Method, device and equipment for optimizing spatial form of block under low-carbon guidance and medium

Abstract

The invention provides a block spatial form optimization method, device and equipment under low-carbon guidance and a medium. The method comprises the following steps: acquiring a spatial form factor of a city to be planned, and constructing a block parameter optimization regulation and control model of the city to be planned based on the spatial form factor; determining current operation simulation data of the city to be planned based on the block parameter optimization regulation and control model; based on the current operation simulation data and a preset optimization target, obtaining a block spatial form optimization scheme of the to-be-planned city; wherein the preset optimization target comprises the building operation carbon emission intensity. According to the invention, the obtained block spatial form system is more comprehensive. Based on operation simulation data including meteorological and equipment operation data, multiple optimization objectives are set, and a block spatial form system is associated with building carbon emission to obtain an optimal block spatial form. In addition, the embodiment of the invention does not depend on historical data, so that the finally obtained low-carbon-oriented block spatial form optimization scheme has scientificity.

Description

Technical Field

[0001] The present invention relates to the technical field of urban layout planning, and in particular to a method, device, equipment and medium for optimizing the spatial form of a block under a low-carbon orientation. Background Art

[0003] Current research shows that the difference in building carbon emissions under different morphological layouts can reach about 10% to 30%. Therefore, the impact of block spatial morphology on building carbon emissions cannot be ignored, and the study of urban block morphology, which is crucial for low-carbon development, should receive more attention and in-depth exploration.

[0004] However, the inventors found that the current methods for optimizing urban block morphology have the following problems: First, the convolutional neural network model is mainly used, and the research object is the environmental microclimate and morphology, which is not linked to building carbon emissions. Second, there are fewer evaluation indicators considered, so that the final block space morphology is not the optimal morphology. Third, the current research on carbon emission accounting for the entire life cycle of buildings basically relies on the BIM model of single buildings, lacks a large range of building carbon emission measurement conditions, and can only calculate implicit carbon emissions through the bill of quantities, and cannot calculate its actual operating carbon emissions. In this way, it is impossible to explore how to achieve the goal of energy conservation and emission reduction by optimizing spatial morphology. Fourth, the research on the mechanism of the impact of spatial morphology on building carbon emissions is relatively single. The current selection of spatial morphology factors is relatively single, and the grasp of the urban morphology system is not comprehensive enough. In addition, some studies simply use typical annual meteorological data to explore the correlation between spatial morphology and building energy consumption and carbon emissions, resulting in a lack of scientific results.

[0005] Based on this, at present there is a lack of effective means to optimize the urban block form in a low-carbon environment. Summary of the invention

[0006] The embodiments of the present invention provide a method, device, equipment and medium for optimizing the spatial form of a block under a low-carbon orientation, so as to solve the problem of lack of effective means for optimizing the form of urban blocks under a low-carbon environment at this stage.

[0007] In a first aspect, an embodiment of the present invention provides a method for optimizing the spatial form of a block under a low-carbon orientation, comprising:

[0008] Obtain the spatial morphological factors of the city to be planned, and build a block parameter optimization and control model for the city to be planned based on the spatial morphological factors;

[0009] Based on the block parameter optimization and control model, determine the current operation simulation data of the city to be planned;

[0010] Based on the current operation simulation data and the preset optimization goals, an optimization plan for the block space form of the city to be planned is obtained; among which, the preset optimization goals include the carbon emission intensity of building operation.

[0011] In a possible implementation, the spatial morphological factors include regulatory factors, descriptive factors, limiting factors, and qualitative analysis factors;

[0012] The regulating factors include street orientation and number of building floors;

[0013] Descriptive factors include sky viewshed, street aspect ratio, maintenance factor, shape factor, and average area-to-perimeter ratio;

[0014] Qualifying factors include typical plot ratios and typical single-building site sizes;

[0015] Qualitative analysis factors include spatial morphological layout;

[0016] Among them, the descriptive factors change with the changes in the regulatory factors.

[0017] In one possible implementation, a block parameter optimization and control model for the city to be planned is constructed based on spatial morphological factors, including:

[0018] Generate the building forms included in the blocks in the city to be planned;

[0019] The building form is adjusted based on the spatial morphological factors, and based on the adjusted building form, a block parameter optimization and control model for the city to be planned is constructed.

[0020] In one possible implementation, the preset optimization targets also include carbon reduction from renewable energy and radiation difference of the building’s exterior skin;

[0021] The current operation simulation data includes the current meteorological simulation data and the equipment energy consumption simulation data;

[0022] Based on the current simulation data and the preset optimization goals, the optimization plan for the block space form of the city to be planned is obtained, including:

[0023] Determine the multi-objective optimization model based on the preset optimization objectives;

[0024] According to the running of the current simulation data and the multi-objective optimization model, the multi-objective optimization model is solved, and based on the solution results, the optimization plan for the block space morphology of the city to be planned is obtained.

[0025] In a possible implementation, the multi-objective optimization model is solved according to the running of the current simulation data and the multi-objective optimization model, and a block spatial morphology optimization scheme of the city to be planned is obtained based on the solution result, including:

[0026] Calculate the current optimization target value based on the current running simulation data and the preset optimization target;

[0027] Inputting the current optimization target value, the morphological control parameters and the optimization algorithm parameters corresponding to the preset optimization algorithm into the multi-objective optimization model to determine the first building morphological cluster; wherein the morphological control parameters include the average number of floors, the building orientation, the shape coefficient, the maintenance coefficient, the average area-to-perimeter ratio, the sky visible area and the east-west street height-to-width ratio;

[0028] Performing fitness evaluation on the first building form cluster, and generating a second building form cluster based on the evaluation result of the first building form cluster;

[0029] Perform fitness evaluation on the second building form cluster, determine the evaluation result of the second building form cluster, and return to the step of generating the second building form cluster based on the evaluation result of the first building form cluster, until the preset conditions are met, and then output the second building form cluster at this time as the solution result;

[0030] Based on the second building form cluster at this time, the optimization plan of the block space form of the city to be planned is determined. In a possible implementation method, the multi-objective optimization model is:

[0031] MinF(X)=[CEI(X),-CRRE(X),RD(X)]

[0032] Subject to:FAR(X)=FAR st

[0033] bt i ∈[T-1,L-2]

[0034] bf i ∈[6,9,24,30]

[0035] O i ∈[-45°,-30°,-15°,0°,15°,30°,45°]

[0036] Among them, CEI(X) is the objective function of the carbon emission intensity of building operation; CRRE(X) is the objective function of the carbon reduction of renewable energy; RD(X) is the objective function of the radiation difference of the building surface; FAR(X) is the block volume ratio determined based on the current operation simulation data; FAR st It is a typical floor area ratio constraint for urban commercial blocks; bt j Indicates the architectural combination form; bf j is the number of building floors; i is the street block orientation; where X={x1,x2,…x7} T is the morphology control parameter.

[0037] In one possible implementation, the carbon emission intensity of building operation is:

[0038]

[0039] EC H =E H ×f h

[0040] Among them, EC E For electricity carbon emissions; EC H is thermal carbon emissions; i is different types of electric energy; E Ei is the consumption of the i-th energy source; f E is the carbon emission factor of electricity; E H is the heating energy consumption; f H is the thermal carbon emission factor; A is the total building area;

[0041] The carbon reduction from renewable energy is:

[0042] CRRE=Q×S×η×EF e

[0043] Where Q is the radiation of the photovoltaic module; S is the area of ​​the photovoltaic module; η is the conversion efficiency of the photovoltaic module; EF e is the carbon emission coefficient of electricity;

[0044] The radiation difference of the building's outer skin is:

[0045] RD=H t1 -H t2

[0046] H t =H b R b +H d R d +H dg

[0047]

[0048] Among them, H t1 is the daily average total radiation energy of the building surface in the cooling season; H t2 H is the daily average total radiation energy of the building surface in the heating season; b H is the average daily direct solar radiation energy received by the horizontal surface; d H is the average daily atmospheric scattered radiation energy received by the horizontal surface; dg R is the daily average ground reflected radiation energy received; b , R d is the correction coefficient; ρ is the ground reflectivity; β is the angle between the wall corner and the horizontal plane.

[0049] In a second aspect, an embodiment of the present invention provides a low-carbon oriented block space morphology optimization device, comprising:

[0050] The acquisition and construction module is used to obtain the spatial morphological factors of the city to be planned, and to construct a block parameter optimization and control model for the city to be planned based on the spatial morphological factors;

[0051] The simulation operation module is used to optimize the control model based on the block parameters and determine the current operation simulation data of the city to be planned;

[0052] The optimization module is used to obtain the block space morphology optimization plan of the city to be planned based on the current operation simulation data and the preset optimization goals; among which the preset optimization goals include the carbon emission intensity of building operation.

[0053] In a third aspect, an embodiment of the present invention provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the processor implements the steps of the method described in the first aspect or any possible implementation manner of the first aspect.

[0054] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method described in the first aspect or any possible implementation method of the first aspect are implemented.

[0055] The embodiments of the present invention provide a method, device, equipment and medium for optimizing the spatial morphology of blocks under the guidance of low carbon. By selecting multiple representative spatial morphology factors in the planned city as the block parameter optimization and control model for constructing the city to be planned, the obtained block spatial morphology system is more comprehensive. Then, based on the block parameter optimization and control model, the corresponding operation simulation data including meteorological and equipment operation data are determined, and multiple optimization goals are set to link the block spatial morphology system with building carbon emissions to obtain the optimal block spatial morphology. In addition, the method provided by the embodiments of the present invention does not rely on historical data, and can make the final low-carbon-oriented block spatial morphology optimization scheme scientific. It can be seen that the embodiments of the present invention provide an effective means for optimizing urban block morphology in a low-carbon environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the embodiments or related technical descriptions are 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.

[0057] Figure 1 It is a flow chart of the implementation of the low-carbon oriented block space morphology optimization method provided by an embodiment of the present invention;

[0058] Figure 2 It is a multi-objective optimization model solution flow chart of the low-carbon oriented block space morphology optimization method provided by an embodiment of the present invention;

[0059] Figure 3 It is a block diagram of a low-carbon oriented block space morphology optimization method provided by another embodiment of the present invention;

[0060] Figure 4 is a schematic diagram of the structure of a low-carbon oriented block space morphology optimization device provided by an embodiment of the present invention;

[0061] Figure 5 is a schematic diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0062] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present invention. However, it should be clear to those skilled in the art that the present invention may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present invention.

[0063] In order to make the purpose, technical solutions and advantages of the present invention more clear, specific embodiments will be described below in conjunction with the accompanying drawings.

[0064] Figure 1 : is a flowchart of the implementation of the low-carbon oriented block space morphology optimization method provided by the embodiment of the present invention. Figure 1 As shown, the method includes:

[0065] Step 110: Obtain the spatial morphological factors of the city to be planned, and construct a block parameter optimization and control model for the city to be planned based on the spatial morphological factors.

[0066] In this embodiment, the spatial morphology factors may include control factors, descriptive factors, limiting factors, and qualitative analysis factors. Among them, the control factors can be actively controlled by humans or algorithms. When the control factors are changed, the remaining spatial morphology parameters will change with the change of the control factors. Therefore, these spatial morphology parameters can be defined as descriptive factors, limiting factors are limiting conditions in construction projects, and qualitative analysis factors are used to characterize spatial morphology. Correspondingly, the specific contents of each item in the spatial morphology factor can be found in Table 1.

[0067] Table 1 Selection and definition of spatial morphological factors

[0068]

[0069] In this embodiment, as shown in Table 1, the regulating factors in the spatial morphology factors may include block orientation and building floors; the descriptive factors may include sky visibility, street aspect ratio, maintenance coefficient, shape coefficient and average area-to-perimeter ratio, which are determined based on the regulating factors; the limiting factors may include typical floor area ratio and typical single building site size, which are determined by the standards of the construction industry and are generally fixed values; the qualitative analysis factors may include spatial morphology layout, which is determined based on the regulating factors and the descriptive factors.

[0070] After determining the spatial morphological factors, constructing a block parameter optimization and control model for the city to be planned based on the spatial morphological factors in step 110 may include:

[0071] Generate the building forms included in the blocks in the city to be planned.

[0072] The building form is adjusted based on the spatial morphological factors, and based on the adjusted building form, a block parameter optimization and control model for the city to be planned is constructed.

[0073] In this embodiment, the generation of the block parameter optimization and control model can include two parts: generating the building form and adjusting the building form. The following is an example of a commercial building to illustrate this embodiment.

[0074] The generation of building forms can be based on the Rhino & Grasshopper parametric platform. Specifically, according to the number of building floors in the limiting factor, it can be controlled through the Gene Pool and List Item components of Grasshopper; among them, the Gene Pool tool is also an important medium component for the subsequent combination with the NSGA-Ⅱ genetic algorithm to dynamically control the number of building floors.

[0075] Considering that commercial buildings can be divided into two types according to their vertical dimensions: towers and low-rise buildings, these two different combinations correspond to two different mathematical relationships between storey height and building base area. Therefore, it is necessary to adjust the number of building floors under limited factors, that is, it is necessary to adjust the number of building floors under the conditions of typical commercial block floor area ratio and typical building base area.

[0076] The generation path of the building form can be simply described as: establish the block plane - establish the building base outline - stretch it into the building form. Correspondingly:

[0077] First, you can use the grid and plane division tools to build the block site, use the Square component to divide the block outline, then extract the outline and generate the block plot plane through the RU component. Then use the Offset component to offset the block road outline to get the road block plane.

[0078] Then, the center point of each road block plane is extracted as the reference point for subsequent parametric modeling, and the corresponding base contour is assigned to each building land area.

[0079] Finally, the building area is given the building height attribute, that is, the floor control results under the limiting factor are associated. The building base outline and building height attributes are connected to the Extrude component to be pulled into the building form.

[0080] Next, the building form is adjusted and assigned according to the spatial morphological factors. Accordingly, the building orientation is used as one of the control factors, where the range of the building orientation can be set to [-45°, 45°]. In this embodiment, the Number Slider component can be used to control the orientation of the block building with 15° as the control basis, and the established orientation control module is connected to the center point of the entire plot, which is used as the rotation center point of all elements of the block. At the same time, by adjusting the different data panels in grasshopper, the spatial morphological factors selected in the previous part are calculated accordingly. Finally, based on the Rhino&Grasshopper parametric platform, a complete block parametric control geometry model is obtained, that is, a block parameter optimization control model.

[0081] In this embodiment, step 110 describes how to determine the block parameter optimization and control model. The following describes the application and control of the block parameter optimization and control model through steps 120 and 130, as well as how to determine the block spatial morphology optimization plan under the low-carbon orientation.

[0082] Step 120: Based on the block parameter optimization control model, determine the current operation simulation data of the city to be planned.

[0083] In this embodiment, after determining the block parameter optimization and control model, the current running simulation data can be obtained based on the Ladybug Tools series components and calling simulation engines such as EnergyPlus and Radiance.

[0084] Since the Ladybug Tools series of components include multiple core plug-ins, these core plug-ins are developed based on the current mainstream computer simulation engines. Each plug-in is designed for different analysis needs and application scenarios, including but not limited to sunshine, shadows, sun tracks, wind flow, temperature and energy consumption. Ladybug mainly includes environmental data analysis and visualization functions. By importing and analyzing meteorological data, basic environmental analysis such as sunshine, shadows, and sun tracks can be performed. Honeybee is based on simulation engines such as Radiance and EnergyPlus. By inputting building physical parameters, it can perform lighting simulation, heat load calculation and energy consumption prediction. Dragonfly focuses on urban-scale simulation and analysis, providing support for urban design and planning, including analysis of heat island effects, urban energy flows and space utilization efficiency. Butterfly can use open source CFD engines to simulate wind and heat environments, and can analyze and optimize air flow and temperature distribution in buildings and urban environments. Accordingly, the current running simulation data may include current meteorological simulation data and equipment energy consumption simulation data; among them, the current meteorological simulation data may include data such as sunshine, shadows, sun tracks, wind flow and temperature. Equipment energy consumption simulation data includes the energy consumption of each device; in addition, it can also include data such as energy flow.

[0085] Step 130: Based on the current operation simulation data and the preset optimization target, an optimization plan for the block space form of the city to be planned is obtained; wherein the preset optimization target includes the carbon emission intensity of building operation.

[0086] In this embodiment, considering that in the whole life cycle of a building, the proportion of operating carbon emission intensity is much higher than the implicit carbon emission intensity and is the main source of total carbon emissions of the building, this embodiment takes the building operation carbon emission intensity as one of the preset optimization targets. Correspondingly, the building operation carbon emission intensity can be expressed as:

[0087]

[0088] EC H =E H ×f h

[0089] Among them, EC E For electricity carbon emissions; EC H is thermal carbon emissions; i is different types of electric energy; E Ei is the consumption of the i-th energy source; f E is the carbon emission factor of electricity; E H is the heating energy consumption; f H is the thermal carbon emission factor; A is the total building area.

[0090] In an optional embodiment, the carbon emission intensity of building operation can be calculated in the Rhino&Grasshopper environment based on the run Energy Simulation energy consumption analysis module, calling the Energyplus simulation engine to calculate the energy consumption intensity values ​​of building cooling, heating, lighting and electrical equipment respectively, and then based on the carbon emission factor calculation method, the carbon emission factor value corresponding to each energy consumption is taken to calculate.

[0091] The amount of carbon reduction from renewable energy refers to the amount of carbon emissions that can be offset by renewable energy stored by energy collection technology. Since the method provided by the embodiment of the present invention can be applied to commercial buildings in the city to be planned, considering that a large amount of unused roof space in commercial buildings provides convenient conditions for the installation of solar panels, and at the same time setting the use of solar energy instead of electricity, the preset optimization target can also include the amount of carbon reduction from renewable energy. Accordingly, the amount of carbon reduction from renewable energy can be expressed as:

[0092] CRRE=Q×S×η×EF e

[0093] Where Q is the radiation of the photovoltaic module; S is the area of ​​the photovoltaic module; η is the conversion efficiency of the photovoltaic module; EF e is the carbon emission coefficient of electricity;

[0094] The radiation difference of the building surface is an important indicator for evaluating the wind-heat environment of the building. It is related to the adaptability and comfort of the block to climate change. The corresponding radiation difference of the building surface can be expressed as:

[0095] RD=H t1 -H t2

[0096] H t =H b R b +H d R d +H dg

[0097]

[0098] Among them, H t1 is the daily average total radiation energy of the building surface in the cooling season; H t2 H is the daily average total radiation energy of the building surface in the heating season; b H is the average daily direct solar radiation energy received by the horizontal surface; d H is the average daily atmospheric scattered radiation energy received by the horizontal surface; dg R is the daily average ground reflected radiation energy received; b , R d is the correction coefficient; ρ is the ground reflectivity; β is the angle between the wall corner and the horizontal plane.

[0099] The main logic of the building skin radiation difference analysis is consistent with the main logic of the building operation carbon emission intensity or the carbon reduction of renewable energy. The analysis of the building skin radiation difference uses the Sun Path operator, the trajectory time is set to one year as the basic scale, and the temperature is used as the judgment condition. In the Sun Path operator, "a<10" and "b>26" are set as the judgment conditions. The judgment condition refers to analyzing the solar radiation of the building facade when the temperature is lower than 10℃ and higher than 26℃ respectively, and taking the difference of the annual average radiation per unit area under two different temperature conditions in a year as the output indicator.

[0100] In an optional embodiment, in step 130, based on the current running simulation data and the preset optimization target, obtaining the block space form optimization scheme of the city to be planned may include:

[0101] Step 131: Determine a multi-objective optimization model based on preset optimization objectives.

[0102] Step 132: According to running the current simulation data and the multi-objective optimization model, the multi-objective optimization model is solved, and based on the solution results, an optimization plan for the block space form of the city to be planned is obtained.

[0103] In step 131, a multi-objective optimization model can be determined based on a preset optimization objective; and a preset optimization algorithm is used to solve the multi-objective optimization model, so as to transform the process of determining the block space form optimization scheme of the city to be planned into the process of solving the multi-objective optimization model. Exemplarily, the preset optimization algorithm can be an NSGA-Ⅱ genetic algorithm. The following relevant embodiments are explained by taking the NSGA-Ⅱ genetic algorithm as an example. It should be understood that the NSGA-Ⅱ genetic algorithm is only an implementation method provided in this embodiment. In practical applications, the optimization algorithm used is not limited to the NSGA-Ⅱ genetic algorithm.

[0104] In this embodiment, the carbon emission intensity of building operation and the carbon reduction of renewable energy can be used as important indicators for measuring energy conservation and emission reduction, and the radiation difference of the building surface can be used as an important indicator for measuring building comfort. Correspondingly, the multi-objective optimization model can be expressed as:

[0105] MinF(X)=[CEI(X),-CRRE(X),RD(X)]

[0106] Subject to:FAR(X)=FAR st

[0107] bt i ∈[T-1,L-2]

[0108] bfi ∈[6,9,24,30]

[0109] O i ∈[-45°,-30°,-15°,0°,15°,30°,45°]

[0110] Among them, CEI(X) is the objective function of the carbon emission intensity of building operation; CRRE(X) is the objective function of the carbon reduction of renewable energy; RD(X) is the objective function of the radiation difference of the building surface; FAR(X) is the block volume ratio determined based on the current operation simulation data; FAR st It is a typical floor area ratio constraint for urban commercial blocks; bt j Indicates the architectural combination form; bf j is the number of building floors; i is the street block orientation; where X={x1,x2,…x7} T are morphological control parameters, which respectively characterize the average number of floors, building orientation, shape coefficient, maintenance coefficient, average area-to-perimeter ratio, sky view, and east-west street height-to-width ratio. In the formula, because of different building types, the classification of building forms is different. When the method provided in this embodiment takes commercial buildings as the object, the combination form is divided into two combination forms: tower building and flat low building. That is, when taking commercial buildings as the object, bt j The two combinations of towers and flat buildings can be adjusted according to the actual situation. This embodiment is only used for explanation and does not constitute any limitation to this solution. In addition, in the formula, the number of building floors and the building orientation are used as the control factors in the optimization process, and the parameters can be dynamically controlled through the algorithm optimization process to generate a new building model.

[0111] In the multi-objective optimization model provided in the present embodiment, the building operation carbon emission intensity and the building exterior skin radiation difference are optimized with the minimum value as the optimization target, and the renewable energy carbon reduction is optimized with the maximum value as the optimization target. Considering that the optimization trend of the genetic algorithm is to take the minimum value as the optimization direction, the multi-objective optimization model provided in the present embodiment takes the renewable energy carbon reduction as a negative value for optimization.

[0112] After the multi-objective optimization model is determined, step 132 is explained below through an optional embodiment:

[0113] In an optional embodiment, in step 132, according to running the current simulation data and the multi-objective optimization model, solving the multi-objective optimization model, and obtaining the block space form optimization plan of the city to be planned based on the solution result, may include:

[0114] Calculate the current optimization target value based on the current running simulation data and the preset optimization target.

[0115] The current optimization target value, morphological control parameters and optimization algorithm parameters corresponding to the preset optimization algorithm are input into the multi-objective optimization model to determine the first building morphology cluster; wherein the morphological control parameters include the average number of floors, building orientation, shape coefficient, maintenance coefficient, average area-to-perimeter ratio, sky visible area and east-west street height-to-width ratio.

[0116] The fitness of the first building form cluster is evaluated, and a second building form cluster is generated based on the evaluation result of the first building form cluster.

[0117] The fitness of the second building form cluster is evaluated, the evaluation result of the second building form cluster is determined, and the step of generating the second building form cluster based on the evaluation result of the first building form cluster is returned until the preset conditions are met, and the second building form cluster at this time is output as the solution result.

[0118] Based on the second building form cluster at this time, determine the optimization plan for the block space form of the city to be planned.

[0119] Figure 2 This is a multi-objective optimization model solution flow chart of the low-carbon oriented block space morphology optimization method provided by the embodiment of the present invention. Figure 2 This embodiment is described:

[0120] In this embodiment, the multi-objective optimization model solution process can be mainly divided into three steps. The first step is to determine the n-generation initial building form, that is, the first building form cluster; the second step is to predict the initial building form optimization target level, that is, to evaluate the fitness of the first building form cluster; the third step is to generate a second building form cluster based on the evaluation result of the first building form cluster, judge the evaluation result of the second building form cluster, and start the execution loop until the preset conditions are met, and the latest building form cluster obtained at this time (that is, the second building form cluster at this time) is output as the solution result of the multi-objective optimization model.

[0121] Exemplarily, in the first step, the current optimization target value, morphological control parameters and NSGA-Ⅱ genetic algorithm optimization parameters are input into the multi-objective optimization model, and the NSGA-Ⅱ genetic optimization algorithm randomly selects the block morphological model corresponding to the feasible solution as the initial building morphology for optimization, that is, the first building morphology cluster. Among them, the current optimization target value is predetermined based on the current running simulation data and the preset optimization target; the morphological control parameters can be determined according to the block parameter optimization and regulation model.

[0122] Step 2: Conduct fitness evaluation on the first building form cluster and obtain the evaluation result, that is, the performance target level feedback optimization model, to predict the performance optimization target level of the first building form cluster.

[0123] Step 3: Based on the fitness evaluation result of the first building form cluster, genetic operations are performed on the selected individuals through crossover and mutation operators to reorganize and generate the second building form cluster. The fitness of the second building form cluster is evaluated to obtain the evaluation result of the second building form cluster. The evaluation result of the second building form cluster is judged. If new individuals are generated in the second building form cluster relative to the first building form cluster, the second building form cluster is used as the first building form cluster, and the step of generating the second building form cluster based on the evaluation result of the first building form cluster is returned until the preset condition is met, and the second building form cluster at this time is output as the solution result; wherein the preset condition can be that the number of iterations is equal to the preset number of iterations, or that the number of individuals in the second building form cluster reaches the preset number, which is not limited here.

[0124] In this embodiment, the solution result of the multi-objective optimization model is the Pareto optimal solution set of the spatial form factor at this time, and the corresponding architectural spatial form can be obtained through the solution result. Since the block parameter optimization and control model constructed in the early stage is established based on the spatial form factor, the spatial form factor can be adjusted by solving the multi-objective optimization model, and then the architectural form can be regulated. Therefore, based on the solution result, the block spatial form optimization plan of the city to be planned can be obtained.

[0125] In an optional embodiment, the method provided in this embodiment can be implemented based on the Rhino&Grasshopper parameterization platform. Figure 3 This is a block diagram of the implementation of the low-carbon oriented block space morphology optimization method provided by another embodiment of the present invention, that is, a block diagram of the implementation under the Rhino&Grasshopper parameterized platform structure. Figure 3 , the method provided in this embodiment is described:

[0126] In this embodiment, based on the Rhino&Grasshopper parametric platform, the method provided in this embodiment can be implemented in three steps. The first step is to construct a parametric controllable block: this step corresponds to step 110. Specifically, a spatial morphological factor is selected, and based on the spatial morphological factor, floor control is performed under a limited factor to generate a building form; wherein generating a building form may include: sequentially establishing a plane block, establishing a building base outline, and stretching it into a building form. Then, the building orientation is controlled under the limited factor, and the morphological index is calculated to construct a block parameter optimization control model.

[0127] The second step is to simulate and calculate the performance indicators. This step corresponds to step 120 and calculates the current optimization target value based on the current operation simulation data and the preset optimization target. Specifically, the block parameter optimization control model is simulated through each plug-in to obtain the current operation simulation data, and then the optimization target values ​​of the building operation carbon emission intensity, renewable energy carbon reduction and building outer skin radiation difference corresponding to the current simulation data are calculated based on the current operation simulation data.

[0128] The third step is a multi-objective optimization algorithm, which corresponds to step 130. Specifically, a multi-objective optimization model is constructed based on the carbon emission intensity of building operation, the carbon reduction of renewable energy, and the radiation difference of the building's outer skin. A preset optimization algorithm, such as the NSGA-Ⅱ genetic optimization algorithm, is used to solve the multi-objective optimization model to obtain the Pareto optimal spatial morphological distribution, that is, the solution result. The corresponding spatial morphological factors are determined based on the solution results, and then the spatial morphological optimization plan of the block of the city to be planned is obtained based on the corresponding spatial morphological factors.

[0129] In summary, compared with the traditional method, firstly, this embodiment can be applied to commercial buildings at the block scale. Unlike single buildings, the embodiment of the present invention takes into account the mutual influence between buildings and calls the Ladybug Tools series components for simulation, including microclimate simulation, light simulation, etc., so that the correlation between spatial form and building carbon emissions is stronger, more scientific and reasonable, and makes up for the lack of research on carbon emissions at the mesoscale. There are many research results on residential buildings, and the embodiment of the present invention can provide certain help for the morphological design of future commercial buildings. Secondly, the parametric block is controlled by different parameter panels. By adjusting the parameters, the block building can be adjusted to make it controllable, which cannot be achieved if the ordinary modeling method in the past is used. Moreover, the parametric model contains a large amount of data information of the building. Only the corresponding parameters need to be changed to quickly generate the building form. The ordinary single modeling in the past is time-consuming and labor-intensive in large-scale applications. Finally, the embodiment of the present invention can finally realize the regulation of the building form of the commercial block by establishing a parametric model, simulation and index calculation, and multi-objective optimization. It can be seen that the embodiment of the present invention is based on the Rhino&Grasshopper parametric platform and establishes a parametric controllable block by setting the corresponding panel components. A series of spatial morphological factors were determined as important parameters for subsequent multi-objective optimization, and the problem of the association between the building model and the spatial morphological factors was solved in the process of generating the building form. The simulation for the block scale is based on the parametric block model established in the previous step, using the ladybug tools series components and calling simulation engines such as EnergyPlus, and considering the interaction between buildings, including microclimate, light effects, wind and heat environment simulation, etc., so the performance indicators and building carbon emissions are associated with the spatial form, which not only makes the correlation between spatial form and building carbon emissions stronger, but also makes the indicator results obtained by simulation and calculation more scientific and reasonable. Finally, based on the parametric platform, the NSGA-Ⅱ genetic algorithm is written in Python language to iterate continuously to achieve multi-objective dynamic optimization, and the obtained solution set corresponds to the corresponding spatial morphological factors, thereby realizing the regulation of the building space form. This step realizes the regulation of spatial form by multi-objective optimization results based on the series of building carbon emission indicators. The whole method forms a closed loop with low carbon as the orientation, and the Pareto optimal solution is obtained through continuous genetic iteration. Each step is closely linked, closely related, and coordinated, realizing the coupling of the three modules.

[0130] It should be understood that the order of execution of the steps in the above embodiment does not necessarily mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present invention.

[0131] The following is an embodiment of the device of the present invention. For details not described in detail therein, reference may be made to the corresponding method embodiment described above.

[0132] Figure 4 The schematic diagram of the structure of the low-carbon oriented block space form optimization device provided by the embodiment of the present invention is shown. For the convenience of explanation, only the part related to the embodiment of the present invention is shown, which is described in detail as follows:

[0133] like Figure 4 As shown, the low-carbon oriented block space form optimization device 4 includes:

[0134] The acquisition and construction module 41 is used to obtain the spatial morphological factors of the city to be planned, and to construct a block parameter optimization and control model of the city to be planned based on the spatial morphological factors;

[0135] The simulation operation module 42 is used to determine the current operation simulation data of the city to be planned based on the block parameter optimization control model;

[0136] The optimization module 43 is used to obtain a block space morphology optimization plan for the city to be planned based on the current operation simulation data and the preset optimization target; wherein the preset optimization target includes the carbon emission intensity of building operation.

[0137] In a possible implementation, the spatial morphological factors include regulatory factors, descriptive factors, limiting factors, and qualitative analysis factors;

[0138] The regulating factors include street orientation and number of building floors;

[0139] Descriptive factors include sky viewshed, street aspect ratio, maintenance factor, shape factor, and average area-to-perimeter ratio;

[0140] Qualifying factors include typical plot ratios and typical single-building site sizes;

[0141] Qualitative analysis factors include spatial morphological layout;

[0142] Among them, the descriptive factors change with the changes in the regulatory factors.

[0143] In a possible implementation, the acquisition construction module 41 is specifically configured to:

[0144] Generate the building forms included in the blocks in the city to be planned;

[0145] The building form is adjusted based on the spatial morphological factors, and based on the adjusted building form, a block parameter optimization and control model for the city to be planned is constructed.

[0146] In one possible implementation, the preset optimization targets also include carbon reduction from renewable energy and radiation difference of the building’s exterior skin;

[0147] The current operation simulation data includes the current meteorological simulation data and the equipment energy consumption simulation data;

[0148] The optimization module 43 is specifically used for:

[0149] Determine the multi-objective optimization model based on the preset optimization objectives;

[0150] According to the running of the current simulation data and the multi-objective optimization model, the multi-objective optimization model is solved, and based on the solution results, the optimization plan for the block space morphology of the city to be planned is obtained.

[0151] In a possible implementation, the optimization module 43 is specifically configured to:

[0152] Calculate the current optimization target value based on the current running simulation data and the preset optimization target;

[0153] Inputting the current optimization target value, the morphological control parameters and the optimization algorithm parameters corresponding to the preset optimization algorithm into the multi-objective optimization model to determine the first building morphological cluster; wherein the morphological control parameters include the average number of floors, the building orientation, the shape coefficient, the maintenance coefficient, the average area-to-perimeter ratio, the sky visible area and the east-west street height-to-width ratio;

[0154] Performing fitness evaluation on the first building form cluster, and generating a second building form cluster based on the evaluation result of the first building form cluster;

[0155] Perform fitness evaluation on the second building form cluster, determine the evaluation result of the second building form cluster, and return to the step of generating the second building form cluster based on the evaluation result of the first building form cluster, until the preset conditions are met, and then output the second building form cluster at this time as the solution result;

[0156] Based on the second building form cluster at this time, the optimization plan of the block space form of the city to be planned is determined. In a possible implementation method, the multi-objective optimization model is:

[0157] MinF(X)=[CEI(X),-CRRE(X),RD(X)]

[0158] Subject to:FAR(X)=FAR st

[0159] bt i ∈[T-1,L-2]

[0160] bf i ∈[6,9,24,30]

[0161] O i∈[-45°,-30°,-15°,0°,15°,30°,45°]

[0162] Among them, CEI(X) is the objective function of the carbon emission intensity of building operation; CRRE(X) is the objective function of the carbon reduction of renewable energy; RD(X) is the objective function of the radiation difference of the building surface; FAR(X) is the block volume ratio determined based on the current operation simulation data; FAR st It is a typical floor area ratio constraint for urban commercial blocks; bt j Indicates the architectural combination form; bf j is the number of building floors; i is the street block orientation; where X={x1,x2,…x7} T is the morphology control parameter.

[0163] In one possible implementation, the carbon emission intensity of building operation is:

[0164]

[0165] EC H =E H ×f h

[0166] Among them, EC E For electricity carbon emissions; EC H is thermal carbon emissions; i is different types of electric energy; E Ei is the consumption of the i-th energy source; f E is the carbon emission factor of electricity; E H is the heating energy consumption; f H is the thermal carbon emission factor; A is the total building area;

[0167] The carbon reduction from renewable energy is:

[0168] CRRE=Q×S×η×EF e

[0169] Where Q is the radiation of the photovoltaic module; S is the area of ​​the photovoltaic module; η is the conversion efficiency of the photovoltaic module; EF e is the carbon emission coefficient of electricity;

[0170] The radiation difference of the building's outer skin is:

[0171] RD=H t1 -H t2

[0172] H t =H b R b +H d R d +Hdg

[0173]

[0174] Among them, H t1 is the daily average total radiation energy of the building surface in the cooling season; H t2 H is the daily average total radiation energy of the building surface in the heating season; b H is the average daily direct solar radiation energy received by the horizontal surface; d H is the average daily atmospheric scattered radiation energy received by the horizontal surface; dg R is the daily average ground reflected radiation energy received; b , R d is the correction coefficient; ρ is the ground reflectivity; β is the angle between the wall corner and the horizontal plane.

[0175] Figure 5 Schematic diagram of an electronic device provided by an embodiment of the present invention. Figure 5 As shown, the electronic device 5 of this embodiment includes: a processor 50, a memory 51, and a computer program 52 stored in the memory 51 and executable on the processor 50. When the processor 50 executes the computer program 52, the steps in the above-mentioned various low-carbon oriented block space form optimization method embodiments are implemented, such as Figure 2 Alternatively, when the processor 50 executes the computer program 52, the functions of each module / unit in the above-mentioned device embodiments are realized, for example, Figure 4 The functions of each module / unit are shown.

[0176] Exemplarily, the computer program 52 may be divided into one or more modules / units, which are stored in the memory 51 and executed by the processor 50 to implement the present invention. The one or more modules / units may be a series of computer program instruction segments capable of implementing specific functions, which are used to describe the execution process of the computer program 52 in the electronic device 5. For example, the computer program 52 may be divided into Figure 4 The modules / units shown.

[0177] The electronic device 5 may be a computing device such as a desktop computer, a notebook, a PDA, or a cloud server. The electronic device 5 may include, but is not limited to, a processor 50 and a memory 51. Those skilled in the art will appreciate that Figure 5 It is only an example of the electronic device 5 and does not constitute a limitation of the electronic device 5. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the electronic device may also include input and output devices, network access devices, buses, etc.

[0178] The processor 50 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0179] The memory 51 may be an internal storage unit of the electronic device 5, such as a hard disk or memory of the electronic device 5. The memory 51 may also be an external storage device of the electronic device 5, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the electronic device 5. Further, the memory 51 may also include both an internal storage unit of the electronic device 5 and an external storage device. The memory 51 is used to store the computer program and other programs and data required by the electronic device. The memory 51 may also be used to temporarily store data that has been output or is to be output.

[0180] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0181] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0182] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0183] In the embodiments provided by the present invention, it should be understood that the disclosed devices / electronic devices and methods can be implemented in other ways. For example, the device / electronic device embodiments described above are only schematic. For example, the division of the modules or units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0184] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0185] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0186] If the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, the steps of the above-mentioned low-carbon-oriented block spatial form optimization method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device that can carry the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium.

[0187] The embodiments described above are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included in the protection scope of the present invention.

Claims

1. A low-carbon oriented block spatial morphology optimization method, characterized in that: include: Acquire spatial morphological factors of the city to be planned, and construct a block parameter optimization and control model for the city to be planned based on the spatial morphological factors; Determining current operation simulation data of the city to be planned based on the block parameter optimization and control model; Based on the current operation simulation data and the preset optimization target, an optimization plan for the block space form of the city to be planned is obtained; wherein the preset optimization target includes the carbon emission intensity of building operation.

2. The low-carbon oriented block spatial morphology optimization method according to claim 1 is characterized in that: The spatial morphological factors include regulatory factors, descriptive factors, limiting factors and qualitative analysis factors; The control factors include street orientation and building floors; The descriptive factors include sky view, street aspect ratio, maintenance coefficient, shape coefficient and average area-to-perimeter ratio; The limiting factors include typical plot ratio and typical single building site size; The qualitative analysis factors include spatial morphology and layout; Wherein, the descriptive factor changes with the change of the regulating factor.

3. The low-carbon oriented block spatial morphology optimization method according to claim 1 is characterized in that: The step of constructing the block parameter optimization and control model of the city to be planned based on the spatial morphological factors includes: Generating the building forms included in the blocks in the city to be planned; The building form is adjusted based on the spatial morphological factors, and a block parameter optimization and control model for the city to be planned is constructed based on the adjusted building form.

4. The low-carbon oriented block spatial morphology optimization method according to claim 1 is characterized in that: The preset optimization targets also include carbon reduction from renewable energy and radiation difference of building exterior skin; The current operation simulation data includes current meteorological simulation data and equipment energy consumption simulation data; The step of obtaining a block space morphology optimization scheme for the city to be planned based on the current operation simulation data and the preset optimization target includes: Based on the preset optimization goal, determining a multi-objective optimization model; According to the running current simulation data and the multi-objective optimization model, the multi-objective optimization model is solved, and based on the solution result, an optimization plan for the block space form of the city to be planned is obtained.

5. The low-carbon oriented block spatial morphology optimization method according to claim 4 is characterized in that: The method of solving the multi-objective optimization model according to the running current simulation data and the multi-objective optimization model, and obtaining a block space morphology optimization plan for the city to be planned based on the solution result, includes: Calculating a current optimization target value based on the current operation simulation data and the preset optimization target; Inputting the current optimization target value, the morphology control parameters and the optimization algorithm parameters corresponding to the preset optimization algorithm into the multi-objective optimization model to determine the first building morphology cluster; wherein the morphology control parameters include the average number of floors, building orientation, shape coefficient, maintenance coefficient, average area-to-perimeter ratio, sky visible area and east-west street height-to-width ratio; Performing fitness evaluation on the first building form cluster, and generating a second building form cluster based on the evaluation result of the first building form cluster; Performing fitness evaluation on the second building form cluster, determining the evaluation result of the second building form cluster, and returning to the step of generating the second building form cluster based on the evaluation result of the first building form cluster, until a preset condition is met, and outputting the second building form cluster at this time as a solution result; Based on the second building form cluster at this time, an optimization plan for the block space form of the city to be planned is determined.

6. The low-carbon oriented block spatial morphology optimization method according to claim 4 is characterized in that: The multi-objective optimization model is: MinF(X)=[CEI(X),-CRRE(X),RD(X)] Subjectto:FAR(X)=FAR st bt i ∈[T-1,L-2] bf i ∈[6,9,24,30] O i ∈[-45°,-30°,-15°,0°,15°,30°,45°] Among them, CEI(X) is the objective function of the carbon emission intensity of building operation; CRRE(X) is the objective function of the carbon reduction of renewable energy; RD(X) is the objective function of the radiation difference of the building surface; FAR(X) is the block volume ratio determined based on the current operation simulation data; FAR st It is a typical floor area ratio constraint for urban commercial blocks; bt j Indicates the architectural combination form; bf j is the number of building floors; i is the street block orientation; where X={x1,x2,…x7} T is the morphology control parameter.

7. The low-carbon oriented block spatial morphology optimization method according to claim 4 is characterized in that: The carbon emission intensity of the building operation is: EC H =E H × f h Among them, EC E For electricity carbon emissions; EC H is thermal carbon emissions; i is different types of electric energy; E Ei is the consumption of the i-th energy; f E is the carbon emission factor of electricity; E H is the heating energy consumption; f H is the thermal carbon emission factor; A is the total building area; The carbon reduction amount of renewable energy is: CRRE=Q×S×η×EF e Where Q is the radiation of the photovoltaic module; S is the area of ​​the photovoltaic module; η is the conversion efficiency of the photovoltaic module; EF e is the carbon emission coefficient of electricity; The radiation difference of the building exterior skin is: RD=H t1 -H t2 H t =H b R b +H d R d +H dg Among them, H t1 is the daily average total radiation energy of the building surface in the cooling season; H t2 H is the daily average total radiation energy of the building surface in the heating season; b H is the average daily direct solar radiation energy received by the horizontal surface; d H is the average daily atmospheric scattered radiation energy received by the horizontal surface; dg R is the daily average ground reflected radiation energy received; b , R d is the correction coefficient; ρ is the ground reflectivity; β is the angle between the wall corner and the horizontal plane.

8. A low-carbon oriented block space morphology optimization device, characterized in that: include: A collection and construction module is used to obtain the spatial morphological factors of the city to be planned, and to construct a block parameter optimization and control model of the city to be planned based on the spatial morphological factors; A simulation operation module, used to determine the current operation simulation data of the city to be planned based on the block parameter optimization control model; The optimization module is used to obtain a block space morphology optimization plan for the city to be planned based on the current operation simulation data and the preset optimization target; wherein the preset optimization target includes the carbon emission intensity of building operation.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method as claimed in any one of claims 1 to 7 are implemented.

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