Building carbon emission analysis method based on STIRPAT algorithm and building information model technology

Abstract

The invention discloses a building carbon emission analysis method based on an STIRPAT algorithm and a building information model technology, and the method comprises the steps: collecting the whole life cycle information of a building through the BIM technology in a data collection and integration stage, covering the data of all links of design, construction and operation, widely collecting the external environment data of a project location, such as social economy, energy technology, etc. Multi-source data such as geographic space and weather are introduced and fused, and real-time monitoring is performed by means of an intelligent sensing technology. Secondly, an extended STIRPAT model is constructed, core independent variables such as population, economy and technology and control variables such as building scale are comprehensively considered, and the influence of factors is accurately quantified through dynamic parameter adjustment and scene simulation prediction; according to the method, carbon emission attributes are given to components and equipment based on a BIM model, full-life-cycle carbon emission is simulated and calculated, integrated optimization is carried out with solar photovoltaic, intelligent control, waste management and other technologies, finally, results are comprehensively analyzed, carbon emission distribution and trend are displayed by utilizing a BIM visualization function, and a building low-carbon decision is provided.

Description

Technical Field

[0001] The present invention belongs to the technical field related to building environment monitoring and energy conservation and emission reduction, and specifically relates to a building carbon emission analysis method based on the STIRPAT algorithm and building information model technology. Background Art

[0002] With the increasing global attention to climate change issues, the construction industry, as a key area of carbon emissions, accurately analyzing and evaluating its carbon emissions is of great significance. Traditional building carbon emission analysis methods have many limitations. On the one hand, when considering the factors affecting building carbon emissions, they are often not comprehensive and in-depth enough. Many methods only focus on a few main factors, such as energy consumption, while ignoring the comprehensive impact of other important factors such as population size, economic development level, and technological progress on building carbon emissions. This makes the assessment of building carbon emissions inaccurate and incomplete, and it is difficult to provide effective decision-making basis.

[0003] On the other hand, existing methods are relatively backward in data processing and analysis means. In the whole life cycle of a building project, a large amount of building information data is involved, including data in various stages such as building design, construction process, and operation management. However, traditional methods cannot efficiently integrate and utilize these massive data, resulting in the inability to fully explore the potential information behind the data when analyzing building carbon emissions, which affects the accuracy and reliability of the analysis results.

[0004] In recent years, the STIRPAT (Stochastic Impacts by Regression on Population, Affluence and Technology) model has received extensive attention in the field of environmental impact assessment because it can comprehensively consider multiple influencing factors. At the same time, building information model (BIM) technology, as an information integration and management technology based on a three-dimensional building model, can effectively integrate and manage the information of the whole life cycle of a building project. Combining the STIRPAT algorithm with BIM technology provides new ideas and methods for building carbon emission analysis. Summary of the Invention

[0005] The purpose of the present invention is to provide a building carbon emission analysis method based on the STIRPAT algorithm and building information model technology to solve the problems of incomplete consideration of factors and backward data processing and analysis means in existing building carbon emission analysis methods, and to achieve comprehensive, accurate, and efficient analysis of building carbon emissions.

[0006] To achieve the above purpose, the present invention provides the following technical solutions:

[0007] A building carbon emission analysis method based on the STIRPAT algorithm and building information model technology includes the following steps:

[0008] Data collection and integration step:

[0009] Using Building Information Modeling (BIM) technology, during the design phase of a construction project, collect the geometric and non-geometric information of the building. During the construction phase, collect data on material usage, energy consumption, and construction progress information during the construction process. During the operation phase, obtain data on the building's energy consumption and personnel flow information, and integrate them to form a BIM database of the building's full life cycle information. At the same time, collect social and economic data such as the population size, per capita GDP, and industrial structure of the project location, as well as external environment data such as the local energy structure and technological development level.

[0010] Expand the scope of data collection, introduce geospatial data, meteorological data, and data on the building's surrounding environment, and through specially developed data fusion algorithms, efficiently integrate and deeply analyze multi-source data, eliminate data redundancy and conflicts, and form a building carbon emission analysis dataset with spatio-temporal correlation.

[0011] Deploy intelligent sensors inside and around the building, including sensors for temperature, humidity, air quality, energy consumption, and personnel activities. Use Internet of Things technology to transmit sensor data to the BIM platform and data analysis center in real time, and based on machine learning algorithms, perform real-time analysis and prediction on the sensor data to achieve real-time and accurate perception of the building's operating status and environmental parameters.

[0012] Steps for factor analysis based on the STIRPAT model:

[0013] Construct an extended STIRPAT model, with the building's carbon emissions as the dependent variable, population size, economic development level, and technological level as the core independent variables, and introduce building scale, building type, and energy structure as control variables. Determine the elasticity coefficients of each variable through statistical analysis and regression methods, and quantify the degree of influence of each factor on the building's carbon emissions.

[0014] Establish a time series analysis module, using methods such as Kalman filtering and state space models to perform time series analysis on the building's carbon emission data and related influencing factor data, estimate the dynamic changes of the influence of each factor in real time, and automatically update the elasticity coefficients of each variable in the STIRPAT model. Develop an adaptive parameter adjustment algorithm for regions and building types, and make targeted adjustments to the model parameters according to the characteristics of the building's location and building type.

[0015] Use the STIRPAT model for multi-scenario simulation and future carbon emission prediction, set scenario assumptions for different population growth, economic development, technological progress, and energy policies, and perform uncertainty analysis on the scenario simulation results through the Monte Carlo simulation method to provide a comprehensive and scientific decision-making basis for building carbon emission management.

[0016] Steps for carbon emission simulation and calculation based on the BIM model:

[0017] Assign carbon emission related attribute information to each building component and facility in the BIM model. Based on the building usage and energy consumption data, use the BIM model to conduct building carbon emission simulation calculations, and comprehensively calculate the carbon emissions throughout the building's life cycle.

[0018] Further improve the full life cycle carbon emission traceability function of the BIM model, assign a unique carbon emission identification code to each building component, material, construction, and operation activity. Develop a carbon emission management module based on the BIM model, set carbon emission targets and thresholds, monitor and manage the carbon emissions throughout the building's life cycle in real time, and use the visualization advantage of BIM to display the time-space carbon emission distribution map.

[0019] Innovatively integrate BIM technology with solar photovoltaic system design software, intelligent building control systems, building waste management systems, and other green building technologies to achieve collaborative optimization of building carbon emissions, including using the BIM model to optimize the design and operation strategies of solar photovoltaic systems, intelligently control the operation modes of building energy equipment, and plan the classified collection and reuse of building waste.

[0020] Result analysis and visualization display steps:

[0021] Integrate the analysis results based on the STIRPAT model and the building carbon emission data calculated based on the BIM model, analyze the contribution ratio of different factors to carbon emissions throughout the building's life cycle, and identify the key influencing factors.

[0022] Use the visualization function of BIM technology to intuitively present the carbon emission distribution of different parts and stages of the building, as well as the changing trends of the impact of various factors on carbon emissions, through the color change of the 3D model and chart display.

[0023] Preferably, in the data collection and integration step, the geospatial data includes the topographic features and land use type information of the building's location area; the meteorological data includes the real-time and historical data of temperature, precipitation, wind speed, and sunshine duration; the building surrounding environment data covers the traffic flow and vegetation coverage data around the building.

[0024] Preferably, in the factor analysis step based on the STIRPAT model, the time series analysis module continuously analyzes the collected data, and the regional and building type adaptive parameter adjustment algorithm adjusts the model parameters according to the geographical, economic, and policy characteristics of the building's location area and the characteristics of the building type.

[0025] Preferably, in the steps of carbon emission simulation and calculation based on the BIM model, the influence of the power generation efficiency of the solar photovoltaic system on building energy consumption is simulated in real time through the BIM model, the operation mode of energy equipment is automatically optimized by the intelligent control system according to the real-time environmental parameters and personnel activities, and the waste generated during the building construction and demolition processes is simulated and predicted through the BIM model.

[0026] Preferably, a system for implementing a building carbon emission analysis method based on the STIRPAT algorithm and building information model technology includes:

[0027] A data collection and integration system for performing the data collection and integration steps, including a BIM data collection module, an external environment data collection module, a multi-source data fusion module, and an intelligent sensor data collection and transmission module;

[0028] An STIRPAT model analysis system for performing the factor analysis steps based on the STIRPAT model, including a model construction module, a time series analysis module, a regional and building type adaptive parameter adjustment module, and a scenario simulation and prediction module;

[0029] A BIM carbon emission simulation, calculation and management system for performing the carbon emission simulation and calculation steps based on the BIM model and the result analysis and visualization display steps, including a BIM model carbon emission attribute assignment module, a carbon emission simulation calculation module, a full life cycle carbon emission traceability and management module, a BIM and other green building technology integration module, and a result analysis and visualization display module.

[0030] Preferably, a computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, it implements the building carbon emission analysis method based on the STIRPAT algorithm and building information model technology according to any one of claims 1 to 4.

[0031] Preferably, an electronic device includes a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, it implements the building carbon emission analysis method based on the STIRPAT algorithm and building information model technology according to any one of claims 1 to 4.

[0032] Compared with the prior art, the present invention provides a building carbon emission analysis method based on the STIRPAT algorithm and building information model technology, having the following beneficial effects:

[0033] Accurate and comprehensive analysis: This invention integrates multi-source data, covering not only the information of the entire building life cycle but also data such as geospatial, meteorological, and surrounding environment, comprehensively reflecting the complex influencing factors of building carbon emissions. Based on the extended STIRPAT model, it accurately quantifies the elasticity coefficients of various factors, combines with the BIM model for detailed simulation calculation, and accurately obtains the carbon emissions of the entire building life cycle, avoiding the one-sidedness and errors of traditional methods and providing a reliable basis for decision-making;

[0034] Dynamic and intelligent model: The STIRPAT model introduces dynamic parameter adjustment, tracks the changes of factors in real time, automatically updates the coefficients, and adapts to different regions, building types, and development stages. The scenario simulation and prediction combined with Monte Carlo analysis show various possibilities of future carbon emissions, helping decision-makers weigh risks and opportunities, flexibly formulate strategies, and cope with uncertainties;

[0035] Real-time perception and early warning: The intelligent perception technology combined with 5G and the Internet of Things, sensors inside and outside the building collect and transmit data in real time, and the BIM platform visualizes and displays it. Based on machine learning, it predicts energy demand and carbon emissions, and gives early warnings in case of anomalies, facilitating maintenance personnel to intervene in advance, optimize building operation, and reduce energy consumption and emissions;

[0036] Function collaboration and expansion: The BIM model deepens its functions, traces the source and process of carbon emissions, and queries the whole process information by associating with identification codes; it is integrated with solar energy, intelligent control, and waste management systems to collaboratively optimize energy utilization, equipment operation, and waste treatment, reducing carbon emissions from multiple links and improving the overall low-carbon performance of the building;

[0037] Intuitive and easy-to-use results: The result analysis synthesizes data from multiple models to mine key factors; the visualization uses 3D models, charts, and VR / AR technologies to intuitively present the carbon emission distribution and trends. The immersive experience and intelligent decision support system help users understand the data, quickly screen the optimal emission reduction strategies, and promote the implementation of low-carbon practices in the building industry. Description of the Drawings

[0038] Figure 1 It is a schematic diagram of the method flow of the present invention. Detailed Embodiments

[0039] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0040] The present invention provides Figure 1 as shown

[0041] A building carbon emission analysis method based on the STIRPAT algorithm and building information model technology, comprising the following steps:

[0042] I. Data collection and integration implementation

[0043] (1) Collection of building life cycle information

[0044] Design stage: Use the professional BIM modeling software, Autodesk Revit, to carefully construct a 3D building model by the design team. During the modeling process, detailed geometric information is entered, such as the length, width, and height of the building, the layout of each floor, the size of the rooms, etc. are accurately recorded; at the same time, non-geometric information cannot be omitted. For example, when selecting a certain brand of concrete, detailed parameters such as its strength grade, density, and carbon emission factor need to be entered. For air conditioning equipment, key information such as the model, cooling and heating power are entered. All the entered information is stored in the BIM database in real time, constructing the basic data framework for the design stage.

[0045] Construction stage: The construction unit uses on-site data collection equipment, such as intelligent electricity meters and electronic weighbridges, to record the usage of various building materials during the construction process in real time and accurately. The changes in the usage of steel, cement, and wood can be captured and uploaded in a timely manner. At the same time, energy consumption data such as the electricity consumption and fuel consumption of construction equipment, as well as construction progress information, the start and end times of each construction stage, are also collected and uploaded synchronously. These data are stably transmitted to the BIM database through the Internet of Things technology, achieving seamless docking with the data in the design stage and gradually improving the building life cycle data chain.

[0046] Operation stage: The building management department uses equipment such as intelligent electricity meters, water meters, and air quality monitors to continuously collect the energy consumption data of the building, covering the changes in the usage of electricity, natural gas, and water resources; through the personnel access management system, methods such as access card swiping records and face recognition statistics are used to accurately count the personnel flow information in the building, including the number of people and the distribution of activity areas at different time periods. The data in the operation stage are updated in real time to the BIM database, providing dynamic and real operation data support for subsequent analysis.

[0047] (2) Collection of external environment data

[0048] Socio-economic data: Through authoritative channels such as the official websites of government statistical departments and annual statistical reports, collect socio-economic data such as the population size, per capita GDP, and industrial structure of the project location. For example, obtain the population growth data of the past ten years from the local statistical bureau and analyze its change trend; extract the per capita GDP data from the report of the economic development research center and study its correlation with the development of the construction industry. These data are updated regularly to ensure that the analysis model is based on the latest socio-economic situation.

[0049] Energy and technology data: Collect local energy structure data, such as the proportion of hydropower, thermal power, wind power, solar power generation, etc. in electricity, which can be obtained from the energy statistical yearbooks published by the energy management department; for the data on the technological development level of the local construction industry, the application proportion of new energy-saving technologies, the degree of building intelligence, etc., collect through channels such as industry association reports and research results of scientific research institutions. These data provide key evidence for analyzing the impact of energy structure and technological progress on building carbon emissions.

[0050] (III) Implementation of multi-source data fusion

[0051] Expansion of data collection: Introduce geospatial data, obtain the topographic and geomorphic data of the area where the building is located from the Geographic Information System (GIS) platform, and analyze the potential impact of terrain undulation on building ventilation and lighting; obtain land use type data and study the differences in building carbon emissions under different land use types (commercial areas, residential areas, industrial areas). For meteorological data, cooperate with the meteorological department to obtain real-time data such as temperature, precipitation, wind speed, and sunshine duration, and connect the data to the analysis system through the meteorological data interface. The data on the surrounding environment of the building is collected through traffic flow monitoring devices, vegetation coverage monitoring cameras, etc. installed around the building to obtain information such as traffic flow and vegetation coverage.

[0052] Application of data fusion algorithms: Develop specialized data fusion algorithms, which are based on big data processing technologies and can automatically identify the data associations and complementary relationships between different data sources. For example, by analyzing the correlation between temperature changes in meteorological data and building energy consumption data, establish a mathematical model between the two to eliminate data redundancy and conflicts. During the data fusion process, standardize the data in different formats, unify the data format and unit, ensure that the data can be effectively integrated, and form a comprehensive, accurate and spatiotemporally correlated building carbon emission analysis data set.

[0053] (IV) Implementation of the application of intelligent sensing technology

[0054] Deployment of sensors: Inside the building, deploy various intelligent sensors according to the needs of different functional areas. In the office area, install temperature sensors, humidity sensors, and air quality sensors to monitor indoor environmental parameters in real time; in the equipment machine room, install energy consumption sensors to accurately monitor the energy consumption of various building equipment; in the public area, install personnel activity sensors, infrared sensors, and cameras to monitor the flow of people and behavior patterns. Around the building, deploy traffic flow sensors to monitor the traffic flow of the surrounding roads and install vegetation growth status sensors to monitor the health status of the surrounding vegetation.

[0055] Data Transmission and Analysis: Leveraging Internet of Things (IoT) technology, the data collected by sensors is transmitted to the BIM platform and data analysis center in real-time and quickly through the 5G communication network. On the BIM platform, a dedicated sensor data visualization plugin is developed, which can display real-time data in an intuitive way on the building's 3D model. For example, in the BIM model, different colors are used to identify the temperature conditions in different areas, with red indicating areas with too high temperature and blue indicating areas with suitable temperature; a flashing effect is used to highlight equipment with excessive energy consumption. At the same time, based on machine learning algorithms and deep learning models, the sensor data is analyzed and predicted in real-time. Using historical energy consumption data and real-time environmental parameters, an energy consumption prediction model is established to predict in advance the energy demand and carbon emission trends of the building in the future for a period of time, and warning messages are sent in a timely manner when abnormal situations are detected.

[0056] II. Implementation of Factor Analysis Based on the STIRPAT Model

[0057] (I) Model Construction and Parameter Determination

[0058] Model Building: Using statistical analysis software such as SPSS and R language, an extended STIRPAT model is constructed. The building carbon emissions are set as the dependent variable, and the population size, per capita GDP, technological level (measured by the application ratio of energy-saving technologies in the local construction industry), building scale (measured by the building area), building type (divided into residential, commercial, public buildings, etc.), energy structure (measured by the proportion of clean energy in the local electricity) are set as independent variables. During the model construction process, according to the data characteristics and research purposes, the model parameters and variable forms are reasonably set to ensure that the model can accurately reflect the relationship between various factors and building carbon emissions.

[0059] Parameter Determination: The collected data is sorted and preprocessed to remove outliers and missing values. Regression analysis methods such as multiple linear regression and stepwise regression are used to fit the data to determine the elastic coefficients of each variable in the model. For example, through multiple experiments and analyses, the elastic coefficient of the population size is determined to be 0.2, indicating that for every 1% increase in the population size, the building carbon emissions will increase by 0.2%; the elastic coefficient of per capita GDP is 0.3, indicating that for every 1% increase in per capita GDP, the building carbon emissions will increase by 0.3%, etc. During the process of determining the elastic coefficients, statistical test methods such as t-test and F-test are used to test the significance of the coefficients to ensure the reliability of the model results.

[0060] (II) Implementation of Dynamic Parameter Adjustment

[0061] Operation of the time series analysis module: Establish a time series analysis module, which is developed based on data analysis libraries such as pandas and numpy in Python. Continuously perform time series analysis on the collected building carbon emission data and related influencing factor data (changes in population size, fluctuations in economic development, dynamics of technological innovation, etc.). Use the Kalman filter algorithm to estimate the dynamic changes in the impact of each factor on building carbon emissions in real time. For example, as new energy-saving technologies are popularized and applied in the construction industry, the impact coefficient of the technological level on building carbon emissions may gradually decrease. The time series analysis module can capture this change in a timely manner and feedback it to the STIRPAT model to automatically adjust the elasticity coefficient of the technological level variable.

[0062] Adaptive parameter adjustment for regions and building types: Develop an adaptive parameter adjustment algorithm for regions and building types, which is based on geographic information analysis and building type feature recognition technologies. According to the geographical, economic, policy and other characteristics of the region where the building is located, as well as the characteristics of the building type (residential, commercial building, industrial building, etc.), the parameters of the STIRPAT model are adjusted specifically. For example, in regions where energy resources are rich and prices are low, the impact of energy prices on building carbon emissions may be relatively small. The algorithm automatically adjusts the elasticity coefficient of the energy price variable according to the regional energy data and economic characteristics; for different types of buildings, residential buildings pay more attention to living comfort, and commercial buildings are more concerned about operating costs. The algorithm adjusts the weights of relevant variables according to the characteristics of the building type to make the model more suitable for the carbon emission characteristics of different building types.

[0063] (III) Implementation of scenario simulation and prediction

[0064] Scenario setting: Set three modes: low-carbon scenario, baseline scenario and high-carbon scenario. In the low-carbon scenario, it is assumed that population growth remains stable, economic development pays more attention to green industries and low-carbon technological innovation, and the proportion of clean energy in the energy structure increases significantly. By 2030, the proportion of local clean energy in the energy structure will increase from the current 30% to 60%; in the baseline scenario, each factor is predicted according to the current development trend, the population growth rate remains at the average growth rate level of the past five years, and the economic growth rate is consistent with the average growth rate of the gross domestic product; in the high-carbon scenario, it is assumed that the population grows rapidly, economic development mainly relies on high-energy-consuming industries, the adjustment of the energy structure is slow, and the proportion of high-energy-consuming industries in the industrial structure only decreases by 5% in the next ten years.

[0065] Simulation and Analysis: Input the predicted values of various factors under different scenarios into the STIRPAT model, and use the Monte Carlo simulation method to generate a large number of possible scenario combinations. Through multiple simulation calculations, obtain the possible values and their probability distributions of building carbon emissions at different future time points. For example, through 1000 Monte Carlo simulations, it is found that under the low-carbon scenario, there is an 80% probability that the building carbon emissions in 2050 will be between 50% and 60% of the current emissions; under the high-carbon scenario, there is a 70% probability that the building carbon emissions in 2050 will be 1.5 to 2 times the current emissions. Using the simulation results, analyze the impact changes of various factors on building carbon emissions under different scenarios, provide a comprehensive and scientific decision-making basis for building industry decision-makers, and help them formulate flexible and effective carbon emission control strategies.

[0066] III. Implementation of Carbon Emission Simulation and Calculation Based on BIM Model

[0067] (1) Assignment of Carbon Emission Attributes

[0068] In the BIM model, use programming interfaces such as Revit API to develop a special plug-in for assigning carbon emission attributes. For each building component, such as walls, roofs, doors and windows, according to the building materials selected, query the carbon emission database, obtain the corresponding carbon emission factors, and assign them to the component attributes. For example, for a certain brand of concrete wall, the carbon emission factor during its production process is X kilograms of carbon dioxide per cubic meter, and this carbon emission factor is accurately entered into the wall component in the BIM model. For building equipment, such as air conditioners and lighting equipment, according to their energy consumption parameters and energy types, combined with the carbon emission conversion relationship of energy, determine the carbon emission attributes during equipment operation and assign them to the equipment model. For a certain model of air conditioner, when operating at its rated power, it generates Y kilograms of carbon dioxide per kilowatt-hour of electricity consumed, and this carbon emission attribute is associated with the BIM model of this air conditioner equipment.

[0069] (2) Implementation of Carbon Emission Simulation Calculation

[0070] Real-time simulation calculation: Based on the developed carbon emission calculation plug-in, combined with the real-time usage situation and energy consumption data of the building, conduct real-time simulation calculations of building carbon emissions. For the lighting system, according to the power of the lamps, the daily usage time, and the carbon emission factor of local electricity, calculate the carbon emissions of the lighting system at different time periods in real time. For example, in the office area, through sensors, obtain the real-time on-time and power data of the lamps, and combine the local electricity carbon emission factor to calculate the carbon emissions of the lighting system in this area per hour. For the air conditioning system, according to its cooling and heating power, operating time, and the carbon emission factors of energy types (electricity, natural gas), real-time simulate the changes in carbon emissions of the air conditioning system. When the load of the air conditioning system changes, the plug-in can adjust the carbon emission calculation results in a timely manner according to the real-time operating parameters.

[0071] Full life cycle calculation: Consider the carbon emissions of building materials during production, transportation and construction, and calculate this part of the carbon emissions through the material usage and corresponding carbon emission factors recorded in the BIM model. For example, for building steel, the transportation distance from the steel production site to the construction site is Z kilometers, and the carbon emissions during the transportation of each ton of steel is W kilograms of carbon dioxide. According to the amount of steel in the BIM model, the total carbon emissions during the transportation of steel are calculated. Summarize the carbon emissions of each part of the building (material production, transportation, construction, equipment operation, maintenance and management, etc.) to obtain the carbon emissions of the building throughout its life cycle. In the calculation process, big data processing technology is used to efficiently calculate massive data to ensure the accuracy and timeliness of the calculation results.

[0072] (III) Implementation of carbon emission traceability and management throughout the life cycle

[0073] Implementation of traceability function: Further improve the carbon emission traceability function of the entire life cycle of the building in the BIM model. Each component, material, and each activity in the construction and operation process of the building is given a unique carbon emission identification code, which is associated with the detailed carbon emission information in the BIM database. Through this identification code, the source, quantity, and related influencing factors of the carbon emissions of the component or activity throughout its life cycle can be traced back. For example, for a building brick, through its carbon emission identification code, you can query the energy consumed in the production process of the brick, the carbon emissions of raw materials, the carbon emissions during transportation, and the carbon emissions generated by maintenance, replacement and other activities during the use of the building. In the BIM model, a special traceability query interface is developed, and users can quickly and easily obtain their carbon emission traceability information by entering the identification code or directly clicking on the component in the model.

[0074] Management module operation: Develop a carbon emission management module based on the BIM model. This module monitors and manages the carbon emissions of the building throughout its life cycle in real time based on the carbon emission traceability data. By setting carbon emission targets and thresholds, when the carbon emissions of a certain stage or area of ​​the building approach or exceed the threshold, the system automatically issues a warning message and provides corresponding emission reduction suggestions and measures. For example, if it is found that the energy consumption of a certain floor leads to excessive carbon emissions during the building operation stage, the management module will analyze the reasons and recommend adjusting the equipment operating parameters, optimizing the use of space, or upgrading equipment based on the equipment information and space layout in the BIM model to reduce carbon emissions. At the same time, the management module can track and evaluate the implementation effect of emission reduction measures, adjust management strategies in a timely manner according to actual conditions, and ensure that the building's carbon emissions are always under control.

[0075] 4. Integration with other green building technologies

[0076] Integration with solar photovoltaic systems: Integrate BIM technology with solar photovoltaic system design and optimization software. In the BIM model, based on information such as the orientation of the building, the area of the roof and walls, and the surrounding shading conditions, use solar photovoltaic system design software to accurately calculate the most suitable locations and capacities for installing solar photovoltaic panels. For example, through the 3D visualization function of the BIM model, visually display the lighting conditions at different locations of the building, and combine with the algorithms of the solar photovoltaic system design software to determine that installing X photovoltaic panels with a power of Y watts in a certain area of the roof can achieve the best power generation efficiency. At the same time, through the BIM model, real-time simulate the power generation efficiency of the solar photovoltaic system under different weather conditions and its impact on building energy consumption, and then optimize the design and operation strategies of the photovoltaic system. In rainy and cloudy weather, according to the simulation results of the BIM model, automatically adjust the building's energy supply strategy, increase the reliance on other energy sources, ensure the stability of the building's energy supply, maximize the utilization of solar energy resources, reduce the building's reliance on traditional energy sources, and reduce carbon emissions.

[0077] Integration with intelligent building control systems: Integrate with intelligent building control systems to achieve intelligent management of building energy. Connect various energy equipment (air conditioning systems, lighting systems, elevators, etc.) in the building to the intelligent control system through the BIM model, and use the intelligent control system to automatically optimize the operation modes of the energy equipment according to real-time indoor and outdoor environmental parameters, personnel activities, and the building's carbon emission targets. For example, when the intelligent control system detects that there are fewer people indoors and the lighting is sufficient, automatically reduce the lighting brightness and adjust the cooling and heating power of the air conditioning system; when the outdoor temperature is appropriate, automatically turn on the natural ventilation system and turn off the air conditioning equipment to achieve the purpose of energy conservation and emission reduction. In the BIM model, develop an interactive interface between the intelligent control system and the energy equipment, and users can visually see the control situation of the intelligent control system on the energy equipment and the real-time feedback of the operating status of the energy equipment, which is convenient for timely adjustment and optimization of the intelligent control strategy and further reduce building carbon emissions.

[0078] Integration with the Construction Waste Management System: Integrating with the construction waste management system to reduce carbon emissions during the construction and demolition phases. In the BIM model, simulate and predict the waste generated during the construction and demolition processes. Through integration with the construction waste management system, achieve classified collection, transportation, and reuse planning of waste. For example, based on the material information and demolition sequence of building components in the BIM model, reasonably arrange the classified collection points and transportation routes of waste to improve the recycling rate of waste. For recyclable waste such as steel and wood, through the collaboration between the BIM model and the construction waste management system, plan the best recycling solutions, and directly transport the demolished steel to a nearby steel processing factory for reprocessing; for non-recyclable waste, reasonably plan the transportation route to a designated landfill or incineration plant to reduce carbon emissions during transportation. At the same time, through the BIM model, monitor the management situation of construction waste in real time, and adjust the management strategy in a timely manner to ensure the effective treatment of construction waste and achieve the low-carbon environmental protection goal of the entire building life cycle.

[0079] IV. Result Analysis and Visualization Display Implementation

[0080] (I) Comprehensive Analysis Implementation

[0081] Comprehensively analyze the impact results of various factors on building carbon emissions obtained from the STIRPAT model analysis and the building carbon emission data calculated based on the BIM model. Use data analysis software, the data analysis library of Python, to deeply mine and compare the data. By comparing the contribution ratios of different factors to carbon emissions during the entire building life cycle, identify the key factors affecting building carbon emissions. For example, through analysis, it is found that in a certain commercial building, the energy structure and building type are the key factors affecting the building's carbon emissions. The low proportion of clean energy in the energy structure leads to a large dependence on traditional high-carbon emission energy during the operation phase of the building; the building type is a commercial building, with long operation hours and frequent equipment use, which is also one of the reasons for the high carbon emissions. Based on the comprehensive analysis results, provide a scientific basis for formulating targeted carbon emission control strategies.

[0082] (II) Visualization Display Implementation

[0083] 3D Model Visualization: By leveraging the visualization capabilities of BIM technology, the building carbon emission analysis results are presented intuitively on a 3D model. In the BIM model, different colors and transparencies are set according to the carbon emissions of different parts. Areas with high carbon emissions are shown in red with lower transparency, while areas with low carbon emissions are shown in green with higher transparency, making the carbon emission situation of different parts of the building clear at a glance. At the same time, the animation effect is used to show the changes in carbon emissions of the building at different stages. From the construction stage to the operation stage, the trend of gradual increase or decrease in the building's carbon emissions helps users understand the dynamic change process of building carbon emissions more clearly.

[0084] Chart Display: Generate various types of charts, such as bar charts, line charts, and pie charts, to display building carbon emission data. The bar chart is used to compare the carbon emissions of different building types, visually presenting the carbon emission differences among various buildings; the line chart is used to show the trend of carbon emissions of the building at different time stages (annual, quarterly), facilitating the observation of the long-term change law of carbon emissions; the pie chart is used to analyze the contribution ratio of various factors to building carbon emissions, clearly showing the key influencing factors. During the chart display process, an interactive function is set, and the user clicks on the chart.

[0085] A system for a building carbon emission analysis method based on the STIRPAT algorithm and building information model technology, including:

[0086] A data collection and integration system for performing data collection and integration steps, including a BIM data collection module, an external environment data collection module, a multi-source data fusion module, and an intelligent sensor data collection and transmission module;

[0087] A STIRPAT model analysis system for performing factor analysis steps based on the STIRPAT model, including a model construction module, a time series analysis module, a regional and building type adaptive parameter adjustment module, and a scenario simulation and prediction module;

[0088] A BIM carbon emission simulation, calculation, and management system for performing carbon emission simulation and calculation steps based on the BIM model and result analysis and visualization display steps, including a BIM model carbon emission attribute assignment module, a carbon emission simulation calculation module, a full life cycle carbon emission traceability and management module, a BIM and other green building technology integration module, and a result analysis and visualization display module.

[0089] A computer-readable storage medium storing a computer program, which when executed by a processor, implements a building carbon emission analysis method based on the STIRPAT algorithm and building information model technology.

[0090] An electronic device includes a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, a building carbon emission analysis method based on the STIRPAT algorithm and building information model technology is implemented.

[0091] Finally, it should be noted that the above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or perform equivalent replacements for some of the technical features. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A building carbon emission analysis method based on the STIRPAT algorithm and building information modeling technology, characterized in that: The following steps are included: Data collection and integration steps: Using Building Information Modeling (BIM) technology, we collect geometric and non-geometric information of buildings during the design phase of construction projects, collect material usage, energy consumption data and construction progress information during the construction phase, and obtain energy consumption data and personnel flow information during the operation phase, and integrate them to form a BIM database of building life cycle information; at the same time, we collect population size, per capita GDP, industrial structure and social and economic data of the project location, as well as local energy structure, technology development level and external environment data; Expand the scope of data collection, introduce geospatial data, meteorological data and building surrounding environment data, and use specially developed data fusion algorithms to efficiently integrate and deeply analyze multi-source data, eliminate data redundancy and conflicts, and form a building carbon emission analysis data set with spatiotemporal correlation; Deploy smart sensors inside and around buildings, including sensors for temperature, humidity, air quality, energy consumption, and personnel activities. Use IoT technology to transmit sensor data to the BIM platform and data analysis center in real time, and use machine learning algorithms to analyze and predict sensor data in real time to achieve real-time and accurate perception of building operating status and environmental parameters. Factor analysis steps based on the STIRPAT model: An extended STIRPAT model was constructed, with building carbon emissions as the dependent variable, population size, economic development level, and technological level as the core independent variables, and building size, building type, and energy structure as control variables; The elasticity coefficient of each variable is determined through statistical analysis and regression methods to quantify the impact of each factor on building carbon emissions; Establish a time series analysis module, use Kalman filtering and state space model methods to conduct time series analysis on building carbon emission data and related influencing factor data, estimate the dynamic changes of various factors in real time, and automatically update the elasticity coefficient of each variable in the STIRPAT model; develop regional and building type adaptive parameter adjustment algorithms, and make targeted adjustments to model parameters according to the characteristics of the region where the building is located and the characteristics of the building type; The STIRPAT model is used to conduct multi-scenario simulations and future carbon emission forecasts, setting different scenario assumptions for population growth, economic development, technological progress, and energy policies. The Monte Carlo simulation method is used to conduct uncertainty analysis on the scenario simulation results, providing a comprehensive and scientific decision-making basis for building carbon emission management. Carbon emission simulation and calculation steps based on BIM model: In the BIM model, each building component and equipment is given carbon emission-related attribute information. Based on the building usage and energy consumption data, the BIM model is used to simulate the building's carbon emissions and comprehensively calculate the carbon emissions over the entire life cycle of the building; Further improve the carbon emission traceability function of the BIM model throughout its life cycle, assign a unique carbon emission identification code to each component, material, construction and operation activity of the building, develop a carbon emission management module based on the BIM model, set carbon emission targets and thresholds, monitor and manage carbon emissions throughout the building life cycle in real time, and use the visualization advantages of BIM to display the time-space carbon emission distribution map; Innovatively integrate BIM technology with solar photovoltaic system design software, intelligent building control systems, construction waste management systems and other green building technologies to achieve collaborative optimization of building carbon emissions, including using BIM models to optimize solar photovoltaic system design and operation strategies, intelligently control building energy equipment operation modes, and plan the classification, collection and reuse of construction waste; Result analysis and visualization steps: Combining the analysis results based on the STIRPAT model with the building carbon emission data calculated based on the BIM model, we analyze the contribution of different factors to carbon emissions throughout the building life cycle and identify the key influencing factors; By utilizing the visualization function of BIM technology, the distribution of carbon emissions in different parts and stages of the building, as well as the changing trend of the impact of various factors on carbon emissions, can be intuitively presented through color changes in the three-dimensional model and graphical display.

2. The building carbon emission analysis method based on the STIRPAT algorithm and building information modeling technology according to claim 1 is characterized in that: In the data collection and integration steps, the geospatial data includes the topography and land use type information of the area where the building is located; the meteorological data includes the real-time and historical data of temperature, precipitation, wind speed, and sunshine duration; and the building surrounding environment data covers the traffic flow and vegetation coverage data around the building.

3. The building carbon emission analysis method based on the STIRPAT algorithm and building information modeling technology according to claim 1 is characterized in that: In the factor analysis step based on the STIRPAT model, the time series analysis module continuously analyzes the collected data, and the regional and building type adaptive parameter adjustment algorithm adjusts the model parameters according to the geographical, economic, and policy characteristics of the region where the building is located and the characteristics of the building type.

4. The building carbon emission analysis method based on the STIRPAT algorithm and building information modeling technology according to claim 1 is characterized in that: In the carbon emission simulation and calculation step based on the BIM model, the BIM model is used to simulate in real time the impact of the power generation efficiency of the solar photovoltaic system on the energy consumption of the building, and the intelligent control system is used to automatically optimize the operation mode of the energy equipment according to the real-time environmental parameters and personnel activities. The BIM model is used to simulate and predict the waste generated during the construction and demolition process.

5. A system for implementing the building carbon emission analysis method based on the STIRPAT algorithm and building information modeling technology as described in any one of claims 1 to 4, characterized in that: include: Data collection and integration system, used to perform data collection and integration steps, including BIM data collection module, external environment data collection module, multi-source data fusion module and intelligent sensor data collection and transmission module; STIRPAT model analysis system, used to perform factor analysis steps based on the STIRPAT model, including a model building module, a time series analysis module, a region and building type adaptive parameter adjustment module, and a scenario simulation and prediction module; The BIM carbon emission simulation, calculation and management system is used to execute carbon emission simulation and calculation steps based on the BIM model, as well as result analysis and visualization display steps, including a BIM model carbon emission attribute assignment module, a carbon emission simulation calculation module, a full life cycle carbon emission traceability and management module, a BIM and other green building technology integration module, and a result analysis and visualization display module.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the building carbon emission analysis method based on the STIRPAT algorithm and building information modeling technology described in any one of claims 1 to 4 is implemented.

7. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the building carbon emission analysis method based on the STIRPAT algorithm and building information modeling technology described in any one of claims 1 to 4 is implemented.

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