A low-carbon park energy-saving and carbon-reducing optimization method and system

Abstract

This invention discloses an energy-saving and carbon-reduction optimization method and system for low-carbon industrial parks, relating to the field of low-carbon building and park design technology. The method includes developing a carbon emission function and quantifying various influencing factors; virtualizing the spatial layout of the park for multi-objective optimization; and establishing a low-carbon and thermal comfort evaluation feedback mechanism through optimized constraint procedures. The method described in this invention promotes carbon emission reduction goals, and the refined measurement and assessment also helps policymakers to scientifically regulate and drive the transformation of parks towards low-carbon development. It improves the efficiency of resource allocation, promotes the implementation of green building concepts, ensures the harmonious coexistence of buildings and the natural environment, and makes the iteration and optimization of park design more efficient. It ensures that various services and facilities can be adjusted and improved in a timely manner during actual operation, enabling the park to continuously develop towards a lower-carbon and more environmentally friendly goal.

Description

Technical Field

[0001] This invention relates to the field of low-carbon building and park design technology, specifically to an energy-saving and carbon-reduction optimization method and system for low-carbon parks. Background Technology

[0002] In recent years, with the intensification of global climate change, the development of low-carbon technologies has gradually become an important direction for urban sustainable development. Many countries and regions have successively introduced technological measures aimed at reducing carbon emissions and promoting the use of renewable energy. Against this backdrop, the construction of low-carbon parks has gradually emerged, becoming an important means to achieve environmentally friendly and sustainable economic development in cities. 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, promoting the continuous progress of related technologies.

[0003] Although current technologies and methods related to low-carbon parks have made some progress, there are still many shortcomings. Existing carbon emission quantification models mostly rely on traditional statistical methods, which are difficult to comprehensively and accurately consider multiple influencing factors, such as traffic patterns and building operating energy consumption. The spatial layout optimization process often lacks a real-time feedback mechanism and cannot effectively integrate optimal building arrangement and thermal comfort assessment, resulting in layout design failing to achieve the expected low-carbon effect. When identifying and dealing with multi-objective optimization problems, existing technologies often focus on a single objective, ignoring the dual consideration of low carbon and thermal comfort, resulting in limitations in design schemes. Therefore, they cannot fully meet the needs of users in different areas. This invention uses a systematic approach, by formulating detailed carbon emission functions, quantifying various influencing factors, and introducing virtualized operation and optimization constraint mechanisms, to effectively improve the flexibility and comprehensiveness of park design and effectively achieve 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 problem solved by this invention is that existing low-carbon building and park design technologies suffer from inaccurate models, lack of effective feedback mechanisms, insufficient multi-objective optimization capabilities, and the problem of how to achieve more efficient low-carbon park design and operation.

[0006] To address the aforementioned technical problems, this invention provides the following technical solution: an energy-saving and carbon-reduction optimization method for low-carbon industrial parks, comprising: formulating a carbon emission function and quantifying various influencing factors; performing virtualization operations on the spatial layout of the park and conducting multi-objective optimization; and establishing a low-carbon and thermal comfort evaluation feedback mechanism through optimization constraint procedures.

[0007] As a preferred embodiment of the energy-saving and carbon-reduction optimization method for low-carbon industrial parks described in this invention, the step of formulating a carbon emission function and quantifying various influencing factors includes calculating CO2 emissions and fuel consumption during transportation. The CO2 emissions are calculated as follows:

[0008]

[0009] Where E1 represents the CO2 emissions per unit of transportation distance, i represents the type of transportation fuel, indicating 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 follows:

[0010] Q = ∑(K × D)

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

[0012] As a preferred embodiment of the energy-saving and carbon-reduction optimization method for low-carbon parks described in this invention, the step of formulating a carbon emission function and quantifying various influencing factors further includes estimating the building's carbon emissions through 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 C per unit building area during the building operation phase are then calculated. M , is represented as:

[0013]

[0014] Among them, C M EF represents the net carbon emissions per unit area of ​​a building during its operational phase, used to evaluate the annual carbon emissions generated per unit area of ​​a building. i C represents the carbon emission factor for the i-th energy type, used to convert energy consumption into corresponding carbon emissions. p The carbon sink obtained from the renewable energy system represents the carbon reduction achieved by the building through the use of clean energy, where y represents the number of years used to accumulate the building's total carbon emissions, and A represents the total building area. The formula for normalizing carbon emissions to the total carbon emissions per unit area of ​​building energy consumption is expressed as:

[0015]

[0016] Where E represents the annual energy consumption of building type i, E i,j ER represents the amount of energy of type i consumed by the type j system in a building. i,j This represents the amount of energy consumed by the j-th type of system from the i-th type of energy provided by the renewable energy system.

[0017] As a preferred embodiment of the energy-saving and carbon-reduction optimization method for low-carbon parks described in this invention, the virtualization of the park's spatial layout includes writing a program in Rhino for site generation and building placement. Based on the actual size of the park, corresponding plots are generated as boundary constraints for design optimization. The building placement process selects building types based on actual park surveys, constructs the structure using 3D data, building area, and building height, confirms the building layout within the specified area, and achieves digital migration of the overall park plan. By defining plot sizes and center points, a simulated site area is established in Rhino using a raster method. Input relevant site environment information, confirm that boundary constraints are followed during subsequent building placement, set limits on the range of building block changes through exhaustive loops in later optimization, establish an initial model library for various building types based on the park's business format, define the quantity and area range of each type of building, construct buildings and their information attributes as intelligent agents through the building placement module, and set restrictions on building height, length, width, and area, confirm that there is no overlap or close proximity between buildings, and assign initial positions to various building intelligent agents based on the park's functional zoning and planning, and generate a preliminary building layout through the generated boundaries and constraints.

[0018] As a preferred embodiment of the energy-saving and carbon-reduction optimization method for low-carbon parks described in this invention, the multi-objective optimization includes: identifying the information of each building agent and the site through the low-carbon optimization module, calculating the overall carbon emission value of the park, evaluating the wind environment comfort of the park using the thermal environment optimization module, 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 iterations of optimization to obtain the optimal building arrangement and size.

[0019] As a preferred embodiment of the energy-saving and carbon-reduction optimization method for low-carbon parks described in this invention, 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. Values ​​are assigned to each item using Python, with transportation and building carbon emissions being positive values ​​and photovoltaic power generation and green space carbon sinks being negative values. Numerical calculations are then performed on the generated 3D model. 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 indicates that the model meets the low-carbon target, True is output, and the model is imported into the next optimization stage until the optimal solution that meets both low-carbon and thermal comfort requirements is found.

[0020] As a preferred embodiment of the energy-saving and carbon-reduction optimization method for low-carbon parks described in this invention, the establishment of a low-carbon and thermal comfort evaluation feedback mechanism includes, after completing carbon emission verification, connecting the digital park model to the Grasshopper platform, using the Butterfly tool in the Ladybug plugin to simulate the wind environment, generating grid data, including the wind speed value of each grid point, using Numpy to count the number of grids with wind speeds between 1-5 m / s, obtaining the proportion of comfortable areas in the park, combining the overall planning model of the park, evaluating the thermal comfort of the park through quantitative data, and exporting relevant data.

[0021] Another objective of this invention is to provide an energy-saving and carbon-reduction optimization system for low-carbon parks, which can establish a low-carbon and thermal comfort evaluation feedback mechanism through optimization constraint procedures, thus solving the problem that current low-carbon building and park design technologies lack effective feedback mechanisms.

[0022] As a preferred embodiment of the energy-saving and carbon-reduction optimization system for low-carbon parks described in this invention, it includes a carbon emission quantification module, a spatial 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 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 through optimization constraint procedures.

[0024] A computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement energy-saving and carbon-reduction optimization methods for low-carbon industrial parks.

[0025] A computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of an energy-saving and carbon-reduction optimization method for a low-carbon industrial park.

[0026] The beneficial effects of this invention are as follows: The energy-saving and carbon-reduction optimization method for low-carbon parks provided by this invention formulates a carbon emission function and quantifies various influencing factors. By formulating the carbon emission function and quantifying various influencing factors, it is possible to calculate in detail the emissions and fuel consumption during transportation, achieving an accurate assessment of the carbon emissions of each fuel and mode of transportation per unit distance. It also quantifies the carbon emission indicators of building heating and equipment use, enabling the carbon emissions per unit area of ​​buildings during operation to be clearly calculated, providing precise 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. The refined measurement and assessment also helps policymakers to scientifically supervise and promote the transformation of parks towards low-carbon development. Virtual operation of the park's spatial layout allows for multi-objective optimization. Optimizing the park's spatial layout using virtual operation not only simulates the actual environment in software such as Rhino, but also ensures the scientific and rational nature of the planning by defining boundary conditions and building constraints. Through multi-objective optimization, the overall park can be comprehensively considered. By considering layout, carbon emission targets, and thermal comfort, and thereby achieving a rational arrangement of building blocks and functional zoning, the design of the park can flexibly adapt to the environment and usage needs, improve the efficiency of resource allocation, promote the implementation of green building concepts, and achieve the long-term effect of reducing energy consumption and carbon emissions in space use. By optimizing constraint procedures and establishing a low-carbon and thermal comfort evaluation feedback mechanism, the carbon emission sources of the park are comprehensively considered, including transportation, buildings, and photovoltaic power generation, to ensure that the final layout meets low-carbon targets. The established feedback mechanism combines carbon emission verification and thermal comfort assessment, which can adjust the park design in a timely manner to ensure that the predetermined environmental benefits and user comfort are achieved. This not only improves the flexibility and adaptability of the design, but also provides strong support for subsequent adjustments through scientific data analysis, effectively enhancing the sustainability of the park and creating 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. This invention has achieved better results in the precise quantification of carbon emissions, intelligent optimization of park spatial layout, and the establishment of a low-carbon and thermal comfort evaluation feedback mechanism. Attached Figure Description

[0027] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0028] Figure 1 The first embodiment of the present invention provides an overall flowchart of an energy-saving and carbon-reduction optimization method for a low-carbon industrial park.

[0029] Figure 2The following is an overall flowchart of an energy-saving and carbon-reduction optimization system for a low-carbon industrial park, provided as a third embodiment of the present invention. Detailed Implementation

[0030] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0031] Example 1, referring to Figure 1 As an embodiment of the present invention, an energy-saving and carbon-reduction optimization method for low-carbon industrial parks is provided, comprising:

[0032] S1: Develop a carbon emission function and quantify the various influencing factors.

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

[0034]

[0035] Where E1 represents the CO2 emissions per unit of transportation distance, i represents the type of transportation fuel, indicating 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 follows:

[0036] Q = ∑(K × D)

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

[0038] It should be noted that developing a carbon emission function and quantifying various influencing factors also includes estimating the building's carbon emissions through carbon emission indicators of building heating consumption and building equipment consumption. Carbon emissions during the building's operation phase are determined based on the consumption of different types of energy in each system and the carbon emission factors of different types of energy. The total carbon emissions C per unit building area during the building's operation phase are then calculated. M , is represented as:

[0039]

[0040] Among them, C M EF represents the net carbon emissions per unit area of ​​a building during its operational phase, used to evaluate the annual carbon emissions generated per unit area of ​​a building. iC represents the carbon emission factor for the i-th energy type, used to convert energy consumption into corresponding carbon emissions. p The carbon sink obtained from the renewable energy system represents the carbon reduction achieved by the building through the use of clean energy, where y represents the number of years used to accumulate the building's total carbon emissions, and A represents the total building area. The formula for normalizing carbon emissions to the total carbon emissions per unit area of ​​building energy consumption is expressed as:

[0041]

[0042] Where E represents the annual energy consumption of building type i, E i,j ER represents the amount of energy of type i consumed by the type j system in a building. i,j This represents the amount of energy consumed by the j-th type of system from the i-th type of energy provided by the renewable energy system.

[0043] It should also be noted that developing carbon emission functions and quantifying various influencing factors plays a crucial role in energy conservation and carbon reduction in low-carbon industrial parks. Detailed calculations of carbon emissions during transportation processes allow for the clear quantification of emissions from different fuel types and modes of transport. Using mathematical models and formulas to calculate emissions per unit transport distance enables a scientific assessment of the ecological impact of various modes of transport. This not only helps identify the most environmentally burdensome modes of transport but also lays the foundation for building a low-carbon transportation system within the park. Furthermore, it provides an important basis for the systematic management of low-carbon industrial parks, enhancing the understanding of the overall carbon emission level of the park and supporting the formulation of scientifically sound 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: Perform virtualization operations on the spatial layout of the park and optimize it for multiple objectives.

[0045] Furthermore, the virtualization of the park's spatial layout involves writing programs in Rhino for site generation and building placement. Based on the actual size of the park, corresponding plots are generated as boundary constraints for design optimization. The building placement process selects building types based on actual park surveys, and constructs the architecture using 3D data, building area, and building height to confirm the layout of buildings within the specified area, achieving digital migration of the overall park plan. By defining plot size and center point, a simulated site area is established in Rhino using a lattice method, inputting relevant site environment information to confirm that boundary constraints are followed during subsequent building placement. Limitations on the range of building block changes can be imposed through exhaustive loops in later optimization. An initial model library of various building types is established based on the park's business format, defining the quantity and area range of each type of building. Through the building placement module, buildings and their information attributes are structured as intelligent agents, and constraints such as building height, length, width, and area are set to ensure that buildings do not overlap or overlap. Based on the park's functional zoning and planning, a random algorithm is used to assign initial positions to various building agents, generating a preliminary building layout based on the generated boundaries and constraints.

[0046] It should be noted that the multi-objective optimization includes identifying the information of each building agent and the site through the low-carbon optimization module, calculating the overall carbon emission value of the park, evaluating the wind environment comfort of the park using the thermal environment optimization module, and using the multi-objective genetic algorithm of the Numpy and SciPy scientific computing libraries in Python to perform multiple iterations of optimization with carbon emissions and outdoor thermal comfort as objective functions to obtain the optimal building arrangement and size.

[0047] It should also be noted that the virtualization of the park's spatial layout and the multi-objective optimization aim to combine digital technology with traditional park design to achieve more efficient and scientific space utilization. By digitally transforming the actual site using 3D modeling software such as Rhino, the existing spatial conditions and design requirements can be presented intuitively. This visualization not only improves the transparency of the design process but also allows stakeholders to participate more effectively in the design phase. During multi-objective optimization, designers use the virtual environment to simulate the functions and impacts of different building combinations, thereby achieving comprehensive optimization. This considers not only the spatial relationships between buildings but also the overall functional distribution and environmental impact of the park, promoting the generation of high-efficiency and low-carbon design schemes. The park becomes not only a space that achieves low-carbon goals but also an intelligent place that applies modern technology and management concepts, thereby enhancing the overall usability and social value.

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

[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. Values ​​are assigned to each item using Python, with transportation and building carbon emissions assigned positive values ​​and photovoltaic power generation and green space carbon sinks assigned negative values. Numerical calculations are then performed on the generated 3D model. If the total carbon emissions are greater than zero, the layout model does not meet the carbon reduction requirements, and the system outputs False and regenerates the model. If the total carbon emissions are less than or equal to zero, the model meets the low-carbon goals, and the system outputs True and proceeds to the next optimization stage until the optimal solution that meets both low-carbon and thermal comfort requirements is found.

[0050] It should be noted that establishing a low-carbon and thermal comfort evaluation feedback mechanism includes, after completing carbon emission verification, connecting the digital park model to the Grasshopper platform, using the Butterfly tool in the Ladybug plugin to simulate the wind environment, generating grid data, including the wind speed value of each grid point, using Numpy to count the number of grids with wind speeds between 1-5 m / s, obtaining the proportion of comfortable zones in the park, combining the overall planning model of the park, evaluating the thermal comfort of the park through quantitative data, and exporting relevant data.

[0051] It should also be noted that by quantifying various carbon emission sources within the park, including transportation, buildings, and green spaces, a comprehensive evaluation model is formed. Based on this, the optimized constraint procedure can promptly detect whether the design scheme meets low-carbon goals and provide real-time feedback to managers. This ensures the flexibility and scientific nature of the park design during implementation, encourages architects, engineers, and planners to consider the comprehensive impact of carbon emissions from the outset, and establishes a low-carbon and thermal comfort evaluation feedback mechanism that directly affects park users. Through simulation and analysis of environmental parameters such as wind speed and temperature, designers can 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 park design also become more efficient, ensuring that various services and facilities can be adjusted and improved in a timely manner during actual operation, enabling the park to continuously develop towards a lower-carbon and more environmentally friendly goal.

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

[0053] First, a carbon emission function was designed based on different modes of transportation and building energy consumption within the park. Transportation modes included public transportation, private cars, and walking; building energy consumption included HVAC, lighting, and equipment operation. Energy consumption data from multiple transportation modes, including different fuel consumption and emission coefficients, was collected to build a model. In the experiment, the total transportation distance within the park was assumed to be 10,000 kilometers, using both diesel and electric fuels to calculate the carbon emissions of each mode of transportation. A 10-hectare park was selected for virtual modeling. Rhino software was used to write programs for site generation and building placement. Based on existing planning data, including building type, area, and floor height, boundary conditions for buildings were set using a lattice method to construct a digital park model. An initial model library was established, defining building height, size, and functional areas for spatial layout optimization. The Numpy and SciPy libraries in Python were used to implement a multi-objective genetic algorithm to calculate the overall carbon emissions of the park, while simultaneously evaluating the thermal comfort of buildings under different wind speeds. After multiple iterations and optimizations, the optimal arrangement of buildings within the park was obtained. After model optimization, the Grasshopper platform was integrated, and the Ladybug plugin was used to simulate the wind environment and evaluate the thermal comfort of the park. The generated grid data provided wind speed information for each location and quantified it into different comfort zone proportions. In summary, this 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, referring to Figure 2 As an embodiment of the present invention, an energy-saving and carbon-reduction optimization system for a low-carbon park is provided, including a carbon emission quantification module, a spatial layout optimization module, and a thermal comfort feedback module.

[0055] The carbon emission quantification module is used to formulate carbon emission functions and quantify various influencing factors; the spatial layout optimization module is used to perform virtual operations on the spatial layout of the park and carry out multi-objective optimization; the thermal comfort feedback module is used to establish a low-carbon and thermal comfort evaluation feedback mechanism through optimization constraint procedures.

[0056] If a function is implemented as 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 this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

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

[0058] More specific examples (a non-exhaustive list) of computer-readable media include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which programs can be printed, because programs can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0059] It should be understood that various parts of the present invention can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc. It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within 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 and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for optimizing energy conservation and carbon reduction in a low-carbon industrial park, characterized in that, include: Develop a carbon emission function and quantify the various influencing factors; The spatial layout of the park is virtualized and optimized for multiple objectives. By optimizing the constraint procedures, a feedback mechanism for evaluating low carbon emissions and thermal comfort is established. The formulation of the carbon emission function and the quantification of various influencing factors include calculations during transportation processes. Emissions and fuel consumption, calculated Emissions, expressed as: in, This represents the amount generated by transportation per unit distance. Emissions This indicates the type of transportation fuel, representing different fuel categories. This indicates the fuel consumption of a mode of transportation. Indicates the carbon dioxide emission coefficient of the fuel; Fuel consumption is calculated and expressed as follows: in, This indicates the amount of a certain fuel consumed in a mode of transportation. Indicates the travel distance by mode of transport. This indicates the fuel consumption per 100 kilometers of transportation equipment; The process of developing a carbon emission function and quantifying various influencing factors also includes estimating the building's carbon emissions through carbon emission indicators of building heating consumption and building equipment consumption. Carbon emissions during the building's operation phase are determined based on the consumption of different types of energy in each system and the carbon emission factors of different types of energy, calculating the total carbon emissions per unit building area during the building's operation phase. , is represented as: in, This represents the net carbon emissions per unit area of ​​a building during its operational phase, and is used to evaluate the annual carbon emissions generated per unit area of ​​a building. Indicates the first Carbon emission factors for energy-related products are used to convert energy consumption into corresponding carbon emissions. Carbon sinks derived from renewable energy systems represent the reduction in carbon emissions achieved by buildings through the use of clean energy. Indicates the number of years used to accumulate the total carbon emissions of buildings. The total building area is used to normalize carbon emissions to the total carbon emissions per unit area of ​​building energy consumption. The formula is as follows: in, Indicates the building number Annual consumption of this type of energy Indicates the first in the building The first type of system consumption Type of energy quantity, Indicates the first The first type of system consumption The amount of energy provided by renewable energy systems in this category; The virtualization of the park's spatial layout includes writing programs in Rhino for site generation and building placement. Based on the actual size of the park, corresponding plots are generated as boundary constraints for design optimization. The building placement part selects building types based on actual park surveys, and constructs the architecture using 3D data, building area, and building height to confirm the layout of buildings within the specified area, achieving digital migration of the overall park plan. By defining plot size and center point, a simulated site area is established in Rhino using a lattice method. Relevant site environment information is input to confirm that boundary constraints are followed during subsequent building placement. Limitations on the range of building block changes can be imposed through exhaustive loops in later optimization. Based on the park's business format, an initial model library of various building types is established, defining the number and area range of each type of building. Through the building placement module, buildings and their information attributes are constructed as intelligent agents, and constraints such as building height, length, width, and area are set to ensure that buildings do not overlap or overlap. Based on the park's functional zoning and planning, a random algorithm is used to assign initial positions to various building intelligent agents, generating a preliminary building layout through the generated boundaries and constraints.

2. The energy-saving and carbon-reduction optimization method for low-carbon industrial parks as described in claim 1, characterized in that: The multi-objective optimization includes identifying the information of each building agent and the site through the low-carbon optimization module, calculating the overall carbon emission value of the park, evaluating the wind environment comfort of the park using the thermal environment optimization module, 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, and performing multiple iterations to obtain the optimal building arrangement and size.

3. The energy-saving and carbon-reduction optimization method for low-carbon industrial parks as described in claim 2, characterized in that: 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. Values ​​are assigned to each item using Python, with transportation and building carbon emissions assigned positive values ​​and photovoltaic power generation and green space carbon sinks assigned negative values. Numerical calculations are then performed on the generated 3D model. If the total carbon emissions are greater than zero, the layout model does not meet the carbon reduction requirements, and the system outputs False and regenerates the model. If the total carbon emissions are less than or equal to zero, the model meets the low-carbon target, and the system outputs True and proceeds to the next optimization stage until the optimal solution that meets both low-carbon and thermal comfort requirements is found.

4. The energy-saving and carbon-reduction optimization method for low-carbon industrial parks as described in claim 3, characterized in that: The establishment of a low-carbon and thermal comfort evaluation feedback mechanism includes, after completing carbon emission verification, connecting the digital park model to the Grasshopper platform, using the Butterfly tool in the Ladybug plugin to simulate the wind environment, generating grid data, including the wind speed value of each grid point, using Numpy to count the number of grids with wind speeds between 1-5 m / s, obtaining the proportion of comfortable zones in the park, combining the overall planning model of the park, evaluating the thermal comfort of the park through quantitative data, and exporting relevant data.

5. A system employing the energy-saving and carbon-reduction optimization method for low-carbon industrial parks as described in any one of claims 1 to 4, characterized in that: Includes a carbon emission quantification module, a spatial layout optimization module, and a 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 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 procedure.

6. A computer 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 energy-saving and carbon-reduction optimization method for low-carbon industrial parks as described in any one of claims 1 to 4.

7. A computer-readable 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 energy-saving and carbon-reduction optimization method for low-carbon industrial parks as described in any one of claims 1 to 4.

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