Architectural design-oriented carbon emission estimation method and device

Abstract

The invention discloses an architectural design-oriented carbon emission estimation method and device. The building design-oriented carbon emission estimation method comprises the steps of obtaining carbon emission information in a preset time period corresponding to a preset building closest to building basic information of a to-be-evaluated building, wherein the carbon emission information is called first carbon emission information; estimating carbon emission information in a preset time period according to the building basic information, wherein the carbon emission information is called second carbon emission information; obtaining a trained prediction neural network; and inputting the first carbon emission information and the second carbon emission information into the trained prediction neural network so as to obtain the carbon emission estimation information of the building to be evaluated. According to the method, on one hand, the carbon emission information of the to-be-evaluated building serves as one feature, on the other hand, the actual carbon emission information of an existing building similar to the to-be-evaluated building serves as the other feature, and therefore data information brought by the two features can be considered at the same time during prediction.

Description

Technical Field

[0001] The present application relates to the technical field of building carbon emissions, and in particular to a carbon emissions estimation method for building design and a carbon emissions estimation device for building design. Background Art

[0002] In theory, in the early stages of building design, an approximate estimate of the carbon emissions after the building is completed should be made, so that when conducting overall urban planning, a better overall planning of carbon emissions can be made.

[0003] In the prior art, the estimation of carbon emissions of buildings that have not yet been built is mainly based on the conditions of the buildings themselves. This estimation completely ignores various changing conditions in actual operation, and therefore often leads to inaccurate estimation. Summary of the invention

[0004] The purpose of the present invention is to provide a carbon emission estimation method for building design to solve at least one of the above technical problems.

[0005] One aspect of the present invention provides a carbon emission estimation method for building design, the carbon emission estimation method for building design comprising:

[0006] Obtain basic architectural information of the building to be assessed;

[0007] Acquire a building database, wherein the building database includes a plurality of preset buildings and basic building information corresponding to each preset building;

[0008] Comparing the basic building information of the building to be evaluated with the basic building information corresponding to each preset building, respectively, to obtain the preset building corresponding to the basic building information of the preset building that is closest to the basic building information of the building to be evaluated;

[0009] Acquire a historical building carbon emission database, wherein the historical carbon emission database includes at least one preset building and carbon emission information within a preset time period corresponding to each preset building;

[0010] Acquire carbon emission information within a preset time period corresponding to a preset building that is closest to the basic building information of the building to be evaluated, the carbon emission information being referred to as first carbon emission information;

[0011] Estimate carbon emission information within a preset time period based on the building basic information of the building to be evaluated, where the carbon emission information is called second carbon emission information;

[0012] Get the trained prediction neural network;

[0013] The first carbon emission information and the second carbon emission information are input into the trained prediction neural network to obtain carbon emission estimation information of the building to be evaluated.

[0014] Optionally, the basic building information of the building to be evaluated includes building type, building area, and basic indoor equipment parameters of the building;

[0015] The basic building information corresponding to the preset building includes the building type of the preset building, the building area of ​​the preset building, and the basic building indoor equipment parameters of the preset building.

[0016] Optionally, comparing the basic building information of the building to be evaluated with the basic building information corresponding to each preset building, and acquiring the preset building corresponding to the basic building information that is closest to the basic building information of the building to be evaluated comprises:

[0017] Obtaining the building area of ​​the preset building corresponding to the building type of each preset building of the same building type and the building basic indoor equipment parameters of the preset building;

[0018] Select a preset building whose building area and building area are less than the threshold value from among the preset buildings, and the preset building that meets the condition is called the first preset building;

[0019] The one whose basic indoor equipment parameters of each building in each first preset building are closest to the basic indoor equipment parameters of the building is obtained as the preset building to be used.

[0020] Optionally, the basic building information includes the designed flow of people in the building, the number of rooms in the building, and preset indoor equipment parameters;

[0021] The method of estimating carbon emission information within a preset time period based on the basic building information of the building to be evaluated includes:

[0022] According to the basic building information, the indoor airflow data of the building to be evaluated is obtained through CFD calculation;

[0023] When the indoor environment of the building to be evaluated meets the human comfort requirements, the energy consumption data of the preset indoor equipment parameters of the building to be evaluated in a preset time period are obtained according to the indoor airflow data of the building to be evaluated;

[0024] According to the energy consumption data of the preset indoor equipment parameters of the building to be evaluated in the preset time period, a set algorithm is adopted to calculate the carbon emissions of the building to be evaluated in the preset time period.

[0025] Optionally, the prediction neural network is an LSTM neural network.

[0026] Optionally, the carbon emission estimation method for building design further comprises:

[0027] The prediction neural network is trained.

[0028] Optionally, the training of the prediction neural network includes:

[0029] Set up the main service system;

[0030] Establishing a prediction neural network in the main service system;

[0031] Distribute the prediction neural network to auxiliary service systems set up in various places, wherein each auxiliary service system has part of the private training data;

[0032] Each auxiliary service system trains the prediction neural network using private training data, thereby obtaining trained parameter information;

[0033] Each auxiliary service system sends the trained parameter information to the main service system, so that the main service system obtains the parameters for the final model based on the acquired parameter information.

[0034] The present application also provides a carbon emission estimation device for building design, the carbon emission estimation device for building design comprising:

[0035] A building basic information acquisition module, which is used to acquire the building basic information of the building to be evaluated;

[0036] A building database acquisition module, the building database acquisition module is used to acquire a building database, the building database includes a plurality of preset buildings and basic building information corresponding to each preset building;

[0037] A comparison module, wherein the comparison module is used to compare the basic building information of the building to be evaluated with the basic building information corresponding to each preset building, and obtain the preset building corresponding to the basic building information of the preset building that is closest to the basic building information of the building to be evaluated;

[0038] A module for acquiring a historical building carbon emission database, wherein the module is used to acquire a historical building carbon emission database, wherein the historical carbon emission database includes at least one preset building and carbon emission information within a preset time period corresponding to each preset building;

[0039] A first carbon emission information acquisition module, the first carbon emission information acquisition module is used to obtain carbon emission information within a preset time period corresponding to a preset building that is closest to the basic building information of the building to be evaluated, and the carbon emission information is called first carbon emission information;

[0040] A second carbon emission information acquisition module, the second carbon emission information acquisition module is used to estimate the carbon emission information within a preset time period according to the building basic information of the building to be evaluated, and the carbon emission information is called the second carbon emission information;

[0041] A prediction neural network acquisition module, wherein the prediction neural network acquisition module is used to acquire a trained prediction neural network;

[0042] A carbon emission estimation information acquisition module is used to input the first carbon emission information and the second carbon emission information into the trained prediction neural network, so as to obtain the carbon emission estimation information of the building to be evaluated.

[0043] The present application also provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and capable of running on the processor. When the processor executes the computer program, the carbon emission estimation method for building design as described above is implemented.

[0044] The present application also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it can implement the carbon emission estimation method for building design as described above.

[0045] Beneficial Effects

[0046] The carbon emission estimation method for building design of the present application uses the carbon emission information of the building to be evaluated as one of the features on the one hand, and the actual carbon emission information of existing buildings similar to the building to be evaluated as another feature on the other hand, so that the data information brought by the two features can be considered simultaneously during the prediction, making the prediction more accurate. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 It is a structural schematic diagram of a carbon emission estimation method for building design according to an embodiment of the present application.

[0048] Figure 2 It is a schematic diagram of an electronic device for implementing a carbon emission estimation method for building design according to an embodiment of the present application. DETAILED DESCRIPTION

[0049] In order to make the purpose, technical scheme and advantages of the implementation of this application clearer, the technical scheme in the embodiment of this application will be described in more detail below in conjunction with the drawings in the embodiment of this application. In the drawings, the same or similar reference numerals throughout represent the same or similar elements or elements with the same or similar functions. The described embodiments are part of the embodiments of this application, not all of them. The embodiments described below with reference to the drawings are exemplary and are intended to be used to explain this application, and should not be construed as limitations on this application. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application. The embodiments of this application are described in detail below in conjunction with the drawings.

[0050] Figure 1 It is a structural schematic diagram of a carbon emission estimation method for building design according to an embodiment of the present application.

[0051] like Figure 1 The carbon emission estimation methods shown for building design include:

[0052] Step 1: Obtain the basic architectural information of the building to be assessed;

[0053] Step 2: Obtain a building database, wherein the building database includes a plurality of preset buildings and basic building information corresponding to each preset building;

[0054] Step 3: Compare the basic building information of the building to be evaluated with the basic building information corresponding to each preset building, and obtain the preset building corresponding to the basic building information that is closest to the basic building information of the building to be evaluated;

[0055] Step 4: Obtain a historical building carbon emission database, wherein the historical carbon emission database includes at least one preset building and carbon emission information within a preset time period corresponding to each preset building;

[0056] Step 5: Obtain carbon emission information within a preset time period corresponding to a preset building that is closest to the basic building information of the building to be evaluated, and the carbon emission information is referred to as first carbon emission information;

[0057] Step 6: Estimate carbon emission information within a preset time period based on the building basic information of the building to be evaluated, and the carbon emission information is called second carbon emission information;

[0058] Step 7: Get the trained prediction neural network;

[0059] Step 8: Input the first carbon emission information and the second carbon emission information into the trained prediction neural network to obtain the carbon emission estimation information of the building to be evaluated.

[0060] The carbon emission estimation method for building design of the present application uses the carbon emission information of the building to be evaluated as one of the features on the one hand, and the actual carbon emission information of existing buildings similar to the building to be evaluated as another feature on the other hand, so that the data information brought by the two features can be considered simultaneously during the prediction, making the prediction more accurate.

[0061] In this embodiment, the basic building information of the building to be evaluated includes building type, building area, and basic indoor equipment parameters of the building;

[0062] The basic building information corresponding to the preset building includes the building type of the preset building, the building area of ​​the preset building, and the basic building indoor equipment parameters of the preset building.

[0063] In this embodiment, comparing the basic building information of the building to be evaluated with the preset basic building information corresponding to each preset building, and obtaining the preset building corresponding to the preset building basic information closest to the basic building information of the building to be evaluated includes:

[0064] Obtaining the building area of ​​the preset building corresponding to the building type of each preset building of the same building type and the building basic indoor equipment parameters of the preset building;

[0065] Select a preset building whose building area and building area are less than the threshold value from among the preset buildings, and the preset building that meets the condition is called the first preset building;

[0066] The one whose basic indoor equipment parameters of each building in each first preset building are closest to the basic indoor equipment parameters of the building is obtained as the preset building to be used.

[0067] In this embodiment, the basic building information includes the designed flow of people in the building, the number of rooms in the building, and preset indoor equipment parameters;

[0068] In this embodiment, the preset indoor equipment parameters are information about electrical equipment in each room or public area, such as air conditioners, exhaust fans, and the like.

[0069] In this embodiment, building types include residential buildings, office buildings, public buildings, etc. Because the working hours of various devices in different building types are different, the carbon emissions of buildings at different working hours can be distinguished by building type.

[0070] In this embodiment, estimating carbon emission information within a preset time period based on the basic building information of the building to be evaluated includes:

[0071] According to the basic building information, the indoor airflow data of the building to be evaluated is obtained through CFD calculation;

[0072] For example, obtaining the set parameters of the building to be evaluated, such as three-dimensional model data, meteorological data, and building design flow;

[0073] According to the three-dimensional model data of the building to be evaluated, grid data of the building to be evaluated for CFD calculation is obtained;

[0074] According to the meteorological data of the building to be evaluated, the wall thermal boundary conditions and the incoming wind boundary conditions of the building to be evaluated within the estimated preset time period are obtained;

[0075] According to the architectural design flow of people in the building to be evaluated, the human-related source items of the building to be evaluated are obtained;

[0076] The indoor airflow data of the building is obtained by CFD calculation based on the grid data of the building to be evaluated, the wall thermal boundary conditions in the estimated preset time period, the incoming wind boundary conditions in the calculation period, the human body related source items in the calculation period, and the design parameters of the indoor related equipment of the building.

[0077] In this embodiment, the estimated preset time period can be set as needed.

[0078] When the indoor environment of the building to be evaluated meets the human comfort requirements, the energy consumption data of the preset indoor equipment parameters of the building to be evaluated in a preset time period are obtained according to the indoor airflow data of the building to be evaluated;

[0079] Specifically, when the temperature data of the indoor air flow data of the building to be evaluated is within the set temperature range and the relative humidity data is within the set humidity range, it is judged that the indoor environment of the building to be evaluated meets the human comfort requirements; when it is judged that the indoor environment of the building meets the human comfort requirements, the energy consumption data of the relevant indoor equipment of the building during the calculation period is calculated.

[0080] According to the energy consumption data of the preset indoor equipment parameters of the building to be evaluated in the preset time period, a set algorithm is adopted to calculate the carbon emissions of the building to be evaluated in the preset time period.

[0081] In this embodiment, the prediction neural network is an LSTM neural network.

[0082] In this embodiment, the carbon emission estimation method for building design further includes:

[0083] The prediction neural network is trained.

[0084] In this embodiment, the training of the prediction neural network includes:

[0085] Set up the main service system;

[0086] Establishing a prediction neural network in the main service system;

[0087] Distribute the prediction neural network to auxiliary service systems set up in various places, wherein each auxiliary service system has part of the private training data;

[0088] Each auxiliary service system trains the prediction neural network using private training data, thereby obtaining trained parameter information;

[0089] Each auxiliary service system sends the trained parameter information to the main service system, so that the main service system obtains the parameters for the final model based on the acquired parameter information.

[0090] In this embodiment, distributed learning can be used to obtain the conditions of buildings in different regions. For example, different regions may have different climate types. However, it is difficult to centralize all data for training. The use of distributed learning methods allows different regions to assist in training. On the one hand, it can speed up the training process, and on the other hand, it can prevent the owners of each building from not providing data due to privacy issues.

[0091] The present application also provides a carbon emission estimation device for building design, which includes a building basic information acquisition module, a building database acquisition module, a comparison module, a building historical carbon emission database acquisition module, a first carbon emission information acquisition module, a second carbon emission information acquisition module, a prediction neural network acquisition module and a carbon emission estimation information acquisition module, wherein:

[0092] The building basic information acquisition module is used to obtain the building basic information of the building to be evaluated;

[0093] The building database acquisition module is used to acquire a building database, wherein the building database includes a plurality of preset buildings and basic building information corresponding to each preset building;

[0094] The comparison module is used to compare the basic building information of the building to be evaluated with the preset basic building information corresponding to each preset building, and obtain the preset building corresponding to the preset basic building information that is closest to the basic building information of the building to be evaluated;

[0095] The building historical carbon emission database acquisition module is used to acquire the building historical carbon emission database, wherein the historical carbon emission database includes at least one preset building and carbon emission information within a preset time period corresponding to each preset building;

[0096] The first carbon emission information acquisition module is used to acquire carbon emission information within a preset time period corresponding to a preset building that is closest to the basic building information of the building to be evaluated, and the carbon emission information is called the first carbon emission information;

[0097] The second carbon emission information acquisition module is used to estimate the carbon emission information within a preset time period according to the building basic information of the building to be evaluated, and the carbon emission information is called the second carbon emission information;

[0098] The prediction neural network acquisition module is used to acquire the trained prediction neural network;

[0099] The carbon emission estimation information acquisition module is used to input the first carbon emission information and the second carbon emission information into the trained prediction neural network, so as to obtain the carbon emission estimation information of the building to be evaluated.

[0100] The present application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and capable of running on the processor. When the processor executes the computer program, the above-mentioned carbon emission estimation method for building design is implemented.

[0101] The present application also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it can implement the above-mentioned carbon emission estimation method for building design.

[0102] Figure 2 It is used to realize Figure 1 Schematic diagram of electronic device of carbon emission estimation method for building design shown.

[0103] like Figure 2 As shown, the electronic device includes an input device 501, an input interface 502, a central processor 503, a memory 504, an output interface 505, and an output device 506. The input interface 502, the central processor 503, the memory 504, and the output interface 505 are interconnected through a bus 507, and the input device 501 and the output device 506 are connected to the bus 507 through the input interface 502 and the output interface 505, respectively, and then connected to other components of the electronic device. Specifically, the input device 501 receives input information from the outside, and transmits the input information to the central processor 503 through the input interface 502; the central processor 503 processes the input information based on the computer executable instructions stored in the memory 504 to generate output information, temporarily or permanently stores the output information in the memory 504, and then transmits the output information to the output device 506 through the output interface 505; the output device 506 outputs the output information to the outside of the electronic device for use by the user.

[0104] That is to say, Figure 2 The electronic device shown can also be implemented as comprising: a memory storing computer executable instructions; and one or more processors, which can implement the combination when executing the computer executable instructions. Figure 1 A carbon emissions estimation method for building design is described.

[0105] In one embodiment, Figure 2 The electronic device shown can be implemented to include: a memory 504 configured to store executable program code; and one or more processors configured to run the executable program code stored in the memory 504 to execute the carbon emission estimation method for building design in the above-mentioned embodiment.

[0106] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0107] The memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0108] Computer readable media include permanent and non-permanent, removable and non-removable, and the media can be implemented by any method or technology to store information. The information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), data versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device.

[0109] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present application. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and a module, a program segment or a part of a code includes one or more executable instructions for realizing the specified logical function. It should also be noted that in some implementations as replacements, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes identified in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or the total flow chart can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0110] The processor referred to in this embodiment 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 the processor may be any conventional processor, etc.

[0111] The memory can be used to store computer programs and / or modules. The processor realizes various functions of the device / terminal equipment by running or executing the computer programs and / or modules stored in the memory and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area can store data created according to the use of the mobile phone (such as audio data, a phone book, etc.), etc. In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0112] In this embodiment, if the module / unit integrated in the device / terminal equipment 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, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. 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 capable of carrying computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. Although the present application is disclosed as above in terms of a preferred embodiment, it is not intended to limit the present application. Any technical personnel in this field may make possible changes and modifications without departing from the spirit and scope of the present application. Therefore, the scope of protection of the present application shall be based on the scope defined by the claims of the present application.

[0113] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0114] In addition, it is obvious that the word "comprising" does not exclude other units or steps. Multiple units, modules or devices stated in the device claim can also be implemented by one unit or the overall device through software or hardware.

[0115] It should be noted that the above explanations of the method embodiment are also applicable to the device of this embodiment and will not be repeated here.

[0116] Although the present invention has been described in detail above with general descriptions and specific embodiments, it is obvious to those skilled in the art that some modifications or improvements may be made thereto based on the present invention. Therefore, these modifications or improvements made without departing from the spirit of the present invention all fall within the scope of protection claimed by the present invention.

Claims

1. A carbon emission estimation method for building design, characterized in that: The carbon emission estimation method for building design includes: Obtain basic architectural information of the building to be assessed; Acquire a building database, wherein the building database includes a plurality of preset buildings and basic building information corresponding to each preset building; Comparing the basic building information of the building to be evaluated with the basic building information corresponding to each preset building, respectively, to obtain the preset building corresponding to the basic building information of the preset building that is closest to the basic building information of the building to be evaluated; Acquire a historical building carbon emission database, wherein the historical carbon emission database includes at least one preset building and carbon emission information within a preset time period corresponding to each preset building; Acquire carbon emission information within a preset time period corresponding to a preset building that is closest to the basic building information of the building to be evaluated, the carbon emission information being referred to as first carbon emission information; Estimate carbon emission information within a preset time period based on the building basic information of the building to be evaluated, where the carbon emission information is called second carbon emission information; Get the trained prediction neural network; The first carbon emission information and the second carbon emission information are input into the trained prediction neural network to obtain carbon emission estimation information of the building to be evaluated.

2. The carbon emission estimation method for building design according to claim 1, characterized in that: The basic building information of the building to be evaluated includes building type, building area, and basic indoor equipment parameters of the building; The basic building information corresponding to the preset building includes the building type of the preset building, the building area of ​​the preset building, and the basic building indoor equipment parameters of the preset building.

3. The carbon emission estimation method for building design according to claim 2, characterized in that: The step of comparing the basic building information of the building to be evaluated with the basic building information corresponding to each preset building to obtain the preset building corresponding to the basic building information closest to the basic building information of the building to be evaluated comprises: Obtaining the building area of ​​the preset building corresponding to the building type of each preset building of the same building type and the building basic indoor equipment parameters of the preset building; Select a preset building whose building area and building area are less than the threshold value from among the preset buildings, and the preset building that meets the condition is called the first preset building; The one whose basic indoor equipment parameters of each building in each first preset building are closest to the basic indoor equipment parameters of the building is obtained as the preset building to be used.

4. The carbon emission estimation method for building design according to claim 3, characterized in that: The basic building information includes the designed flow of people, the number of rooms in the building, and the preset indoor equipment parameters; The method of estimating carbon emission information within a preset time period based on the basic building information of the building to be evaluated includes: According to the basic building information, the indoor airflow data of the building to be evaluated is obtained through CFD calculation; When the indoor environment of the building to be evaluated meets the human comfort requirements, the energy consumption data of the preset indoor equipment parameters of the building to be evaluated in a preset time period are obtained according to the indoor airflow data of the building to be evaluated; According to the energy consumption data of the preset indoor equipment parameters of the building to be evaluated in the preset time period, a set algorithm is adopted to calculate the carbon emissions of the building to be evaluated in the preset time period.

5. The carbon emission estimation method for building design according to claim 4, characterized in that: The prediction neural network is an LSTM neural network.

6. The carbon emission estimation method for building design according to claim 5, characterized in that: The carbon emission estimation method for building design further comprises: The prediction neural network is trained.

7. The carbon emission estimation method for building design according to claim 6, characterized in that: The training of the prediction neural network comprises: Set up the main service system; Establishing a prediction neural network in the main service system; Distribute the prediction neural network to auxiliary service systems set up in various places, wherein each auxiliary service system has part of the private training data; Each auxiliary service system trains the prediction neural network using private training data, thereby obtaining trained parameter information; Each auxiliary service system sends the trained parameter information to the main service system, so that the main service system obtains the parameters for the final model based on the acquired parameter information.

8. A carbon emission estimation device for building design, characterized in that: The carbon emission estimation device for building design comprises: A building basic information acquisition module, which is used to acquire the building basic information of the building to be evaluated; A building database acquisition module, the building database acquisition module is used to acquire a building database, the building database includes a plurality of preset buildings and basic building information corresponding to each preset building; A comparison module, wherein the comparison module is used to compare the basic building information of the building to be evaluated with the basic building information corresponding to each preset building, and obtain the preset building corresponding to the basic building information of the preset building that is closest to the basic building information of the building to be evaluated; A module for acquiring a historical building carbon emission database, wherein the module is used to acquire a historical building carbon emission database, wherein the historical carbon emission database includes at least one preset building and carbon emission information within a preset time period corresponding to each preset building; A first carbon emission information acquisition module, the first carbon emission information acquisition module is used to obtain carbon emission information within a preset time period corresponding to a preset building that is closest to the basic building information of the building to be evaluated, and the carbon emission information is called first carbon emission information; A second carbon emission information acquisition module, the second carbon emission information acquisition module is used to estimate the carbon emission information within a preset time period according to the building basic information of the building to be evaluated, and the carbon emission information is called the second carbon emission information; A prediction neural network acquisition module, wherein the prediction neural network acquisition module is used to acquire a trained prediction neural network; A carbon emission estimation information acquisition module is used to input the first carbon emission information and the second carbon emission information into the trained prediction neural network, so as to obtain the carbon emission estimation information of the building to be evaluated.

9. An electronic device, characterized in that: The electronic device includes a memory, a processor, and a computer program stored in the memory and capable of running on the processor. When the processor executes the computer program, the carbon emission estimation method for building design as described in any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, it can implement the carbon emission estimation method for building design as described in any one of claims 1 to 7.

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