A method for automatic generation and energy-saving optimization of three-dimensional building functional layout

Abstract

This invention provides a method for automatic generation and energy-saving optimization of three-dimensional building functional layout, belonging to the field of automatic generation and optimization of building models in computational design. The method includes: acquiring basic modeling data of the target building; inputting the basic modeling data into model generation software; determining fixed parameters for the layout scheme and generating rule parameters; using a three-dimensional spatial layout algorithm to perform three-dimensional spatial layout on the mesh model; calculating the annual load per unit building area and the applicability score of the building functional layout of the basic building model; setting the running parameters of a multi-objective evolutionary algorithm; optimizing the basic building model using the multi-objective evolutionary algorithm; and performing cluster analysis and visualization processing. This invention provides an automatic three-dimensional modeling method for energy-saving buildings, simultaneously utilizing a three-dimensional spatial layout algorithm and a multi-objective evolutionary algorithm to increase the efficiency of modeling and optimization, and resulting in a model with better applicability.

Description

TECHNICAL FIELD

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

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

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

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

[0005] To achieve the above purpose, the present application provides the following scheme:

[0006] A three-dimensional building functional layout automatic generation and energy-saving optimization method comprises:

[0007] Obtaining basic modeling data of a target building;

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

[0009] Determining fixed parameters of a layout scheme and generating rule parameters;

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

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

[0012] According to the load and score data, the operation parameters of the multi-objective evolutionary algorithm are set;

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

[0014] The building function layout applicability score result is analyzed by using the k-means algorithm to obtain an analysis result, and the analysis result is visualized by using grasshopper and python to obtain a target visual optimization result.

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

[0016] Preferably, the fixed parameters include: function area names, function area areas, function area area tolerances, function area public attributes, function area specified floors, a number of vertical traffic cores, and a vertical traffic area proportion.

[0017] Preferably, the rule parameters include: a starting unit of each function area, a growth step length of each function area in each direction, a growth order of each function area in each direction, a starting unit of a first floor plane of a vertical traffic core, a growth step length of each direction of the first floor plane of the vertical traffic core, and a growth order of 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 by using a three-dimensional spatial layout algorithm includes:

[0019] When the percentage of the vertical traffic area in the total area and the number of vertical traffic cores are input to the grid model, vertical shape growth is performed on the vertical traffic plane of the grid model.

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

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

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

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

[0024] The following technical effects are disclosed in the present application:

[0025] The present application provides a three-dimensional building function layout automatic generation and energy-saving optimization method, which solves the defects of the conventional building layout method limited to two-dimensional space by three-dimensional space layout algorithm and multi-objective evolutionary algorithm, realizes the growth and optimization of the three-dimensional structure of the building model, solves the defects of the conventional layout relying on artificial evaluation by constructing a scoring system, and realizes the objective analysis of the model structure. BRIEF DESCRIPTION OF DRAWINGS

[0026] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed to be used in the embodiments will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor on the basis of these drawings.

[0027] Figure 1 The automatic generation and optimization method flowchart provided for the embodiments of the present application is provided.

[0028] Figure 2 The ideal grid model and its adjacent space unit schematic diagram provided for the embodiments of the present application is provided.

[0029] Figure 3 The projection schematic diagram of the ideal grid model in the building three-dimensional model provided for the embodiments of the present application is provided.

[0030] Figure 4 The generation logic schematic diagram provided for the embodiments of the present application is provided.

[0031] Figure 5 The functional space layout random generation parameter automatic arrangement part result schematic diagram provided for the embodiments of the present application is provided.

[0032] Figure 6 The city complex building block axonometric drawing and first floor plan provided for the embodiments of the present application is provided.

[0033] Figure 7 The optimization result statistical diagram provided for the embodiments of the present application is provided.

[0034] Figure 8 The change schematic diagram of the annual load per unit building area in the 80 evolutionary iteration calculations provided for the embodiments of the present application is provided.

[0035] Figure 9 The change schematic diagram of the privacy score in the 80 evolutionary iteration calculations provided for the embodiments of the present application is provided.

[0036] Figure 10 A variation diagram of floor score in 80 evolutionary iteration calculation provided by the embodiment of the present application is shown in the figure.

[0037] Figure 11 A clustering analysis result diagram of non-dominated solution obtained after optimization provided by the embodiment of the present application is shown in the figure. DETAILED DESCRIPTION

[0038] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work fall within the protection scope of the present application.

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

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

[0041] Figure 1 A flow chart of the automatic generation and optimization method provided by the embodiment of the present application is shown in the figure, and the present application provides a three-dimensional building function layout automatic generation and energy-saving optimization method, which comprises: Figure 1

[0042] Step 100: obtaining basic modeling data of a target building;

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

[0044] Step 300: determining fixed parameters of a layout scheme and generating rule parameters;

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

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

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

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

[0049] Step 800: performing cluster analysis on the building function layout applicability score results by using a k-means algorithm to obtain analysis results, and performing visual processing on the analysis results by using grasshopper and python to obtain target visual optimization results.

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

[0051] Optionally, the fixed parameters include: function area names, function area areas, function area area tolerances, function area public attributes, function area specified floors, a number of vertical traffic cores, and a vertical traffic area proportion.

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

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

[0054] When the vertical traffic area proportion of the total area and the number of vertical traffic cores are input to the grid model, vertical shape growth is performed on the vertical traffic plane of the grid model.

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

[0056] Optionally, the unit building area annual load is a ratio of total energy consumption of a building in a year to a total building area of the building; and the total energy consumption includes: building heating energy consumption, refrigeration energy consumption, lighting energy consumption, and equipment energy consumption.

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

[0058] Specifically, the optimization target includes: the unit building area annual load, the floor score, and the privacy score.

[0059] Further, based on the reverse workflow in automated architectural design, this embodiment develops a three-dimensional space allocation algorithm tool for building function layout, uses multi-objective evolutionary algorithm to take the best building function applicability and the lowest building energy consumption as optimization objectives, establishes an automatic process framework for three-dimensional space layout automatic generation and energy consumption simulation evaluation, and takes a high-rise comprehensive office building as an example to verify the feasibility.

[0060] Further, based on the reverse workflow, this embodiment constructs an energy-saving-oriented three-dimensional building layout optimization framework, aiming to screen out the overall function layout scheme that is energy-saving and meets 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 function space based on discrete grid space layout. This part realizes the automatic layout of building function space and the creation of building model through the linkage of rhino-grasshopper and python self-programming program. The second part is the optimization of scheme combining the building layout applicability scoring system and the annual load simulation calculation of building unit area. This part uses the honeybee energy consumption simulation tool of grasshopper platform to establish the energy consumption model and perform the annual load simulation of building unit area, and uses the Octopus tool in the grasshopper platform for multi-objective optimization. Finally, combining grasshopper and python, the k-means algorithm is used for clustering and visual display of the optimization results. Through the integration of computational design and energy simulation, this framework provides a user-friendly and scientifically guided energy-saving building function layout design method for architects.

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

[0062] Reference Figure 2The ideal grid model is composed of several spatial units. The information of six adjacent spatial units can be obtained by discrete coordinate search. If the states of the adjacent spatial units are consistent, the two spatial units are connected and form a connected domain with other connected units. When the states of adjacent spatial units in the same horizontal layer are the same, they are considered as the same connected domain, i.e. a functional partition. In addition, the distribution and specific attributes of the region can be defined through this model to meet the needs of architectural planning. The model is used to represent the distribution and attributes of different regions in the form of ideal grid in architectural design and space planning, and to describe the layout of architectural space for automatic generation and optimization.

[0063] Reference Figure 3 For the actual building model, the actual operation steps in grasshopper are as follows: input the building's outline, then input the layer height, grid auxiliary surface and column grid axis. By inputting these parameters, the building body can be divided into a three-dimensional irregular grid model by the program, and the area of each spatial unit is calculated. The area data of each spatial unit is transmitted to the core layout generation module, and the area attribute of the ideal model spatial unit is updated. This method takes each space across the column grid as the minimum unit, which corresponds to the spatial unit in the ideal grid model, and realizes the goal of identifying the irregular block and non-orthogonal grid building model.

[0064] Reference Figure 4 The three-dimensional space layout generation module needs to input two groups of parameters. One group is the fixed parameter info_data (see Table 1) containing the specific scheme information of each partition according to the user's needs, which is used to define the requirements of scheme generation and the end conditions of generation. The other group 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] Reference Figure 4The three-dimensional space layout algorithm takes into account the area requirement and regional connectivity of each functional space. In this embodiment, the so-called "growth" refers to changing the adjacent space unit from empty to the same state as the space unit to increase the area of a certain entity region. The three-dimensional space layout algorithm can be divided into three steps in total, and the pseudo code is shown in Algorithm 1 and Algorithm 2. The first step is to generate a vertical traffic core: this is an optional step. By inputting the percentage of the vertical traffic area in the total area and the number of vertical traffic cores, the step will generate a specified number of vertical shapes that can reach all floor levels within the building shape. The growth area of the vertical traffic plane is limited to the space unit in which the minimum projection plane is located, so as to ensure that the vertical traffic area will not be interrupted in the vertical direction. The second step is to generate each functional space region: first, according to the generation rule parameters of each functional space, select a starting unit for each functional space and set its attribute to the corresponding state. Then, each region is grown 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 length in the generation rule parameters. The growth process continues until the area requirement is met or there are no adjacent space units left for growth. When there are no adjacent space units left for growth, but the area that has been grown does not meet the requirement, the space unit with the smallest linear serial number in the optional space unit is selected as the new starting unit for growth, until the requirement is met or growth cannot continue. The third step is to fill the remaining empty units: when all regions complete the growth in the second step and meet the minimum area requirement, if there are still unallocated space units, each region will continue to grow according to the growth rule until all remaining empty units are filled.

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

[0073] Input: program, gene_data, info_data

[0074] Output: result_program

[0075] Generate the plane of vertical traffic

[0076] vt_plan = creat_transport_plan (program, gene_data, info_data) Perform vertical growth of vertical traffic

[0077] program = creat_vertical_mass (program, vt_plan)

[0078] Synchronous growth of multiple connected domains

[0079] result program = grow program (program, gene data, info data) if len (program.get attri unit seq (0)) > 0 do result program = fill program (result program, gene data, info data)

[0080] end

[0081] Algorithm 2, grow program (grow program):

[0082] Input: program, gene data, info data

[0083] Output: program

[0084] Set start point according to gene group

[0085] program = set start point (program, gene data.start unit)

[0086] Grow according to gene group until all regions are grown

[0087] While bool (finish check) = True do

[0088] Stop check = [True] * info.zone_count

[0089] Grow according to direction order in gene group

[0090] For dir in gene data.direction do

[0091] For zone in info data.zone do

[0092] Grow according to direction step length in gene group

[0093] For step in gene data.step_len[zone][dir]do

[0094] Traverse space units in this region

[0095] For unit in program[zone]do

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

[0097] if grow_unit.attri == 0 do

[0098] program[grow_unit].attri = zone

[0099] stop_check[zone] = False

[0100] check if the region meets the area requirement, if it does, the region is grown to completion

[0101] if

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

[0103] finish_check[zone] = True

[0104] if the region cannot continue to grow and does not meet the area requirement, select a vacant unit as the new growth point

[0105] for zone in info_data.zone do

[0106] if finish_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] Further, the specific implementation process is written in Python. The implementation of this layout generation method relies on the state change of adjacent space units under different generation rule parameters. Through reasonable start unit allocation and continuous growth and filling process, the method can quickly and effectively generate three-dimensional space layout with specified area and layout characteristics. Figure 5Two groups of partial scheme results generated by randomly generated rule parameters are shown, and 50 groups of schemes can be generated per second on average. The robustness of the method is demonstrated by testing different plane shapes and height features.

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

[0112] Specifically, the decision variable is the spatial layout mode under the same environment, function and specified area. The genetic parameters for the evolutionary algorithm have a total of three groups, which are the linear index of the starting space unit of each region, the growth direction sequence and the step length of each growth, and each group of parameters corresponds to a space layout scheme. The space layout scheme image and the generated rule parameters correspond to the phenotype and genotype in the genetic algorithm. The advantage of using generated rule parameters instead of space layout images as genotypes is that it can optimize with fewer genetic parameters while solving the continuity problem between space layout units. On the other hand, since the original parameters are discontinuous and the value range is not the same, in this link, all genetic variables are linearly mapped to the [0, 1] interval, so that the value range of each gene bit is consistent, avoiding the problem of value range mismatch.

[0113] The embodiment adopts the building unit area annual load E annualLoad for evaluating the energy efficiency of the building scheme, with the unit of kW·h / (m 2 ). The building unit area annual load refers to the total amount of energy consumed by the building in a year divided by the total building area of the building, including building heating energy consumption, cooling energy consumption, lighting energy consumption and equipment energy consumption, and 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 the building, the embodiment adopts 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 buildings and can perform visual analysis. Through the Honeybee plug-in, we can simulate the energy consumption of the building under different conditions and evaluate the energy consumption performance of different design schemes.

[0116] Further, the floor score is designed to evaluate the rationality of the floor arrangement of the multi-storey building. In a multi-storey or high-rise building, different floor heights have different attributes, for example, commercial catering, guest hall and other public functions are often arranged on the lower floors to facilitate more people to reach and use. While functions such as office and residence are arranged on high floors to obtain better landscape and privacy. Users can set the specified function to be arranged on high floors or low floors according to the needs, and 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 needs, and the closer to 0, the more reasonable the floor arrangement is. Finally, the weighted average score calculation method is used to assign a larger weight to a larger value score, thereby reducing the unreasonable arrangement results. The floor score calculation formula is as follows:

[0117]

[0118] The weight distribution formula is as follows:

[0119]

[0120] Wherein, S f : the floor score of the functional plan area; F: the total number of floors of the building; F z : the floor number where the functional area is located; D f : the layer height demand setting of the functional area, h for arrangement on high floors, and l for arrangement on low floors.

[0121] Further, the privacy score refers to the connectivity concept in space syntax, which reflects the rationality of the functional area in the overall building and other areas arranged adjacent to the functional area, thereby evaluating the degree of privacy of the functional area. Connectivity refers to the sum of the number of nodes connected to other nodes, which is used to describe and analyze the connection, relationship and accessibility between different space elements. In this score, each functional plan area is regarded as an independent space element, and its connectivity is the number of adjacent functional categories. The more adjacent functional categories, 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 plan area with privacy requirements, the embodiment will count the number of adjacent functions that do not match, thereby calculating the privacy score, and the closer to 0, the better the privacy of the functional area. Similarly, the overall privacy score of the final scheme is calculated by using the weighted average score calculation method to reduce the unreasonable scheme in the optimization. The privacy score calculation formula is as follows:

[0122]

[0123] wherein, S p : the privacy score of the functional plan area; Fu i : the function of the adjacent plan area; Fu m : the function matched with the function; C a : the number of other plan areas adjacent to the functional plan area; C f : the number of all functional categories; C m : the number of functional categories matched with the function.

[0124] Specifically, a series of Pareto front solutions can be obtained after multi-objective optimization, all of which constitute the Pareto front curve. The Pareto front solution represents the best balance point in the multi-objective optimization problem, and cannot be improved by improving a single target. The embodiment adopts the k-means clustering algorithm to perform clustering analysis on the generated Pareto front solution, and the clustering basis is the similarity of the optimization targets of the solutions. The function algorithm is written by python. K-means is an unsupervised learning technique, 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 clusters while minimizing the similarity of data points between different clusters, thereby achieving effective grouping of data. The number of clusters is determined by the user. Through k-means clustering analysis of 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 building unit area annual load of the lowest solution in each cluster is visualized in the visualization module of Grasshopper. The visualization content includes the plan of each layer of each solution, building energy consumption related data, building partition model and building energy consumption model diagram, etc. Through these intuitive images and data, architects are provided with direct and easy-to-understand design references.

[0125] Specifically, the embodiment takes a certain comprehensive high-rise office building as an example to generate a three-dimensional layout test, evaluate the energy saving and practical effect of the generated building layout scheme, and verify the effectiveness of the framework of the embodiment. The case building has 19 floors, a total building area of 19400 square meters, and the building shape and the function area to be arranged are as shown in Figure 5 The building has a total of seven functional areas, including unit office, open office, electronic research and development room, conference room, and commercial supporting, canteen and staff dormitory. The basic parameter information of each functional area is shown in Table 3. The time and manner of personnel activities in each functional area are different, and their lighting usage habits and heating and cooling settings also differ. These differences combined with the arrangement position of different functions will affect the overall energy performance of the building. The embodiment adopts traditional lighting design, and the lighting load is only related to the building area.

[0126] Table 3

[0127]

[0128]

[0129] Optionally, the embodiment adopts the classic meteorological year data of a certain city for simulation analysis. The building envelope materials and related parameters are shown in Table 4. The window-to-wall ratio of the front facade and the rear facade 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 functional 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 in the honeybee-energy module of Rhino-grasshopper, which prepares for the simulation and evaluation of building energy consumption in combination with the energy consumption simulation model.

[0130] Table 4

[0131]

[0132] Specifically, the commercial and dining functions are arranged close to the low floors to obtain better traffic accessibility, and the dormitory is arranged close to the high floors to reduce interference from other functions. The commercial and dining functions are set as public areas, that is, the relevant scores of the two areas will not be calculated in the privacy score. The dormitory is set as an independent private area and has no associated functions, that is, the dormitory function is expected to be arranged in a completely independent area. The cellular office, open office, research and development room, and conference room are set as a group of interrelated functions, and the corresponding scheme will obtain a higher privacy score when they are arranged adjacent to each other and have no other adjacent functions. The corresponding module will calculate and analyze the floor scores and privacy scores of different functional layout schemes according to these settings to evaluate the rationality of the schemes.

[0133] Further, the population size in the evolutionary algorithm is set to 30. The elite strategy helps to preserve the best solution and improve the performance of the algorithm by preserving individuals with high fitness and directly passing them to the next generation. Considering that too many elite individuals will lead to too little diversity and fall into local optimal solution, the proportion of elite individuals is set to 10% in this embodiment. Mutation operation is to increase the diversity of the search space and avoid falling into 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 operation to increase diversity. The maximum number of iterations 30 is taken as the termination condition of optimization in this embodiment to avoid unnecessary calculation. After 80 iterations of calculation, a total of 7368 historical solutions and 28 non-dominated solutions are generated. The historical solutions from generation 1 to generation 80 and the Pareto front surface are shown in Figure 6 Table 5 shows the target parameter range and maximum reduction amplitude of all historical solutions. The annual load per unit area of all historical solutions ranges from 253.3kW·h / (m2) to 268.08kW·h / (m2), with a difference of 14.78kW·h / (m2). The maximum reduction amplitude of the annual load per unit area is 0.05kW·h / (m2), which means that the annual load per unit area of the best solution is 0.05kW·h / (m2) lower than that of the second best solution. 2 ) between 253.3kW·h / (m2 The maximum decrease was 5.51%, with the reduction in cooling load playing a major role.

[0134] Table 5

[0135]

[0136]

[0137] Furthermore, although the optimization objective parameters change differently during the iteration process, the average shortest distance of all solutions continuously decreases during the iterative calculation until it reaches a stable value, such as... Figure 8 As shown in Figures 9 and 10. This embodiment employs a three-cluster clustering approach, dividing the 28 Pareto front solutions from the experimental results into three categories. The scheme with the lowest annual load per unit area of ​​building within each cluster is selected for visualization (see reference). Figure 11 (See Table 6). All three schemes performed well in terms of functional layout rationality. The canteen and supporting commercial facilities are located on the lower floors, while the dormitory area is concentrated on the top floor, and their floor scores are close to 0. The optimization process also considered the matching of adjacent functions, and the privacy scores of the three schemes also showed good scores among all historical solutions. Scheme A has the lowest annual load per unit area of ​​building area among all historical solutions. Similar to Schemes B and C, the commercial functions are mainly located on the northwest side, while the canteen function is located on the southeast side. Open offices and R&D rooms are arranged adjacent to each other, with the R&D rooms biased towards the south side. Scheme clustering and visualization analysis not only provide an intuitive way to present the optimization results, but also enable architects to have a more convenient way of thinking and making decisions regarding the optimization of functional layout and energy performance during the design process.

[0138] Table 6

[0139]

[0140]

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

[0142] This invention provides an automatic 3D modeling method for energy-saving buildings, which utilizes a 3D spatial layout algorithm and a multi-objective evolutionary algorithm to increase the efficiency of modeling and optimization, and the resulting model has better applicability.

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

[0144] The principles and implementation manners of the present application are described by using specific examples in the present application, and the above examples are only used to help understand the method of the present application and its core idea; meanwhile, for the general technical personnel in the art, the specific implementation manners and application ranges will be changed according to the idea of the present application. In conclusion, the content of the present specification should not be understood as the limitation of the present application.

Claims

1. A method for automatically generating and optimizing energy saving of three-dimensional building function layout, characterized in that, The method comprises the following steps: obtaining basic modeling data of a target building; inputting the basic modeling data into model generation software to obtain a grid model; determining fixed parameters of a layout scheme and generating rule parameters; the fixed parameters include: names of each functional area, areas of each functional area, area tolerances of each functional area, public attributes of each functional area, specified floors of each functional area, numbers of vertical traffic cores, and area proportions of vertical traffic; the rule parameters include: starting units of each functional area, growth step lengths of each functional area in each direction, growth sequences of each functional area in each direction, starting units of a first floor plane of a vertical traffic core, growth step lengths of the first floor plane of the vertical traffic core in each direction, and growth sequences of the first floor plane of the vertical traffic core in each direction; based on the fixed parameters and the rule parameters, performing three-dimensional space layout on the grid model by using a three-dimensional space layout algorithm to obtain a basic building model; calculating annual unit building area load and building function layout applicability scores of the basic building model to obtain load and score data; setting operation parameters of a multi-objective evolutionary algorithm according to the load and score data; optimizing optimization targets of the basic building model by using the multi-objective evolutionary algorithm to obtain building models of each iteration, annual unit building area load data of the building models of each iteration, and building function layout applicability score results of the building models of each iteration; performing clustering analysis on the building function layout applicability score results by using a k-means algorithm to obtain analysis results, and performing visual processing on the analysis results by using grasshopper and python to obtain target visual optimization results. The step of performing three-dimensional space layout on the grid model by using the three-dimensional space layout algorithm comprises the following steps: after inputting the percentage of the area of vertical traffic in the total area and the number of vertical traffic cores into the grid model, performing vertical shape growth on the vertical traffic plane of the grid model; after the vertical shape growth is completed, determining starting units of each functional space of the grid model and attribute parameters of the starting units according to the rule parameters, and performing functional area growth to obtain the basic building model.

2. The method of claim 1, wherein, The basic modeling data includes a building shape model of the target building, a grid auxiliary surface, a column grid arrangement mode, and a number of floors.

3. The method of claim 1, wherein, The annual unit building area load is the ratio of total energy consumption of the building in one year to the total building area of the building; The total energy consumption includes: building heating energy consumption, refrigeration energy consumption, daylighting energy consumption, and equipment energy consumption.

4. The method of claim 1, wherein the method further comprises: The building function layout applicability scores include floor scores and privacy scores.

5. The method of claim 4, wherein the method further comprises: The optimization targets include: the annual unit building area load, the floor scores, and the privacy scores.

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