data per unit building area of ​​the building model of each iteration and the building function layout suitability score result; The k-means algorithm is used to perform cluster analysis on the building function layout suitability scoring results to obtain analysis results, and grasshopper and python are used to visualize the analysis results to obtain target visualization optimization results.

2. A method for automatic generation and energy-saving optimization of three-dimensional building functional layout according to claim 1, characterized in that: The basic modeling data includes the building form model, grid auxiliary surface, column grid arrangement and number of floors of the target building.

3. The method for automatic generation and energy-saving optimization of three-dimensional building functional layout according to claim 1, characterized in that: The fixed parameters include: the name of each functional area, the area of ​​each functional area, the area tolerance of each functional area, the common attributes of each functional area, the specified floors of each functional area, the number of vertical traffic cores and the proportion of vertical traffic area.

4. The method for automatic generation and energy-saving optimization of three-dimensional building functional layout according to claim 1, characterized in that: The rule parameters include: the starting unit of each functional area, the growth step size of each functional area in each direction, the growth order of each functional area in each direction, the starting unit of the first floor plane of the vertical traffic core, the growth step size of each direction of the first floor plane of the vertical traffic core, and the growth order of the direction of the first floor plane of the vertical traffic core.

5. The method for automatic generation and energy-saving optimization of three-dimensional building functional layout according to claim 1, characterized in that: The step of performing three-dimensional spatial layout on the grid model using a three-dimensional spatial layout algorithm comprises: After the percentage of the vertical traffic area to the total area and the number of vertical traffic cores are input into the grid model, vertical body growth is performed on the vertical traffic plane of the grid model; After the vertical body growth is completed, the starting unit of each functional space of the grid model and the attribute parameters of the starting unit are determined according to the rule parameters, and functional area growth is performed to obtain a basic building model.

6. The method for automatic generation and energy-saving optimization of three-dimensional building functional layout according to claim 1, characterized in that: The annual load per unit building area is the ratio of the total energy consumed by the building in one year to the total building area of ​​the building; The total amount of energy consumed includes: building heating energy consumption, cooling energy consumption, lighting energy consumption and equipment energy consumption.

7. The method for automatic generation and energy-saving optimization of three-dimensional building functional layout according to claim 1, characterized in that: The building functional layout suitability score includes floor score and privacy score.

8. The method for automatic generation and energy-saving optimization of three-dimensional building functional layout according to claim 7, characterized in that: The optimization objectives include: the annual load per unit building area, the floor score, and the privacy score.

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