A building multifunctional carbon emission control method and related device

Abstract

This invention provides a multifunctional carbon emission control method and related device for buildings, belonging to the field of carbon emission control technology. The invention acquires real-time carbon emission data and ventilation equipment operating power both inside and outside the building; based on the carbon emission data and ventilation equipment operating power, it calculates the dynamic distribution of carbon dioxide concentration within the building using partial differential equations; it inputs the dynamic distribution of carbon dioxide concentration into an optimization control model, solves the optimization control model to obtain the optimal ventilation equipment operating power within the building; the objective function of the optimization control model is to optimize the carbon dioxide concentration, ventilation equipment energy consumption, and temperature and humidity environmental deviations within the building; and it controls the ventilation equipment to operate at the optimal ventilation equipment operating power. This invention solves the problem of low precision in building carbon emission control.

Description

Technical Field

[0001] This invention belongs to the field of carbon emission control technology, specifically relating to a multifunctional carbon emission control method and related device for buildings. Background Technology

[0002] In the operation of modern buildings, the monitoring and control of carbon emissions has become an important area of ​​research and application.

[0003] Current multifunctional carbon emission control methods for buildings typically use sensor networks to collect data such as carbon dioxide concentration, temperature, humidity, and air velocity inside the building, and combine this with the operation and control of building equipment (such as ventilation and air conditioning systems) to manage the building's internal environment.

[0004] Existing technologies also employ preset rules or fixed thresholds to regulate ventilation equipment in order to maintain indoor air quality and reduce carbon emissions.

[0005] In addition, there are energy-optimized solutions that utilize collected data for localized adjustments to achieve a trade-off between energy consumption and environmental quality. These technologies can improve building operational efficiency to some extent, especially in small or single-purpose building environments where they can better meet usage needs.

[0006] However, the effectiveness of existing technologies remains limited in complex architectural environments.

[0007] First, monitoring a single data point is insufficient to reflect the dynamic changes and spatial distribution characteristics of carbon dioxide concentration inside a building, and cannot fully capture the coupling relationship between carbon emissions and environmental factors.

[0008] Secondly, fixed-rule equipment control methods lack flexibility in responding to real-time data, which can easily lead to low operating efficiency of ventilation equipment or insufficient optimization of carbon emissions.

[0009] Furthermore, in large-scale sensor networks, data transmission delays and losses can affect the accuracy of dynamic calculations, and the lack of communication modules' ability to process data with different priorities can also limit the efficient operation of multi-module collaboration. Therefore, existing technologies still need to further improve the monitoring accuracy of carbon emissions, control optimization capabilities, and the overall real-time performance and stability of the system when facing dynamic and multi-dimensional complex environments. Summary of the Invention

[0010] The purpose of this invention is to provide a multifunctional carbon emission control method and related device for buildings, which solves the problem of low accuracy in the control of carbon emissions from buildings in the prior art.

[0011] To achieve the above objectives, the present invention adopts the following technical solution: In a first aspect, the present invention provides a multifunctional carbon emission control method for buildings, comprising the following steps: Real-time acquisition of carbon emission data inside and outside buildings and the operating power of ventilation equipment; Based on carbon emission data and ventilation equipment operating power, the dynamic distribution of carbon dioxide concentration inside the building is calculated using partial differential equations. The dynamic distribution of carbon dioxide concentration inside the building is input into the optimization control model, and the optimal operating power of the ventilation equipment inside the building is obtained by solving the optimization control model; the objective function of the optimization control model is to optimize the carbon dioxide concentration, ventilation equipment energy consumption and temperature and humidity environmental deviation inside the building. Control ensures that the ventilation equipment operates at its optimal operating power.

[0012] A further improvement of this invention is that the calculation formula for the partial differential equation is as follows:

[0013] in, This is the partial derivative of carbon dioxide concentration with respect to time, used to represent the rate of change of carbon dioxide concentration over time. The carbon dioxide diffusion coefficient is... The Laplace operator for carbon dioxide concentration. For airflow velocity field, The gradient of carbon dioxide concentration, This refers to the input source of carbon dioxide. The carbon dioxide removal efficiency coefficient of building ventilation equipment. The operating power of the ventilation equipment, Spatial location and time The concentration of carbon dioxide on the surface.

[0014] A further improvement of this invention is that the objective function of the optimized control model is:

[0015] in, To optimize the objective function of the control model, , and These are the weighting coefficients for carbon dioxide concentration, temperature and humidity deviation, and building ventilation equipment energy consumption, respectively. Spatial location and time carbon dioxide concentration on The gradient of the temperature field inside the building. For an ideal temperature gradient, The operating power of the ventilation equipment, For the time change, This represents the concentration of carbon dioxide at a given spatial location.

[0016] A further improvement of this invention is that the method for obtaining the optimal operating power of ventilation equipment within a building by solving the optimization control model includes: The solution is obtained through an optimal control algorithm.

[0017] A further improvement of this invention is that the optimal control algorithm uses a combination of Lagrange function formal constraints and objective function.

[0018] A further improvement of this invention is that the expression for the Lagrange function is:

[0019] in, For Lagrange functions, Let be the objective function. For Lagrange multipliers, A function that describes the constraints.

[0020] A further improvement of the present invention is that the constraints include nonnegativity constraints, maximum operating power constraints, and dynamic state constraints.

[0021] Secondly, the present invention provides a multifunctional carbon emission control system for buildings, comprising: The data acquisition module is used to acquire real-time carbon emission data and ventilation equipment operating power inside and outside the building; The calculation module is used to calculate the dynamic distribution of carbon dioxide concentration in a building using partial differential equations based on carbon emission data and the operating power of ventilation equipment. The solution module is used to input the dynamic distribution of carbon dioxide concentration in the building into the optimization control model, and solve the optimization control model to obtain the optimal operating power of the ventilation equipment in the building; the objective function of the optimization control model is to optimize the carbon dioxide concentration, ventilation equipment energy consumption and temperature and humidity environmental deviation in the building. The control module is used to control the ventilation equipment to operate at the optimal ventilation equipment operating power.

[0022] Thirdly, the present invention provides an electronic device including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the multifunctional carbon emission control method for buildings described above.

[0023] Fourthly, the present invention provides a storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the multifunctional carbon emission control method for buildings described above.

[0024] Compared with the prior art, the present invention has the following beneficial effects: The multifunctional carbon emission control method for buildings proposed in this invention, on the one hand, calculates the dynamic distribution of carbon dioxide concentration in buildings through partial differential equations. Partial differential equations can accurately reflect the spatiotemporal evolution characteristics of carbon dioxide concentration in buildings. On the other hand, when constructing the objective function of the optimized control model, three factors are comprehensively considered: carbon dioxide concentration in buildings, energy consumption of ventilation equipment, and temperature and humidity environmental deviations. Considering a variety of factors can also improve the accuracy of subsequent carbon emission control of buildings, thereby effectively solving the problem of low accuracy in carbon emission control of buildings in existing technologies. Attached Figure Description

[0025] Figure 1 This is a flowchart of the multifunctional carbon emission control method for buildings according to the present invention; Figure 2 This is a schematic diagram of the multifunctional carbon emission control system for buildings according to the present invention; Figure 3 This is a schematic diagram of the structure of the electronic device of the present invention. Detailed Implementation

[0026] To further understand the content of this invention, the invention will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the embodiments are merely illustrative and not limiting of the invention.

[0027] Example 1: The flowchart of the multifunctional carbon emission control method for buildings of the present invention is as follows: Figure 1 As shown, the multifunctional carbon emission control method for buildings of the present invention includes the following steps: S1. Real-time acquisition of carbon emission data and ventilation equipment operating power inside and outside the building; S2. Based on carbon emission data and ventilation equipment operating power, calculate the dynamic distribution of carbon dioxide concentration inside the building using partial differential equations; S3. Input the dynamic distribution of carbon dioxide concentration in the building into the optimization control model, and solve the optimization control model to obtain the optimal operating power of the ventilation equipment in the building; the objective function of the optimization control model is to optimize the carbon dioxide concentration, energy consumption and temperature and humidity environmental deviation in the building. S4. Control the ventilation equipment to operate at the optimal ventilation equipment operating power.

[0028] In step S2 of this invention, the dynamic distribution of carbon dioxide concentration in a building is calculated using partial differential equations, which can accurately reflect the spatiotemporal evolution characteristics of carbon dioxide concentration in a building. In step S3, when constructing the objective function of the optimization control model, three factors are comprehensively considered: carbon dioxide concentration in the building, energy consumption of ventilation equipment, and temperature and humidity environmental deviation. The diverse factors considered improve the accuracy of carbon emission control in buildings.

[0029] Example 2: A schematic diagram of the multifunctional carbon emission control system for buildings of this invention is shown below. Figure 2 As shown, the multifunctional carbon emission control system for buildings of the present invention includes: The data acquisition module is used to acquire real-time carbon emission data and ventilation equipment operating power inside and outside the building; The calculation module is used to calculate the dynamic distribution of carbon dioxide concentration in a building using partial differential equations based on carbon emission data and the operating power of ventilation equipment. The solution module is used to input the dynamic distribution of carbon dioxide concentration in the building into the optimization control model, and solve the optimization control model to obtain the optimal operating power of the ventilation equipment in the building; the objective function of the optimization control model is to optimize the carbon dioxide concentration, ventilation equipment energy consumption and temperature and humidity environmental deviation in the building. The control module is used to control the ventilation equipment to operate at the optimal ventilation equipment operating power.

[0030] Example 3: The multifunctional carbon emission control method for buildings of the present invention includes the following steps: S1. Real-time acquisition of carbon emission data inside and outside buildings and the operating power of ventilation equipment.

[0031] First, real-time carbon emission data (carbon dioxide concentration and airflow velocity field inside the building) and ventilation equipment operating power are acquired.

[0032] S2. Based on carbon emission data and the operating power of ventilation equipment, calculate the dynamic distribution of carbon dioxide concentration in the building using partial differential equations.

[0033] The formula for calculating the partial differential equation in this step is:

[0034] in, This is the partial derivative of carbon dioxide concentration with respect to time, used to represent the rate of change of carbon dioxide concentration over time. The carbon dioxide diffusion coefficient is... The Laplace operator for carbon dioxide concentration. This refers to the airflow velocity field, the direction and magnitude of which are determined by the ventilation equipment and the building's airflow design. The gradient of carbon dioxide concentration is used to describe the effect of airflow on the concentration distribution. This refers to the input source of carbon dioxide. The carbon dioxide removal efficiency coefficient of building ventilation equipment. The operating power of the ventilation equipment, Spatial location and time The carbon dioxide concentration is expressed in ppm.

[0035] The following is about spatial location and time carbon dioxide concentration The boundary conditions are explained in detail: For the ventilation opening boundary, the Dirichlet boundary condition is used:

[0036] in, The background carbon dioxide concentration in the outside air is provided by an outdoor weather station.

[0037] For the wall boundary, Neumann boundary conditions are applied:

[0038] in, The normal derivative indicates that there is no carbon dioxide permeation to the wall surface.

[0039] The following is about spatial location and time carbon dioxide concentration The initial conditions will be explained in detail:

[0040] in, This represents the initial carbon dioxide concentration distribution inside the building.

[0041] To transform continuous partial differential equations into a solvable system of algebraic equations, the following numerical discretization technique is employed in this embodiment: In general, time discretization uses the implicit Euler method, and the discretization form is as follows:

[0042] in, and The first Time and the The carbon dioxide concentration at time [time]. For time step.

[0043] Spatial discretization employs the finite difference method. For the Laplace operator, this embodiment uses the second-order central difference method for discretization:

[0044] in, For grid nodes , and carbon dioxide concentration on , and These represent the spacing of the grid in each direction.

[0045] S3. Input the dynamic distribution of carbon dioxide concentration in the building into the optimization control model, and solve the optimization control model to obtain the optimal operating power of the ventilation equipment in the building; the objective function of the optimization control model is to optimize the carbon dioxide concentration, ventilation equipment energy consumption and temperature and humidity environmental deviation in the building.

[0046] The objective function for optimizing the control model in this step is:

[0047] in, To optimize the objective function of the control model, , and These are the weighting coefficients for carbon dioxide concentration, temperature and humidity deviation, and ventilation equipment energy consumption, respectively. Spatial location and time carbon dioxide concentration on The gradient of the temperature field inside the building. For an ideal temperature gradient, The operating power of the ventilation equipment, For the time change, This represents the concentration of carbon dioxide at a given spatial location.

[0048] The method for solving the optimal control model to obtain the optimal operating power of the ventilation equipment in the building in this step includes: The solution is obtained through an optimal control algorithm.

[0049] The optimal control algorithm uses the Lagrangian function to formalize the combination of constraints and objective function.

[0050] The expression for the Lagrange function is:

[0051] in, For Lagrange functions, Let be the objective function. For Lagrange multipliers, Functions that describe constraints (such as building energy consumption limits and temperature and humidity limits).

[0052] Taking the gradient of the Lagrange function as zero, we obtain the necessary condition:

[0053] The constraints include nonnegativity constraints, maximum operating power constraints, and dynamic state constraints.

[0054] The formula for calculating nonnegativity constraints is:

[0055] in, This refers to the operating power of the ventilation equipment.

[0056] The formula for calculating the maximum operating power constraint is:

[0057] in, This refers to the maximum operating power of the ventilation equipment within the building.

[0058] The formula for calculating dynamic state constraints is:

[0059] in, This is the partial derivative of carbon dioxide concentration with respect to time. The Laplace operator for carbon dioxide concentration. For airflow velocity field, The gradient of carbon dioxide concentration, This refers to the input source of carbon dioxide. This is the carbon dioxide removal efficiency coefficient of the ventilation equipment. The operating power of the ventilation equipment, This represents the carbon dioxide concentration.

[0060] This embodiment uses the adjoint variable method and numerical discretization method to solve the optimization control model and obtain the optimal ventilation equipment operating power in the building. The core idea of ​​the adjoint variable method is to introduce adjoint variables and analyze the relationship between the objective function and the state variables to help the optimal control algorithm improve efficiency.

[0061] An adjoint equation is constructed using the adjoint variable method. This equation expresses the rate of change of the objective function with respect to the state variables, and its specific form is as follows:

[0062] in, As an accompanying variable, For the objective function For state variables The partial derivatives of the equation represent the impact of changes in the system state on the objective value. This allows us to calculate the degree of influence of each state variable during the optimization process, thereby enabling effective trade-offs among multiple objectives.

[0063] To determine the optimal operating power of ventilation equipment within a building, the finite difference method is used to discretize the state equations. By dividing time into several equally spaced time points, the continuous state equations are transformed into a discrete form:

[0064] in, In time step The state value, For the control input at this time step, This is a function describing the dynamic relationships of the system. This discretization method allows the state to be updated at each time step, and the control variables to be adjusted according to the adjoint equation.

[0065] Throughout the optimization process, the optimized control model can update the state and control input in a timely manner based on real-time environmental feedback. When sensors collect environmental data (such as changes in temperature and humidity), real-time feedback is used to adjust the control input. Adjustments are made to ensure the system adapts to new environmental conditions, thereby maintaining high efficiency and user comfort. This process allows the optimal control algorithm to be continuously corrected and updated at each optimization step, thus achieving dynamic control.

[0066] By combining the adjoint variable method and the finite difference method, the optimized control model can not only theoretically solve complex multi-objective optimization problems, but also demonstrates strong adaptability and the ability to handle complex environments in practical applications, providing a solid technical foundation for the sustainable development of future intelligent buildings.

[0067] S4. Control the ventilation equipment to operate at the optimal ventilation equipment operating power.

[0068] Example 4: Please see Figure 3 As shown, the present invention also provides an electronic device 100 for a multifunctional carbon emission control method for buildings; the electronic device 100 includes a memory 101, at least one processor 102, a computer program 103 stored in the memory 101 and executable on the at least one processor 102, and at least one communication bus 104.

[0069] The memory 101 can be used to store the computer program 103. The processor 102 implements the steps of the multifunctional carbon emission control method for buildings described in Embodiment 1 by running or executing the computer program stored in the memory 101 and calling the data stored in the memory 101. The memory 101 may mainly include a program storage area and a data storage area. The program storage area may store the operating system, at least one application program required for a function (such as sound playback function, image playback function, etc.), etc.; the data storage area may store data created according to the use of the electronic device 100 (such as audio data), etc. In addition, the memory 101 may include non-volatile memory, such as hard disk, memory, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one disk storage device, flash memory device, or other non-volatile solid-state storage device.

[0070] The at least one processor 102 may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The processor 102 may be a microprocessor or any conventional processor. The processor 102 is the control center of the electronic device 100, connecting various parts of the electronic device 100 via various interfaces and lines.

[0071] The memory 101 in the electronic device 100 stores multiple instructions to implement a multi-functional carbon emission control method for buildings, and the processor 102 can execute the multiple instructions to achieve the following: Real-time acquisition of carbon emission data inside and outside buildings and the operating power of ventilation equipment; Based on carbon emission data and ventilation equipment operating power, the dynamic distribution of carbon dioxide concentration inside the building is calculated using partial differential equations. The dynamic distribution of carbon dioxide concentration inside the building is input into the optimization control model, and the optimal operating power of the ventilation equipment inside the building is obtained by solving the optimization control model; the objective function of the optimization control model is to optimize the carbon dioxide concentration, ventilation equipment energy consumption and temperature and humidity environmental deviation inside the building. Control ensures that the ventilation equipment operates at its optimal operating power.

[0072] Example 5: If the modules / units integrated in the electronic device 100 are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, and a read-only memory (ROM).

[0073] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0074] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0075] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0076] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0077] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A multifunctional carbon emission control method for buildings, wherein the building is equipped with ventilation equipment; characterized in that, Includes the following steps: Real-time acquisition of carbon emission data inside and outside buildings and the operating power of ventilation equipment; Based on carbon emission data and ventilation equipment operating power, the dynamic distribution of carbon dioxide concentration inside the building is calculated using partial differential equations. The formula for calculating the partial differential equation is as follows: in, This is the partial derivative of carbon dioxide concentration with respect to time, used to represent the rate of change of carbon dioxide concentration over time. The diffusion coefficient of carbon dioxide. The Laplace operator for carbon dioxide concentration. For airflow velocity field, The gradient represents the carbon dioxide concentration. This refers to the input source of carbon dioxide. The carbon dioxide removal efficiency coefficient of ventilation equipment in a building. For the operating power of ventilation equipment, For spatial location and time The carbon dioxide concentration on; The dynamic distribution of carbon dioxide concentration inside the building is input into the optimization control model, and the optimal operating power of the ventilation equipment inside the building is obtained by solving the optimization control model; the objective function of the optimization control model is to optimize the carbon dioxide concentration, ventilation equipment energy consumption and temperature and humidity environmental deviation inside the building. The objective function of the optimized control model is: in, To optimize the objective function of the control model, , and These are the weighting coefficients for carbon dioxide concentration, temperature and humidity deviation, and ventilation equipment energy consumption, respectively. For spatial location and time carbon dioxide concentration on This represents the gradient of the temperature field inside the building. For an ideal temperature gradient, The operating power of the ventilation equipment, For the time change, This represents the concentration of carbon dioxide at a spatial location. Control ensures that the ventilation equipment operates at its optimal operating power.

2. The multifunctional carbon emission control method for buildings according to claim 1, characterized in that, The method for obtaining the optimal operating power of ventilation equipment within a building by solving the optimization control model includes: The solution is obtained through an optimal control algorithm.

3. The multifunctional carbon emission control method for buildings according to claim 2, characterized in that, The optimal control algorithm uses the Lagrange function to formalize the combination of constraints and objective function.

4. The multifunctional carbon emission control method for buildings according to claim 3, characterized in that, The expression for the Lagrange function is: in, For Lagrange functions, Let be the objective function. For Lagrange multipliers, A function that describes the constraints.

5. The multifunctional carbon emission control method for buildings according to claim 3, characterized in that, The constraints include nonnegativity constraints, maximum operating power constraints, and dynamic state constraints.

6. A multifunctional carbon emission control system for buildings, characterized in that, include: The data acquisition module is used to acquire real-time carbon emission data and ventilation equipment operating power inside and outside the building; The calculation module is used to calculate the dynamic distribution of carbon dioxide concentration in a building using partial differential equations based on carbon emission data and the operating power of ventilation equipment. The formula for calculating the partial differential equation is as follows: in, This is the partial derivative of carbon dioxide concentration with respect to time, used to represent the rate of change of carbon dioxide concentration over time. The diffusion coefficient of carbon dioxide. The Laplace operator for carbon dioxide concentration. For airflow velocity field, The gradient represents the carbon dioxide concentration. This refers to the input source of carbon dioxide. The carbon dioxide removal efficiency coefficient of ventilation equipment in a building. For the operating power of ventilation equipment, For spatial location and time The carbon dioxide concentration on; The solution module is used to input the dynamic distribution of carbon dioxide concentration in the building into the optimization control model, and solve the optimization control model to obtain the optimal operating power of the ventilation equipment in the building; the objective function of the optimization control model is to optimize the carbon dioxide concentration, ventilation equipment energy consumption and temperature and humidity environmental deviation in the building. The objective function of the optimized control model is: in, To optimize the objective function of the control model, , and These are the weighting coefficients for carbon dioxide concentration, temperature and humidity deviation, and ventilation equipment energy consumption, respectively. For spatial location and time carbon dioxide concentration on This represents the gradient of the temperature field inside the building. For an ideal temperature gradient, The operating power of the ventilation equipment, For the time change, This represents the concentration of carbon dioxide at a spatial location. The control module is used to control the ventilation equipment to operate at the optimal ventilation equipment operating power.

7. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the building multifunctional carbon emission control method according to any one of claims 1 to 5.

8. A storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the building multifunctional carbon emission control method according to any one of claims 1 to 5.

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