BIM-driven dynamic evaluation and management system for carbon emission of green building

Abstract

The invention discloses a BIM-driven green building carbon emission dynamic evaluation and management system, which relates to the technical field of carbon emission evaluation and management and comprises a data acquisition module, a region determination module, a power consumption fluctuation coefficient acquisition module, an air quality abnormal coefficient acquisition module, a region division module and an early warning module. The system collects personnel flow data in a building through intelligent access control equipment, and analyzes a personnel flow concentration area; on the basis of the areas, the system collects power consumption and air quality data in real time, a power consumption fluctuation coefficient and an air quality abnormal coefficient are extracted through Fourier transform and wavelet transform respectively, and carbon emission fluctuation and abnormal conditions are reflected; by comprehensively analyzing the power consumption fluctuation coefficient and the air quality abnormal coefficient, the system can accurately judge whether the carbon emission of each area in the building is normal or not, identify the abnormal carbon emission area 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 usually rely on static data collection and analysis, and cannot reflect the volatility and anomalies of carbon emissions in various areas of the building in real time, which makes many carbon emission management measures lack timeliness and pertinence. In addition, most of the existing air quality monitoring systems and power consumption analysis tools work independently, lack synergy, and it is difficult to fully and accurately capture the actual situation of carbon emissions during building operations, resulting in poor carbon emission management results. Although 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 in one system, and are unable to analyze and predict carbon emission fluctuations through efficient algorithms, lacking comprehensive dynamic management capabilities.

[0003] In order to deal with these problems, the research on dynamic assessment and management technology of carbon emissions in green buildings has gradually become a key direction for the development of the construction industry. Based on the application of building information modeling (BIM) technology, it is possible to achieve a comprehensive assessment and dynamic regulation of carbon emissions in various areas of the building through data integration, intelligent analysis and real-time monitoring.

[0004] There is a lack of intelligent access control system to collect data on the flow of people in buildings, combined with power consumption and air quality monitoring data to identify areas with intensive human activities; 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 background problems.

[0006] In order to achieve the above-mentioned purpose, the present invention provides the following technical solution: BIM-driven green building carbon emission dynamic assessment and management system, comprising:

[0007] A data acquisition module, the data acquisition module is used to obtain personnel flow data, power consumption data and air quality monitoring data of the green building 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, the area determination module is used to analyze the personnel flow data and determine the personnel flow concentrated area; a power consumption fluctuation coefficient acquisition module, the power consumption fluctuation coefficient acquisition module extracts the power consumption fluctuation characteristics by processing the power consumption data, and generates the power consumption fluctuation coefficient; an air quality abnormality coefficient acquisition module, the air quality abnormality coefficient acquisition module extracts the air quality characteristics by processing the air quality monitoring data, and generates the air quality abnormality coefficient; an area division module, the area division module is used to comprehensively analyze the power consumption fluctuation coefficient and the air quality abnormality coefficient of the personnel concentrated area, judge whether the carbon emission of the green building is normal, and mark the personnel concentrated area according to the judgment result, and divide it into a normal carbon emission area and an abnormal carbon emission area; an early warning module, the early warning module is used to warn and process the abnormal carbon emission area.

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

[0009] Use the smart access control system to identify areas where people gather; use the access control system to analyze the frequency of entry and exit of each area, combined with the length of time people stay, to generate personnel flow data for the area; by comprehensively analyzing 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 people stay in the area; the system will generate a dynamic heat map, showing high-frequency areas of personnel flow, marked as areas where personnel flow is concentrated.

[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] In the operation stage of green buildings, based on the personnel concentration area, the power consumption data within the monitoring period is collected in real time according to the time series; the power consumption data of each collection point is obtained, which is 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, and the calculation expression is: In the formula, 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. According to the Fourier transform result, the frequency domain signal P(f) is obtained, and the amplitude spectrum of the power consumption signal P(f) is calculated to represent the fluctuation intensity of power consumption. The calculation expression is: In the formula, 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 in the amplitude spectrum; identify the fluctuation characteristics in power consumption by comparing the amplitudes of each frequency component; calculate the average amplitude μ through the mean calculation formula v , the standard deviation value σ of the amplitude is calculated by 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 green buildings in the operation stage based on the personnel concentration area, processing the air quality monitoring data, extracting air quality characteristics, and generating air quality anomaly coefficients specifically includes:

[0013] In the green building operation stage, based on the personnel concentration area, the air quality monitoring data within the monitoring period is collected in real time according to the time series; the air quality monitoring data of each collection point is obtained; the air quality monitoring data of each collection point is subjected to wavelet transformation, and the characteristics of the data at different scales are extracted through multi-scale analysis, which is recorded as x(t); wherein the calculation expression of the wavelet transformation 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 , the air quality anomaly coefficient is calculated based on the average wavelet amplitude and the standard deviation of the wavelet amplitude.

[0014] Preferably, the area division module is used to comprehensively analyze the power consumption fluctuation coefficient and the air quality abnormality coefficient in the personnel concentrated area, and calculate the carbon emission abnormality coefficient, specifically including:

[0015] The power consumption fluctuation coefficient and air quality anomaly coefficient of each concentrated area are obtained, and the power consumption fluctuation coefficient and air quality anomaly coefficient are normalized to calculate the carbon emission anomaly coefficient.

[0016] 6. The BIM-driven green building carbon emission dynamic assessment and management system according to claim 1 is characterized in that the determination of whether the green building carbon emission is normal and marking of the personnel concentration area according to the determination result specifically include:

[0017] Determine whether the carbon emission variation coefficient of the personnel-concentrated area is greater than or equal to the preset carbon emission variation 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] Analyze the abnormal carbon emissions due to the decline in air quality or excessive carbon dioxide concentration. The system will automatically adjust the ventilation equipment in the area, increase the ventilation rate or start the air purification equipment to dilute and reduce the concentration of carbon emissions in the air;

[0021] Retest the carbon emission level of the adjusted area 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 in buildings through an intelligent access control system, combines power consumption and air quality monitoring data, and identifies areas with dense personnel activities; then, Fourier transform and wavelet transform techniques are used to analyze power consumption fluctuation characteristics and air quality abnormal characteristics, 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; the early warning information not only includes the specific location and degree of the abnormality, but also provides possible cause analysis, 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 continuously optimizing carbon emission management of 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 abnormal carbon emission areas, 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 drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded 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] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are 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 collection 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 of the concentrated areas 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 a concentrated area of ​​personnel flow;

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

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

[0036] A regional division module, which is used to comprehensively analyze the power consumption fluctuation coefficient and air quality abnormality coefficient of the personnel concentrated area, judge whether the carbon emission of the green building is normal, and mark the personnel concentrated area according to the judgment result, and divide it into a normal carbon emission area and an abnormal carbon emission area;

[0037] The early warning module is used to warn and process abnormal carbon emission areas.

[0038] In the area determination module, the personnel flow data of the green building during the operation stage is obtained, and the personnel flow data is analyzed to determine the concentrated areas of personnel flow, 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, lounge 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 time people stay, to generate personnel flow data for that area;

[0044] The system will analyze the number of people entering and leaving and the length of stay based on different time periods (such as morning and evening peaks, lunch time, etc.), thereby identifying which areas have a high concentration 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 in-and-out 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, showing 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 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 includes:

[0052] During the operation phase of green buildings, based on the concentrated areas of personnel, real-time data on power consumption 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, 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] According to the Fourier transform result, the frequency domain signal P(f) is obtained, and the amplitude spectrum (i.e., amplitude) of the power consumption signal P(f) is calculated to represent the fluctuation intensity of power consumption. The calculation expression is: Wherein, 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 in 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 through the mean calculation formula v, the standard deviation value σ of the amplitude is calculated by the standard deviation calculation formula v ; Calculate the power consumption fluctuation coefficient, the calculation expression is: In the formula, Z V represents the power consumption fluctuation coefficient, a 1 and a 2 is the preset proportional coefficient, and a 1 and a 2 are all greater than 0, e represents the logarithm of the base of a natural number, 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, and the larger the power consumption fluctuation coefficient is, the more unstable the power consumption in the corresponding area is. 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 the periodic fluctuations in power consumption.

[0059] In the air quality anomaly coefficient acquisition module, based on the personnel concentration area, the regional air quality monitoring data of the green building in the operation stage is obtained, the air quality monitoring data is processed, the air quality characteristics are extracted, and the air quality anomaly coefficient is generated, which includes:

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

[0061] The air quality monitoring data of each collection point is obtained; the air quality monitoring data of each collection point is subjected to wavelet transformation, and the features of the data at different scales are extracted through multi-scale analysis, which is recorded as x(t); wherein the calculation expression of the wavelet transformation 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, and the calculation expression is: In the formula, Z q Indicates the air quality anomaly coefficient in the area where people gather, a 3 and a4 is the preset proportional coefficient, and a 3 and a 4 All greater than 0, μ W represents the average wavelet amplitude, σ W represents 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 with concentrated personnel meets the standards, and the larger the value of the air quality anomaly coefficient, the worse the air quality in the corresponding area, and there may be abnormal carbon emissions.

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

[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, and FD j represents the carbon emission variation coefficient of the jth population concentration area, Z 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, b 1 and b 2 is the preset proportional coefficient, and b 1 and b 2 All are greater than 0;

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

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

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

[0069] It should be noted that the carbon emission variation coefficient reflects the carbon emission situation in areas with concentrated populations, and the larger the value of the carbon emission variation 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 warning is 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 degree of abnormal carbon emissions, and possible causes, so that managers can quickly grasp the situation and assess potential impacts;

[0073] After issuing an early warning, the system monitors the abnormal area in real time, collects and records data such as power consumption, air quality, and personnel flow, and uses this data for further analysis, for example, to analyze whether the abnormal carbon emissions are caused by an increase in personnel or equipment power consumption. The data analysis results can help managers locate the problem more accurately and determine its severity so that they can take appropriate countermeasures.

[0074] If the analysis confirms that the abnormal carbon emissions are caused by the decline in air quality or excessive carbon dioxide concentration, the system will automatically adjust the ventilation equipment in the area, increase the ventilation rate or start the 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 the 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 formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula 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 by 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 programs 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 site, computer, server or data center to another website site, computer, server or data center by wired or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can access 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 hard disk.

[0080] It should be understood that the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. A and B can be singular or plural. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship, but it may also indicate an "and / or" relationship. Please refer to the context for specific understanding.

[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, but the content is only a preferred embodiment of the present invention and cannot be considered to limit the scope of implementation 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 of the green building 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 personnel flow data and determine the personnel flow concentration area; an electric power consumption fluctuation coefficient acquisition module, which processes the electric power consumption data, extracts the electric power consumption fluctuation characteristics, and generates the electric power consumption fluctuation coefficient; An air quality anomaly coefficient acquisition module, wherein the air quality anomaly coefficient acquisition module processes air quality monitoring data, extracts air quality characteristics, and generates an air quality anomaly coefficient; A regional division module, which is used to comprehensively analyze the power consumption fluctuation coefficient and air quality abnormality coefficient of the personnel concentrated area, judge whether the carbon emission of the green building is normal, and mark the personnel concentrated area according to the judgment result, and divide it into a normal carbon emission area and an abnormal carbon emission area; The early warning module is used to warn and process abnormal carbon emission areas.

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 the smart access control system to identify areas where people gather; use the access control system to analyze the frequency of entry and exit of each area, combined with the length of time people stay, to generate personnel flow data for the area; by comprehensively analyzing 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 people stay in the area; the system will generate a dynamic heat map, showing high-frequency areas of personnel flow, marked as areas where personnel flow is concentrated.

3. The BIM-driven green building carbon emission dynamic assessment and management system according to claim 1 is characterized in that: Based on the concentrated personnel 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, including: In the operation stage of green buildings, based on the personnel concentration area, the power consumption data within the monitoring period is collected in real time according to the time series; the power consumption data of each collection point is obtained, which is 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, and the calculation expression is: In the formula, P*f( represents the power consumption signal in the frequency domain, 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; according to the Fourier transform result, the frequency domain signal P(f) is obtained, and the amplitude spectrum of the power consumption signal P(f) is calculated to represent the fluctuation intensity of power consumption. The calculation expression is: In the formula, 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 in the amplitude spectrum; identify the fluctuation characteristics in power consumption by comparing the amplitudes of each frequency component; calculate the average amplitude μ through the mean calculation formula v , the standard deviation value σ of the amplitude is calculated by the standard deviation calculation formula v , that is, calculate the power consumption fluctuation coefficient.

4. The BIM-driven green building carbon emission dynamic assessment and management system according to claim 1, characterized in that: The method of obtaining regional air quality monitoring data of green buildings in the operation stage based on the concentrated personnel area, processing the air quality monitoring data, extracting air quality characteristics, and generating air quality anomaly coefficients specifically includes: In the green building operation stage, based on the personnel concentration area, the air quality monitoring data within the monitoring period is collected in real time according to the time series; the air quality monitoring data of each collection point is obtained; the air quality monitoring data of each collection point is subjected to wavelet transformation, and the characteristics of the data at different scales are extracted through multi-scale analysis, which is recorded as x(t); wherein the calculation expression of the wavelet transformation 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 , the air quality anomaly coefficient is calculated based on the average wavelet amplitude and the standard deviation of the wavelet amplitude.

5. 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 abnormality coefficient in the personnel concentration area, and calculate the carbon emission abnormality coefficient, specifically including: The power consumption fluctuation coefficient and air quality anomaly coefficient of each concentrated area are obtained, and the power consumption fluctuation coefficient and air quality anomaly coefficient are normalized to calculate the carbon emission anomaly coefficient.

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

7. The BIM-driven green building carbon emission dynamic assessment and management system according to claim 1, 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.

8. The BIM-driven green building carbon emission dynamic assessment and management system according to claim 7 is characterized in that: The further analysis specifically includes: Analyze the abnormal carbon emissions due to the decline in air quality or excessive carbon dioxide concentration. The system will automatically adjust the ventilation equipment in the area, increase the ventilation rate or start the 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 retested 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 optimization for carbon emission management in the area.

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