BIM-driven green building carbon emission dynamic assessment and management system

Abstract

The present invention discloses a BIM-driven green building carbon emission dynamic assessment and management system, which relates to the field of carbon emission assessment and management technology. The system includes a data acquisition module, a region determination module, a power consumption fluctuation coefficient acquisition module, an air quality anomaly coefficient acquisition module, a region division module, and an early warning module. The system collects personnel flow data within the building through intelligent access control equipment and analyzes areas where personnel flow is concentrated. Based on these areas, the system collects power consumption and air quality data in real time, and uses Fourier transform and wavelet transform to extract the power consumption fluctuation coefficient and air quality anomaly coefficient, respectively, to reflect carbon emission fluctuations and anomalies. By comprehensively analyzing the power consumption fluctuation coefficient and air quality anomaly coefficient, the system can accurately determine whether the carbon emissions of various areas within the building are normal, identify areas with abnormal carbon emissions, and trigger an early warning mechanism.

Description

Technical Field

[0001] The present invention relates to the technical field of carbon emission assessment and management, and in particular to a BIM-driven green building carbon emission dynamic assessment and management system. Background Art

[0002] Traditional carbon emission assessment methods typically rely on static data collection and analysis, and are unable to reflect the volatility and anomalies of carbon emissions in various areas of a building in real time. This makes many carbon emission management measures lack timeliness and pertinence. In addition, existing air quality monitoring systems and power consumption analysis tools mostly work independently and lack synergy, making it difficult to fully and accurately capture the actual carbon emissions during building operations, resulting in poor carbon emission management results. While existing smart building systems can collect some environmental data, they are often unable to fully integrate data from multiple dimensions such as personnel flow, power consumption, and air quality into a single system. Furthermore, they are unable to analyze and predict carbon emission fluctuations through efficient algorithms, and lack comprehensive dynamic management capabilities.

[0003] To address these issues, research into dynamic carbon emission assessment and management technologies for green buildings has gradually become a key development focus in the construction industry. Building Information Modeling (BIM) technology enables comprehensive assessment and dynamic regulation of carbon emissions across all building areas through data integration, intelligent analysis, and real-time monitoring.

[0004] There is a lack of data on the flow of people in buildings collected through intelligent access control systems, combined with power consumption and air quality monitoring data to identify areas with intensive human activity; Fourier transform and wavelet transform techniques are used to analyze the fluctuation characteristics of power consumption and the abnormal characteristics of air quality respectively, and generate corresponding fluctuation coefficients and abnormal coefficients; through comprehensive analysis of these two coefficients, the system can determine the carbon emission status of each area, divide the internal space of the building into normal and abnormal carbon emission areas, and trigger early warnings for abnormal areas. Summary of the Invention

[0005] The purpose of the present invention is to provide a BIM-driven green building carbon emission dynamic assessment and management system to solve the above-mentioned problems.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a BIM-driven green building carbon emission dynamic assessment and management system, comprising:

[0007] a data acquisition module, which is used to obtain personnel flow data, power consumption data and air quality monitoring data of green buildings during the operation stage; wherein the power consumption data is the total power consumption of the personnel concentrated area in the building, and the air quality monitoring data includes: carbon dioxide concentration, temperature and humidity data and air circulation speed; an area determination module, which is used to analyze the personnel flow data and determine the personnel flow concentrated area; a power consumption fluctuation coefficient acquisition module, which extracts the power consumption fluctuation characteristics by processing the power consumption data and generates the power consumption fluctuation coefficient; an air quality anomaly coefficient acquisition module, which extracts the air quality characteristics by processing the air quality monitoring data and generates the air quality anomaly coefficient; an area division module, which is used to comprehensively analyze the power consumption fluctuation coefficient and the air quality anomaly coefficient of the personnel concentrated area to determine whether the carbon emissions of the green building are normal, and according to the judgment result, mark the personnel concentrated area and divide it into normal carbon emission area and abnormal carbon emission area; an early warning module, which is used to issue an early warning for the abnormal carbon emission area and perform processing.

[0008] Preferably, analyzing the personnel flow data to determine the personnel flow concentrated area specifically includes:

[0009] Use an intelligent access control system to identify areas where people gather; analyze the frequency of entry and exit of each area through the access control system, and combine it with the length of time people stay in the area to generate personnel flow data for the area; through comprehensive analysis of the entry and exit data of each area, the system identifies the regularity of areas where people gather; and marks the areas according to the number of entries and exits and the length of time they stay in the area; the system will generate a dynamic heat map, showing high-frequency areas of personnel flow, marked as areas of concentrated personnel flow.

[0010] Preferably, based on the personnel concentration area, the power consumption data of the green building in the operation stage is obtained, the power consumption data is processed, the power consumption fluctuation characteristics are extracted, and the power consumption fluctuation coefficient is generated, which specifically includes:

[0011] During the green building operation phase, power consumption data within the monitoring period is collected in real time according to the time series based on the personnel concentration area. The power consumption data of each collection point is obtained and recorded as P(t). Where t represents the number of collection points and t is a positive integer greater than 0. The power consumption data P(t) at each time point is Fourier transformed to convert it from the time domain to the frequency domain. The calculation expression is: Where P(f) represents the power consumption signal in the frequency domain, indicating the components of power consumption data at different frequencies, f represents the frequency, t represents the number of acquisition points, and i represents the imaginary unit. Based on the Fourier transform results, the frequency domain signal P(f) is obtained. The amplitude spectrum of the power consumption signal P(f) is calculated to represent the intensity of power consumption fluctuations. The calculation expression is: Where R(P(f)) represents the real part of P(f), CR(P(f)) represents the imaginary part of P(f), and |P(f)| represents the amplitude of the power consumption signal P(f). Analyze the high-frequency and low-frequency parts of the amplitude spectrum. Identify the fluctuation characteristics of power consumption by comparing the amplitudes of each frequency component. Calculate the average amplitude μ using the mean calculation formula. v , calculate the standard deviation value σ of the amplitude through the standard deviation calculation formula v , that is, calculate the power consumption fluctuation coefficient.

[0012] Preferably, the method of obtaining regional air quality monitoring data of the green building during the operation phase based on the personnel concentration area, processing the air quality monitoring data, extracting air quality characteristics, and generating an air quality anomaly coefficient specifically includes:

[0013] During the green building operation phase, air quality monitoring data is collected in real time within the monitoring period based on a time series in areas where people gather. Air quality monitoring data is obtained at each collection point. The air quality monitoring data at each collection point is subjected to a wavelet transform, and features of the data at different scales are extracted through multi-scale analysis, which is recorded as x(t). The calculation expression of the wavelet transform is: Where W(s, τ) is the result of wavelet transform, which represents the eigenvalue of τ at scale s and time offset, and x(t) represents the air quality monitoring data at time point t. Represents the mother function, s represents the scale parameter, and τ represents the time offset parameter; the high-frequency components extracted by wavelet transform are used to calculate the average wavelet amplitude μ W , calculate the standard deviation σ of the wavelet amplitude W ,According to the average wavelet amplitude and the standard deviation of the wavelet amplitude, the air quality anomaly coefficient is calculated.

[0014] Preferably, the area division module is used to conduct a comprehensive analysis of the power consumption fluctuation coefficient and the air quality anomaly coefficient in the personnel concentration area, and calculate the carbon emission anomaly coefficient, specifically including:

[0015] Obtain the power consumption fluctuation coefficient and air quality anomaly coefficient of each concentrated area, normalize them, and calculate the carbon emission fluctuation coefficient.

[0016] 6. The BIM-driven green building carbon emission dynamic assessment and management system according to claim 1 is characterized in that the process of determining whether green building carbon emissions are normal and marking areas where personnel are concentrated based on the determination result specifically includes:

[0017] Determine whether the carbon emission fluctuation coefficient of the personnel concentration area is greater than or equal to the preset carbon emission fluctuation coefficient threshold. If so, it is recorded as an abnormal carbon emission area; if not, it is recorded as a normal carbon emission area.

[0018] Preferably, the warning and processing of abnormal carbon emission areas specifically include: based on the abnormal carbon emission areas, the system automatically generates abnormal warning information and sends it to management personnel; the warning information includes the specific location of the abnormal area and the degree of carbon emission abnormality.

[0019] Preferably, the further analysis specifically includes:

[0020] Analyzing abnormal carbon emissions due to deteriorating air quality or excessively high carbon dioxide concentrations, the system will automatically adjust the ventilation equipment in the area, increase the ventilation rate, or activate air purification equipment to dilute and reduce the concentration of carbon emissions in the air;

[0021] For the adjusted areas, retest the carbon emission levels to determine whether the regional air quality meets the green building standards;

[0022] After the test, if the green building standards are met, the system monitors the area for a period of time and records the adjusted carbon emission data;

[0023] If abnormal situations occur frequently, the system will analyze historical data and provide optimized processing for carbon emission management in the area.

[0024] In the above technical solution, the technical effects and advantages provided by the present invention are:

[0025] 1. The present invention collects personnel flow data within a building through an intelligent access control system, and combines it with power consumption and air quality monitoring data to identify areas with intensive human activity. Subsequently, Fourier transform and wavelet transform techniques are used to analyze the fluctuation characteristics of power consumption and the abnormal characteristics of air quality, respectively, to generate corresponding fluctuation coefficients and abnormality coefficients. By comprehensively analyzing these two coefficients, the system can determine the carbon emission status of each area, divide the interior space of the building into normal and abnormal carbon emission areas, and trigger early warnings for abnormal areas. The early warning information not only includes the specific location and extent of the abnormality, but also provides an analysis of the possible causes, assisting managers in taking quick action, such as adjusting the ventilation system or optimizing energy efficiency strategies, to reduce carbon emissions and improve indoor environmental quality. In addition, the system supports subsequent data analysis and processing effect evaluation, providing a scientific basis for the continuous optimization of carbon emission management in green buildings.

[0026] 2. The present invention optimizes building energy efficiency and environmental protection levels through systematic data analysis and dynamic control; through dynamic evaluation of carbon emissions in different areas of the building, the system can not only detect areas of abnormal carbon emissions, but also automatically adjust ventilation systems, air-conditioning equipment and other environmental control facilities according to different carbon emission data characteristics; for example, when air quality monitoring data shows that the carbon dioxide concentration is too high, the system will automatically increase the ventilation rate or enable air purification equipment to reduce indoor pollutant concentrations, thereby reducing carbon emissions and improving indoor air quality; in addition, by intelligently adjusting power consumption and equipment operating status, the system can reduce unnecessary energy consumption in the building, minimize carbon emissions, and improve the building's energy efficiency level; the entire process does not require human intervention, and automated control makes building management more efficient, reduces human errors, and greatly reduces management costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments described in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0028] Figure 1 It is a system module diagram of the present invention. DETAILED DESCRIPTION

[0029] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0030] For examples, see Figure 1 As shown, the BIM-driven green building carbon emission dynamic assessment and management system described in this embodiment includes:

[0031] A data acquisition module, which is used to obtain personnel flow data, power consumption data and air quality monitoring data of the green building during the operation stage;

[0032] Among them, the power consumption data is the total power consumption in the concentrated area of ​​​​people in the building, and the air quality monitoring data includes: carbon dioxide concentration, temperature and humidity data, and air circulation speed;

[0033] An area determination module, which is used to analyze personnel flow data and determine areas where personnel flow is concentrated;

[0034] a power consumption fluctuation coefficient acquisition module, which processes power consumption data, extracts power consumption fluctuation characteristics, and generates a power consumption fluctuation coefficient;

[0035] An air quality anomaly coefficient acquisition module, which processes air quality monitoring data, extracts air quality characteristics, and generates an air quality anomaly coefficient;

[0036] A regional division module is used to comprehensively analyze the power consumption fluctuation coefficient and air quality anomaly coefficient in the personnel concentration area to determine whether the carbon emissions of the green building are normal. Based on the judgment results, the personnel concentration area is marked and divided into a normal carbon emission area and an abnormal carbon emission area;

[0037] The early warning module is used to issue early warnings to abnormal carbon emission areas and perform processing.

[0038] In the area determination module, the personnel flow data of the green building during the operation phase is obtained and analyzed to determine the areas where personnel flow is concentrated, including:

[0039] Use smart access control systems to identify areas where people gather;

[0040] By installing access control devices in all areas of the building (such as meeting rooms, restaurants, rest areas, etc.), these devices usually include RFID cards, fingerprint recognition or facial recognition technology;

[0041] When a person passes through the access control, the system will automatically record the person's identity information, entry time, exit time and length of stay;

[0042] This information is synchronized with the central database in real time through the system to ensure the accuracy and timeliness of the data;

[0043] The access control system will analyze the frequency of entry and exit of each area, combined with the length of stay of people, to generate personnel flow data for that area;

[0044] The system analyzes the number of people entering and leaving the area and their length of stay based on different time periods (such as morning and evening peaks, lunch periods, etc.), thereby identifying areas with high concentrations of people and frequent flow patterns.

[0045] For example, conference rooms have a higher frequency of entry and exit during the working hours during the day, while restaurants have a peak flow during meal times;

[0046] By comprehensively analyzing the entry and exit data of each area, the system identifies the regularity of areas where people are concentrated;

[0047] And mark the area according to the number of entries and exits and the length of stay in the area;

[0048] The system will generate a dynamic heat map to show the high-frequency areas of personnel flow, which are recorded as concentrated areas of personnel flow;

[0049] These data will be used for further carbon emission assessments to help managers understand energy consumption changes and carbon emission patterns in different regions;

[0050] Finally, based on the data collected by the access control system, managers can implement dynamic energy efficiency management strategies.

[0051] In the power consumption fluctuation coefficient acquisition module, based on the personnel concentration area, the power consumption data of the green building during the operation phase is obtained, the power consumption data is processed, the power consumption fluctuation characteristics are extracted, and the power consumption fluctuation coefficient is generated. Specifically, the following steps are performed:

[0052] During the green building operation phase, based on the concentration of personnel in the area, real-time power consumption data within the monitoring period is collected in time series (e.g., every minute);

[0053] Obtain the power consumption data of each collection point, recorded as P(t);

[0054] Wherein, t represents the number of collection points, and t is a positive integer greater than 0;

[0055] Perform Fourier transform on the power consumption data P(t) at each time point to convert it from the time domain to the frequency domain. The calculation expression is: Where P(f) represents the power consumption signal in the frequency domain, which represents the components of the power consumption data at different frequencies, f represents the frequency, t represents the number of acquisition points, and i represents the imaginary unit;

[0056] Based on the Fourier transform results, the frequency domain signal P(f) is obtained. The amplitude spectrum (i.e., magnitude) of the power consumption signal P(f) is calculated to represent the fluctuation intensity of power consumption. The calculation expression is: Where R(P(f)) represents the real part of P(f), CR(P(f)) represents the imaginary part of P(f), and |P(f)| represents the amplitude of the power consumption signal P(f).

[0057] Analyze the high-frequency and low-frequency parts of the amplitude spectrum; high-frequency components correspond to rapid fluctuations, and low-frequency components usually reflect long-term energy consumption trends; by comparing the amplitudes of each frequency component, identify the fluctuation characteristics in power consumption; calculate the average amplitude μ using the mean calculation formula v, calculate the standard deviation value σ of the amplitude through the standard deviation calculation formula v ; Calculate the power consumption fluctuation coefficient, the calculation expression is: Where Z V represents the power consumption fluctuation coefficient, a1 and a2 are preset proportional coefficients, and both a1 and a2 are greater than 0, e represents the logarithm of the natural number base, μ v represents the average amplitude, σ v Indicates the standard deviation of the amplitude;

[0058] It should be noted that the power consumption fluctuation coefficient reflects the fluctuation of power consumption in areas with concentrated personnel. When the power consumption fluctuation coefficient is larger, the power consumption in the corresponding area is more unstable. The fluctuation intensity of the power consumption signal is directly quantified in the frequency domain, and the fluctuation coefficient based on the amplitude spectrum is obtained. This frequency domain analysis can reveal periodic fluctuations in power consumption.

[0059] In the air quality anomaly coefficient acquisition module, based on the personnel concentration area, regional air quality monitoring data of the green building during the operation phase is obtained, the air quality monitoring data is processed, the air quality characteristics are extracted, and the air quality anomaly coefficient is generated. Specifically, the following steps are performed:

[0060] During the operation phase of a green building, based on the concentration of people, real-time air quality monitoring data within the monitoring period is collected in a time series (e.g., every minute), including carbon dioxide concentration, temperature and humidity data, and air circulation speed;

[0061] Acquire the air quality monitoring data of each collection point; perform wavelet transform on the air quality monitoring data of each collection point, and extract the features of the data at different scales through multi-scale analysis, which are recorded as x(t); wherein the calculation expression of the wavelet transform is: Where W(s, τ) is the result of wavelet transform, which represents the eigenvalue of τ at scale s and time offset, and x(t) represents the air quality monitoring data at time point t. represents the mother function, s represents the scale parameter, and τ represents the time offset parameter;

[0062] The high-frequency components extracted by wavelet transform are used to calculate the average wavelet amplitude μ W , calculate the standard deviation σ of the wavelet amplitude W , according to the average wavelet amplitude and the standard deviation of the wavelet amplitude, the air quality anomaly coefficient is calculated. The calculation expression is: Where Z q Indicates the air quality abnormality coefficient in the area where people gather. a3 and a4 are preset proportional coefficients, and both a3 and a4 are greater than 0. μ W represents the average wavelet amplitude, σ Wrepresents the standard deviation of the wavelet amplitude;

[0063] It should be noted that the air quality anomaly coefficient reflects whether the air quality in areas where people are concentrated meets the standards, and the larger the value of the air quality anomaly coefficient, the worse the air quality in the corresponding area, and the possibility of abnormal carbon emissions.

[0064] In the regional division module, a comprehensive analysis of the power consumption fluctuation coefficient and air quality anomaly coefficient in the personnel concentration area is conducted to determine whether the carbon emissions of the green building are normal. Based on the judgment results, the personnel concentration area is marked and divided into normal carbon emission areas and abnormal carbon emission areas. Specifically, the following are included:

[0065] Obtain the power consumption fluctuation coefficient and air quality anomaly coefficient of each concentrated area, normalize them, and calculate the carbon emission fluctuation coefficient. The calculation expression is: In the formula, j represents the number of personnel concentration areas, j is a positive integer greater than 0, FD j Z represents the carbon emission fluctuation coefficient of the jth population concentration area, Vj represents the power consumption fluctuation coefficient of the jth population concentration area, Z qj represents the air quality anomaly coefficient of the jth concentrated area, b1 and b2 are preset proportional coefficients, and both b1 and b2 are greater than 0;

[0066] Compare the carbon emission fluctuation coefficient of the population-concentrated area with the preset carbon emission fluctuation coefficient threshold;

[0067] If the carbon emission fluctuation coefficient of a population-concentrated area is greater than or equal to the preset carbon emission fluctuation coefficient threshold, it indicates that the carbon emission of the corresponding population-concentrated area is abnormal and is recorded as an abnormal carbon emission area;

[0068] If the carbon emission fluctuation coefficient of the population-concentrated area is less than the preset carbon emission fluctuation coefficient threshold, it means that the carbon emissions of the corresponding population-concentrated area are normal and it is recorded as a normal carbon emission area;

[0069] It should be noted that the carbon emission fluctuation coefficient reflects the carbon emission situation in areas with concentrated populations, and the larger the value of the carbon emission fluctuation coefficient, the higher the degree of carbon emission abnormality in the corresponding area, and timely processing is required.

[0070] In the early warning module, early warnings are issued for abnormal carbon emission areas and processed, including:

[0071] Based on the abnormal carbon emission areas, the system automatically generates abnormal warning information and sends it to the manager's device (such as mobile phone, computer);

[0072] Early warning information should include detailed information such as the specific location of the abnormal area, the extent of the carbon emission abnormality, and possible causes, so that managers can quickly grasp the situation and assess potential impacts;

[0073] After issuing an alert, the system monitors the abnormal area in real time, collecting and recording data such as power consumption, air quality, and personnel flow. This data is then used for further analysis, such as determining whether abnormal carbon emissions are due to an increase in personnel or equipment power consumption. This data analysis can help managers more accurately identify the problem and determine its severity, allowing them to take appropriate countermeasures.

[0074] If analysis confirms that abnormal carbon emissions are due to deteriorating air quality or excessively high carbon dioxide concentrations, the system will automatically adjust the ventilation equipment in that area, increasing the ventilation rate or activating air purification equipment to dilute and reduce the concentration of carbon emissions in the air. This real-time dynamic adjustment can effectively reduce the carbon emission level in the area and ensure that the air quality in the building meets green building standards.

[0075] After completing the response measures, the system monitors the area for a period of time, records the adjusted carbon emission data, and generates a processing report for management personnel to evaluate;

[0076] If abnormal situations occur frequently, the system will analyze historical data and provide optimized processing for carbon emission management in the area.

[0077] The working principle of the present invention is as follows: the personnel flow data in the building is collected through the intelligent access control system, and combined with the power consumption and air quality monitoring data, the areas with dense personnel activities are identified. Subsequently, the Fourier transform and wavelet transform techniques are used to analyze the fluctuation characteristics of power consumption and the abnormal characteristics of air quality respectively, and generate corresponding fluctuation coefficients and abnormal coefficients. By comprehensively analyzing these two coefficients, the system can determine the carbon emission status of each area, divide the internal space of the building into normal and abnormal carbon emission areas, and trigger early warnings for abnormal areas. The early warning information not only includes the specific location and degree of the abnormality, but also provides an analysis of possible causes to assist managers in taking quick action, such as adjusting the ventilation system or optimizing energy efficiency strategies to reduce carbon emissions and improve indoor environmental quality. In addition, the system supports subsequent data analysis and processing effect evaluation, providing a scientific basis for the continuous optimization of carbon emission management in green buildings.

[0078] The above formulas are all dimensionless and numerical calculations. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formulas are set by technicians in this field according to actual conditions.

[0079] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center via wired or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state drive.

[0080] It should be understood that the term "and / or" as used herein simply describes a relationship between associated objects, indicating that three possible relationships exist. For example, "A and / or B" can represent: A alone, A and B together, or B alone. A and B can be singular or plural. Furthermore, the character " / " as used herein generally indicates an "or" relationship between the associated objects, but it may also indicate an "and / or" relationship. For specific understanding, please refer to the context.

[0081] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0082] The above is a detailed description of an embodiment of the present invention. However, the content described is only a preferred embodiment of the present invention and should not be considered to limit the scope of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. BIM-driven green building carbon emission dynamic assessment and management system, characterized by: include: A data acquisition module, which is used to obtain personnel flow data, power consumption data, and air quality monitoring data during the operation phase of the green building; wherein the power consumption data is the total power consumption of the personnel concentrated area in the building, and the air quality monitoring data includes: carbon dioxide concentration, temperature and humidity data, and air circulation speed; An area determination module, which is used to analyze personnel flow data and determine areas where personnel flow is concentrated; Based on the concentrated areas of people, the power consumption data of green buildings during the operation phase is obtained, the power consumption data is processed, the power consumption fluctuation characteristics are extracted, and the power consumption fluctuation coefficient is generated. Specifically, the following are performed: During the green building operation phase, electricity consumption data within the monitoring period is collected in real time according to a time series based on the concentration of personnel in the area. The electricity consumption data at each collection point is obtained. The electricity consumption data at each time point is Fourier transformed to convert it from the time domain to the frequency domain. Based on the Fourier transform results, the frequency domain signal is obtained and the amplitude spectrum of the electricity consumption signal is calculated to represent the intensity of fluctuations in electricity consumption. The high-frequency and low-frequency parts of the amplitude spectrum are analyzed. The fluctuation characteristics in electricity consumption are identified by comparing the amplitudes of each frequency component. The average amplitude is calculated using the mean calculation formula, and the standard deviation of the amplitude is calculated using the standard deviation calculation formula, that is, the electricity consumption fluctuation coefficient is calculated. The method of obtaining regional air quality monitoring data of green buildings in the operation phase based on the concentrated areas of people, processing the air quality monitoring data, extracting air quality characteristics, and generating air quality anomaly coefficients specifically includes: During the green building operation phase, based on the concentration of personnel in the area, real-time air quality monitoring data within the monitoring period is collected in a time series. Air quality monitoring data is obtained at each collection point. The air quality monitoring data at each collection point is subjected to wavelet transformation, and the characteristics of the data at different scales are extracted through multi-scale analysis. The average wavelet amplitude and standard deviation of the wavelet amplitude are calculated based on the high-frequency components extracted by wavelet transformation. The air quality anomaly coefficient is calculated based on the average wavelet amplitude and the standard deviation of the wavelet amplitude. a power consumption fluctuation coefficient acquisition module, which processes power consumption data, extracts power consumption fluctuation characteristics, and generates a power consumption fluctuation coefficient; An air quality anomaly coefficient acquisition module, which processes air quality monitoring data, extracts air quality characteristics, and generates an air quality anomaly coefficient; A regional division module is used to comprehensively analyze the power consumption fluctuation coefficient and air quality anomaly coefficient in the personnel concentration area to determine whether the carbon emissions of the green building are normal. Based on the judgment results, the personnel concentration area is marked and divided into a normal carbon emission area and an abnormal carbon emission area; The early warning module is used to issue early warnings to abnormal carbon emission areas and perform processing.

2. The BIM-driven green building carbon emission dynamic assessment and management system according to claim 1 is characterized in that: The analysis of personnel flow data to determine the concentrated areas of personnel flow specifically includes: Use an intelligent access control system to identify areas where people gather; analyze the frequency of entry and exit of each area through the access control system, and combine it with the length of time people stay in the area to generate personnel flow data for the area; through comprehensive analysis of the entry and exit data of each area, the system identifies the regularity of areas where people gather; and marks the areas according to the number of entries and exits and the length of time they stay in the area; the system will generate a dynamic heat map, showing high-frequency areas of personnel flow, marked as areas of concentrated personnel flow.

3. The BIM-driven green building carbon emission dynamic assessment and management system according to claim 1 is characterized in that: The regional division module is used to comprehensively analyze the power consumption fluctuation coefficient and air quality anomaly coefficient in the concentrated area, and calculate the carbon emission anomaly coefficient, specifically including: Obtain the power consumption fluctuation coefficient and air quality anomaly coefficient of each concentrated area, normalize them, and calculate the carbon emission fluctuation coefficient.

4. The BIM-driven green building carbon emission dynamic assessment and management system according to claim 1 is characterized in that: The judgment of whether the carbon emissions of green buildings are normal and the marking of areas where people gather based on the judgment results specifically include: Determine whether the carbon emission fluctuation coefficient of the personnel concentration area is greater than or equal to the preset carbon emission fluctuation coefficient threshold. If so, it is recorded as an abnormal carbon emission area; if not, it is recorded as a normal carbon emission area.

5. The BIM-driven green building carbon emission dynamic assessment and management system according to claim 1 is characterized in that: The aforementioned early warning and processing of abnormal carbon emission areas specifically include: based on the abnormal carbon emission areas, the system automatically generates abnormal early warning information and sends it to management personnel; the early warning information includes the specific location of the abnormal area and the degree of carbon emission abnormality.

6. The BIM-driven green building carbon emission dynamic assessment and management system according to claim 5 is characterized in that: Further analysis is conducted, including: Analyzing abnormal carbon emissions due to deteriorating air quality or excessively high carbon dioxide concentrations, the system will automatically adjust the ventilation equipment in the area, increase the ventilation rate, or activate air purification equipment to dilute and reduce the concentration of carbon emissions in the air; For the adjusted area, the carbon emission level will be re-tested to determine whether the regional air quality meets the green building standards. After the test, if the green building standards are met, the system will monitor the area for a period of time and record the adjusted carbon emission data. If abnormal situations occur frequently, the system will analyze historical data and provide optimized processing for carbon emission management in the area.

Images