Carbon footprint tracking management method and system under building life cycle

Abstract

The invention discloses a carbon footprint tracking management method and system under a building life cycle, and relates to the technical field of carbon footprint tracking management, and the method comprises the steps: collecting the function attributes of a target building, analyzing the operation life cycle rule of the target building, and dividing a plurality of carbon footprint management stages; building structure attributes and function system operation attributes are combined, key monitoring card points are extracted to deploy building monitoring devices, and real-time operation monitoring is carried out; on the basis of monitoring data, personalized carbon footprint analysis is carried out on each management stage, and carbon emission dynamic characteristics are accurately identified; and implementing intelligent energy regulation and control according to an analysis result, and dynamically optimizing the building energy consumption. The technical problem that accurate dynamic tracking and real-time dynamic response of the building full-life-cycle carbon footprint cannot be achieved through a traditional method in the prior art is solved, and the technical effect that dynamic monitoring and accurate regulation and control of the building full-life-cycle carbon footprint are achieved through full-life-cycle stage division and stage analysis and optimization is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of carbon footprint tracking and management, and specifically to a carbon footprint tracking and management method and system under the building life cycle. Background Art

[0002] With the advancement of the global carbon neutrality goal, the construction industry, as one of the main areas of energy consumption and carbon emissions, urgently needs to achieve precise management of carbon footprints. However, existing technologies usually focus on a single operation stage or static energy consumption analysis, lacking the ability to dynamically track the carbon emissions throughout the building life cycle and unable to comprehensively reflect the carbon emission patterns at different stages. In addition, traditional methods are difficult to combine real-time monitoring data for personalized regulation, resulting in insufficient timeliness and pertinence in carbon emission management. Summary of the Invention

[0003] This application provides a carbon footprint tracking and management method and system under the building life cycle, which is used to solve the technical problem that traditional methods in the prior art cannot achieve precise dynamic tracking and real-time dynamic response of the carbon footprint throughout the building life cycle.

[0004] In the first aspect of this application, a carbon footprint tracking and management method under the building life cycle is provided. The method includes: collecting the building function attributes of the target building, analyzing the laws of the building operation life cycle according to the building function attributes, and dividing the entire life operation cycle into multiple carbon footprint management stages according to the cycle analysis results; obtaining the target building structure attributes and functional system operation attributes, analyzing and extracting key monitoring points, and deploying building monitoring devices for the key monitoring points; using the building monitoring devices to conduct real-time operation monitoring of the target building to obtain real-time monitoring data; based on the real-time monitoring data, conducting personalized carbon footprint tracking analysis for each carbon footprint management stage, and performing intelligent energy regulation according to the analysis results.

[0005] In the second aspect of the present application, a carbon footprint tracking and management system under the building life cycle is provided. The system includes: a management stage division module, which is used to collect the building function attributes of the target building, analyze the laws of the building operation life cycle according to the building function attributes, and divide the whole life operation cycle into multiple carbon footprint management stages according to the cycle analysis results; a building monitoring device deployment module, which is used to obtain the building structure attributes and the operation attributes of the functional system of the target building, analyze and extract key monitoring points, and deploy building monitoring devices for the key monitoring points; a real-time operation monitoring module, which is used to use the building monitoring devices to conduct real-time operation monitoring of the target building and obtain real-time monitoring data; a personalized carbon footprint tracking and management module, which is used to conduct personalized carbon footprint tracking analysis for each carbon footprint management stage based on the real-time monitoring data, and conduct intelligent energy regulation according to the analysis results.

[0006] One or more technical solutions provided in the present application have at least the following technical effects or advantages:

[0007] The carbon footprint tracking and management method and system under the building life cycle provided by the present application relate to the technical field of carbon footprint tracking and management. By collecting building function attributes and structural information, analyzing the laws of its operation life cycle, dividing carbon footprint management stages, extracting key monitoring points and deploying real-time monitoring devices, obtaining operation data for personalized carbon footprint analysis, optimizing carbon emission characteristics, and implementing intelligent energy regulation, accurate carbon footprint management of the whole building life cycle is realized, solving the technical problem in the prior art that the traditional method cannot achieve accurate dynamic tracking and real-time dynamic response of the carbon footprint of the whole building life cycle, and achieving the technical effect of realizing dynamic monitoring and accurate regulation of the carbon footprint of the whole building life cycle through stage division and stage analysis optimization of the whole life cycle. Brief Description of the Drawings

[0008] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention, and those of ordinary skill in the art can also obtain other drawings based on these drawings without creative efforts.

[0009] Figure 1 It is a schematic flow chart of the carbon footprint tracking and management method under the building life cycle provided by the embodiments of the present application;

[0010] Figure 2 It is a schematic structural diagram of the carbon footprint tracking and management system under the building life cycle provided by the embodiments of the present application.

[0011] Description of the accompanying drawing reference numerals: management stage division module 11, building monitoring device deployment module 12, real-time operation monitoring module 13, personalized carbon footprint tracking and management module 14. Detailed implementation manners

[0012] This application provides a carbon footprint tracking and management method and system under the building life cycle, which is used to solve the technical problem that the traditional method in the prior art cannot achieve accurate dynamic tracking and real-time dynamic response of the carbon footprint of the entire building life cycle.

[0013] Next, the technical solutions in the embodiments of this application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of this application. Obviously, the described embodiments are only a part of the embodiments of this application, rather than all the embodiments. Based on the embodiments in this application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of this application.

[0014] It should be noted that the terms "first", "second", etc. in the specification of this application and the above accompanying drawings are used to distinguish similar objects, and do not necessarily need to be used to describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances so that the embodiments of this application described here can be implemented in an order other than those illustrated or described here. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or server that includes a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or modules that are not clearly listed or are inherent to these processes, methods, products, or devices.

[0015] Embodiment 1, as Figure 1 shown, this application provides a carbon footprint tracking and management method under the building life cycle, and the method includes:

[0016] P10: Collect the building function attributes of the target building, analyze the laws of the building operation life cycle according to the building function attributes, and divide the entire life operation cycle into multiple carbon footprint management stages according to the cycle analysis results. Among them, the target building is a functional building used to support power transmission, distribution, and management.

[0017] Further, when analyzing the laws of the building operation life cycle according to the building function attributes, step P10 of the embodiment of this application further includes:

[0018] P11: Obtain the building function attributes of the target building, where the building function attributes include design attributes, operation attributes, and energy usage patterns; P12: Analyze the core function characteristics of the target building according to the building function attributes, where the core function characteristics include scheduling function, power supply service function, and office support function; P13: Based on the core function characteristics, extract historical operation data respectively, and perform building energy consumption distribution and carbon emission analysis based on the historical operation data to generate an energy consumption distribution map and a carbon emission distribution map; P14: Perform trend analysis through the energy consumption distribution map and the carbon emission distribution map to generate the cycle analysis result.

[0019] It should be understood that by obtaining and analyzing the function attributes of the target building, the operation life cycle law of the building is revealed, and the whole life cycle is divided into multiple carbon footprint management stages to achieve precise management. Among them, the target building is a functional building used to support power transmission, distribution, and management, such as a power grid dispatching building, a rural power supply station, etc.

[0020] First, it is necessary to comprehensively collect the building function attributes of the target building, including design attributes, operation attributes, and energy usage patterns. Design attributes involve building layout, equipment selection, and energy-saving measure planning. For example, a power grid dispatching building needs to reserve an independent area for high-load dispatching equipment to ensure its stable operation; while a rural power supply station focuses on a compact layout to optimize space utilization. Operation attributes reflect the operation frequency and functional area usage characteristics of the building, such as the all-weather operation of dispatching equipment and the intermittent use of the office area. Energy usage patterns cover the energy demand distribution of the building, the law of peak electricity consumption, and the configuration of backup energy sources, etc. The acquisition of these data can be completed through building design documents, operation records, and real-time monitoring information in the energy management system, providing basic data support for subsequent analysis.

[0021] After obtaining the building function attributes, it is necessary to further analyze its core function characteristics, which determine the main energy consumption sources and carbon footprint distribution of the building operation. For example, the scheduling function of a power grid dispatching building usually involves the continuous operation of a large number of high-performance devices, which is the main energy consumption source; the power supply service function supports power grid maintenance and power distribution, and its equipment energy consumption is relatively stable but cannot be ignored; the office support function mainly consumes energy for lighting, air conditioning, and office equipment, showing intermittent energy consumption characteristics. Through the analysis of these core function characteristics, the key energy consumption areas of the building can be initially divided, and the key nodes of high carbon emissions can be identified.

[0022] Based on the core functional characteristics, extract the historical operation data of the building to support energy consumption and carbon emission analysis. These data include equipment operation records, regional energy consumption distribution, and phased carbon emission data. For example, through monitoring data, it can be found that high-power equipment (such as servers) in the dispatching functional area account for more than 70% of the total energy consumption, while the office support function only accounts for 20%. At the same time, generate energy consumption distribution maps and carbon emission distribution maps based on historical data to visually reveal the energy consumption and carbon emission characteristics of different functional areas and time periods of the building. These charts provide a clear reference basis for subsequent trend analysis.

[0023] Finally, combine the energy consumption distribution map and the carbon emission distribution map to conduct trend analysis on the energy and carbon emission laws during the building's life cycle. For example, in summer, the high-load operation of dispatching equipment is superimposed with the high energy consumption of the air conditioning system, resulting in a significant increase in total carbon emissions, while the energy consumption ratio of heating equipment in the office area increases in winter. Through trend analysis, the entire life cycle of the building can be divided into multiple carbon footprint management stages, such as the "peak dispatching period", the "normal operation period", and the "energy-saving maintenance period". The results of these cycle analyses not only reveal the energy consumption and carbon emission characteristics of different stages but also provide a scientific basis for formulating intelligent energy regulation strategies.

[0024] Furthermore, according to the results of the cycle analysis, divide the entire life operation cycle into multiple carbon footprint management stages. Step P10 of the embodiment of the present application further includes:

[0025] P15: Based on the results of the cycle analysis, respectively extract the trend changes of energy consumption at multiple points and the trend changes of carbon emissions at multiple points; P16: Conduct centralized trend extraction and discrete trend fusion based on the trend changes of energy consumption at multiple points and the trend changes of carbon emissions at multiple points, extract multiple representative cycles, and divide the entire life operation cycle into multiple carbon footprint management stages according to the multiple representative cycles.

[0026] In a possible embodiment of the present application, to achieve accurate phased management of the carbon footprint, by deeply analyzing the cycle law, extract the key change trends of energy consumption and carbon emissions, and divide the entire life operation cycle into multiple carbon footprint management stages based on these trends.

[0027] First, based on the periodic analysis results generated in the early stage, extract the multi-point energy consumption trend changes and multi-point carbon emission trend changes during building operation. Here, multi-point refers to the energy consumption and carbon emission data collected in multiple functional areas within the building (such as the scheduling function area, office support area, and power supply service area) and time nodes (such as the daytime peak period and the nighttime low period). The energy consumption trend change reflects the variation law of energy consumption in each area over time. For example, the energy consumption of equipment in the scheduling area rises rapidly during the electricity peak period. The carbon emission trend change reveals the dynamic changes in carbon emissions at different stages. For example, the carbon emissions of the air conditioning system in the office area increase significantly during summer use. By extracting these trends, the energy use and carbon footprint distribution characteristics of each area and time period within the building can be comprehensively understood.

[0028] Next, perform the extraction of central tendency and the fusion of dispersion tendency on the extracted multi-point energy consumption and carbon emission trends. The extraction of central tendency means identifying the overall change trend of energy consumption or carbon emissions within the same functional area or time period. For example, the continuous high energy consumption characteristic of equipment operation throughout the day in the scheduling function area. The fusion of dispersion tendency is to integrate the individual change trends in different areas or time periods into a global rule. For example, combining the fluctuation rules of peak energy consumption during the day and low energy consumption at night in the office area to form the overall energy consumption trend model of the office area. This process can be achieved through data clustering algorithms (such as K-Means) and time series analysis techniques (such as ARIMA models) to ensure the reasonable extraction and integration of each trend change.

[0029] Subsequently, by analyzing the overall change rules of energy consumption and carbon emissions, extract multiple representative cycles within the building life cycle. For example, it is possible to identify the "all-day operation peak period" in winter and the "partial load adjustment period" in summer in the scheduling function area. These representative cycles can effectively reflect the energy consumption and carbon emission characteristics of the building at specific stages.

[0030] Finally, based on these representative cycles, divide the entire life cycle into multiple carbon footprint management stages. Each stage represents the specific rules and energy characteristics of building operation. For example, during the "peak energy consumption period", it may be necessary to focus on monitoring and optimizing the operation efficiency of scheduling function equipment, while during the "low valley energy saving period", energy saving measures implementation in the office area can be emphasized. This phased carbon footprint management strategy can more accurately match the actual operation needs of the building and provide a clear implementation path for subsequent intelligent energy regulation and energy saving optimization.

[0031] P20: Obtain the target building structure attributes and functional system operation attributes, analyze and extract key monitoring checkpoints, and deploy building monitoring devices for the key monitoring checkpoints.

[0032] Furthermore, step P20 of the embodiment of the present application further includes:

[0033] P21: Obtain the building structure attributes and functional system operation attributes of the target building; P22: Extract the building function partition information and plane layout information based on the building structure attributes; P23: Extract the power supply system topology information and equipment function information based on the functional system operation attributes; P24: Use the power supply system topology information and equipment function information to analyze and determine the key equipment components for carbon emissions; P25: For the key equipment components for carbon emissions, carry out monitoring design with reference to the building function partition information and plane layout information, arrange multiple key monitoring points, and deploy building monitoring devices at multiple key monitoring points.

[0034] Optionally, by extracting the structural attributes and functional system operation attributes of the building, gradually analyze and deploy key monitoring points and building monitoring devices, providing technical support for the accurate monitoring and management of carbon emissions.

[0035] First, comprehensively obtain the building structure attributes and functional system operation attributes of the target building. The building structure attributes include the external shape characteristics, number of floors, material composition, and structural type of the building. For example, a power grid dispatching building usually has a large-area computer room and a high-load-bearing design, while a rural power supply station may adopt a compact building design. The functional system operation attributes cover the operation mode of internal equipment, energy distribution, and electricity demand characteristics in the building. For example, the 24-hour high-intensity electricity consumption mode of the power grid dispatching building and the intermittent electricity consumption mode of the office area. By integrating these attributes, a panoramic model of the building's structure and function can be initially constructed.

[0036] Next, based on the obtained building structure attributes, further extract the building function partition information and plane layout information. The building function partition information defines the uses and functions of different areas in the building, such as the dispatching center, equipment computer room, office area, and public facilities area. The plane layout information describes the spatial distribution of each area in the building, including the area of the area, relative position, and functional path. For example, in a power grid dispatching building, the dispatching center is usually located at the core of the building to ensure stable power supply, while the computer room is close to the equipment area to reduce energy consumption losses. These information provide the spatial and functional basis for the subsequent layout of monitoring points.

[0037] Next, combined with the functional system operation attributes, extract the power supply system topology information and equipment function information inside the building. The power supply system topology information describes the structure of the internal power transmission and distribution network in the building, such as the main power line, branch line, and load distribution. The equipment function information covers the types, operating states, and functional characteristics of key equipment in the building. For example, the load capacity of dispatching equipment and the energy consumption of air conditioning equipment in the office area. By constructing a power supply system topology diagram, the energy consumption contribution and carbon emission characteristics of each equipment can be intuitively displayed.

[0038] Furthermore, by using the power supply system topology information and equipment function information, analyze and determine the key equipment components of carbon emissions in the building. These devices are usually the main sources of building carbon emissions, such as high-energy-consuming server groups, power conversion equipment, or air-conditioning systems. During the analysis process, focus on the operating duration, power demand, and carbon emission intensity of these devices. For example, the servers in the dispatch center are key components of carbon emissions due to their round-the-clock operation and high load; the air-conditioning system in the office area is a secondary key device during summer operation. Through this analysis, it is possible to focus on the core nodes of building carbon emissions and improve the pertinence of monitoring and management.

[0039] Finally, for the determined key equipment components of carbon emissions, refer to the functional zoning information and floor plan layout information of the building, and design and arrange multiple key monitoring points. The arrangement of these points needs to combine equipment distribution and regional characteristics. For example, in the server area of the dispatch center, monitoring points can be arranged on the main power supply line and the equipment operation port; in the air-conditioning system of the office area, monitoring points can be arranged at the cooling device and the fan operation position. In addition, according to the floor plan layout information, monitoring devices such as intelligent energy consumption sensors, carbon emission monitors, and data acquisition terminals can be preferentially deployed in high-density equipment areas and key energy paths. These devices will collect the energy consumption and carbon emission data of key equipment in real time, providing real-time data support for subsequent carbon footprint analysis and optimization control.

[0040] This method combines spatial layout, functional zoning, and equipment characteristics. Through multi-dimensional information analysis and intelligent deployment, it ensures the comprehensiveness and accuracy of carbon emission monitoring, laying a foundation for subsequent carbon footprint tracking and intelligent control.

[0041] P30: Use the building monitoring device to conduct real-time operation monitoring of the target building and obtain real-time monitoring data.

[0042] Specifically, through the deployed building monitoring device, conduct real-time operation monitoring of the target building to obtain accurate and dynamic energy consumption and carbon emission data, providing core support for subsequent carbon footprint tracking and intelligent control.

[0043] First, use the building monitoring devices deployed in the early stage to collect real-time data from multiple key monitoring points in the building. These monitoring devices include energy consumption sensors, carbon emission monitors, environmental data recorders, etc., which can efficiently sense the energy use and carbon emission characteristics of the building during operation. For example, at the server group monitoring point in the dispatching center, the energy consumption sensor records the device power consumption, and the carbon emission monitor calculates the indirect carbon emissions generated by electricity use; at the office area air conditioning system monitoring point, the energy consumption data of the device operation and the dynamic energy consumption caused by the change of temperature and humidity are recorded. These data are transmitted to the central data management system through the real-time transmission module of the building monitoring device. The central system summarizes, cleans, and preliminarily analyzes the monitoring data. For example, after removing abnormal data (such as sensor errors or temporary shutdown data), a structured data set that meets the analysis requirements is generated. This real-time monitoring method improves the accuracy and timeliness of the data, providing strong support for the energy conservation optimization and low-carbon operation of the building.

[0044] P40: Based on the real-time monitoring data, conduct personalized carbon footprint tracking analysis for each carbon footprint management stage, and perform intelligent energy regulation according to the analysis results.

[0045] Further, step P40 of the embodiment of the present application further includes:

[0046] P41: Extract multivariate sample data from the historical carbon emission records of the target building to establish a general carbon footprint analysis model; P42: For the multiple carbon footprint management stages, extract the stage characteristics respectively, and based on the stage characteristics, perform migration optimization on the general carbon footprint analysis model to generate a stage carbon footprint analysis model; P43: Use the stage carbon footprint analysis model to conduct personalized carbon footprint tracking analysis for the target stage.

[0047] It should be understood that by combining real-time monitoring data with historical records, personalized analysis models are constructed for different carbon footprint management stages, and intelligent energy regulation is implemented accordingly to achieve low-carbon optimized management in the building operation stage.

[0048] First, extract multivariate sample data covering equipment energy consumption, regional carbon emission distribution, environmental conditions (such as seasonal temperature changes), etc. from the historical carbon emission records of the target building, and train to generate a general carbon footprint analysis model. This model aims to summarize the overall carbon emission law of the building and provide a unified basic framework for subsequent stage analysis. Through multivariate regression analysis or machine learning algorithms (such as random forest or gradient boosting decision tree), this model can capture the complex relationship between energy consumption driving factors and carbon emissions. For example, in the power grid dispatching building, the continuous high-load operation of the server group is the main driving factor for carbon emissions, while the air conditioning equipment in the office area is significantly affected by seasonal changes. The construction of the general model provides a comprehensive global perspective on the carbon emission characteristics of the building.

[0049] Furthermore, in order to more precisely adapt to the characteristics of different carbon footprint management stages, phased features are extracted from the general model and migrated and optimized to generate a phased carbon footprint analysis model adapted to each stage. The phased features include equipment load fluctuations during peak periods, equipment no-load characteristics during valley periods, and key equipment energy consumption characteristics during energy-saving maintenance periods, etc. Through transfer learning, such as the fine-tuning method based on neural networks, the general model is adjusted to a model that pays more attention to specific stage features. For example, during the peak scheduling period, by increasing the weights of server equipment energy consumption and carbon emission factors, the model can more accurately predict the carbon emission dynamics of this stage; during the energy-saving maintenance period, the impact of equipment optimization in the office area is highlighted. The generated phased model can perform more fine-grained carbon footprint analysis for each stage, ensuring the pertinence and accuracy of the analysis results.

[0050] Next, the phased carbon footprint analysis model is used to conduct personalized carbon footprint tracking analysis for each carbon footprint management stage. First, through dynamic trend analysis, the time-series changes of carbon emissions within the stage are monitored in real time. For example, how the operating load changes of the server group during the peak period drive the emergence of carbon emission peaks. Secondly, regional comparison analysis is carried out to identify key areas with higher carbon emissions, such as the energy consumption distribution differences between the equipment area and the office area of the dispatching center. In addition, through the anomaly detection function, abnormal high-emission equipment or areas are identified. For example, the energy consumption of air-conditioning equipment suddenly increases during non-operating hours. Based on the analysis results, specific optimization suggestions are generated, such as reducing the operating frequency of specific equipment, adjusting the load distribution during peak electricity consumption, etc. Through personalized analysis, the accuracy and real-time nature of carbon emission management are ensured.

[0051] Next, based on the personalized analysis results, intelligent energy regulation strategies are implemented. Through the building management system, the operating parameters of equipment are adjusted in real time. For example, the server load balance is optimized to reduce energy consumption during peak periods, or unnecessary equipment is turned off during valley periods. In addition, through long-term planning, energy-saving plans can be formulated, such as equipment upgrades or process optimizations, to reduce the carbon footprint of the building in the long term. By combining real-time monitoring and intelligent regulation, the building can dynamically adapt to the operating requirements of different stages, achieving dynamic optimization of carbon emissions and maximizing energy utilization efficiency.

[0052] Furthermore, step P42 of the embodiment of the present application further includes:

[0053] P42-1: For the multiple carbon footprint management phases, extract the first carbon footprint management phase and the second carbon footprint management phase, where the first carbon footprint management phase and the second carbon footprint management phase are adjacent management phases; P42-2: Based on the phased characteristics of the first carbon footprint management phase and the second carbon footprint management phase, conduct feature correlation analysis to generate the feature space of the target phase; P42-3: According to the feature space of the target phase, migrate and optimize the general carbon footprint analysis model to generate the phased carbon footprint analysis model of the target phase.

[0054] Optionally, by conducting correlation analysis on the features of adjacent carbon footprint management phases, construct the feature space of the target phase, and based on this, optimize the general model to generate the phased carbon footprint analysis model adapted to the target phase.

[0055] Among the multiple carbon footprint management phases, first extract the first carbon footprint management phase and the second carbon footprint management phase, which are adjacent phases in terms of time or functional characteristics. The selection of adjacent phases is based on the building operation rules. For example, in a power grid dispatching building, the "peak dispatching period" is usually adjacent to the "normal operation period". During the peak period, the equipment operates at full load, while during the normal operation period, the equipment load is relatively reduced. This adjacent relationship provides a basis for analyzing the energy consumption and carbon emission transition characteristics during the phase conversion process. By determining these two phases, the dynamic characteristics during the phase switch can be systematically captured, laying a foundation for subsequent analysis.

[0056] Next, for the extracted first and second carbon footprint management phases, conduct feature correlation analysis based on the phased characteristics to reveal the core correlation rules between adjacent phases and generate the feature space of the target phase. Feature correlation analysis can extract the change trends of key features by comparing the real-time monitoring data and historical data of the two phases. For example, when the equipment load during the peak dispatching period transitions from high intensity to low intensity, the dynamic changes in server energy consumption and air conditioner power. The feature space of the target phase consists of multi-dimensional characteristics, such as equipment energy consumption fluctuations, regional carbon emission intensity, and seasonal environmental impacts. By constructing the feature space, the carbon emission characteristics of the target phase can be accurately described, reflecting the key changes during the phase transition.

[0057] Furthermore, based on the feature space of the target phase, migrate and optimize the general carbon footprint analysis model to generate the phased carbon footprint analysis model adapted to the target phase. Migration and optimization make the model more prominent in the characteristics of the target phase by adjusting the model parameters and feature weights. For example, during the transition from the peak dispatching period to the normal operation period, the load change characteristics of server equipment may be the main driving factor for carbon emissions. Therefore, during the optimization process, the feature weight of it will be increased, while the weight of relatively minor characteristics (such as lighting energy consumption) will be reduced.

[0058] The finally generated phased model can accurately predict the carbon footprint dynamics in the target phase, providing support for energy consumption optimization and carbon emission management within the phase. This process ensures the phase adaptability of the model, provides a scientific basis for intelligent regulation strategies in different phases, and improves the efficiency and accuracy of carbon footprint management.

[0059] Furthermore, step P42-3 of the embodiment of the present application further includes:

[0060] P42-31: The general carbon footprint analysis model includes a feature extraction layer, a task classification layer, and a carbon footprint analysis layer; P42-32: Freeze the feature extraction layer, and use the feature space of the target phase to perform feature migration on the task classification layer and the carbon footprint analysis layer, and train to generate the phased carbon footprint analysis model of the target phase.

[0061] Specifically, a phased carbon footprint analysis model adapted to the target phase can be generated by freezing and transfer training the feature levels of the general carbon footprint analysis model.

[0062] Among them, the general carbon footprint analysis model is composed of three parts: a feature extraction layer, a task classification layer, and a carbon footprint analysis layer, forming a complete hierarchical structure. The feature extraction layer is responsible for extracting global features from the input multi-dimensional energy consumption and carbon emission data, such as equipment operation load fluctuations, seasonal electricity consumption trends, and regional carbon emission distributions. Through a large amount of training with historical data, the function of this layer has established a general feature recognition ability. The function of the task classification layer is to classify according to the characteristics of the management phase to which the input data belongs on the basis of the extracted features, such as identifying the high-load mode of equipment during the "peak scheduling period" or the low-energy consumption characteristics during the "energy-saving maintenance period". The carbon footprint analysis layer further quantitatively analyzes the classification results and outputs key indicators, such as the total carbon emission within the phase, peak distribution, and contribution ratio of the main equipment. This hierarchical design ensures the generalization ability of the model and provides structural support for subsequent phased optimization.

[0063] To adapt to the feature space of the target stage, the task classification layer and the carbon footprint analysis layer of the general model can be optimized through a transfer learning strategy. In this process, first freeze the feature extraction layer to retain its existing global feature extraction ability. This layer does not participate in the optimization, but the high-quality features it extracts will be used as input and passed to subsequent layers, reducing the training time and ensuring the basic stability of the model. Next, use the feature space of the target stage to perform feature transfer training on the task classification layer and the carbon footprint analysis layer. The transfer process adjusts the weights of the task classification layer to make it more adaptable to the characteristics of the target stage. For example, during the transition from the peak scheduling period to the normal operation period, enhance the attention to the downward trend of equipment load, and at the same time weaken the weight of seasonal air conditioning changes. In the carbon footprint analysis layer, optimize the analysis function parameters so that it can accurately quantify the carbon emission dynamics of the target stage. This transfer training uses the backpropagation algorithm to update the weights and adjusts the parameters in combination with the weight distribution of the target stage features to ensure the adaptability and prediction accuracy of the model in the target stage.

[0064] In summary, the embodiments of the present application at least have the following technical effects:

[0065] The present application collects building function attributes and structural information, analyzes the operation life cycle law thereof, and divides the carbon footprint management stage; extracts key monitoring points and deploys real-time monitoring devices to obtain operation data; conducts personalized carbon footprint analysis based on the data, optimizes the carbon emission characteristics, and implements intelligent energy regulation to achieve precise carbon footprint management throughout the building life cycle and improve the energy efficiency and carbon emission optimization effect.

[0066] It achieves the technical effect of realizing dynamic monitoring and precise regulation of the carbon footprint throughout the building life cycle through the division of the entire life cycle stage and phased analysis and optimization.

[0067] Embodiment 2, based on the same inventive concept as the carbon footprint tracking management method in the building life cycle in the foregoing embodiment, as Figure 2 shown, the present application provides a carbon footprint tracking management system in the building life cycle. The system in the embodiments of the present application and the method embodiments are based on the same inventive concept. Among them, the system includes:

[0068] A management stage division module 11, which is used to collect the building function attributes of the target building, analyze the operation life cycle law of the building according to the building function attributes, and divide the entire life operation cycle into multiple carbon footprint management stages according to the cycle analysis results.

[0069] A building monitoring device deployment module 12, which is used to obtain the building structure attributes and the operation attributes of the functional system of the target building, analyze and extract key monitoring checkpoints, and deploy building monitoring devices for the key monitoring checkpoints.

[0070] A real-time operation monitoring module 13, which is used to perform real-time operation monitoring of a target building using the building monitoring device and obtain real-time monitoring data.

[0071] A personalized carbon footprint tracking and management module 14, which is used to perform personalized carbon footprint tracking analysis for each carbon footprint management stage based on the real-time monitoring data, and perform intelligent energy regulation according to the analysis results.

[0072] Furthermore, the management stage division module 11 is also used to perform the following steps:

[0073] Obtain the building function attributes of the target building, where the building function attributes include design attributes, operation attributes, and energy usage patterns; according to the building function attributes, analyze the core function characteristics of the target building, where the core function characteristics include scheduling functions, power supply service functions, and office support functions; based on the core function characteristics, respectively extract historical operation data, and perform building energy consumption distribution and carbon emission analysis based on the historical operation data to generate an energy consumption distribution map and a carbon emission distribution map; perform trend analysis through the energy consumption distribution map and the carbon emission distribution map to generate the cycle analysis result. Among them, the target building is a functional building used to support power transmission, distribution, and management.

[0074] Furthermore, the management stage division module 11 is also used to perform the following steps:

[0075] Based on the cycle analysis result, respectively extract the multi-point energy consumption trend change and the multi-point carbon emission trend change; perform centralized trend extraction and discrete trend fusion according to the multi-point energy consumption trend change and the multi-point carbon emission trend change, extract multiple representative cycles, and divide the full-life operation cycle into multiple carbon footprint management stages according to the multiple representative cycles.

[0076] Furthermore, the building monitoring device deployment module 12 is also used to perform the following steps:

[0077] Obtain the building structure attributes and functional system operation attributes of the target building; based on the building structure attributes, extract building function partition information and plane layout information; based on the functional system operation attributes, extract power supply system topology information and equipment function information; use the power supply system topology information and equipment function information to analyze and determine key carbon emission equipment components; for the key carbon emission equipment components, perform monitoring design with reference to the building function partition information and plane layout information, arrange multiple key monitoring points, and deploy building monitoring devices at the multiple key monitoring points.

[0078] Further, the personalized carbon footprint tracking and management module 14 is further configured to perform the following steps:

[0079] Extract multi-source sample data based on the historical carbon emission records of the target building to establish a general carbon footprint analysis model; extract stage features for each of the multiple carbon footprint management stages, and based on the stage features, perform transfer optimization on the general carbon footprint analysis model to generate a stage carbon footprint analysis model; use the stage carbon footprint analysis model to perform personalized carbon footprint tracking analysis for the target stage.

[0080] Further, the personalized carbon footprint tracking and management module 14 is further configured to perform the following steps:

[0081] For each of the multiple carbon footprint management stages, extract the first carbon footprint management stage and the second carbon footprint management stage, where the first carbon footprint management stage and the second carbon footprint management stage are adjacent management stages; perform feature correlation analysis based on the stage features of the first carbon footprint management stage and the second carbon footprint management stage to generate a feature space for the target stage; based on the feature space of the target stage, perform transfer optimization on the general carbon footprint analysis model to generate a stage carbon footprint analysis model for the target stage.

[0082] Further, the personalized carbon footprint tracking and management module 14 is further configured to perform the following steps:

[0083] The general carbon footprint analysis model includes a feature extraction layer, a task classification layer, and a carbon footprint analysis layer; freeze the feature extraction layer, and use the feature space of the target stage to perform feature transfer on the task classification layer and the carbon footprint analysis layer, and train to generate the stage carbon footprint analysis model for the target stage.

[0084] It should be noted that the above sequence of embodiments of the present application is only for description and does not represent the advantages and disadvantages of the embodiments. And the above specifically describes certain embodiments of this specification. Additionally, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multi-task processing and parallel processing are also possible or may be advantageous.

[0085] The above are only the preferred embodiments of the present application and are not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application shall be included within the protection scope of the present application.

[0086] This specification and the drawings are merely illustrative of the present application and are considered to cover any and all modifications, variations, combinations, or equivalents within the scope of the present application. Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalent technologies, the present application is intended to cover these changes and modifications.

Claims

1. A carbon footprint tracking and management method under the building life cycle, characterized in that The method includes: Collect the building function attributes of the target building, analyze the laws of the building operation life cycle according to the building function attributes, and divide the whole life operation cycle into multiple carbon footprint management stages according to the cycle analysis results; Obtain the target building structure attributes and functional system operation attributes, analyze and extract key monitoring points, and deploy building monitoring devices for the key monitoring points; Use the building monitoring devices to conduct real-time operation monitoring of the target building and obtain real-time monitoring data; Based on the real-time monitoring data, conduct personalized carbon footprint tracking analysis for each carbon footprint management stage, and conduct intelligent energy regulation according to the analysis results.

2. The carbon footprint tracking and management method under the building life cycle according to claim 1, wherein the target building is a functional building for supporting power transmission, distribution and management.

3. The carbon footprint tracking and management method under the building life cycle according to claim 1, collecting the building function attributes of the target building and analyzing the laws of the building operation life cycle according to the building function attributes, including: Obtain the building function attributes of the target building, where the building function attributes include design attributes, operation attributes, and energy usage patterns; According to the building function attributes, analyze the core function characteristics of the target building, where the core function characteristics include scheduling function, power supply service function, and office support function; Based on the core function characteristics, respectively extract historical operation data, and conduct building energy consumption distribution and carbon emission analysis based on the historical operation data to generate an energy consumption distribution map and a carbon emission distribution map; Conduct trend analysis through the energy consumption distribution map and the carbon emission distribution map to generate the cycle analysis result.

4. The carbon footprint tracking and management method under the building life cycle according to claim 3, dividing the whole life operation cycle into multiple carbon footprint management stages according to the cycle analysis result, including: Based on the cycle analysis result, respectively extract multi-point energy consumption trend changes and multi-point carbon emission trend changes; Conduct central tendency extraction and discrete trend fusion according to the multi-point energy consumption trend changes and multi-point carbon emission trend changes, extract multiple representative cycles, and divide the whole life operation cycle into multiple carbon footprint management stages according to the multiple representative cycles.

5. The carbon footprint tracking and management method under the building life cycle according to claim 1, conducting personalized carbon footprint tracking analysis for each carbon footprint management stage based on the real-time monitoring data, including: According to the historical carbon emission records of the target building, extract multivariate sample data to establish a general carbon footprint analysis model; For the multiple carbon footprint management stages, respectively extract stage characteristics, and based on the stage characteristics, perform migration optimization on the general carbon footprint analysis model to generate a stage carbon footprint analysis model; Use the stage carbon footprint analysis model to conduct personalized carbon footprint tracking analysis for the target stage.

6. The carbon footprint tracking and management method under the building life cycle according to claim 5, characterized in that For the multiple carbon footprint management stages, respectively extract stage characteristics, and based on the stage characteristics, perform migration optimization on the general carbon footprint analysis model, including: For the multiple carbon footprint management stages, extract the first carbon footprint management stage and the second carbon footprint management stage, where the first carbon footprint management stage and the second carbon footprint management stage are adjacent management stages; Based on the stage characteristics of the first carbon footprint management stage and the second carbon footprint management stage, conduct feature correlation analysis to generate the feature space of the target stage; According to the feature space of the target stage, perform migration optimization on the general carbon footprint analysis model to generate the stage-specific carbon footprint analysis model of the target stage.

7. The carbon footprint tracking and management method under the building life cycle according to claim 6, characterized in that, According to the feature space of the target stage, perform migration optimization on the general carbon footprint analysis model to generate the stage-specific carbon footprint analysis model of the target stage, including: The general carbon footprint analysis model includes a feature extraction layer, a task classification layer, and a carbon footprint analysis layer; Freeze the feature extraction layer, and use the feature space of the target stage to perform feature migration on the task classification layer and the carbon footprint analysis layer, and train to generate the stage-specific carbon footprint analysis model of the target stage.

8. The carbon footprint tracking and management method under the building life cycle according to claim 1, characterized in that Obtain the target building structure attributes and functional system operation attributes, analyze and extract key monitoring checkpoints, and deploy building monitoring devices for the key monitoring checkpoints, including: Obtain the building structure attributes and functional system operation attributes of the target building; Based on the building structure attributes, extract building function partition information and floor plan layout information; Based on the functional system operation attributes, extract power supply system topology information and equipment function information; Use the power supply system topology information and equipment function information to analyze and determine the key equipment components for carbon emissions; For the key equipment components for carbon emissions, refer to the building function partition information and floor plan layout information for monitoring design, arrange multiple key monitoring checkpoints, and deploy building monitoring devices at the multiple key monitoring checkpoints.

9. Carbon footprint tracking and management system under the building life cycle, characterized in that, The system includes: A management stage division module, which is used to collect the building function attributes of the target building, analyze the laws of the building operation life cycle according to the building function attributes, and divide the whole life operation cycle into multiple carbon footprint management stages according to the cycle analysis results; A building monitoring device deployment module, which is used to obtain the target building structure attributes and functional system operation attributes, analyze and extract key monitoring checkpoints, and deploy building monitoring devices for the key monitoring checkpoints; A real-time operation monitoring module, which is used to use the building monitoring device to perform real-time operation monitoring of the target building and obtain real-time monitoring data; A personalized carbon footprint tracking and management module, which is used to perform personalized carbon footprint tracking analysis for each carbon footprint management stage based on the real-time monitoring data, and perform intelligent energy regulation according to the analysis results.

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