Energy-saving and carbon-reducing optimization method and system for low-carbon park

Abstract

The invention discloses an energy-saving and carbon-reducing optimization method and system for a low-carbon park, and relates to the technical field of low-carbon building and park design, and the method comprises the steps: formulating a carbon emission function, and quantifying each influence factor; performing virtualization operation on the spatial layout of the park, and performing multi-objective optimization; and establishing a low-carbon and thermal comfort evaluation feedback mechanism by optimizing a constraint program. According to the method, the goal of promoting carbon emission reduction is achieved, and refined measurement and evaluation are also beneficial to scientific supervision of policy formulators, promote transformation of a park to low-carbon development, improve the efficiency of resource allocation, promote implementation of a green building concept, ensure harmonious coexistence of a building and a natural environment, and improve the economic benefit. Iteration and optimization of the park design become more efficient, it is guaranteed that various services and facilities can be adjusted and improved in time in the actual operation process, and the park continuously develops towards the lower-carbon and more environment-friendly target.

Description

Technical Field

[0001] The present invention relates to the technical field of low-carbon building and park design, and in particular to an energy-saving and carbon-reduction optimization method and system for a low-carbon park. Background Art

[0002] In recent years, with the intensification of global climate change issues, the development of low-carbon technologies has gradually become an important direction for sustainable urban development. Many countries and regions have successively introduced technical measures to reduce carbon emissions and promote the use of renewable energy. Against this background, the construction of low-carbon parks has gradually emerged and become an important means to achieve urban environmental friendliness and economic sustainable development. Low-carbon parks are committed to building energy-saving and environmentally friendly living and working environments by optimizing spatial layout, improving energy efficiency and reducing carbon emissions. The development of modern information technology, architectural design theory and environmental science has provided new perspectives and tools for the system design of low-carbon parks and promoted the continuous advancement of related technologies.

[0003] Although the current technologies and methods related to low-carbon parks have made certain progress, there are still many shortcomings. The existing carbon emission quantification models mostly rely on traditional statistical methods, which are difficult to comprehensively and accurately consider a variety of influencing factors, such as traffic patterns and building operation energy consumption. The spatial layout optimization process often lacks a real-time feedback mechanism and cannot effectively integrate the optimal building arrangement and thermal comfort evaluation, resulting in the layout design being unable to achieve the expected low-carbon effect. When identifying and responding to multi-objective optimization problems, the existing technology often focuses on a single goal, ignoring the dual considerations of low carbon and thermal comfort, resulting in limitations in the design scheme, and therefore cannot fully meet the needs of users in different regions. The present invention uses a systematic approach to formulate a detailed carbon emission function, quantify various influencing factors, and introduce virtualization operations and optimization constraint mechanisms, thereby effectively improving the flexibility and comprehensiveness of park design and effectively achieving energy conservation and carbon reduction. Summary of the invention

[0004] In view of the above-mentioned problems, the present invention is proposed.

[0005] Therefore, the technical problems solved by the present invention are: the existing low-carbon building and park design technologies have inaccurate models, lack of effective feedback mechanisms, insufficient multi-objective optimization capabilities, and how to achieve more efficient low-carbon park design and operation.

[0006] To solve the above technical problems, the present invention provides the following technical solutions: an energy-saving and carbon-reduction optimization method for a low-carbon park, comprising formulating a carbon emission function and quantifying various influencing factors; virtualizing the spatial layout of the park and performing multi-objective optimization; and establishing a low-carbon and thermal comfort evaluation feedback mechanism by optimizing the constraint program.

[0007] As a preferred solution of the energy-saving and carbon-reduction optimization method of the low-carbon park described in the present invention, wherein: the formulation of the carbon emission function and the quantification of various influencing factors include calculating the CO2 emissions and fuel consumption during transportation, and calculating the CO2 emissions, which is expressed as:

[0008]

[0009] Among them, E1 represents the CO2 emissions generated by each unit of transportation distance, i is the type of transportation fuel, representing different fuel categories, Q represents the fuel consumption of the transportation mode, and C represents the carbon dioxide emission coefficient of the fuel; the fuel consumption is calculated as:

[0010] Q = ∑(K × D)

[0011] Among them, Q represents the consumption of a certain fuel in the transportation mode, K represents the transportation distance of the transportation mode, and D represents the fuel consumption per 100 kilometers of the transportation equipment.

[0012] As a preferred solution of the energy-saving and carbon-reduction optimization method of the low-carbon park described in the present invention, wherein: the formulation of the carbon emission function and the quantification of various influencing factors also include estimating the carbon emission of the building through the carbon emission indicators of building heating consumption and building equipment consumption, the carbon emission in the building operation stage is determined according to the different types of energy consumption of each system and the carbon emission factors of different types of energy, and the total carbon emission per unit building area in the building operation stage is calculated. M , expressed as:

[0013]

[0014] Among them, C M It represents the net carbon emissions per unit area during the operation phase of a building, and is used to evaluate the annual carbon emissions per unit area of ​​a building. i represents the carbon emission factor of the i-th energy type, which is used to convert energy consumption into corresponding carbon emissions, C p is the carbon sink obtained from the renewable energy system, which indicates the carbon emission reduced by the building through the use of clean energy. y represents the number of years, which is used to accumulate the total carbon emissions of the building. A represents the total building area, which is used to normalize the carbon emissions to the total carbon emissions calculation formula of the building energy consumption per unit area, which is expressed as:

[0015]

[0016] Where E represents the annual energy consumption of the i-th type of building, E i,j represents the amount of energy consumed by the jth system in the building, ER i,j It represents the amount of the i-th energy consumed by the j-th system that is provided by the renewable energy system.

[0017] As a preferred solution of the energy-saving and carbon-reduction optimization method of the low-carbon park described in the present invention, the virtualization operation of the spatial layout of the park includes writing a program for site generation and building placement in Rhino, generating corresponding plots based on the actual site size of the park as boundary constraints for design optimization, selecting building types based on actual surveys of the park, and constructing a structure through three-dimensional data, building area and building floor height to confirm the layout of buildings in the specified area, thereby realizing the digital migration of the overall planning of the park, and using the dot matrix method in Rhino to establish a simulated site area by defining the plot size and center point. Input relevant site environment information, confirm that boundary constraints are followed during subsequent building placement, set up to limit the range of building block changes through exhaustive loops in later optimization, establish an initial model library of various building types according to the park's business format, define the number and area range of each building, and structure the building and its information attributes into intelligent entities through the building placement module. Set restrictions such as the building's height, length, width and area, and confirm that there is no overlap or close contact between buildings. Based on the functional zoning and planning of the park, use a random algorithm to assign initial positions to various building intelligent entities, and generate a preliminary building layout through the generated boundaries and restrictions.

[0018] As a preferred solution of the energy-saving and carbon-reduction optimization method for the low-carbon park described in the present invention, the multi-objective optimization includes identifying each building intelligent body and site information through the low-carbon optimization module, calculating the overall carbon emission value of the park, and using the thermal environment optimization module to evaluate the wind environment comfort of the park. Through the multi-objective genetic algorithm of the Numpy and Scipy scientific computing libraries in Python, carbon emissions and outdoor thermal comfort are used as objective functions, and multiple iterative optimizations are performed to obtain the optimal building arrangement combination and volume.

[0019] As a preferred solution of the energy-saving and carbon-reduction optimization method for the low-carbon park described in the present invention, the optimization constraint program includes calculating the total carbon emissions of the park, involving traffic carbon emissions, building carbon emissions, photovoltaic power generation carbon sinks and green space carbon sinks, assigning values ​​to each item through Python, traffic and building carbon emissions are positive values, photovoltaic power generation and green space carbon sinks are negative values, and the generated three-dimensional model is numerically calculated. If the total carbon emissions are greater than zero, the layout model does not meet the carbon reduction requirements, False is output and the model is regenerated. If the total carbon emissions are less than or equal to zero, it means that the model meets the low-carbon target, True is output, and it is imported into the next optimization link until the optimal solution that meets the low-carbon and thermal comfort requirements is found.

[0020] As a preferred solution of the energy-saving and carbon-reduction optimization method for the low-carbon park described in the present invention, the establishment of a low-carbon and thermal comfort evaluation feedback mechanism includes connecting the digital park model to the Grasshopper platform after completing the carbon emission verification, using the Butterfly tool in the Ladybug plug-in to simulate the wind environment, and generating grid data, including the wind speed value of each grid point. The number of grids with a wind speed between 1-5m / s is counted by Numpy to obtain the proportion of the comfortable area of ​​the park. Combined with the overall planning model of the park, the thermal comfort of the park is evaluated through quantitative data, and relevant data is exported.

[0021] Another object of the present invention is to provide an energy-saving and carbon-reduction optimization system for a low-carbon park, which can establish a low-carbon and thermal comfort evaluation feedback mechanism by optimizing the constraint program, thereby solving the problem that current low-carbon building and park design technologies contain a lack of an effective feedback mechanism.

[0022] As a preferred solution of the energy-saving and carbon-reduction optimization system for the low-carbon park described in the present invention, it includes: a carbon emission quantification module, a space layout optimization module, and a thermal comfort feedback module.

[0023] The carbon emission quantification module is used to formulate a carbon emission function and quantify various influencing factors; the spatial layout optimization module is used to virtualize the spatial layout of the park and perform multi-objective optimization; the thermal comfort feedback module is used to establish a low-carbon and thermal comfort evaluation feedback mechanism by optimizing the constraint program.

[0024] A computer device includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement a step of an energy-saving and carbon-reduction optimization method for a low-carbon park.

[0025] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of a method for optimizing energy conservation and carbon reduction in a low-carbon park.

[0026] Beneficial effects of the present invention: The energy-saving and carbon-reduction optimization method for a low-carbon park provided by the present invention formulates a carbon emission function and quantifies various influencing factors. By formulating a carbon emission function and quantifying various influencing factors, the emissions and fuel consumption in the transportation process can be calculated in detail, and the carbon emissions of each fuel and mode of transportation within a unit distance can be accurately evaluated. The carbon emission indicators of building heating and equipment use can be quantified, so that the carbon emissions per unit area of ​​the building during the operation stage can also be clearly calculated, providing accurate data support for subsequent optimization, helping to design low-carbon strategies and measures, and providing a scientific basis for decision-making, ultimately promoting the goal of carbon emission reduction. Refined measurement and evaluation can also help policymakers to supervise scientifically and promote the transformation of the park to low-carbon development. The spatial layout of the park is virtualized and multi-objective optimization is performed. The spatial layout of the park is optimized using virtualization operations. Not only can the actual environment be simulated in software such as Rhino, but also the scientificity and rationality of the planning can be ensured by defining boundary conditions and building constraints. Through multi-objective optimization, the overall planning of the park can be comprehensively considered. The layout, carbon emission targets and thermal comfort of the park are considered, and then the reasonable arrangement and functional zoning of the building blocks are realized, so that the design of the park can flexibly adapt to the environment and usage needs, improve the efficiency of resource allocation, promote the implementation of the concept of green building, and achieve the long-term effect of reducing energy consumption and carbon emissions in the use of space. By optimizing the constraint procedure, a low-carbon and thermal comfort evaluation feedback mechanism is established, and the carbon emission sources of the park are comprehensively considered, including transportation, construction and photovoltaic power generation, to ensure that the final layout meets the low-carbon target. The established feedback mechanism combines carbon emission verification and thermal comfort assessment, and can adjust the park design in time to ensure that the predetermined environmental benefits and user comfort are achieved. It not only improves the flexibility and adaptability of the design, but also provides strong support for subsequent adjustments through scientific data analysis, which can effectively improve the sustainability of the park and create a healthier and more comfortable living environment for users. At the same time, it provides data support for the formulation of relevant policies and standards. The present invention has achieved better results in the accurate quantification of carbon emissions, the intelligent optimization of the park space layout, and the establishment of a low-carbon and thermal comfort evaluation feedback mechanism. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0028] Figure 1 An overall flow chart of an energy-saving and carbon-reduction optimization method for a low-carbon park provided in the first embodiment of the present invention.

[0029] Figure 2An overall flow chart of an energy-saving and carbon-reduction optimization system for a low-carbon park provided in the third embodiment of the present invention. DETAILED DESCRIPTION

[0030] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0031] Example 1, reference Figure 1 , which is an embodiment of the present invention, provides an energy-saving and carbon-reduction optimization method for a low-carbon park, comprising:

[0032] S1: Develop a carbon emission function and quantify each influencing factor.

[0033] Furthermore, a carbon emission function is formulated and various influencing factors are quantified, including the calculation of CO2 emissions and fuel consumption during transportation. CO2 emissions are calculated and expressed as:

[0034]

[0035] Among them, E1 represents the CO2 emissions generated by each unit of transportation distance, i is the type of transportation fuel, representing different fuel categories, Q represents the fuel consumption of the transportation mode, and C represents the carbon dioxide emission coefficient of the fuel; the fuel consumption is calculated as:

[0036] Q = ∑(K × D)

[0037] Among them, Q represents the consumption of a certain fuel in the transportation mode, K represents the transportation distance of the transportation mode, and D represents the fuel consumption per 100 kilometers of the transportation equipment.

[0038] It should be noted that the formulation of carbon emission functions and the quantification of various influencing factors also include estimating the carbon emissions of buildings through the carbon emission indicators of building heating consumption and building equipment consumption. The carbon emissions during the building operation phase are determined based on the energy consumption of different types of systems and the carbon emission factors of different types of energy. The total carbon emissions per unit building area during the building operation phase are calculated as C M , expressed as:

[0039]

[0040] Among them, C M It represents the net carbon emissions per unit area during the operation phase of a building, and is used to evaluate the annual carbon emissions per unit area of ​​a building. irepresents the carbon emission factor of the i-th energy type, which is used to convert energy consumption into corresponding carbon emissions, C p is the carbon sink obtained from the renewable energy system, which indicates the carbon emission reduced by the building through the use of clean energy. y represents the number of years, which is used to accumulate the total carbon emissions of the building. A represents the total building area, which is used to normalize the carbon emissions to the total carbon emissions calculation formula of the building energy consumption per unit area, which is expressed as:

[0041]

[0042] Where E represents the annual energy consumption of the i-th type of building, E i,j represents the amount of energy consumed by the jth system in the building, ER i,j It represents the amount of the i-th energy consumed by the j-th system that is provided by the renewable energy system.

[0043] It should also be noted that the formulation of a carbon emission function and the quantification of various influencing factors play an important role in energy conservation and carbon reduction in low-carbon parks. Detailed calculations of carbon emissions in the transportation process enable emissions from different fuel types and modes of transportation to be clearly quantified. Mathematical models and formulas are used to calculate emissions per unit transportation distance, enabling scientific assessments of the ecological impacts of various types of transportation. This not only helps identify the most environmentally burdensome modes of transportation, but also lays the foundation for the construction of a low-carbon transportation system within the park. It also provides an important basis for the systematic management of low-carbon parks, enhances the understanding of the overall carbon emission level of the park, and provides support for the formulation of scientific and reasonable carbon reduction targets. When park managers can clearly see the specific contribution of each factor, they can further improve design decisions and implement more efficient management measures, laying a solid foundation for subsequent energy conservation and emission reduction strategies.

[0044] S2: Virtualize the spatial layout of the park and perform multi-objective optimization.

[0045] Furthermore, the virtualization operation of the spatial layout of the park includes writing the site generation and building placement program in Rhino. Based on the actual site size of the park, the corresponding plots are generated as the boundary constraints of the design optimization. The building placement part selects the building type according to the actual survey of the park, and the structure is constructed through three-dimensional data, building area and building floor height to confirm the layout of the building in the specified area, so as to realize the digital migration of the overall planning of the park. By defining the plot size and center point, the dot matrix method is used in Rhino to establish the simulated site area, input the relevant site environment information, confirm that the boundary constraints are followed in the subsequent building placement process, and set the scope of the building block change to be restricted through exhaustive loops in the later optimization. According to the business format of the park, an initial model library of various building types is established, and the number and area range of each building are defined. Through the building placement module, the building and its information attributes are structured as intelligent entities, and the building height, length, width and area and other constraints are set to confirm that there is no overlap or close contact between the buildings. Based on the functional zoning and planning of the park, a random algorithm is used to assign initial positions to various building intelligent entities, and a preliminary building layout is generated through the generated boundaries and constraints.

[0046] It should be noted that the multi-objective optimization includes identifying each building intelligent entity and site information through the low-carbon optimization module, calculating the overall carbon emission value of the park, using the thermal environment optimization module to evaluate the wind environment comfort of the park, and using the multi-objective genetic algorithm of the Numpy and Scipy scientific computing libraries in Python, taking carbon emissions and outdoor thermal comfort as objective functions, performing multiple iterative optimizations, and obtaining the optimal building arrangement and volume.

[0047] It should also be noted that the virtualization of the spatial layout of the park and the multi-objective optimization are aimed at combining digital technology with traditional park design to achieve more efficient and scientific space utilization. The actual site is digitally transformed through 3D modeling software such as Rhino, which can intuitively present the existing space conditions and design requirements. The visualization method not only improves the transparency of the design process, but also enables stakeholders to better participate in the design process. When performing multi-objective optimization, designers use virtual environments to simulate the functions and impacts of different building combinations, thereby achieving comprehensive optimization. Not only the spatial relationship between buildings is considered, but also the overall functional distribution and environmental impact of the park are taken into account, promoting the generation of high-efficiency and low-carbon design solutions. The park has not only become a space to achieve low-carbon goals, but also an intelligent place that applies modern technology and management concepts, thereby improving the overall use effect and social value.

[0048] S3: Establish a low-carbon and thermal comfort evaluation feedback mechanism by optimizing the constraint program.

[0049] Furthermore, the optimization constraint procedure includes calculating the total carbon emissions of the park, involving transportation carbon emissions, building carbon emissions, photovoltaic power generation carbon sinks and green space carbon sinks. Python is used to assign values ​​to each item, with transportation and building carbon emissions as positive values ​​and photovoltaic power generation and green space carbon sinks as negative values. The generated three-dimensional model is then numerically calculated. If the total carbon emissions are greater than zero, the layout model does not meet the carbon reduction requirements, and False is output and the model is regenerated. If the total carbon emissions are less than or equal to zero, it means that the model meets the low-carbon target, True is output, and it is imported into the next optimization step until the optimal solution that meets the low-carbon and thermal comfort requirements is found.

[0050] It should be noted that the establishment of a low-carbon and thermal comfort evaluation feedback mechanism includes connecting the digital park model to the Grasshopper platform after completing the carbon emission verification, using the Butterfly tool in the Ladybug plug-in to simulate the wind environment, and generating grid data, including the wind speed value of each grid point. The number of grids with wind speeds between 1-5m / s is counted by Numpy to obtain the proportion of the park's comfortable area. Combined with the overall planning model of the park, the thermal comfort of the park is evaluated through quantitative data, and relevant data is exported.

[0051] It should also be noted that by quantifying various carbon emission sources in the park, including transportation, construction and greening, a comprehensive evaluation model is formed. On this basis, the optimization constraint program can timely detect whether the design plan meets the low-carbon goals and provide real-time feedback to managers, ensuring the flexibility and scientificity of the park design during implementation. Architects, engineers and planners are encouraged to consider the comprehensive impact of carbon emissions from the beginning of the design. The establishment of a low-carbon and thermal comfort evaluation feedback mechanism has a direct impact on the users of the park. Through the simulation and analysis of environmental parameters such as wind speed and temperature, designers can clearly identify which areas are more suitable for people's living and activities, ensuring the harmonious coexistence of buildings and the natural environment. The iteration and optimization of the park design has also become more efficient, ensuring that in the actual operation process, various services and facilities can be adjusted and improved in a timely manner, so that the park continues to develop towards the goal of lower carbon and more environmentally friendly.

[0052] Example 2 is an embodiment of the present invention, which provides an energy-saving and carbon-reduction optimization method for a low-carbon park. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0053] Firstly, a carbon emission function is designed according to different modes of transportation and building energy consumption in the park. The modes of transportation include public transportation, private cars and walking, and the building energy consumption includes HVAC, lighting and equipment operation. The model is established by collecting energy consumption data of multiple modes of transportation, including different fuel consumption and emission coefficients. During the experiment, it is assumed that the total transportation distance in the park is 10,000 kilometers, and diesel and electric fuels are used to calculate the carbon emissions of various modes of transportation. A park with an area of ​​10 hectares is selected for virtual modeling, and the program for site generation and building placement is written using Rhino software. Based on the existing planning data, including building type, area and floor height, the boundary conditions of the building are set by the lattice method, and a digital park model is constructed. An initial model library is established, the height, size and functional area of ​​the building are defined, and the spatial layout is optimized. The Numpy and Scipy libraries in Python are used to implement a multi-objective genetic algorithm to calculate the overall carbon emission value of the park, and the thermal comfort of the building at different wind speeds is evaluated. After multiple iterations of optimization, the optimal arrangement and combination of buildings in the park was obtained; after completing the model optimization, the Grasshopper platform was connected, and the Ladybug plug-in was used to simulate the wind environment and evaluate the thermal comfort of the park. The generated grid data provided wind speed information at each location and was quantified into different comfort zone ratios; in summary, the present invention is effective and innovative in energy conservation and carbon reduction, and has practical application value in improving the overall energy efficiency of the park and reducing environmental impact.

[0054] Example 3, reference Figure 2 , which is an embodiment of the present invention, provides an energy-saving and carbon-reduction optimization system for a low-carbon park, including a carbon emission quantification module, a space layout optimization module, and a thermal comfort feedback module.

[0055] The carbon emission quantification module is used to formulate the carbon emission function and quantify various influencing factors; the spatial layout optimization module is used to virtualize the spatial layout of the park and perform multi-objective optimization; the thermal comfort feedback module is used to establish a low-carbon and thermal comfort evaluation feedback mechanism by optimizing the constraint program.

[0056] If the function 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 technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0057] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0058] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering, or processing in another suitable manner as necessary, and then stored in a computer memory.

[0059] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc. It should be noted that the above embodiments are only used to illustrate the technical solution of the present invention and are not limited. Although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention, which should be included in the scope of the claims of the present invention.

[0060] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for optimizing energy conservation and carbon reduction in a low-carbon park, characterized in that: include: Develop a carbon emission function and quantify the various influencing factors; Virtualize the spatial layout of the park and perform multi-objective optimization; By optimizing the constraint program, a low-carbon and thermal comfort evaluation feedback mechanism is established.

2. The energy-saving and carbon-reduction optimization method for a low-carbon park according to claim 1, characterized in that: The formulation of the carbon emission function and quantification of various influencing factors include calculating CO2 emissions and fuel consumption during transportation, and calculating CO2 emissions, which is expressed as: Among them, E1 represents the CO2 emission generated by each unit of transportation distance, i is the type of transportation fuel, representing different fuel categories, Q represents the fuel consumption of the transportation mode, and C represents the CO2 emission coefficient of the fuel; Calculate the fuel consumption, expressed as: Q = ∑(K × D) Among them, Q represents the consumption of a certain fuel in the transportation mode, K represents the transportation distance of the transportation mode, and D represents the fuel consumption per 100 kilometers of the transportation equipment.

3. The energy-saving and carbon-reduction optimization method for a low-carbon park according to claim 2, characterized in that: The formulation of the carbon emission function and quantification of various influencing factors also includes estimating the carbon emission of the building through the carbon emission indicators of building heating consumption and building equipment consumption. The carbon emission during the building operation phase is determined according to the different types of energy consumption of each system and the carbon emission factors of different types of energy. The total carbon emission per unit building area during the building operation phase is calculated. M , expressed as: Among them, C M It represents the net carbon emissions per unit area during the operation phase of a building, and is used to evaluate the annual carbon emissions per unit area of ​​a building. i represents the carbon emission factor of the i-th energy type, which is used to convert energy consumption into corresponding carbon emissions, C p is the carbon sink obtained from the renewable energy system, which indicates the carbon emission reduced by the building through the use of clean energy. y represents the number of years, which is used to accumulate the total carbon emissions of the building. A represents the total building area, which is used to normalize the carbon emissions to the total carbon emissions calculation formula of the building energy consumption per unit area, which is expressed as: Where E represents the annual energy consumption of the i-th type of building, E i,j represents the amount of energy consumed by the jth system in the building, ER i,j It represents the amount of the i-th energy consumed by the j-th system that is provided by the renewable energy system.

4. The energy-saving and carbon-reduction optimization method for a low-carbon park according to claim 3, characterized in that: The virtualization operation of the spatial layout of the park includes writing a program for site generation and building placement in Rhino, generating corresponding plots based on the actual site size of the park as boundary constraints for design optimization, selecting building types based on actual surveys of the park, and structuring through three-dimensional data, building area and building floor height to confirm the layout of buildings in a specified area, thereby realizing the digital migration of the overall planning of the park, defining the plot size and center point, using a dot matrix method in Rhino to establish a simulated site area, inputting relevant site environment information, confirming that boundary constraints are followed during subsequent building placement, and setting a setting to allow the scope of change of building blocks to be restricted through exhaustive loops in later optimization, establishing an initial model library of various building types based on the business format of the park, defining the number and area range of each building, structuring the building and its information attributes into intelligent entities through the building placement module, and setting restrictions such as the height, length, width and area of ​​the building, confirming that there is no overlap or close contact between the buildings, and using a random algorithm to assign initial positions to various building intelligent entities based on the functional zoning and planning of the park, and generating a preliminary building layout through the generated boundaries and restrictions.

5. The energy-saving and carbon-reduction optimization method for a low-carbon park according to claim 4, characterized in that: The multi-objective optimization includes identifying each building intelligent body and site information through a low-carbon optimization module, calculating the overall carbon emission value of the park, using a thermal environment optimization module to evaluate the wind environment comfort of the park, and using a multi-objective genetic algorithm of the Numpy and Scipy scientific computing libraries in Python, taking carbon emissions and outdoor thermal comfort as objective functions, performing multiple iterative optimizations, and obtaining the optimal building arrangement combination and volume.

6. The energy-saving and carbon-reduction optimization method for a low-carbon park according to claim 5, characterized in that: The optimization constraint program includes calculating the total carbon emissions of the park, involving transportation carbon emissions, building carbon emissions, photovoltaic power generation carbon sinks and green space carbon sinks. Python is used to assign values ​​to each item, with transportation and building carbon emissions as positive values ​​and photovoltaic power generation and green space carbon sinks as negative values. The generated three-dimensional model is numerically calculated. If the total carbon emissions are greater than zero, the layout model does not meet the carbon reduction requirements, False is output and the model is regenerated. If the total carbon emissions are less than or equal to zero, it means that the model meets the low-carbon target, True is output, and it is imported into the next optimization step until the optimal solution that meets the low-carbon and thermal comfort requirements is found.

7. The energy-saving and carbon-reduction optimization method for a low-carbon park according to claim 6, characterized in that: The establishment of a low-carbon and thermal comfort evaluation feedback mechanism includes connecting the digital park model to the Grasshopper platform after completing the carbon emission verification, using the Butterfly tool in the Ladybug plug-in to simulate the wind environment, generating grid data, including the wind speed value of each grid point, and using Numpy to count the number of grids with wind speeds between 1-5m / s to obtain the proportion of the park's comfortable area. Combined with the overall planning model of the park, the thermal comfort of the park is evaluated through quantitative data, and related data is exported.

8. A system using the energy-saving and carbon-reduction optimization method for a low-carbon park as claimed in any one of claims 1 to 7, characterized in that: Including carbon emission quantification module, space layout optimization module, thermal comfort feedback module; The carbon emission quantification module is used to formulate a carbon emission function and quantify various influencing factors; The spatial layout optimization module is used to perform virtualization operations on the spatial layout of the park and perform multi-objective optimization; The thermal comfort feedback module is used to establish a low-carbon and thermal comfort evaluation feedback mechanism by optimizing the constraint program.

9. A computer 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 energy-saving and carbon-reduction optimization method for a low-carbon park described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the energy-saving and carbon-reduction optimization method for a low-carbon park according to any one of claims 1 to 7 are implemented.

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