A blockchain-based green building carbon footprint management method and system

Abstract

The application relates to the technical field of carbon footprint management, and discloses a green building carbon footprint management method and system based on a blockchain, which comprises the following steps: determining a plurality of building spaces based on space division data, mapping building equipment to corresponding building spaces based on equipment ownership data, identifying the sources of operation collection data, collecting various types of data to corresponding building spaces based on space operation objects, determining space carbon footprint data, extracting data demand items corresponding to each evaluation index based on evaluation index data, determining the correlation between building spaces, space operation objects, space carbon footprint data and data demand items based on an association rule algorithm, performing index ownership processing on the space carbon footprint data corresponding to each building space based on the correlation, determining target building spaces that deviate under the same evaluation index, and writing carbon footprint management records into the blockchain. The application ensures the reliability of carbon footprint management of green buildings.

Description

Technical Field

[0001] This invention relates to the field of carbon footprint management technology, and more specifically, to a blockchain-based method and system for managing the carbon footprint of green buildings. Background Technology

[0002] Green buildings, characterized by energy conservation, efficient resource utilization, and low environmental impact, have been widely applied in the construction of public and residential buildings. During the construction and operation of green buildings, carbon emissions are generated from building materials, construction equipment, HVAC, lighting, elevators, and water supply and drainage. Because HVAC, lighting, and elevator equipment operate dynamically, their energy consumption data fluctuates with time, location, number of users, and environmental conditions. Recording carbon emissions only during the construction phase or a single production stage would negatively impact the low-carbon operation management and carbon reduction assessment of green buildings.

[0003] Chinese patent application publication number CN118365495B discloses a carbon footprint management system for prefabricated component production that integrates the Internet of Things and blockchain. It collects material information of prefabricated components through RFID and uses smart contracts to complete carbon footprint calculation, transaction legality verification, and blockchain storage. However, it is evident that simply converting material and energy consumption into carbon footprint values ​​and writing them into the blockchain without further linking the carbon footprint data with green buildings makes it difficult to determine the impact of carbon emission data on indicators such as building energy efficiency and renewable energy utilization rate of green buildings. Furthermore, when spatial carbon emission anomalies occur during the building operation phase, it is difficult to monitor and evaluate them, resulting in insufficient reliability of carbon footprint management.

[0004] Therefore, it is necessary to design a blockchain-based method and system for managing the carbon footprint of green buildings to address the problems existing in current technologies. Summary of the Invention

[0005] In view of this, the present invention proposes a blockchain-based method and system for managing the carbon footprint of green buildings, aiming to solve the problem that the lack of association between carbon footprint data and green buildings makes it difficult to determine the impact of carbon emission data on indicators such as building energy efficiency and renewable energy utilization rate of green buildings, and makes it impossible to monitor and evaluate operation and maintenance, resulting in insufficient reliability of carbon footprint management.

[0006] In one aspect, this invention proposes a blockchain-based method for managing the carbon footprint of green buildings, comprising:

[0007] Acquire the spatial division data, equipment ownership data, operation collection data and evaluation index data of the green building, determine several building spaces based on the spatial division data, and map the building equipment to the corresponding building spaces based on the equipment ownership data to determine the space operation objects;

[0008] The source of the collected operational data is identified, and the data is divided into equipment operation data, energy consumption data, renewable energy supply data, water use data, and waste treatment data based on the source. The data is then aggregated into the corresponding building space based on the spatial operation object to determine the spatial carbon footprint data.

[0009] Based on the evaluation index data, extract the data requirement items corresponding to each evaluation index, and determine the association relationship between the building space, the space operation object, the space carbon footprint data and the data requirement items based on the association rule algorithm. Based on the association relationship, perform index attribution processing on the space carbon footprint data corresponding to each building space, determine the carbon footprint association data corresponding to each evaluation index, and determine the index support record of the corresponding building space based on the carbon footprint association data.

[0010] By comparing the indicator support records of different building spaces, the target building spaces that deviate under the same evaluation indicator are identified. Based on the correlation, the source of the indicator deviation of the target building space is determined. The source of the indicator deviation is bound to the corresponding evaluation indicator, building space and indicator support record to determine the carbon footprint management record, and the carbon footprint management record is written into the blockchain.

[0011] Furthermore, when determining the space operation object, the process includes: hierarchically splitting the building number, floor number, and room number in the space division data to determine the building space of the green building; extracting the equipment number, installation location, metering point number, and service area from the equipment ownership data; mapping building equipment with independent installation locations to corresponding building spaces; dividing building equipment whose service areas cover several building spaces into several equipment service items; mapping each equipment service item to a corresponding building space; and determining the space operation object based on the mapped building spaces.

[0012] Furthermore, when classifying equipment operation data, energy consumption data, renewable energy supply data, water usage data, and waste treatment data, the process includes: obtaining the collection point number, data name, unit of measurement, and collection time from the operation data; matching the collection point number with the metering point number in the spatial operation object; and classifying the operation data based on the data name and unit of measurement. Specifically, the start / stop status, operating level, and running time are classified as equipment operation data; electricity and gas consumption are classified as energy consumption data; photovoltaic power generation and waste energy recovery are classified as renewable energy supply data; water supply and recycled water consumption are classified as water usage data; and the amount of waste collected, recycled, and disposed of are classified as waste treatment data.

[0013] Furthermore, in determining spatial carbon footprint data, the process includes: matching the collection point numbers in the equipment operation data, energy consumption data, renewable energy supply data, water usage data, and waste treatment data with the spatial operation objects to determine the building spaces corresponding to each type of data. When the building space corresponding to the collection point number is unique, the corresponding data is assigned to that building space. When the building space corresponding to the collection point number is not unique, the service area in the spatial operation object is extracted, the corresponding data is split based on the service area, and the splitting results are assigned to the corresponding building spaces. Building space identifiers, equipment identifiers, and collection source identifiers are added to the collected data, and spatial carbon footprint data is determined.

[0014] Furthermore, when extracting data requirement items corresponding to each evaluation indicator based on the evaluation indicator data, the process includes: parsing the fields of indicator name, indicator category, evaluation object, statistical scope, and evaluation period in the evaluation indicator data to determine the indicator limitation information corresponding to each evaluation indicator; identifying the data type referenced by each evaluation indicator based on the indicator limitation information; determining the evaluation indicator with data type "building energy consumption" as an energy-saving data requirement item; determining the evaluation indicator with data type "photovoltaic, energy storage, and waste energy recovery" as a renewable energy data requirement item; determining the evaluation indicator with data type "water supply, reclaimed water, and drainage" as a water resource data requirement item; and determining the evaluation indicator with data type "waste generation, collection, recycling, and disposal" as a waste data requirement item.

[0015] Furthermore, in determining the association between the building space, the space operation object, the space carbon footprint data, and the data requirement items, the process includes: generating several candidate item sets for the building space, the space operation object, the space carbon footprint data, and the data requirement items according to the Eclat algorithm; determining frequent itemsets based on the support of the candidate item sets; and determining the association between the building space, the space operation object, the space carbon footprint data, and the data requirement items based on the frequent itemsets.

[0016] Furthermore, when determining the indicator support records for the corresponding building space, the process includes: extracting the spatial operation objects and data requirement items corresponding to the same evaluation indicator in each building space based on the aforementioned relationship; grouping spatial carbon footprint data from the same spatial operation object and corresponding to the same data requirement item into the indicator data group; determining carbon footprint related data; and determining the indicator support records for the building space corresponding to the carbon footprint related data based on the correspondence between the building space, evaluation indicator, and spatial operation object.

[0017] Furthermore, when determining the target building space that deviates under the same evaluation index, the process includes: extracting the spatial operation object and index support record corresponding to each building space under the same evaluation index, dividing the building spaces with the same spatial operation object into comparison groups, comparing the corresponding index support records within the comparison group, determining the candidate building spaces with inconsistent index support records within the comparison group, and determining the candidate building space corresponding to the data requirement item of the same evaluation index as the target building space.

[0018] Furthermore, when writing the carbon footprint management record into the blockchain, the process includes: using the target building space and the corresponding evaluation index as search conditions, determining the source of index deviation by identifying the spatial operation object and data type corresponding to the record difference under the same evaluation index; determining the carbon footprint management record based on the source of index deviation, each evaluation index, the target building space, the index supporting record, the collection source, and the collection time; and writing the carbon footprint management record into the blockchain based on a hash algorithm.

[0019] Compared with existing technologies, the advantages of this invention are as follows: By acquiring spatial division data, equipment attribution data, operational data collection data, and evaluation index data of green buildings, and mapping building equipment to corresponding building spaces based on equipment attribution data, the dispersed building equipment and operational data collection data during the green building operation phase can be incorporated into specific building spaces for management, avoiding the risk of unclear spatial sources caused by recording carbon footprints only for the entire building or a single device. By identifying the source of operational data collection and dividing it into equipment operation data, energy consumption data, renewable energy supply data, water use data, and waste treatment data, and then aggregating it to the corresponding building spaces based on the spatial operation objects, spatial carbon footprint data with spatial attribution and data source is formed. Based on the evaluation index data, the data requirement items corresponding to each evaluation index are extracted, and the correlation relationship between building space, space operation object, space carbon footprint data and data requirement items is determined based on the association rule algorithm. The space carbon footprint data can establish a corresponding relationship with the evaluation index of green building, which is no longer limited to material consumption or energy consumption. Furthermore, by comparing the index support records of different building spaces, the target building spaces that deviate under the same evaluation index and the source of the index deviation are identified. This enables the space carbon emissions during the building operation phase to form a traceable on-chain record, thereby improving the reliability of green building carbon footprint management.

[0020] On the other hand, this application also provides a blockchain-based green building carbon footprint management system for applying the above-mentioned blockchain-based green building carbon footprint management method, including:

[0021] The space division unit is configured to acquire the space division data, equipment ownership data, operation collection data and evaluation index data of the green building, determine several building spaces based on the space division data, and map building equipment to the corresponding building spaces based on the equipment ownership data to determine the space operation objects.

[0022] The first unit of the footprint is configured to identify the source of the operational data, and divide the operational data into equipment operation data, energy consumption data, renewable energy supply data, water use data and waste treatment data based on the source of collection, and collect the various types of data into the corresponding building space based on the spatial operation object to determine the spatial carbon footprint data;

[0023] The second footprint unit is configured to extract data requirement items corresponding to each evaluation indicator based on the evaluation indicator data, and determine the association relationship between the building space, the space operation object, the space carbon footprint data and the data requirement items based on the association rule algorithm. Based on the association relationship, the spatial carbon footprint data corresponding to each building space is processed for indicator attribution, the carbon footprint association data corresponding to each evaluation indicator is determined, and the indicator support record of the corresponding building space is determined based on the carbon footprint association data.

[0024] The footprint management unit is configured to compare the indicator support records of different building spaces, identify target building spaces that deviate under the same evaluation indicator, determine the source of the indicator deviation of the target building space based on the correlation, bind the source of the indicator deviation with the corresponding evaluation indicator, building space and indicator support record, determine the carbon footprint management record, and write the carbon footprint management record into the blockchain.

[0025] It is understandable that the aforementioned blockchain-based green building carbon footprint management method and system have the same beneficial effects, and will not be elaborated further here. Attached Figure Description

[0026] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0027] Figure 1 A flowchart illustrating a blockchain-based green building carbon footprint management method provided in this embodiment of the invention;

[0028] Figure 2 This is a functional block diagram of a blockchain-based green building carbon footprint management system provided in an embodiment of the present invention. Detailed Implementation

[0029] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. 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 skilled in the art without creative effort are within the scope of protection of the present invention.

[0030] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0031] See Figure 1 As shown in some embodiments of this application, a blockchain-based method for managing the carbon footprint of green buildings includes:

[0032] S100: Acquire spatial division data, equipment attribution data, operation data and evaluation index data of green buildings, determine several building spaces based on spatial division data, and map building equipment to the corresponding building spaces based on equipment attribution data to determine the space operation objects;

[0033] S200: Identify the source of operational data and classify it into equipment operation data, energy consumption data, renewable energy supply data, water use data, and waste treatment data based on the source. Then, based on the spatial operation object, collect the various types of data into the corresponding building space to determine the spatial carbon footprint data.

[0034] S300: Extract the data requirement items corresponding to each evaluation indicator based on the evaluation indicator data, and determine the association relationship between building space, space operation object, space carbon footprint data and data requirement items based on the association rule algorithm. Based on the association relationship, perform indicator attribution processing on the space carbon footprint data corresponding to each building space, determine the carbon footprint association data corresponding to each evaluation indicator, and determine the indicator support record of the corresponding building space based on the carbon footprint association data.

[0035] S400: Compare the indicator support records of different building spaces to identify target building spaces that deviate under the same evaluation indicator, and determine the source of the indicator deviation of the target building space based on the correlation. Bind the source of the indicator deviation to the corresponding evaluation indicator, building space and indicator support record to determine the carbon footprint management record, and write the carbon footprint management record into the blockchain.

[0036] Specifically, the carbon footprint of a green building during its operation is not determined by a single piece of equipment or a single energy consumption data point. Instead, it is formed by the combined effects of building space, building equipment, operational data, and evaluation indicators. Without spatial partitioning data, one can only know the total energy consumption of the entire building, not which floor, functional area, or room the energy consumption is concentrated on. Without equipment attribution data, one can only know that a certain piece of equipment is operating, not which building space it serves. Without evaluation indicator data, it is impossible to determine whether this operational data ultimately affects building energy efficiency, renewable energy utilization, water resource utilization efficiency, or waste recycling rate. Therefore, several building spaces are identified based on spatial partitioning data. Spatial partitioning data represents the division of internal spaces within a green building, such as buildings, floors, functional areas, and rooms. Building spaces are the spatial units within a green building that carry operational and carbon footprint data, such as a lobby on the first floor, an office area on the third floor, a meeting room, or an equipment room. After identifying the building spaces, building equipment is mapped to the corresponding building spaces based on equipment attribution data. Equipment attribution data represents the installation location, service scope, or metering relationship between building equipment and building space. Building equipment refers to objects in green buildings that generate operational data, energy consumption data, renewable energy supply data, water usage data, or waste disposal data, such as HVAC, lighting, elevators, water supply and drainage equipment, and photovoltaic equipment. Through mapping, building equipment is placed into the building space where it is actually installed or serves. Each building space is no longer just a location concept, but forms a corresponding spatial operation object. Spatial operation objects represent building equipment that has established a corresponding relationship with the building space and its operational attribution relationship, such as "second-floor office area - lighting equipment," "second-floor office area - air conditioning terminal," "public corridor - lighting equipment," and "entire building - elevator system." Operational data collection represents data obtained from sensors, meters, equipment control systems, or management systems during building operation. Source identification determines which equipment, metering point, business link, and corresponding data content each operational data collection piece comes from. For example, a data piece from an electricity meter with the unit being kWh is more likely to correspond to energy consumption, while a data piece from a photovoltaic inverter with the content being power generation is more likely to correspond to renewable energy supply. Based on the data collection source, the operational data is divided into equipment operation data, energy consumption data, renewable energy supply data, water consumption data, and waste disposal data. Equipment operation data represents the operating status of the equipment itself, such as start / stop status, operating duration, and operating speed. Energy consumption data represents the energy consumed by the building's operation, such as electricity and gas consumption. Renewable energy supply data represents data on photovoltaic power generation and energy storage discharge, etc. Water consumption data represents data on water supply and recycled water consumption, etc. Waste disposal data represents data on collection and recycling volume, etc. By classifying the operational data, each type of data can be entered into different green building evaluation paths.For example, energy consumption data will be included in the evaluation path for building energy efficiency or operational energy intensity, while renewable energy supply data will be included in the evaluation path for renewable energy utilization rate. Based on the spatial operation object, various data are aggregated to the corresponding building space. For example, if a lighting circuit belongs to the third-floor conference room, then the electricity consumption data for that lighting circuit will be aggregated to the third-floor conference room. Spatial carbon footprint data is carbon footprint-related data organized by building space, including equipment operation data, energy consumption data, renewable energy supply data, water consumption data, and waste disposal data within the corresponding building space. It should be noted that spatial carbon footprint data can be converted into carbon emissions according to GB / T 51366-2019.

[0037] By transforming the carbon footprint management of green buildings during operation from recording the total amount of the entire building to collecting data from specific building spaces, it becomes possible to determine which building space an anomaly in a certain evaluation indicator occurs in and which space operation object is involved. This forms spatial carbon footprint data with the building space as the attribute and the space operation object as the source. When there are local spaces with high energy consumption, abnormal renewable energy supply, abnormal water use, or abnormal waste disposal during the building operation phase, the spatial carbon footprint data can be used to determine the building space where the anomaly occurs and the corresponding space operation object, ensuring the reliability of monitoring and evaluating spatial carbon emissions during the green building operation phase.

[0038] Understandably, since green building evaluation indicators cannot directly utilize all operational data, they require selecting data relevant to the evaluation indicator from spatial carbon footprint data. Evaluation indicator data represents the content of indicators involved in the green building evaluation process, such as building energy efficiency, renewable energy utilization rate, water resource utilization efficiency, and construction waste recycling rate. Data requirement items represent the data types or content that a particular evaluation indicator needs to reference. For example, building energy efficiency requires energy consumption data, renewable energy utilization rate requires renewable energy supply data, water resource utilization efficiency requires water usage data, and construction waste recycling rate requires waste treatment data. After extracting data requirement items, the association relationship between building space, spatial operation objects, spatial carbon footprint data, and data requirement items is determined based on association rule algorithms. In other words, it determines "which evaluation indicator should be supported by a certain type of spatial carbon footprint data generated by a certain spatial operation object in a certain building space." For example, lighting equipment in a three-story office area generates energy consumption data, while the data requirement for building energy efficiency rate requires energy consumption data. Therefore, a relationship is formed between "three-story office area—lighting equipment—energy consumption data—building energy efficiency rate data requirement." Similarly, rooftop photovoltaic equipment generates renewable energy supply data, while the data requirement for renewable energy utilization rate requires renewable energy supply data. Thus, a relationship is formed between "rooftop area—photovoltaic equipment—renewable energy supply data—renewable energy utilization rate data requirement." Within the same building space, there may be multiple spatial operating objects, and each spatial operating object may generate various spatial carbon footprint data. Different evaluation indicators will reference different data requirement items. Without identifying these relationships, spatial carbon footprint data will remain merely recorded and cannot determine the evaluation indicators for green buildings. However, after identifying these relationships, the spatial carbon footprint data for each building space is assigned to an indicator based on these relationships. This means that the spatial carbon footprint data is categorized under the corresponding evaluation indicator according to the relationship. For example, the energy consumption data for lighting and air conditioning equipment in the three-story office area is categorized under the data range corresponding to the building energy efficiency rate. After indicator assignment, the carbon footprint correlation data corresponding to each evaluation indicator is determined. Carbon footprint correlation data refers to spatial carbon footprint data that has already established a correspondence with evaluation indicators. This means it's no longer just about a specific space generating a specific data point, but rather further clarifies how a specific data point in a specific space supports a specific evaluation indicator. Based on carbon footprint correlation data, corresponding indicator support records are determined for the building spaces. These records include the building space corresponding to the evaluation indicator, the objects operating within that space, and the carbon footprint correlation data. For example, a three-story office area might generate an indicator support record for building energy efficiency. This record could include lighting energy consumption data, air conditioning energy consumption data, and the corresponding objects operating within the three floors of the office area. Since anomalies in the operation phase of green buildings typically manifest as differences between spaces, this is important.For example, even within an office area, if the energy efficiency record for one floor differs significantly from other floors, it might indicate an anomaly in the air conditioning, lighting, or their usage on that floor. Therefore, the record support data for different building spaces is compared. The target building space represents the space that deviates under the same evaluation indicator; that is, the record support data for that building space differs from the records of other building spaces under the same evaluation indicator. After identifying the target building space, the source of the deviation is determined based on correlations. The source of deviation indicates the spatial operating object or spatial carbon footprint data source that caused the deviation under the corresponding evaluation indicator. For example, if a floor deviates in terms of energy efficiency, the correlation can be traced from the floor's record support data to the corresponding air conditioning or lighting energy consumption data, and further to the corresponding spatial operating object. Similarly, if an area deviates in terms of renewable energy utilization, the renewable energy supply data and its corresponding spatial operating object can be traced back. By linking the source of deviation from the indicator with the corresponding evaluation indicator, building space, and indicator supporting records, a carbon footprint management record is established, specifying "which building space deviated, which evaluation indicator the deviation corresponds to, which data supports the deviation, and which space operation object or data source the deviation source points to." The carbon footprint management record is then written to the blockchain. The blockchain is a chain-like data structure maintained by multiple nodes. Writing to the blockchain saves the carbon footprint management record as an on-chain record. By further transforming the spatial carbon footprint data formed during the operation phase into indicator supporting records under the evaluation indicators, the carbon footprint data is no longer just an on-chain record of material or energy consumption, but can be correlated with indicators such as green building energy efficiency, renewable energy utilization rate, water resource utilization efficiency, and construction waste recycling rate. Furthermore, when different building spaces show deviations in indicator supporting records under the same evaluation indicator, the source of the deviation in the target building space can be traced based on the correlation. This source of deviation, along with the corresponding evaluation indicator, building space, and indicator supporting records, is then written to the blockchain, forming a carbon footprint monitoring, indicator evaluation, and deviation tracing system for the green building operation phase, ensuring the reliability of green building carbon footprint management.

[0039] In some embodiments of this application, determining the space operation object includes: hierarchically splitting the building number, floor number, and room number in the space division data to determine the building space of the green building; extracting the equipment number, installation location, metering point number, and service area from the equipment ownership data; mapping building equipment with independent installation locations to the corresponding building space; dividing building equipment whose service area covers several building spaces into several equipment service items; mapping each equipment service item to the corresponding building space; and determining the space operation object based on the mapped building space.

[0040] Specifically, building numbers represent the identification of different individual buildings within a green building, such as Building 1 and Building 2; floor numbers represent different height levels within the same building, such as the 3rd floor and 4th floor of Building 1; and room numbers represent different rooms or areas within the same floor, such as Room 301 and Room 302 on the 3rd floor of Building 1. The hierarchical breakdown follows a hierarchical relationship of building number—floor number—room number, organizing the originally mixed spatial data into hierarchical building spaces. Equipment attribution data includes equipment number, installation location, metering point number, and service area. Equipment attribution data represents the attribution information of the building equipment itself and the relationship between the equipment and the building space. Equipment number represents the unique identification information of building equipment, such as air conditioning terminal A-301, lighting circuit L-301, and water meter W-301. Installation location indicates the actual location where the equipment is installed, such as in room 301, a corridor, an equipment room, or the roof. Metering point number represents the identification information of the metering point used to collect the operating or consumption data of the equipment, such as the electricity meter number, water meter number, or equipment control point number. Service area indicates the actual building space range in which the equipment functions or serves; for example, an air conditioning terminal may only serve room 301, meaning the equipment is searched within a defined building space based on its installation location. However, determining equipment ownership solely by installation location is not always accurate. For example, an air conditioning unit may be installed on the roof, but its service area is not the roof but multiple floors; a water pump may be installed in an equipment room, but its service area may be the entire building's water supply system; a lighting device installed in a corridor may only serve that corridor. Building equipment whose service area covers several building spaces is divided into several equipment service items. An equipment service item represents a service unit separated from a building equipment serving multiple spaces, corresponding to different building spaces. For example, if a single fresh air handling unit serves rooms 301, 302, and 303, it can be divided into "Fresh Air Handling Unit - Room 301 Service Item," "Fresh Air Handling Unit - Room 302 Service Item," and "Fresh Air Handling Unit - Room 303 Service Item." After dividing the equipment service items, each service item is mapped to its corresponding building space. This breaks down the original holistic service relationship of cross-space building equipment into multiple spatial correspondences, allowing a single building device to access different building spaces through multiple service items. The mapped building space is the building space that has already been connected to building equipment or service items. The space operation object is the operation and management object formed by the building space and its corresponding building equipment, service items, and metering point numbers. For example, "Room 301 - Lighting Equipment - Electricity Meter Location" and "Room 301 - Fresh Air Handling Unit Room 301 Service Item - Control Point Location."By unifying the installation location, service area, and metering point number of building equipment into the spatial attribution relationship, carbon footprint data of cross-space building equipment that is assigned solely based on installation location is avoided from falling into the wrong building space. At the same time, cross-space equipment can be mapped separately through equipment service items, which improves the reliability of spatial attribution in green building carbon footprint management.

[0041] In some embodiments of this application, the process of classifying equipment operation data, energy consumption data, renewable energy supply data, water usage data, and waste disposal data includes: obtaining the collection point number, data name, unit of measurement, and collection time from the operation data; matching the collection point number with the metering point number in the spatial operation object; and classifying the operation data based on the data name and unit of measurement. Specifically, the start / stop status, operating level, and running time are classified as equipment operation data; electricity and gas consumption are classified as energy consumption data; photovoltaic power generation and waste energy recovery are classified as renewable energy supply data; water supply and recycled water consumption are classified as water usage data; and the amount of waste collected, recycled, and disposed of are classified as waste disposal data.

[0042] Specifically, the collection point number indicates the location or metering point that generates the operational data, such as a specific electricity meter number, water meter number, or gas meter number. The data name indicates the specific content of the operational data, such as start / stop status, electricity consumption, photovoltaic power generation, water supply, or waste collection volume. The unit of measurement indicates the unit used for this operational data, such as kWh, m³, kg, t, or h. The collection time indicates the time when the operational data was generated or recorded. The collection point number is matched with the metering point number within the spatial operational object. The metering point number is already included in the spatial operational object and corresponds to building equipment, equipment services, and building space. When the collection point number of operational data matches a metering point number within a spatial operational object, it can be determined that the operational data originated from that spatial operational object. The collection point number alone can only determine where the data comes from, not its category; however, the data name and unit of measurement together reflect the data's business and metering attributes. For example, if the data name is "water supply" and the unit of measurement is m³, this data is classified as water consumption data. Equipment operation data is categorized into start / stop status, operating level, and runtime. This data represents the operational status and process of building equipment, such as whether an air conditioner is turned on, at what operating level, and for how long it has been running continuously. It illustrates the operational behavior of the building equipment. Electricity and gas consumption data are categorized into energy consumption data, representing the actual amount of energy consumed during the operation of a green building, such as electricity for lighting, air conditioning, and gas for boilers. This data corresponds to the main sources of carbon emissions during the operation phase. Photovoltaic power generation and waste heat recovery data are categorized into renewable energy supply data, representing the supply data generated by renewable or recyclable energy during the operation of a green building. This includes electricity generated by rooftop photovoltaic systems, electricity released to the building by energy storage systems, and heat provided by waste heat recovery systems. This data is related to the utilization of renewable energy in green buildings. Water supply and recycled water consumption are categorized into water usage data, representing the use and reuse of water resources during the operation of a green building, such as domestic water supply and rainwater reuse. The volume of waste collected, recycled, and disposed of is categorized into waste management data. This data represents the quantity of waste collected, recycled, or disposed of during building operation, such as the volume of construction waste collected and the volume of recyclables recycled. When managing the carbon footprint of green buildings, the data collected during operation is specifically assigned to the operational objects in the space and categorized into equipment operation data, energy consumption data, renewable energy supply data, water consumption data, and waste management data. This improves the reliability of data classification and evaluation support for green building carbon footprint management.

[0043] In some embodiments of this application, determining spatial carbon footprint data includes: matching the collection point numbers and spatial operation objects in equipment operation data, energy consumption data, renewable energy supply data, water use data, and waste treatment data to determine the building spaces corresponding to each type of data; when the building space corresponding to the collection point number is unique, the corresponding data is assigned to that building space; when the building space corresponding to the collection point number is not unique, the service area in the spatial operation object is extracted, the corresponding data is split based on the service area, and the splitting results are assigned to the corresponding building spaces; building space identifiers, equipment identifiers, and collection source identifiers are added to the collected data, and spatial carbon footprint data is determined.

[0044] Specifically, the data collection point number corresponds to the metering point number or equipment collection relationship within the spatial operation object. This correspondence identifies the spatial operation object to which the data collection point number belongs, and the spatial operation object then determines the corresponding building space. For example, if the data collection point number for a certain energy consumption data point corresponds to the spatial operation object "Room 301 - Lighting Equipment," then the building space corresponding to this energy consumption data is Room 301. When the building space corresponding to a data collection point number is unique, the corresponding data is assigned to that building space. A unique building space means that the data collection point number corresponds to only one specific building space, and does not involve multiple building spaces sharing or providing services. For example, if the meter for the independent lighting circuit in Room 301 only collects electricity consumption for lighting in Room 301, the corresponding data is directly assigned to that building space without further splitting. Since there is already a one-to-one correspondence between the data collection point number and the building space, the spatial affiliation of the data is clear, and it can be directly assigned to that building space. When the building space corresponding to a collection point number is not unique, meaning the same collection point number corresponds to multiple building spaces, this typically occurs when a single building device, metering point, or system serves multiple building spaces simultaneously. For example, a single fresh air unit might serve multiple offices. The service area represents the area range of each building space served by the operating object. For instance, if a fresh air unit serves room 301, each room has a corresponding service area. When a public lighting circuit covers a corridor and elevator lobby, the corridor area and elevator lobby area are the service areas corresponding to that public lighting circuit. Based on the service area, the corresponding data is split, meaning that a single data point or category generated by the same collection point number is allocated to different building spaces according to the proportion of the service area it serves. For example, if a fresh air handling unit generates 100 kWh of electricity at a certain data collection time, serving rooms 301, 302, and 303, with service areas of 40 m², 30 m², and 30 m² respectively, then the 100 kWh can be divided into 40 kWh, 30 kWh, and 30 kWh based on the service area, and assigned to rooms 301, 302, and 303 respectively. Since it's impossible to assign all data from devices or metering points across building spaces to their installation location, nor can it be evenly distributed across all building spaces, otherwise it could easily lead to spatial carbon footprint data bias, splitting based on service area allows cross-space data to be spatially assigned according to the actual service range. After data collection is complete, building space identifiers, device identifiers, and data collection source identifiers are added to the collected data. Building space identifiers indicate the building space number or name to which the collected data belongs, such as "Room 301, 3rd Floor, Building 1". Equipment identifiers indicate the building equipment number that generated or is associated with the data, such as lighting equipment number, air conditioning equipment number, and water meter equipment number. Collection source identifiers indicate the collection point, metering point, or data source mark corresponding to the data, such as electricity meter collection point number, water meter collection point number, and photovoltaic inverter collection point number.Adding these identifiers ensures that equipment operation data, energy consumption data, renewable energy supply data, water usage data, and waste disposal data, after being assigned to a specific building space, also include the building space source, equipment source, and data collection source. By categorizing data from the green building operation phase, spatial attribution and source labeling are further completed. This prevents data from equipment across different building spaces from being mistakenly aggregated into a single building space, and also prevents data from independent building spaces from being mixed into the total data for the entire building. This improves the reliability of data traceability and anomaly location in green building carbon footprint management.

[0045] In some embodiments of this application, when extracting data requirement items corresponding to each evaluation indicator based on the evaluation indicator data, the process includes: parsing the fields of indicator name, indicator category, evaluation object, statistical scope and evaluation period in the evaluation indicator data, determining the indicator limitation information corresponding to each evaluation indicator, identifying the data type referenced by each evaluation indicator based on the indicator limitation information, determining the evaluation indicator with data type of building energy consumption as energy-saving data requirement item, determining the evaluation indicator with data type of photovoltaic, energy storage and waste energy recovery as renewable energy data requirement item, determining the evaluation indicator with data type of water supply, reclaimed water and drainage as water resource data requirement item, and determining the evaluation indicator with data type of waste generation, collection, recycling and disposal as waste data requirement item.

[0046] Specifically, the indicator name represents the name of the specific evaluation indicator, such as building energy efficiency rate, renewable energy utilization rate, water resource utilization efficiency, and waste recycling rate. The indicator category represents the evaluation direction to which the indicator belongs, such as energy saving, renewable energy, water resources, and waste. The evaluation object represents the management object corresponding to the evaluation indicator, which can be the entire building, a specific floor, a specific building space, or a type of space operation object. The statistical scope represents the statistical range and data boundaries used when the evaluation indicator references data, such as only counting building electricity consumption during the operation phase, or counting photovoltaic power generation and waste energy recovery, or counting water supply and recycled water. The evaluation period represents the time range for statistical and evaluation purposes, such as daily, monthly, quarterly, or annually. Field parsing then extracts these fields from the evaluation indicator data and determines, based on the correspondence between the fields, which data needs to be used for each evaluation indicator, which evaluation object it applies to, and what statistical scope and evaluation period should be used for processing. The indicator limitation information is the usage conditions of the indicator jointly defined by the indicator name, indicator category, evaluation object, statistical scope, and evaluation period. For example, "Building Energy Efficiency Rate—Energy Saving Category—Office Area—Monthly Statistics—Referencing Building Energy Consumption Data." Based on the indicator limitation information, the data type referenced by each evaluation indicator is identified. The data type indicates the category of data content that the evaluation indicator needs to access, such as building energy consumption, photovoltaics, energy storage, waste recovery, water supply, recycled water, drainage, waste generation, waste collection, waste recycling, and waste disposal. For example, if the indicator name is "Building Energy Efficiency Rate," the indicator category is "Energy Saving Category," and the statistical scope is limited to electricity and gas consumption during the operation phase, then its referenced data type should be identified as "Building Energy Consumption." After identifying the data type referenced by each evaluation indicator, evaluation indicators with the data type "Building Energy Consumption" are determined as energy-saving data requirement items. Energy-saving data requirement items represent the data content that energy-saving evaluation indicators need to access, such as electricity consumption, gas consumption, heating and cooling data, and other building energy-related data. Evaluation indicators with the data types "Photovoltaics," "Energy Storage," and "Waste Energy Recovery" are determined as renewable energy data requirement items. Renewable energy data requirements represent the data content needed for renewable energy evaluation indicators, such as photovoltaic power generation and waste energy recovery. Evaluation indicators with data types of water supply, reclaimed water, and wastewater are defined as water resource data requirements. Water resource data requirements represent the data content needed for water resource evaluation indicators, such as water supply volume, reclaimed water volume, and wastewater discharge volume. Evaluation indicators with data types of waste generation, collection, recycling, and disposal are defined as waste data requirements. Waste data requirements represent the data content needed for waste evaluation indicators, such as waste generation volume, collection volume, recycling volume, and disposal volume. Each evaluation indicator is converted into a data requirement item that directly points to a specific data type. For example, building energy efficiency rate is converted into an energy efficiency data requirement item, which requires access to building energy consumption-related data.Renewable energy utilization rate is converted into a renewable energy-related data requirement item, which needs to access data related to photovoltaics, energy storage, and waste energy recovery. By converting green building evaluation indicators into data requirement items with clearly defined evaluation objects, statistical scopes, evaluation periods, and reference data types, spatial carbon footprint data can be assigned according to the actual data needs of different green building evaluation indicators. This avoids the inability to determine the impact of carbon footprint data on indicators such as building energy efficiency, renewable energy utilization rate, water resource utilization efficiency, and waste recycling rate. At the same time, by defining the evaluation objects and evaluation periods, the supporting relationships between indicators in green building carbon footprint management are clarified, thereby improving the reliability of indicator correlation and data reference in green building carbon footprint management.

[0047] In some embodiments of this application, determining the association between building space, space operation object, space carbon footprint data and data requirement items includes: generating several candidate item sets for building space, space operation object, space carbon footprint data and data requirement items according to the Eclat algorithm; determining frequent itemsets based on the support of the candidate item sets; and determining the association between building space, space operation object, space carbon footprint data and data requirement items based on the frequent itemsets.

[0048] Specifically, the Eclat algorithm is an association rule algorithm based on itemset intersection relationships to extract frequently co-occurring items. It considers building space, spatial operation objects, spatial carbon footprint data, and data demand items as objects eligible for association analysis, and organizes spatial operation objects, spatial carbon footprint data, and data demand items that co-occur within the same building space into data combinations. For example, "three-story office area, lighting equipment, electricity data, and energy-saving data demand items" can be one combination, while "roof area, photovoltaic equipment, photovoltaic power generation data, and renewable energy data demand items" can be another. Candidate itemsets represent sets of objects that may have association relationships, formed by combinations of objects, such as "lighting equipment—electricity data—energy-saving data demand items" or "three-story office area—air conditioning equipment—energy consumption data—energy-saving data demand items." Support indicates the degree to which a candidate itemset appears simultaneously in all data combinations. If a candidate itemset repeatedly appears in the same type of building space or the same evaluation process, it indicates that it is not a random combination but has a stable association basis. Frequent itemsets are sets of objects that appear multiple times in green building operation data and reflect stable correspondences. By determining the relationships between building space, space operation objects, space carbon footprint data, and data requirement items based on frequent itemsets, green building carbon footprint management can determine the relationships based on the combinations of objects that appear repeatedly in actual operation data. This reduces mismatches and omissions among building space, space operation objects, space carbon footprint data, and data requirement items, and improves the reliability of data association and indicator attribution in green building carbon footprint management.

[0049] In some embodiments of this application, determining the indicator support records for a corresponding building space includes: extracting the spatial operation objects and data requirement items corresponding to the same evaluation indicator in each building space based on the correlation relationship; grouping spatial carbon footprint data from the same spatial operation object and corresponding to the same data requirement item into the indicator data group; determining carbon footprint related data; and determining the indicator support records for the building space corresponding to the carbon footprint related data based on the correspondence between building space, evaluation indicator and spatial operation object.

[0050] Specifically, the "same evaluation indicator" means that data is collected under the same evaluation objective, rather than mixing data from different evaluation indicators. Taking a specific building space as the processing object, the system searches along relationships to find which spatial operating objects within that building space correspond to the current evaluation indicator, and then identifies the corresponding data requirements for these spatial operating objects. For example, in the building space of a "three-story office area," lighting and air conditioning equipment may both correspond to building energy efficiency rates, and their corresponding data requirements may both point to energy consumption-related data. Spatial carbon footprint data originating from the same spatial operating object and corresponding to the same data requirement are grouped into an indicator data group. An indicator data group represents a collection of data within the same building space that revolves around the same spatial operating object and the same data requirement. For example, multiple energy consumption data points generated by the lighting equipment in the three-story office area all correspond to the building energy efficiency rate data requirement; these spatial carbon footprint data are grouped into the same indicator data group. The air conditioning equipment in the three-story office area also generates energy consumption data, but it originates from a different spatial operating object, thus forming another indicator data group. Data under the same evaluation indicator is differentiated not only by building space but also by the space's operating object and data requirement, avoiding the mixing of data from different equipment sources or different indicator requirements. Carbon footprint-related data is spatial carbon footprint data organized according to evaluation indicators; that is, this data not only has a building space source but also clearly identifies the corresponding space operating object and data requirement. Based on the correspondence between building space, evaluation indicator, and space operating object, the indicator support records for the building space corresponding to the carbon footprint-related data are determined. The indicator support record represents the supporting record formed by a specific building space for a specific evaluation indicator. Its content should at least reflect which space operating objects provide the data for that evaluation indicator within that building space, which data requirement items these data correspond to, and the corresponding carbon footprint-related data. For example, the indicator support record for "three-story office area—building energy efficiency rate" could include carbon footprint-related data for energy consumption of lighting equipment and carbon footprint-related data for energy consumption of air conditioning equipment. By organizing scattered spatial carbon footprint data into indicator support records, green buildings establish a record relationship between building space, evaluation indicator, space operating object, and carbon footprint-related data, improving the reliability of green building carbon footprint management data traceability.

[0051] In some embodiments of this application, when determining the target building space that deviates under the same evaluation index, the method includes: extracting the spatial operation object and index support record corresponding to each building space under the same evaluation index, dividing the building spaces with the same spatial operation object into comparison groups, comparing the corresponding index support records within the comparison group, determining the candidate building spaces with inconsistent index support records within the comparison group, and determining the candidate building space corresponding to the data requirement item of the same evaluation index as the target building space.

[0052] Specifically, using the same evaluation index as a premise, for example, extracting the spatial operation objects and index support records of each building space under the evaluation index of building energy efficiency, and dividing building spaces with the same spatial operation objects into comparison groups. A comparison group is a set of comparisons consisting of multiple building spaces with the same spatial operation objects. For example, multiple office rooms equipped with lighting and air conditioning equipment can form the same comparison group. Only building spaces with the same or similar spatial operation objects have comparable index support records. For example, office spaces equipped with lighting and air conditioning equipment should not be directly compared with restrooms equipped with only water supply equipment; otherwise, the difference may come from differences in spatial function and equipment configuration, rather than from abnormal carbon footprint management. After forming comparison groups, the corresponding index support records are compared within the comparison group. That is, under the premise of the same evaluation index, the same type of spatial operation object, and the same comparison group, the index support records of each building space are compared accordingly. For example, comparing whether the index support records formed by lighting equipment in each office room under the building energy efficiency index are consistent, or comparing whether the index support records formed by water supply equipment in each restroom are consistent under the water resource utilization efficiency index. Inconsistent indicator support records indicate that the recorded content of a certain building space under the same evaluation indicator differs from that of other building spaces in the comparison group. When a building space with inconsistent indicator support records is found within the comparison group, it is first identified as a candidate building space. The candidate building spaces corresponding to the data requirement items of the same evaluation indicator are then identified as target building spaces. A target building space is a building space that deviates under the same evaluation indicator, and this deviation corresponds to a data requirement item of that evaluation indicator. For example, under the building energy efficiency rate evaluation indicator, if the energy consumption indicator support record for lighting equipment in a certain office room is inconsistent with that of other office rooms in the same group, and this difference corresponds to a data requirement item for the building energy efficiency rate, then that office room can be identified as a target building space. Identifying target building spaces with deviations under the constraints of the same evaluation indicator, the same spatial operating object, and the same data requirement item ensures that spatial carbon footprint anomalies during the green building operation phase can be detected in comparable building spaces. This reduces misjudgments caused by differences in building space function, equipment configuration, or inconsistent data requirement items, improving the reliability of spatial anomaly identification and operation and maintenance evaluation in green building carbon footprint management.

[0053] In some embodiments of this application, when writing carbon footprint management records into the blockchain, the process includes: using the target building space and the corresponding evaluation index as search conditions, determining the source of index deviation by identifying the spatial operation object and data type corresponding to the record difference under the same evaluation index; determining the carbon footprint management record based on the source of index deviation, each evaluation index, the target building space, the index supporting record, the collection source, and the collection time; and writing the carbon footprint management record into the blockchain based on a hash algorithm.

[0054] Specifically, the target building space defines the spatial location where the deviation occurs, and the corresponding evaluation index defines the evaluation direction of the deviation. For example, if the target building space is a three-story office area and the corresponding evaluation index is building energy efficiency rate, the search scope will only fall on the supporting records of the index related to building energy efficiency rate in the three-story office area, and will not include data related to water resource utilization efficiency or waste recycling rate in the three-story office area. The spatial operation objects and data types corresponding to the record differences under the same evaluation index are used to determine the source of the index deviation. Record differences indicate the differences between the supporting records of the target building space and the supporting records of other building spaces under the same evaluation index. Data types include energy consumption data, renewable energy supply data, water use data, or waste treatment data. For example, if there are record differences in the three-story office area under building energy efficiency rate, and the differences are concentrated in the energy consumption data corresponding to air conditioning equipment, then the air conditioning equipment and energy consumption data of the three-story office area are determined to be the source of the index deviation. If there are record differences in the roof area under renewable energy utilization rate, and the differences are concentrated in the renewable energy supply data corresponding to photovoltaic equipment, then the photovoltaic equipment and renewable energy supply data of the roof area are determined to be the source of the index deviation. Carbon footprint management records are determined based on the source of deviation from indicators, various evaluation indicators, target building spaces, supporting records for indicators, data collection sources, and collection times. These records comprehensively reflect "which target building space, under which evaluation indicator, based on which supporting indicator, from which source of deviation, where the data comes from, and when it occurred." A hash algorithm converts the carbon footprint management records into summary values. Any change in any content within the record will alter its corresponding summary value. Writing to the blockchain saves the carbon footprint management record or its corresponding summary value as on-chain data, enabling verification based on the on-chain record. By refining spatial deviation results during the green building operation phase into combinations of target building spaces, corresponding evaluation indicators, sources of deviation, supporting records for indicators, data collection sources, and collection times, and writing these to the blockchain using a hash algorithm, carbon footprint management records can be matched to specific spaces, specific indicators, specific operating objects within those spaces, and specific data types during verification. This reduces situations where the source of records is unclear, the cause of deviation is ambiguous, or alterations are difficult to detect, thus improving the reliability of verification and operational evaluation in green building carbon footprint management.

[0055] In summary, the beneficial effects of this invention are as follows: By acquiring spatial division data, equipment attribution data, operational data collection data, and evaluation index data of green buildings, and mapping building equipment to corresponding building spaces based on equipment attribution data, the dispersed building equipment and operational data collection data during the green building operation phase can be incorporated into specific building spaces for management, avoiding the risk of unclear spatial sources caused by recording carbon footprints only for the entire building or a single device. By identifying the sources of operational data collection and dividing it into equipment operation data, energy consumption data, renewable energy supply data, water use data, and waste treatment data, and then aggregating it to the corresponding building spaces based on the spatial operation objects, spatial carbon footprint data with spatial attribution and data source is formed. Based on the evaluation index data, the data requirement items corresponding to each evaluation index are extracted, and the correlation relationship between building space, space operation object, space carbon footprint data and data requirement items is determined based on the association rule algorithm. The space carbon footprint data can establish a corresponding relationship with the evaluation index of green building, which is no longer limited to material consumption or energy consumption. Furthermore, by comparing the index support records of different building spaces, the target building spaces that deviate under the same evaluation index and the source of the index deviation are identified. This enables the space carbon emissions during the building operation phase to form a traceable on-chain record, thereby improving the reliability of green building carbon footprint management.

[0056] In another preferred embodiment based on the above embodiments, see [reference] Figure 2 As shown, this embodiment provides a blockchain-based green building carbon footprint management system for applying a blockchain-based green building carbon footprint management method, including:

[0057] The spatial division unit is configured to acquire spatial division data, equipment attribution data, operation data and evaluation index data of green buildings, determine several building spaces based on the spatial division data, and map building equipment to the corresponding building spaces based on the equipment attribution data to determine the space operation objects;

[0058] The first unit of the footprint is configured to identify the source of the operational data, and divide the operational data into equipment operation data, energy consumption data, renewable energy supply data, water use data and waste treatment data based on the source of collection. Based on the spatial operation object, the various types of data are collected into the corresponding building space to determine the spatial carbon footprint data.

[0059] The second unit of the footprint is configured to extract the data requirement items corresponding to each evaluation indicator based on the evaluation indicator data, and determine the relationship between building space, space operation object, space carbon footprint data and data requirement items based on the association rule algorithm. Based on the relationship, the spatial carbon footprint data corresponding to each building space is processed for indicator attribution, the carbon footprint associated data corresponding to each evaluation indicator is determined, and the indicator support record of the corresponding building space is determined based on the carbon footprint associated data.

[0060] The carbon footprint management unit is configured to compare the indicator support records of different building spaces, identify target building spaces that deviate under the same evaluation indicator, determine the source of the indicator deviation of the target building space based on the correlation, bind the source of the indicator deviation with the corresponding evaluation indicator, building space and indicator support record, determine the carbon footprint management record, and write the carbon footprint management record into the blockchain.

[0061] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program goods according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0062] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A blockchain-based method for managing the carbon footprint of green buildings, characterized in that, include: Acquire the spatial division data, equipment ownership data, operation collection data and evaluation index data of the green building, determine several building spaces based on the spatial division data, and map the building equipment to the corresponding building spaces based on the equipment ownership data to determine the space operation objects; The source of the collected operational data is identified, and the data is divided into equipment operation data, energy consumption data, renewable energy supply data, water use data, and waste treatment data based on the source. The data is then aggregated into the corresponding building space based on the spatial operation object to determine the spatial carbon footprint data. Based on the evaluation index data, extract the data requirement items corresponding to each evaluation index, and determine the association relationship between the building space, the space operation object, the space carbon footprint data and the data requirement items based on the association rule algorithm. Based on the association relationship, perform index attribution processing on the space carbon footprint data corresponding to each building space, determine the carbon footprint association data corresponding to each evaluation index, and determine the index support record of the corresponding building space based on the carbon footprint association data. By comparing the indicator support records of different building spaces, the target building spaces that deviate under the same evaluation indicator are identified. Based on the correlation, the source of the indicator deviation of the target building space is determined. The source of the indicator deviation is bound to the corresponding evaluation indicator, building space and indicator support record to determine the carbon footprint management record, and the carbon footprint management record is written into the blockchain.

2. The blockchain-based green building carbon footprint management method according to claim 1, characterized in that, When determining the space operation object, the process includes: hierarchically splitting the building number, floor number, and room number in the space division data to determine the building space of the green building; extracting the equipment number, installation location, metering point number, and service area from the equipment ownership data; mapping building equipment with independent installation locations to corresponding building spaces; dividing building equipment whose service area covers several building spaces into several equipment service items; mapping each equipment service item to a corresponding building space; and determining the space operation object based on the mapped building spaces.

3. The blockchain-based green building carbon footprint management method according to claim 2, characterized in that, When classifying equipment operation data, energy consumption data, renewable energy supply data, water consumption data, and waste disposal data, the process includes: obtaining the collection point number, data name, unit of measurement, and collection time from the operation data; matching the collection point number with the metering point number in the spatial operation object; and classifying the operation data based on the data name and unit of measurement. Specifically, the start / stop status, operating level, and running time are classified as equipment operation data; electricity and gas consumption are classified as energy consumption data; photovoltaic power generation and waste energy recovery are classified as renewable energy supply data; water supply and recycled water consumption are classified as water consumption data; and the amount of waste collected, recycled, and disposed of are classified as waste disposal data.

4. The blockchain-based green building carbon footprint management method according to claim 3, characterized in that, When determining spatial carbon footprint data, the process includes: matching the collection point numbers in the equipment operation data, energy consumption data, renewable energy supply data, water use data, and waste treatment data with the spatial operation objects to determine the building spaces corresponding to each type of data. When the building space corresponding to the collection point number is unique, the corresponding data is assigned to that building space. When the building space corresponding to the collection point number is not unique, the service area in the spatial operation object is extracted, the corresponding data is split based on the service area, and the split results are assigned to the corresponding building spaces. Building space identifiers, equipment identifiers, and collection source identifiers are added to the collected data, and spatial carbon footprint data is determined.

5. The blockchain-based green building carbon footprint management method according to claim 4, characterized in that, When extracting data requirement items corresponding to each evaluation indicator based on the evaluation indicator data, the process includes: parsing the fields of indicator name, indicator category, evaluation object, statistical scope and evaluation period in the evaluation indicator data; determining the indicator limitation information corresponding to each evaluation indicator; identifying the data type referenced by each evaluation indicator based on the indicator limitation information; determining the evaluation indicator with data type of building energy consumption as energy-saving data requirement item; determining the evaluation indicator with data type of photovoltaic, energy storage and waste energy recovery as renewable energy data requirement item; determining the evaluation indicator with data type of water supply, reclaimed water and drainage as water resource data requirement item; and determining the evaluation indicator with data type of waste generation, collection, recycling and disposal as waste data requirement item.

6. The blockchain-based green building carbon footprint management method according to claim 5, characterized in that, When determining the association between the building space, the space operation object, the space carbon footprint data, and the data requirement items, the process includes: generating several candidate item sets for the building space, the space operation object, the space carbon footprint data, and the data requirement items according to the Eclat algorithm; determining frequent itemsets based on the support of the candidate item sets; and determining the association between the building space, the space operation object, the space carbon footprint data, and the data requirement items based on the frequent itemsets.

7. The blockchain-based green building carbon footprint management method according to claim 6, characterized in that, When determining the indicator support records for the corresponding building space, the process includes: extracting the spatial operation objects and data requirement items corresponding to the same evaluation indicator in each building space based on the relationship; grouping the spatial carbon footprint data from the same spatial operation object and corresponding to the same data requirement item into the indicator data group; determining the carbon footprint related data; and determining the indicator support records for the building space corresponding to the carbon footprint related data based on the correspondence between the building space, the evaluation indicator, and the spatial operation object.

8. The blockchain-based green building carbon footprint management method according to claim 7, characterized in that, When identifying target building spaces that deviate under the same evaluation index, the process includes: extracting the spatial operation objects and index support records corresponding to each building space under the same evaluation index; dividing building spaces with the same spatial operation objects into comparison groups; comparing the corresponding index support records within the comparison groups; identifying candidate building spaces with inconsistent index support records within the comparison groups; and identifying the candidate building spaces corresponding to the data requirement items of the same evaluation index as target building spaces.

9. The blockchain-based green building carbon footprint management method according to claim 8, characterized in that, When writing the carbon footprint management record into the blockchain, the process includes: using the target building space and the corresponding evaluation index as search conditions, determining the source of index deviation by identifying the spatial operation object and data type corresponding to the record difference under the same evaluation index; determining the carbon footprint management record based on the source of index deviation, each evaluation index, the target building space, the index supporting record, the collection source, and the collection time; and writing the carbon footprint management record into the blockchain based on a hash algorithm.

10. A blockchain-based green building carbon footprint management system, used to apply the blockchain-based green building carbon footprint management method as described in any one of claims 1-9, characterized in that, include: The space division unit is configured to acquire the space division data, equipment ownership data, operation collection data and evaluation index data of the green building, determine several building spaces based on the space division data, and map building equipment to the corresponding building spaces based on the equipment ownership data to determine the space operation objects. The first unit of the footprint is configured to identify the source of the operational data, and divide the operational data into equipment operation data, energy consumption data, renewable energy supply data, water use data and waste treatment data based on the source of collection, and collect the various types of data into the corresponding building space based on the spatial operation object to determine the spatial carbon footprint data; The second footprint unit is configured to extract data requirement items corresponding to each evaluation indicator based on the evaluation indicator data, and determine the association relationship between the building space, the space operation object, the space carbon footprint data and the data requirement items based on the association rule algorithm. Based on the association relationship, the spatial carbon footprint data corresponding to each building space is processed for indicator attribution, the carbon footprint association data corresponding to each evaluation indicator is determined, and the indicator support record of the corresponding building space is determined based on the carbon footprint association data. The footprint management unit is configured to compare the indicator support records of different building spaces, identify target building spaces that deviate under the same evaluation indicator, determine the source of the indicator deviation of the target building space based on the correlation, bind the source of the indicator deviation with the corresponding evaluation indicator, building space and indicator support record, determine the carbon footprint management record, and write the carbon footprint management record into the blockchain.

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