Building natural ventilation evolution iteration design method and device oriented to complex meteorological conditions and electronic equipment

Abstract

The invention relates to a complex meteorological condition-oriented building natural ventilation evolution iterative design method and device and electronic equipment, and the method comprises the steps: building a three-dimensional model of a corresponding building, and obtaining the indoor volume of a closed geometry in the three-dimensional model; the initial opening sash model and the building main body model are operated, and a middle opening sash setting rule meeting the ventilation requirement is obtained; establishing a simulation working condition according to the hourly meteorological parameters and carrying out hourly analog simulation to obtain the ventilation quantity of a preset analysis time period in the sub-region; according to the indoor volume of the corresponding sub-region, the number of ventilation times in the preset analysis time period and the average number of ventilation times of the building main body model are obtained; according to the method, an opening fan setting scheme with the highest ventilation frequency under a set condition is searched in combination with algorithm evolution iteration, more effective partition processing is carried out on a complex building space by applying a multi-region grid method, and through multi-working-condition analog simulation and weighted averaging of hourly meteorological data, the multi-working-condition meteorological data is obtained. And carrying out quantitative evaluation on the natural ventilation effect of the design building in a given analysis time period.

Description

Technical Field

[0001] The present invention relates to the field of building technology, and in particular to an evolutionary iterative design method, device and electronic equipment for building natural ventilation under complex meteorological conditions. Background Art

[0002] In current architectural design work, the two commonly used natural ventilation simulation algorithms, the multi-region grid method and computational fluid dynamics (CFD) and their derived tools, are generally less adaptable to curved and irregular building forms. The former has simple operation logic and can quickly provide an evaluation of the natural ventilation effect of the building. The latter uses fluid mechanics calculation tools to evaluate the flow of air in the building interior, and the effect of natural ventilation analysis is more refined and closer to the real effect, but its single round of calculation takes a long time.

[0003] In the simulation stage, the traditional typical tool algorithms are based on given fixed wind direction and wind speed for simulation. The fixed wind speed mainly comes from the average value of meteorological data statistics, and the wind direction comes from the mode of meteorological data statistics. It is difficult to simulate the complex actual wind environment state that changes every moment by using typical working conditions in a statistical sense, and the simulation results are also distorted to a certain extent. Summary of the invention

[0004] Based on this, it is necessary to provide an evolutionary iterative design method, device and electronic equipment for natural ventilation of buildings under complex meteorological conditions, which can perform real dynamic simulation and quickly evaluate ventilation effects, in order to address the above technical problems.

[0005] The present invention provides an evolutionary iterative design method for building natural ventilation under complex meteorological conditions, the method comprising:

[0006] Establishing a three-dimensional model of the corresponding building based on the plan view, section view and elevation view of the building, and obtaining the indoor volume of the closed geometric body in the three-dimensional model;

[0007] Based on the existing opening fan form of the building or the design language of the expected outer surface, initial opening fan setting parameters are obtained and preprocessed, an initial opening fan model is established according to the initial opening fan setting parameters, and a building main body model is established according to the indoor volume;

[0008] Running the initial opening fan model and the building main body model to obtain the intermediate opening fan setting rules that can meet the ventilation requirements;

[0009] Collect hourly meteorological parameters for the preset analysis period;

[0010] The main building model space is divided into multiple sub-areas, and a target opening fan model is established according to the intermediate opening fan setting rule, a simulation condition is established according to the hourly meteorological parameters, and hourly simulation is performed to obtain the ventilation volume in the sub-area during the preset analysis period, and the ventilation air exchange times in the preset analysis period and the average ventilation air exchange times of the main building model are obtained in combination with the indoor volume of the sub-area;

[0011] Based on the target opening fan model and the average ventilation times of the main building model, a genetic algorithm is iterated, and variable parameters in the intermediate opening fan setting rules are obtained as independent variables, and simulation result data is obtained as dependent variables. Based on the target opening fan model and the average ventilation times of the main building model, a genetic algorithm is used to iterate, and the opening fan setting scheme with the highest ventilation times under set conditions is searched, and a three-dimensional opening fan model is established according to the target opening fan setting rules corresponding to the iteration results, and a building drawing is output. In one embodiment, the establishment of the main building model and the initial opening fan model includes:

[0012] Acquire a logical relationship between the initial opening fan setting parameters and the indoor volume, wherein the initial opening fan setting parameters include constant parameters and variable parameters;

[0013] Matching the variable parameter corresponding to the indoor volume according to the logical relationship;

[0014] Establishing a main building model according to the indoor volume;

[0015] An initial opening fan model is established according to the constant parameters and the variable parameters.

[0016] In one embodiment, the step of obtaining a setting rule for the intermediate opening fan that can meet the ventilation requirements includes:

[0017] Obtain theoretical calculation data of target ventilation effect;

[0018] Running the building main body model and the initial opening fan model, and obtaining ventilation times;

[0019] Compare the ventilation air exchange rate with the theoretical calculation data of the target ventilation effect;

[0020] In response to the comparison result being non-compliance, changing the variable parameters and continuing to run the building main body model and the initial opening fan model;

[0021] In response to the comparison result being in compliance, the middle opening fan setting rule is outputted.

[0022] In one embodiment, collecting hourly meteorological parameters of a preset analysis period includes:

[0023] Obtaining a meteorological data file, wherein the meteorological data file is provided by a meteorological station or measured on site, recorded in one-hour steps, and includes wind speed, wind direction, and temperature data;

[0024] Meteorological data of a preset analysis period is intercepted, and the preset analysis period is a period when the building adopts a natural ventilation strategy.

[0025] In one embodiment, the ventilation times during the preset analysis period are obtained by calculating the formula It is calculated that, among them, ACH i is the ventilation rate, V i is the ventilation volume V in the ith hour r is the indoor volume;

[0026] The average ventilation rate is calculated by the formula It is calculated that, is the average ventilation rate, ACH i is the ventilation frequency and i is the number of hours.

[0027] In one embodiment, the genetic algorithm iteration based on the target opening fan model and the average ventilation times of the building main body model includes:

[0028] Obtain the variable parameters in the setting rules of the intermediate opening fan as independent variables, and obtain the simulation result data as dependent variables, and use the genetic algorithm to iterate based on the average ventilation times of the target opening fan model and the main building model;

[0029] A three-dimensional model of the opening sash is established according to the target opening sash setting rules corresponding to the iteration results, and the architectural drawings are output.

[0030] In one embodiment, the iterating using a genetic algorithm comprises:

[0031] Generate an initial population based on the number of samples set by the user or by default;

[0032] Sort the previous population according to the evaluation index;

[0033] Based on the sorting results, an elite retention strategy is adopted to retain the best solution in the previous population to the next generation;

[0034] Generate the next generation population based on the user-set or default crossover rate and mutation rate;

[0035] Repeat the above steps based on the number of optimization generations set by the user or by default.

[0036] The present invention also provides a building natural ventilation evolution iteration device for complex meteorological conditions, the device comprising:

[0037] A modeling module, used to establish a three-dimensional model of the corresponding building based on the floor plan, section view and elevation view of the building, and obtain the indoor volume of the closed geometric body in the three-dimensional model;

[0038] An acquisition module is used to acquire and pre-process initial opening fan setting parameters based on the existing opening fan form of the building or the design language of the expected external surface, establish an initial opening fan model according to the initial opening fan setting parameters, and establish a building main body model according to the indoor volume;

[0039] A calibration module runs the initial opening fan model and the building main body model to obtain the intermediate opening fan setting rules that can meet the ventilation requirements;

[0040] A collection module is used to collect hourly meteorological parameters during a preset analysis period;

[0041] A simulation module is used to divide the main building model space into multiple sub-areas, and establish a target opening fan model according to the intermediate opening fan setting rule, establish a simulation condition according to the hourly meteorological parameters and perform hourly simulation, obtain the ventilation volume in the sub-area during the preset analysis period, and obtain the ventilation air exchange rate in the preset analysis period and the average ventilation air exchange rate of the main building model in combination with the indoor volume of the sub-area;

[0042] The evolutionary iteration module is used to perform genetic algorithm iteration based on the target opening fan model and the average ventilation times of the building main model, obtain the variable parameters in the intermediate opening fan setting rules as independent variables, and obtain the simulation result data as dependent variables. Based on the target opening fan model and the average ventilation times of the building main model, the genetic algorithm is used to iterate, search for the opening fan setting scheme with the highest ventilation times under the set conditions, establish the opening fan three-dimensional model according to the target opening fan setting rules corresponding to the iteration results, and output the building drawings.

[0043] The present invention also provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, it implements any of the above-mentioned evolutionary iterative design methods for natural ventilation of buildings under complex meteorological conditions.

[0044] The above-mentioned evolutionary iterative design method, device and electronic equipment for building natural ventilation under complex meteorological conditions can more accurately focus on the time period that has a significant impact on the natural ventilation of the building by specifying the analysis time period, avoid redundant calculations of irrelevant time, and use the multi-region grid method to more effectively partition the complex building space and reduce unnecessary calculations. Therefore, when processing hourly meteorological data, results can be obtained faster. The method combines multi-condition simulation and weighted averaging of hourly meteorological data to quantitatively evaluate the natural ventilation effect of the designed building within a given analysis time period. This method is closer to the actual natural ventilation condition, more scientific than traditional natural ventilation simulation from a methodological point of view, and can better reflect the ventilation effect of the actual building. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the technical solutions in the present invention or the prior art, a brief introduction is given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0046] Figure 1 A flowchart of an evolutionary iterative design method for natural ventilation of buildings under complex meteorological conditions according to an embodiment;

[0047] Figure 2 A flowchart of the implementation steps of an evolutionary iterative design method for natural ventilation of buildings under complex meteorological conditions according to an embodiment;

[0048] Figure 3 A flow chart of establishing a building main body model and an initial opening fan model according to an embodiment;

[0049] Figure 4 A flowchart of obtaining a fan opening setting rule that can meet ventilation requirements according to an embodiment;

[0050] Figure 5 A flowchart of collecting hourly meteorological parameters for a preset analysis period in one embodiment

[0051] Figure 6 A flowchart of an embodiment of performing genetic algorithm evolution iteration based on the target fan opening model and the average ventilation times of the building main body model;

[0052] Figure 7 A genetic algorithm operation flow chart of an embodiment;

[0053] Figure 8 It is a data graph of evolutionary iteration results of an embodiment;

[0054] Fig. 9This is a schematic diagram of the structure of an evolutionary iterative device for natural ventilation of buildings under complex meteorological conditions according to an embodiment;

[0055] Fig.10 The figure is a diagram showing the internal structure of a computer device according to an embodiment. DETAILED DESCRIPTION

[0056] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0057] Natural ventilation in buildings refers to the phenomenon of using natural wind pressure, air temperature difference, air density difference and other factors to promote air flow, so that the indoor and outdoor air of the building are exchanged. Using natural factors to achieve air exchange can reduce energy consumption in the indoor ventilation process while removing indoor pollutants and improving environmental comfort. Therefore, it is considered a green and healthy building design measure. In current architectural design work, obstacles to the application of natural ventilation techniques include the problem that typical simulation working methods are insufficient.

[0058] At present, typical natural ventilation simulation optimization tools in the industry have shortcomings in both three-dimensional modeling and simulation. Common natural ventilation simulation algorithms include multi-region grid method and computational fluid dynamics (CFD). The former has simple operation logic and can quickly provide an evaluation of the natural ventilation effect of the building, but the analysis does not include the specific flow path of the air in the room, so it cannot be used to evaluate indicators such as indoor air quality. Similarly, it is not completely suitable for spaces with high net heights such as building atriums; the latter uses fluid mechanics calculation tools to evaluate the flow of air in the building, and the effect of natural ventilation analysis is more refined and closer to the real effect, but its single-round operation takes a long time and has high requirements for computer hardware.

[0059] In terms of three-dimensional modeling, the two commonly used algorithms and their derived tools generally have poor adaptability to curves and special-shaped building forms, and do not have the function of parametric adjustment of models. Manual modification of building models is time-consuming and labor-intensive. In the simulation link, traditional typical tool algorithms are based on given fixed wind direction and wind speed for simulation. This fixed wind speed mainly comes from the average value of meteorological data statistics, and the wind direction comes from the mode of meteorological data statistics, which is not accurate enough.

[0060] It is difficult to represent the complex actual wind environment state that changes every moment by using typical working conditions in a statistical sense for simulation, and the simulation results also have certain distortion.

[0061] In view of the above technical problems, the following Figure 1-Figure 9 The invention describes the evolutionary iterative design method, device and electronic equipment for natural ventilation of buildings under complex meteorological conditions.

[0062] like Figure 1 and Figure 2 As shown, in one embodiment, an evolutionary iterative design method for building natural ventilation under complex meteorological conditions includes the following steps:

[0063] Step S110, establishing a three-dimensional model of the corresponding building based on the plan view, section view and elevation view of the building, and obtaining the indoor volume of the closed geometric body in the three-dimensional model.

[0064] Specifically, the model should have opaque exterior walls, light-transmitting enclosure structures, roofs, floors, building interior floors and partitions. The combination of opaque exterior walls, light-transmitting enclosure structures, roofs and floors should be able to form a continuous and closed complete geometric surface to obtain the estimated indoor volume V of the building. r , used to calculate ventilation times.

[0065] Step S120, based on the existing opening fan form of the building or the design language of the expected exterior surface, initial opening fan setting parameters are obtained and preprocessed, an initial opening fan model is established according to the initial opening fan setting parameters, and a building main body model is established according to the indoor volume.

[0066] Specifically, the initial opening sash setting parameters include the size (length, width, height) of the opening sash, the opening angle and the number of opening sashes, which are read from the existing or preliminarily designed model of the building, or are parameterized by establishing rules based on the exterior surface design language expected by the architectural design (including but not limited to the distribution ratio and step length of the glass curtain wall and the solid wall, the setting spacing between two or more opening sashes, and the offset distance of the opening sashes between the upper and lower floors); after obtaining the initial opening sash setting parameters, data processing is performed. If some size parameters of the opening sash are missing, reasonable supplements are made based on similar models or common standards. For obviously unreasonable oversized or undersized size data, they need to be marked for further verification or correction. Subsequently, the initial opening sash setting parameters of different units or magnitudes are uniformly converted to the same unit, and the numerical range is normalized to the [0,1] interval. Then, key features such as the area, shape category and position category of the opening sash are extracted from numerous parameters for model construction.

[0067] Step S130, running the initial opening fan model and the building main body model to obtain the intermediate opening fan setting rules that can meet the ventilation requirements.

[0068] Specifically, the rules should include the facade where the opening sash is located, the restrictions on the total area of ​​the building's opening sash, the distribution ratio of the total area of ​​the opening sash on each facade, the linkage setting rules of the opening sash length and width, etc.

[0069] Step S140, collecting hourly meteorological parameters of a preset analysis period.

[0070] For example, suppose we have the following weather data:

[0071]

[0072]

[0073] If, after analysis, it is determined that the meteorological conditions during the period from 10:00 to 14:00 are most suitable for natural ventilation, this part of the data can be intercepted for simulation.

[0074] Step S150, divide the main building model space into multiple sub-areas, and establish a target opening fan model according to the intermediate opening fan setting rules, establish a simulation condition according to the hourly meteorological parameters and perform hourly simulation, obtain the ventilation volume of the preset analysis period in the sub-area, and calculate the ventilation exchange times of the preset analysis period and the average ventilation exchange times of the main building model in combination with the indoor volume of the sub-area.

[0075] Specifically, during the hourly simulation process, according to the requirements of the national standard GB / T 50378-2019 "Green Building Evaluation Standard", 2 times / hour is used as the basis for meeting the standard of natural ventilation of the building. If the simulation analysis result is lower than 2 times / hour, the window-to-floor ratio will be increased, otherwise it will be reduced until the analysis result is just higher than 2 times / hour.

[0076] The average ventilation rate is an important indicator to measure the comprehensive performance of the ventilation system under various complex conditions. By calculating the average ventilation rate, it is possible to determine whether the ventilation effect of the current design meets the requirements. If not, the length, width and opening angle of the opening fan can be optimized and adjusted according to the analysis results to improve the ventilation efficiency.

[0077] Step S160, based on the target opening fan model and the average ventilation times of the main building model, a genetic algorithm is iterated, variable parameters in the intermediate opening fan setting rules are obtained as independent variables, and simulation result data are obtained as dependent variables, based on the target opening fan model and the average ventilation times of the main building model, a genetic algorithm is used to iterate, search for the opening fan setting scheme with the highest ventilation times under the set conditions, establish an opening fan three-dimensional model according to the target opening fan setting rules corresponding to the iteration results, and output the building drawings. .

[0078] Specifically, a genetic algorithm is introduced, and the characteristic of fast simulation feedback of the "multi-region grid method" is used to perform algorithm evolution and iteration on the opening fan setting scheme.

[0079] The evolutionary iterative design method for natural ventilation of buildings under complex meteorological conditions in this embodiment can more accurately focus on the time periods that have a significant impact on the natural ventilation of buildings by specifying the analysis time period, avoid redundant calculations of irrelevant time, and use the multi-region grid method to more effectively partition the complex building space and reduce unnecessary calculations, so that when processing hourly meteorological data, results can be obtained faster. The method combines multi-condition simulation and weighted averaging of hourly meteorological data to quantitatively evaluate the natural ventilation effect of the designed building within a given analysis time period, which is closer to the actual natural ventilation condition. From a methodological point of view, it is more scientific than traditional natural ventilation simulation and can better reflect the ventilation effect of the actual building.

[0080] like Figure 3 As shown, in one embodiment, establishing the building main body model and the initial opening fan model includes:

[0081] Step S122, obtaining the logical relationship between the initial opening fan setting parameters and the indoor volume, the initial opening fan setting parameters including constant parameters and variable parameters.

[0082] Step S124, matching corresponding variable parameters for the indoor volume according to the logical relationship.

[0083] Step S126, establishing a main building model according to the indoor volume.

[0084] Step S128, establishing an initial opening fan model according to constant parameters and variable parameters.

[0085] Specifically, the light-transmitting part and the light-impermeable part in the main structure of the building are distinguished, the light-transmitting part is used as the opening fan arrangement area, the opening fan setting spacing, the opening height, and the proportion of the opening fan area of ​​each facade to the total opening fan area are set as variable parameters, and a step size is given.

[0086] like Figure 4 As shown, in one embodiment, the intermediate opening fan setting rule that can meet the ventilation requirements is obtained, including:

[0087] Step S121, obtaining theoretical calculation data of target ventilation effect.

[0088] Step S123, running the main building model and the initial fan opening model, and obtaining the ventilation times.

[0089] Step S125, comparing the ventilation frequency with the theoretical calculation data of the target ventilation effect.

[0090] Step S127, in response to the comparison result being non-compliant, changing the variable parameters and continuing to run the building main body model and the initial opening fan model.

[0091] Step S129: in response to the comparison result being in compliance, outputting the middle open fan setting rule.

[0092] Preferably, the spacing can dynamically change between 0.5m and 3m, the opening height can dynamically change between 0.6m and 2m, the sum of the opening sash area ratios of the four facades facing east, south, west and north should be 100%, and the opening sash area set on a single facade should not be greater than 50% of the total opening sash area. The ratio of the opening sash area of ​​each facade to the total opening sash area is reset to 1:1:1:1, and the window-to-floor ratio (total opening sash area / building area) is set to 1%.

[0093] like Figure 5 As shown, in one embodiment, collecting hourly meteorological parameters of a preset analysis period includes:

[0094] Step S131, obtaining a meteorological data file, which is provided by a meteorological station or measured on site, recorded in one-hour steps, and includes wind speed, wind direction and temperature data.

[0095] Step S133, intercepting the meteorological data of a preset analysis period, where the preset analysis period is a period when the building adopts a natural ventilation strategy.

[0096] In this embodiment, the ventilation times during the preset analysis period are obtained by calculating the formula It is calculated that, among them, ACH i is the ventilation rate, V i is the ventilation volume V in the ith hour r is the indoor volume.

[0097] The average ventilation rate is calculated by the formula It is calculated that, is the average ventilation rate, ACH i is the ventilation frequency and i is the number of hours.

[0098] Specifically, the meteorological parameters of the project site are selected, and according to the provisions of the national standard GB / T 42074-2022 "Climate Season Division", the two periods of spring-summer and autumn-winter at the project site are extracted as simulation analysis sections based on the average temperature dropping / rising to 10℃ / 22℃ for five consecutive days.

[0099] At the same time, in current architectural design work, the obstacles to the application of natural ventilation techniques also include the complexity of the natural ventilation mechanism.

[0100] Natural ventilation can be caused by factors such as wind pressure and thermal pressure. The mechanisms of action of different factors are different, and most architectural designers do not have an intuitive understanding of them. The evaluation of natural ventilation effects is still based on simulation work by professional green building engineers based on drawings, and then the simulation results are fed back to the designer, who then makes corresponding adjustments to the plan. This workflow involves repeated transmission of files and information, and the time cost of communication between the two parties is high, and it is difficult to guarantee the number of optimization rounds and optimization effects.

[0101] Due to the above obstacles, the current natural ventilation design of buildings in the industry is mainly achieved by opening windows in large areas, increasing the number of opening panels or increasing the area of ​​a single opening panel. However, designers usually have no quantitative understanding of the opening panel area that should be set at the initial stage of the plan, and subsequent adjustments to the plan will increase the workload of the design.

[0102] like Figure 6 As shown, in one embodiment, the genetic algorithm evolution iteration is performed based on the target opening fan model and the average ventilation times of the building main body model, including:

[0103] Step S162, obtaining the variable parameters in the intermediate opening fan setting rules as independent variables, and obtaining the simulation result data as dependent variables, and using a genetic algorithm to perform evolutionary iteration based on the average ventilation times of the target opening fan model and the building main body model.

[0104] Specifically, the spacing, opening height, and the ratio of the opening area of ​​the four facades of east, south, west and north set in the opening fan setting rules were used as independent variables, and the number of ventilation times was input into the genetic algorithm as the dependent variable. The mutation rate was set to 0.9, the crossover rate was set to 0.2, the sample size per generation was set to 20, the sample generation number was set to 20, and the condition of "terminating the evolutionary iteration early when all samples are consistent" was set to carry out computer automated simulation and evolutionary iteration.

[0105] Step S164, establishing a three-dimensional model of the opening sash according to the opening sash setting rules corresponding to the evolutionary iteration result, and outputting the architectural drawing.

[0106] Specifically, after the evolutionary iteration is terminated, the optimal solution output by the algorithm is selected, and the independent variable is set to the value corresponding to the optimal solution. At this time, the parametric modeling tool will generate the opening fan shape under this condition in the drawing space. The corresponding frame of the opening fan shape in the three-dimensional model is extracted and copied to the architectural drawing for drawing.

[0107] The evolutionary iterative design method for natural ventilation of buildings under complex meteorological conditions in this embodiment has a simpler process and operation method compared with the traditional natural ventilation optimization design working method. After the software is formed, the designer can realize the automated natural ventilation simulation optimization work, eliminating the lengthy steps of reciprocating adjustment and feedback. The genetic algorithm is combined to evolve and iterate the window opening form of the architectural scheme to obtain the optimal opening fan setting position and form under the shape conditions of the architectural scheme. This design method can obtain a large number of examples in a short time, and obtain the most cost-effective opening fan setting scheme through comparison and selection. The convergence speed of the genetic algorithm is faster than that of the exhaustive method.

[0108] like Figure 7 As shown, in one embodiment, a genetic algorithm is used to perform evolutionary iterations, including:

[0109] Step S161, generating an initial population according to the sample quantity set by the user or by default.

[0110] Step S163, sorting the preceding population according to the evaluation index.

[0111] Step S165, adopting an elite retention strategy based on the sorting result to retain the optimal solution in the previous population to the next generation.

[0112] Step S167, generating the next generation population based on the crossover rate and mutation rate set by the user or by default.

[0113] Step S169, repeating the above steps based on the evolutionary iteration number set by the user or by default.

[0114] The evolutionary iterative device for building natural ventilation for complex meteorological conditions provided by the present invention is described below. The evolutionary iterative device for building natural ventilation for complex meteorological conditions described below and the evolutionary iterative design method for building natural ventilation for complex meteorological conditions described above can be referenced to each other.

[0115] like Figure 8 As shown, based on the same building model, under the meteorological conditions of different cities, the above-mentioned evolutionary iterative design method for building natural ventilation under complex meteorological conditions is adopted to carry out evolutionary iteration of opening fan design.

[0116] Among them, taking Lanzhou as an example, under the condition of a square plane, assuming that the building area is 1000 square meters, the total opening fan area required is 1000*1.7%=17 square meters, of which the north and south sides are both 17*18.75%=3.1875 square meters, and the east and west sides are both 17*31.25%=5.3125 square meters.

[0117] It should be noted that Figure 8The background color of the area distribution ratio of the center opening fan on the four external surfaces of the building is a gradient color formed based on the area distribution ratio. When the area distribution ratio is 0%, the displayed background color is red. When the ratio reaches 50%, the displayed background color is blue. That is, in the range of 0% to 50%, the background color will gradually transition from red to blue.

[0118] like Fig. 9 As shown, in one embodiment, a natural ventilation evolution iteration device for buildings facing complex meteorological conditions includes:

[0119] The modeling module 610 is used to establish a three-dimensional model of the corresponding building based on the floor plan, section view and elevation view of the building, and obtain the indoor volume of the closed geometric body in the three-dimensional model.

[0120] The acquisition module 620 is used to acquire initial opening fan setting parameters and perform preprocessing, establish an initial opening fan model according to the initial opening fan setting parameters, and establish a building main body model according to the indoor volume.

[0121] The calibration module 630 runs the initial opening fan model and the building main body model to obtain the intermediate opening fan setting rules that can meet the ventilation requirements.

[0122] The collection module 640 is used to collect hourly meteorological parameters during a preset analysis period.

[0123] The simulation module 650 is used to divide the main building model space into multiple sub-areas, and establish a target opening fan model according to the intermediate opening fan setting rules, establish a simulation condition according to the hourly meteorological parameters and perform hourly simulation, obtain the ventilation volume in the sub-area during the preset analysis period, and calculate the ventilation exchange times in the preset analysis period and the average ventilation exchange times of the main building model in combination with the indoor volume of the sub-area.

[0124] The evolutionary iteration module 660 is used to perform genetic algorithm evolutionary iteration based on the target opening fan model and the average ventilation times of the building main body model, and search for the opening fan setting scheme with the highest ventilation times under the set conditions.

[0125] Fig.10 An example of a physical structure diagram of an electronic device is shown. The electronic device may be a smart terminal, and its internal structure diagram may be as follows: Fig.10As shown. The electronic device includes a processor, a memory and a network interface connected via a system bus. The processor of the electronic device is used to provide computing and control capabilities. The memory of the electronic device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the electronic device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, an evolutionary iterative design method for natural ventilation of a building facing complex meteorological conditions is implemented, and the method includes:

[0126] A three-dimensional model of the corresponding building is established based on the floor plan, section view and elevation view of the building, and the indoor volume of the closed geometric body in the three-dimensional model is obtained.

[0127] Initial opening fan setting parameters are obtained and preprocessed, an initial opening fan model is established according to the initial opening fan setting parameters, and a building main body model is established according to the indoor volume.

[0128] Run the initial opening fan model and the main building model to obtain the intermediate opening fan setting rules that can meet the ventilation requirements.

[0129] Collect hourly meteorological parameters for the preset analysis period.

[0130] The main building model space is divided into multiple sub-areas, and the target opening fan model is established according to the setting rules of the middle opening fan. The simulation working condition is established according to the hourly meteorological parameters and hourly simulation is performed to obtain the ventilation volume in the preset analysis period in the sub-area. Combined with the indoor volume of the sub-area, the ventilation air exchange times in the preset analysis period and the average ventilation air exchange times of the main building model are calculated.

[0131] Based on the target opening fan model and the average ventilation times of the building main model, a genetic algorithm evolutionary iteration is performed to search for the opening fan setting scheme with the highest ventilation times under the set conditions.

[0132] Those skilled in the art will understand that Fig.10 The structure shown in the figure is only a block diagram of a part of the structure related to the scheme of the present invention, and does not constitute a limitation on the electronic device to which the scheme of the present invention is applied. The specific electronic device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0133] On the other hand, the present invention further provides a computer storage medium storing a computer program, which, when executed by a processor, implements an evolutionary iterative design method for building natural ventilation under complex meteorological conditions, the method comprising:

[0134] A three-dimensional model of the corresponding building is established based on the floor plan, section view and elevation view of the building, and the indoor volume of the closed geometric body in the three-dimensional model is obtained.

[0135] Based on the existing opening fan form of the building or the design language of the expected exterior surface, the initial opening fan setting parameters are obtained and preprocessed, an initial opening fan model is established according to the initial opening fan setting parameters, and the main building model is established according to the indoor volume.

[0136] Run the initial opening fan model and the main building model to obtain the intermediate opening fan setting rules that can meet the ventilation requirements.

[0137] Collect hourly meteorological parameters for the preset analysis period.

[0138] The main building model space is divided into multiple sub-areas, and the target opening fan model is established according to the setting rules of the middle opening fan. The simulation working condition is established according to the hourly meteorological parameters and hourly simulation is performed to obtain the ventilation volume in the preset analysis period in the sub-area. Combined with the indoor volume of the sub-area, the ventilation air exchange times in the preset analysis period and the average ventilation air exchange times of the main building model are calculated.

[0139] Genetic algorithm iteration is performed based on the target opening fan model and the average ventilation times of the main building model, variable parameters in the intermediate opening fan setting rules are obtained as independent variables, and simulation result data are obtained as dependent variables. Based on the target opening fan model and the average ventilation times of the main building model, genetic algorithm is used to iterate, search for the opening fan setting scheme with the highest ventilation times under the set conditions, establish the opening fan three-dimensional model according to the target opening fan setting rules corresponding to the iteration results, and output the building drawings. .

[0140] In another aspect, a computer program product or a computer program is provided, the computer program product or the computer program comprising computer instructions, the computer instructions being stored in a computer-readable storage medium. A processor of an electronic device reads the computer instructions from the computer-readable storage medium, and when the processor executes the computer instructions, an evolutionary iterative design method for natural ventilation of a building facing complex meteorological conditions is implemented, the method comprising:

[0141] A three-dimensional model of the corresponding building is established based on the floor plan, section view and elevation view of the building, and the indoor volume of the closed geometric body in the three-dimensional model is obtained.

[0142] Based on the existing opening fan form of the building or the design language of the expected exterior surface, the initial opening fan setting parameters are obtained and preprocessed, an initial opening fan model is established according to the initial opening fan setting parameters, and the main building model is established according to the indoor volume.

[0143] Run the initial opening fan model and the main building model to obtain the intermediate opening fan setting rules that can meet the ventilation requirements.

[0144] Collect hourly meteorological parameters for the preset analysis period.

[0145] The main building model space is divided into multiple sub-areas, and the target opening fan model is established according to the setting rules of the middle opening fan. The simulation working condition is established according to the hourly meteorological parameters and hourly simulation is performed to obtain the ventilation volume in the preset analysis period in the sub-area. Combined with the indoor volume of the sub-area, the ventilation air exchange times in the preset analysis period and the average ventilation air exchange times of the main building model are calculated.

[0146] Based on the target opening fan model and the average ventilation times of the main building model, a genetic algorithm is iterated to obtain variable parameters in the intermediate opening fan setting rules as independent variables, and simulation result data is obtained as dependent variables. Based on the target opening fan model and the average ventilation times of the main building model, a genetic algorithm is used to iterate to search for an opening fan setting scheme with the highest ventilation times under set conditions, and a three-dimensional opening fan model is established according to the target opening fan setting rules corresponding to the iteration results, and a building drawing is output.

[0147] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing related hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided by the present invention may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory.

[0148] By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0149] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0150] The above-described embodiments only express several implementation methods of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the present invention. It should be pointed out that, for a person of ordinary skill in the art, several modifications and improvements can be made without departing from the concept of the present invention, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the attached claims.

Claims

1. An evolutionary iterative design method for building natural ventilation under complex meteorological conditions, characterized in that: The method comprises: Establishing a three-dimensional model of the corresponding building based on the plan view, section view and elevation view of the building, and obtaining the indoor volume of the closed geometric body in the three-dimensional model; Based on the existing opening fan form of the building or the design language of the expected outer surface, initial opening fan setting parameters are obtained and preprocessed, an initial opening fan model is established according to the initial opening fan setting parameters, and a building main body model is established according to the indoor volume; Running the initial opening fan model and the building main body model to obtain the intermediate opening fan setting rules that can meet the ventilation requirements; Collect hourly meteorological parameters for the preset analysis period; The main building model space is divided into multiple sub-areas, and a target opening fan model is established according to the intermediate opening fan setting rule, a simulation condition is established according to the hourly meteorological parameters, and hourly simulation is performed to obtain the ventilation volume in the sub-area during the preset analysis period, and the ventilation air exchange times in the preset analysis period and the average ventilation air exchange times of the main building model are obtained in combination with the indoor volume of the sub-area; Based on the target opening fan model and the average ventilation times of the main building model, a genetic algorithm is iterated to obtain variable parameters in the intermediate opening fan setting rules as independent variables, and simulation result data is obtained as dependent variables. Based on the target opening fan model and the average ventilation times of the main building model, a genetic algorithm is used to iterate to search for an opening fan setting scheme with the highest ventilation times under set conditions, and a three-dimensional opening fan model is established according to the target opening fan setting rules corresponding to the iteration results, and a building drawing is output.

2. The evolutionary iterative design method for building natural ventilation under complex meteorological conditions according to claim 1 is characterized in that: The step of establishing the building main body model and the initial opening fan model includes: Acquire a logical relationship between the initial opening fan setting parameters and the indoor volume, wherein the initial opening fan setting parameters include constant parameters and variable parameters; Matching the variable parameter corresponding to the indoor volume according to the logical relationship; Establishing a main building model according to the indoor volume; An initial opening fan model is established according to the constant parameters and the variable parameters.

3. The evolutionary iterative design method for building natural ventilation under complex meteorological conditions according to claim 2 is characterized in that: The intermediate opening fan setting rules that can meet the ventilation requirements include: Obtain theoretical calculation data of target ventilation effect; Running the building main body model and the initial opening fan model, and obtaining ventilation times; Compare the ventilation air exchange rate with the theoretical calculation data of the target ventilation effect; In response to the comparison result being non-compliance, changing the variable parameters and continuing to run the building main body model and the initial opening fan model; In response to the comparison result being in compliance, the middle opening fan setting rule is outputted.

4. The evolutionary iterative design method for building natural ventilation under complex meteorological conditions according to claim 1 is characterized in that: The collecting of hourly meteorological parameters during a preset analysis period includes: Obtaining a meteorological data file, wherein the meteorological data file is provided by a meteorological station or measured on site, recorded in one-hour steps, and includes wind speed, wind direction, and temperature data; Meteorological data of a preset analysis period is intercepted, and the preset analysis period is a period when the building adopts a natural ventilation strategy.

5. The evolutionary iterative design method for building natural ventilation under complex meteorological conditions according to claim 1 is characterized in that: The ventilation times during the preset analysis period are obtained by calculating the formula It is calculated that, among them, ACH i is the ventilation rate, V i is the ventilation volume V in the ith hour r is the indoor volume; The average ventilation rate is calculated by the formula It is calculated that, is the average ventilation rate, ACH i is the ventilation frequency and i is the number of hours.

6. The evolutionary iterative design method for building natural ventilation under complex meteorological conditions according to claim 1 is characterized in that: The iterative process using a genetic algorithm comprises: Generate an initial population based on the number of samples set by the user or by default; Sort the previous population according to the evaluation index; Based on the sorting results, an elite retention strategy is adopted to retain the best solution in the previous population to the next generation; Generate the next generation population based on the user-set or default crossover rate and mutation rate; Repeat the above steps based on the number of iterations set by the user or by default.

7. An evolutionary iterative design device for building natural ventilation under complex meteorological conditions, characterized in that: The device comprises: A modeling module, used to establish a three-dimensional model of the corresponding building based on the floor plan, section view and elevation view of the building, and obtain the indoor volume of the closed geometric body in the three-dimensional model; An acquisition module is used to acquire and pre-process initial opening fan setting parameters based on the existing opening fan form of the building or the design language of the expected external surface, establish an initial opening fan model according to the initial opening fan setting parameters, and establish a building main body model according to the indoor volume; A calibration module runs the initial opening fan model and the building main body model to obtain the intermediate opening fan setting rules that can meet the ventilation requirements; A collection module is used to collect hourly meteorological parameters during a preset analysis period; A simulation module is used to divide the main building model space into multiple sub-areas, and establish a target opening fan model according to the intermediate opening fan setting rule, establish a simulation condition according to the hourly meteorological parameters and perform hourly simulation, obtain the ventilation volume in the sub-area during the preset analysis period, and obtain the ventilation air exchange rate in the preset analysis period and the average ventilation air exchange rate of the main building model in combination with the indoor volume of the sub-area; The iteration module is used to perform genetic algorithm iteration based on the target opening fan model and the average ventilation times of the building main model, obtain the variable parameters in the intermediate opening fan setting rules as independent variables, and obtain the simulation result data as dependent variables. Based on the target opening fan model and the average ventilation times of the building main model, the genetic algorithm is used to iterate, search for the opening fan setting scheme with the highest ventilation times under the set conditions, establish an opening fan three-dimensional model according to the target opening fan setting rules corresponding to the iteration results, and output the building drawings.

8. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. 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 according to any one of claims 1 to 6 are implemented.

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