A system and method for personal carbon emission monitoring within a building area

Abstract

The application provides a personal carbon emission monitoring system and method in a building area, which comprises a lean energy metering module, a high-precision space-time positioning module, a behavior trajectory intelligent analysis module and a personal carbon emission metering and tracking module. The application realizes accurate tracking of carbon emission based on the actual activity duration of a person and an area by fusing high-precision positioning, personnel trajectory analysis and real-time energy data, and provides reliable data basis for a personal carbon account and a carbon incentive policy.

Description

Technical Field

[0001] This invention relates to the field of energy and carbon management system technology, and in particular to a personal carbon emission monitoring system and method for use within a building area. Background Technology

[0002] Current energy carbon management methods are generally crude, only covering regional energy consumption statistics or equipment meter readings, and failing to calculate carbon emissions at the individual level. In practice, it is difficult to monitor and calculate carbon emissions for individuals. Specific problems are as follows:

[0003] (1) Coarse measurement granularity: Traditional methods can only obtain the total energy consumption of water, electricity, gas and other resources of the building as a whole, and cannot accurately link carbon emissions to specific areas and individuals inside the building.

[0004] (2) Vague responsibility allocation: Since it is impossible to know the specific activity trajectory and time of people in the building, it is difficult to fairly and reasonably allocate the carbon emissions of public areas (such as lighting, air conditioning, etc.) to individuals, which is not conducive to stimulating individuals' awareness and behavior of energy conservation and emission reduction.

[0005] (3) Data and behavior are disconnected: macro-level energy data is separated from micro-level human energy use behavior, making it impossible to analyze the impact of different work habits and activity patterns on carbon emissions and making it difficult to provide effective personalized carbon emission reduction guidance. Summary of the Invention

[0006] To address the aforementioned technical problems, this invention proposes a personal carbon emission monitoring system and method for building areas. The technical solution of this invention is implemented as follows:

[0007] The first aspect of this invention discloses a personal carbon emission monitoring system for building areas, the system comprising a lean energy metering module, a high-precision spatiotemporal positioning module, a behavior flow intelligent analysis module, a personal carbon emission metering and tracking module, and a digital twin module;

[0008] The lean energy metering module includes IoT data acquisition devices installed at key energy consumption nodes in the building. These IoT data acquisition devices collect energy consumption information and aggregate and preliminarily process it through IoT gateways to form a standardized real-time energy data stream.

[0009] The high-precision spatiotemporal positioning module includes a UWB positioning base station installed inside the building and a UWB tag installed on the target to be monitored; the high-precision spatiotemporal positioning module locates the position of the target to be monitored through the UWB positioning base station and the UWB tag and forms a positioning data stream;

[0010] The behavior trajectory intelligent analysis module uses AI algorithms to perform pattern recognition and trajectory analysis on the positioning data stream and outputs the dwell time of the target to be monitored.

[0011] The personal carbon emission metering and tracking module calculates the total carbon emissions of the building area based on real-time energy data streams and emission factors, and matches personal carbon emissions with the residence time of the target to be monitored.

[0012] The digital twin module establishes a digital twin platform based on the building area and maps it one-to-one with the sub-areas of the building area.

[0013] Furthermore, the key energy consumption nodes of the building include lighting equipment, air conditioning equipment, office equipment, and water supply equipment; the IoT data collection devices include smart meters, smart water meters, and smart gas meters.

[0014] Furthermore, the AI ​​algorithms in the behavior trajectory intelligent analysis module include time series analysis and clustering algorithms.

[0015] Furthermore, the behavioral patterns include the duration of time spent at workstations, movement between meeting rooms, and activities in public areas.

[0016] Furthermore, the specific process for calculating individual carbon emission matching is as follows:

[0017] Calculate the total energy consumption of region z in time t:

[0018] E_z,t = Σ(E_z,t,e);

[0019] Calculate the carbon emissions of region z within time t:

[0020] C_z,t = Σ(E_z,t,e×EF_e);

[0021] Calculate the total residence time of all monitored units in region z within time t:

[0022] T_total,z,t = Σ(T_i,z,t);

[0023] Calculate the percentage of time a single monitored unit spends in region z within time t:

[0024] R_i,z,t=T_i,z,t / T_total,z,t;

[0025] Calculate the carbon emissions allocated to the monitored unit within region z:

[0026] C_i,z,t = C_z,t × R_i,z,t;

[0027] Calculate the total carbon emissions of the unit to be monitored throughout the entire building area:

[0028] C_i,t = Σ(C_i,z,t);

[0029] The meanings of the symbols in the above formulas are as follows:

[0030] i represents the unit to be monitored; t represents the time period; e represents the energy type; T_i,z,t represents the dwell time of the unit to be monitored in region z within time t; T_total,z,t represents the total dwell time of all units to be monitored in region z within time t; R_i,z,t represents the percentage of time the unit to be monitored spends in region z; E_z,t,e represents the amount of energy consumed in region z within time t; EF_e represents the carbon emission factor of energy; C_z,t represents the total carbon emissions generated in region z within time t; C_i,z,t represents the carbon emissions of a single unit to be monitored in region z within time t; C_i,t represents the total carbon emissions of the unit to be monitored in the entire building area within time t.

[0031] A second aspect of this invention discloses a method for monitoring individual carbon emissions within a building area, the method being implemented using the system disclosed in the first aspect of this invention, the method comprising the following steps:

[0032] S1: Data Acquisition: The lean energy metering module collects energy consumption data of the building area in real time; the high-precision spatiotemporal positioning module collects the precise location coordinates of the unit to be monitored in real time through UWB positioning;

[0033] S2. Data Processing and Area Division: Divide the building area into different functional sub-regions in the digital twin platform; map the continuous sequence of positioning coordinates to these sub-regions;

[0034] S3. Movement Analysis and Dwell Time Calculation: The intelligent movement analysis module uses AI algorithms to analyze the location data of each monitored unit, identify its entry and exit times in each sub-area, and calculate its precise dwell time in each sub-area.

[0035] S4. Calculation of total carbon emissions in the region: Based on energy consumption data and corresponding carbon emission factors, calculate the total carbon emissions of each sub-region during the calculation period.

[0036] S5. Individual carbon emission allocation: For each sub-region, its total carbon emissions are allocated according to the proportion of residence time of each individual monitoring unit obtained in S3.

[0037] S6. Personal Carbon Footprint Summary and Traceability: The carbon emissions allocated to the same monitored unit in all sub-regions are summed to obtain the total carbon emissions of the monitored unit in the building area, and a visualized carbon footprint report is generated to support the tracing of the time and spatial distribution of its carbon emissions.

[0038] The advantages of this invention are as follows:

[0039] 1. Accuracy: This invention achieves 10cm-level positioning accuracy through UWB technology and combines it with AI movement analysis to ensure the accuracy of personnel dwell time statistics, providing a reliable basis for fair allocation of carbon emissions and avoiding unfairness in average allocation.

[0040] 2. Dynamic and real-time: Based on real-time energy internet technology and continuous data stream processing, it can reflect changes in energy consumption and personal activities in near real-time, transforming carbon monitoring from static reports to dynamic management.

[0041] 3. Standard Compliance and Evolvability: The carbon measurement method of this invention is a beneficial exploration and deepening of the application of internationally accepted standards (PAS2060, ISO14064) at the individual level, ensuring the scientific nature and credibility of the method, and providing a practical basis for the evolution of future carbon accounting standards.

[0042] 4. Clear incentive effect: The "precise personal carbon tracking" ultimately achieved by this invention directly links carbon emissions to personal behavior, enabling individuals to clearly understand the environmental impact of their activities, and providing solid technical support for building an effective carbon inclusiveness and carbon incentive system. Detailed Implementation

[0043] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0044] Unless otherwise defined, all technical and scientific terms used in this invention have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains; the terminology used in the detailed description is for the purpose of describing particular embodiments only and is not intended to limit the invention; the terms “comprising” and “having”, and any variations thereof, in the specification and claims of this invention are intended to cover non-exclusive inclusion.

[0045] In the description of specific embodiments of the present invention, technical terms such as "first" and "second" are used only to distinguish different objects and should not be construed as indicating or implying relative importance or implicitly specifying the number, specific order, or primary and secondary relationship of the indicated technical features. In the description of the embodiments of the present invention, "multiple" means two or more, unless otherwise explicitly defined.

[0046] In this invention, the reference to "embodiment" means that a specific feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described in this invention can be combined with other embodiments.

[0047] In the description of the embodiments of this invention, the term "and / or" is merely a description of the relationship between associated objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, in this invention, the character " / " generally indicates that the preceding and following associated objects have an "or" relationship.

[0048] Current energy carbon management is generally based on crude statistics, only covering regional energy consumption statistics or equipment meter readings, and failing to calculate carbon emissions at the individual level. In practice, it is very difficult to monitor and calculate carbon emissions for individuals.

[0049] To address the aforementioned issues, this invention discloses a personal carbon emission monitoring system and method for building areas, resolving the problem that existing technologies cannot accurately monitor and fairly allocate dynamic carbon emissions from individuals within buildings. This invention aims to achieve precise carbon emission tracking based on individual activity duration and location by integrating high-precision positioning, personnel movement analysis, and real-time energy data, providing a reliable data foundation for personal carbon accounts and carbon incentive policies.

[0050] The embodiments of the present invention will be described in more detail below through examples. It should be noted that the embodiments of the present invention are not limited to these examples.

[0051] Example: A personal carbon emission monitoring system for building areas, including lean energy metering.

[0052] Lean Energy Metering Module: This module is built upon "real-time energy internet technology." The system deploys smart meters, smart water meters, and smart gas meters at key energy-consuming nodes in the building (such as lighting, air conditioning, office equipment circuits, and water supply pipes) to collect real-time data on total electricity consumption, water consumption, and other energy consumption within the building area. The collected data is aggregated and preliminarily processed through IoT gateways to form a standardized real-time energy data stream.

[0053] High-precision spatiotemporal positioning module: This module adopts UWB (Ultra-Wideband) indoor positioning technology. UWB positioning base stations are deployed within the building area, and personnel requiring monitoring are issued or wear tags (such as name tags or wristbands) with built-in UWB chips. The system calculates the signal transmission time difference between the tag and the base station to achieve real-time three-dimensional spatial positioning of personnel within the venue with an accuracy of up to 10 centimeters.

[0054] Intelligent Behavior Flow Analysis Module: This module's core technology is artificial intelligence, with a particular focus on machine vision-based pattern recognition and trajectory analysis of location data streams. It receives continuous location data from a high-precision spatiotemporal positioning module and reconstructs each person's movement trajectory (flow path) within the building using AI algorithms (including time series analysis and clustering algorithms). It intelligently identifies their behavioral patterns, including dwell time at workstations, movement between meeting rooms, and activities in public areas. The core output is the precise dwell time of each person in specific functional areas.

[0055] Personal Carbon Emission Measurement and Tracking Module: This module is the core computing engine of the system. It connects to the three modules mentioned above: its inputs come from the "Total Carbon Emissions of the Building Area" (calculated based on energy data and emission factors) from the Lean Energy Metering module and the "Time Spent by Each Person in the Area" from the Behavioral Flow Intelligent Analysis module. This module uses a "time-allocation method" to match personal carbon emissions. This method is an evolution and application of standards such as PAS2060 and ISO14064 at the micro-individual level. The system iteratively calculates the carbon emissions of all areas and all personnel, ultimately summarizing to obtain a precise personal carbon footprint for a specified time period.

[0056] The specific calculation method is described below.

[0057] Setting symbols:

[0058] i: refers to the individual being monitored (i1, i2, i3...);

[0059] z: refers to a specific building area or a sub-area (z1, z2, z3, ...) within a building;

[0060] t: refers to the time period for statistical calculation (in minutes);

[0061] e: refers to energy type (e 电力 e 天然气 e 水 wait);

[0062] T_i,z,t: The time that individual i stays in region z within time t, which is obtained from high-precision positioning and AI motion analysis;

[0063] T_total,z,t: The total time all personnel spend in region z within time t;

[0064] T_total,z,t=Σ(T_i,z,t)

[0065] R_i,z,t: The percentage of time that individual i spends in region z;

[0066] E_z,t,e: The amount of energy e consumed by region z in time t, which is measured by a smart meter;

[0067] EF_e: Carbon emission factor of energy e, unit kg CO 2e / kWh, based on official data or standards;

[0068] C_z,t: Total carbon emissions generated in region z during time t, in kg CO2. 2e ;

[0069] C_i,z,t: Carbon emissions of individual i allocated to region z within time t, in kg CO2. 2e ;

[0070] C_i,t: Total carbon emissions of individual i within the entire building area during time t, in kg CO2. 2e，

[0071] C_i,t=Σ(C_i,z,t).

[0072] The calculation steps are as follows:

[0073] Step 1: Calculate the region's total energy consumption and carbon emissions

[0074] Formula (1): E_z,t=Σ(E_z,t,e)

[0075] The total energy consumption of region z within time t is the sum of the consumption of various energy sources e (such as electricity, water, and gas).

[0076] Formula (2): C_z,t=Σ(E_z,t,e×EF_e)

[0077] The total carbon emissions of region z within time t are calculated by multiplying the consumption of various energy sources by their corresponding carbon emission factors EF_e and then summing the results.

[0078] Step 2: Calculate the percentage of time an individual spends within the area.

[0079] Formula (3): T_total,z,t=Σ(T_i,z,t)

[0080] The total time spent by all people in region z within time t is the sum of the time spent by each individual i.

[0081] Formula (4): R_i,z,t=T_i,z,t / T_total,z,t

[0082] Calculate the percentage of time individual i spends in region z within time t.

[0083] Step 3: Calculate your individual carbon emissions allocated within the region.

[0084] C_i,z,t=C_z,t×R_i,z,t

[0085] Step 4: Calculate your total carbon emissions within the entire building area.

[0086] Formula (5): C_i,t=Σ(C_i,z,t)

[0087] The total carbon emissions of individual i over time t are calculated as the sum of their emissions distributed across all regions z.

[0088] The operation steps of this embodiment are as follows:

[0089] S1: Data Acquisition: Real-time data collection of energy consumption data such as water, electricity, and gas in the building area through smart meters; real-time collection of precise location coordinates of personnel through a UWB positioning system.

[0090] S2. Data Processing and Area Division: Divide the building area into different functional sub-areas (such as office areas, meeting areas, corridors, etc.) in the digital twin platform. Map a continuous sequence of location coordinates to these sub-areas.

[0091] S3. Movement Analysis and Dwell Time Calculation: Utilize AI algorithms to analyze the location data of each person, identify their entry and exit times in each sub-area, and calculate their precise dwell time in each sub-area.

[0092] S4. Calculation of Total Carbon Emissions in the Region: Based on energy consumption data and corresponding carbon emission factors, calculate the total carbon emissions of each sub-region during the calculation period.

[0093] S5. Individual carbon emission allocation: For each sub-region, its total carbon emissions are allocated according to the proportion of each person's stay time obtained in step 3.

[0094] S6. Personal Carbon Footprint Summary and Traceability: The carbon emissions allocated to the same person in all sub-areas are summed to obtain the total carbon emissions of the individual in the building area, and a visualized carbon footprint report is generated to support the tracing of the time and spatial distribution of their carbon emissions.

[0095] It should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A personal carbon emission monitoring system for use within a building area, characterized in that, It includes a lean energy metering module, a high-precision spatiotemporal positioning module, a behavior flow intelligent analysis module, a personal carbon emission metering and tracking module, and a digital twin module; The lean energy metering module includes IoT data acquisition devices installed at key energy consumption nodes in the building. These IoT data acquisition devices collect energy consumption information and aggregate and preliminarily process it through IoT gateways to form a standardized real-time energy data stream. The high-precision spatiotemporal positioning module includes a UWB positioning base station installed inside the building and a UWB tag installed on the target to be monitored; the high-precision spatiotemporal positioning module locates the position of the target to be monitored through the UWB positioning base station and the UWB tag and forms a positioning data stream; The behavior trajectory intelligent analysis module uses AI algorithms to perform pattern recognition and trajectory analysis on the positioning data stream and outputs the dwell time of the target to be monitored. The personal carbon emission metering and tracking module calculates the total carbon emissions of the building area based on real-time energy data streams and emission factors, and matches personal carbon emissions with the residence time of the target to be monitored. The digital twin module establishes a digital twin platform based on the building area and maps it one-to-one with the sub-areas of the building area.

2. The system according to claim 1, characterized in that, The key energy consumption nodes of the building include lighting equipment, air conditioning equipment, office equipment, and water supply equipment; the IoT data collection devices include smart meters, smart water meters, and smart gas meters.

3. The system according to claim 1, characterized in that, The AI ​​algorithms in the behavior trajectory intelligent analysis module include time series analysis and clustering algorithms.

4. The system according to claim 1, characterized in that, The behavioral patterns include the duration of time spent at workstations, movement between meeting rooms, and activities in public areas.

5. The system according to claim 1, characterized in that, The specific process for calculating personal carbon emission matching is as follows: Calculate the total energy consumption of region z in time t: E_z,t = Σ(E_z,t,e); Calculate the carbon emissions of region z within time t: C_z,t = Σ(E_z,t,e×EF_e); Calculate the total residence time of all monitored units in region z within time t: T_total,z,t = Σ(T_i,z,t); Calculate the percentage of time a single monitored unit spends in region z within time t: R_i,z,t=T_i,z,t / T_total,z,t; Calculate the carbon emissions allocated to the monitored unit within region z: C_i,z,t = C_z,t × R_i,z,t; Calculate the total carbon emissions of the unit to be monitored throughout the entire building area: C_i,t = Σ(C_i,z,t); The meanings of the symbols in the above formulas are as follows: i represents the unit to be monitored; t represents the time period; e represents the energy type; T_i,z,t represents the dwell time of the unit to be monitored in region z within time t; T_total,z,t represents the total dwell time of all units to be monitored in region z within time t; R_i,z,t represents the percentage of time the unit to be monitored spends in region z; E_z,t,e represents the amount of energy consumed in region z within time t; EF_e represents the carbon emission factor of energy; C_z,t represents the total carbon emissions generated in region z within time t; C_i,z,t represents the carbon emissions of a single unit to be monitored in region z within time t; C_i,t represents the total carbon emissions of the unit to be monitored in the entire building area within time t.

6. A method for monitoring individual carbon emissions within a building area, using the system as described in any one of claims 1-5, characterized in that, The method includes the following steps: S1: Data Acquisition: The lean energy metering module collects energy consumption data of the building area in real time; the high-precision spatiotemporal positioning module collects the precise location coordinates of the unit to be monitored in real time through UWB positioning; S2. Data Processing and Area Division: Divide the building area into different functional sub-regions in the digital twin platform; map the continuous sequence of positioning coordinates to these sub-regions; S3. Movement Analysis and Dwell Time Calculation: The intelligent movement analysis module uses AI algorithms to analyze the location data of each monitored unit, identify its entry and exit times in each sub-area, and calculate its precise dwell time in each sub-area. S4. Calculation of total carbon emissions in the region: Based on energy consumption data and corresponding carbon emission factors, calculate the total carbon emissions of each sub-region during the calculation period. S5. Individual carbon emission allocation: For each sub-region, its total carbon emissions are allocated according to the proportion of residence time of each individual monitoring unit obtained in S3. S6. Personal Carbon Footprint Summary and Traceability: The carbon emissions allocated to the same monitored unit in all sub-regions are summed to obtain the total carbon emissions of the monitored unit in the building area, and a visualized carbon footprint report is generated to support the tracing of the time and spatial distribution of its carbon emissions.