A method, device and storage medium for tracking carbon emissions for a building

Abstract

The present application relates to carbon emission management technical field, specifically to a kind of method, equipment and storage medium for tracking carbon emission of building, comprising: collecting building energy consumption parameter, establish cloud database, based on cloud database, cumulative storage is carried out to building energy consumption parameter;Iterate the building energy consumption parameter accumulated and stored in cloud database, according to building energy consumption parameter analysis building energy consumption anomaly characteristics;Set building carbon emission tracking operation trigger threshold, the present application provides whole, distributed two kinds of tracking logic to building carbon emission tracking by the way of comprehensive collection of building energy consumption parameter and the construction building energy consumption equipment distribution topology, so that building carbon emission can be captured based on distributed tracking logic in time when carbon emission anomaly is found based on whole tracking, effectively improve the comprehensiveness and pertinence of building carbon emission management, ensure the long-term health of building carbon emission, realize that building can continue to operate in the state of lower carbon emission and smaller cost.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of carbon emission management, and in particular to a method and device for tracking carbon emissions of a building and a storage medium. BACKGROUND

[0002] Building carbon emission management aims to monitor, control and reduce carbon emissions in the whole life cycle of a building through scientific means. It covers the accurate statistics of carbon emissions generated by energy consumption, waste disposal, etc., analyzes data to find optimization points, and formulates strategies for energy saving and green operation, which is of great significance to promote low-carbon transformation of the building industry and achieve sustainable development.

[0003] The patent for invention with publication number CN115860338A discloses a carbon emission monitoring method, which comprises: collecting internal environment parameters of a target building in real time; calculating a target comfort level corresponding to the target building according to the internal environment parameters and a preset comfort field; calculating a carbon emission amount matching the target comfort level according to a carbon emission function based on the comfort level; and calculating the target comfort level corresponding to the target building according to the internal environment parameters and the preset comfort field, which comprises: obtaining comfort threshold parameters and comfort weight parameters representing the comfort field; calculating a comfort level corresponding to the internal environment parameters as the target comfort level corresponding to the target building according to the comfort threshold parameters and the comfort weight parameters.

[0004] The application aims to solve the problem that the current carbon emission evaluation method for a building does not consider human comfort and other factors, and lacks rationality and humanization.

[0005] However, most of the existing technologies track and analyze carbon emissions from equipment in a building uniformly to achieve the purpose of maintaining carbon emissions in the building. However, when there is an abnormality in the carbon emissions of the building, it is difficult to quickly determine the source of the abnormal carbon emission equipment, so that the carbon emission control and processing work cannot be quickly responded to and carried out.

[0006] Therefore, a method, device and storage medium for tracking carbon emissions of a building are provided. SUMMARY

[0007] In view of the above-mentioned shortcomings of the prior art, the present application provides a method, device and storage medium for tracking carbon emissions of a building, which solves the problems raised in the background art.

[0008] To achieve the above-mentioned purposes, the present application is realized by the following technical solutions:

[0009] In a first aspect, a method for tracking carbon emissions of a building comprises:

[0010] Collecting building energy consumption parameters, establishing a cloud database, and accumulating and storing the building energy consumption parameters based on the cloud database; traversing the accumulated and stored building energy consumption parameters in the cloud database, analyzing building energy consumption anomaly characteristics based on the building energy consumption parameters; setting a building carbon emission tracking operation trigger threshold, comparing the trigger threshold with the building energy consumption anomaly characteristic analysis result, and executing a building carbon emission tracking operation when the building energy consumption anomaly characteristic analysis result meets the trigger threshold; selecting a continuous tracking target based on the building carbon emission tracking operation result; obtaining the selected continuous tracking target, configuring a tracking period for the continuous tracking target, and continuously tracking the continuous tracking target based on the tracking period configured for the continuous tracking target.

[0011] Further, the building energy consumption parameters include daily cumulative electricity consumption and daily cumulative gas consumption. In the first collection stage of the building energy consumption parameters, the distribution information of the internal power distribution line and the distribution information of the gas distribution line are uploaded synchronously. The power distribution line topology and the gas distribution line topology are constructed based on the distribution information of the power distribution line and the distribution information of the gas distribution line. The power distribution line topology and the gas distribution line topology are further forwarded to the cloud database. The energy consumption parameters generated by the devices deployed at each node position on the power distribution line topology and the gas distribution line topology are stored in the corresponding differentiated storage interval of the node position.

[0012] Further, when the number of stored building energy consumption parameters in each differentiated storage interval in the cloud database is not less than three groups, the analysis operation of the building energy consumption anomaly characteristics is executed.

[0013] The building energy consumption anomaly characteristic analysis logic is represented as:

[0014] ;

[0015] In the formula: is the building power level energy consumption anomaly characteristic value; is the total number of nodes in the power distribution line topology; is the total amount of daily cumulative electricity consumption parameters stored in the differentiated storage interval; , is the electricity consumption on the jth day and the j+1th day; is the earliest cumulative electricity consumption mean value stored in the corresponding differentiated storage interval of each node in the power distribution line topology; is the building gas level energy consumption anomaly characteristic value; is the total number of nodes in the gas distribution line topology; is the total amount of daily cumulative gas consumption parameters stored in the differentiated storage interval; is the gas consumption on the pth day and the p+1th day; The earliest cumulative gas consumption average stored in the storage interval corresponding to each node in the gas delivery route topology; These are characteristic values ​​of building energy consumption variation. , As weight;

[0016] Among them, weight , The sum is 1, and the weights are... , All are positive numbers, weights , The value is defined by the system user, and the weight... , Initially set to 0.5, 0.5, characteristic value of building energy consumption variation. The larger the value, the more likely it is to be used to obtain the characteristic value of building energy consumption variation. The more unstable the building energy consumption is during the corresponding period of the parameter source, the better; conversely, the more unstable the building energy consumption is during the same period, the more stable the building energy consumption becomes. The more stable the building's energy consumption is during the period corresponding to the parameter source period.

[0017] Furthermore, the trigger threshold for the building carbon emission tracking operation is user-defined on the system side;

[0018] The analysis of the building energy consumption variation characteristics is performed continuously based on adaptive logic;

[0019] The adaptive logic is expressed as follows:

[0020] ;

[0021] In the formula: The cycle of analysis operations for the characteristics of building energy consumption anomalies; This serves as the initial periodic base. Compared to The previously obtained characteristic value of energy consumption variation at the building power level; These are the latest obtained characteristic values ​​of energy consumption anomalies at the building electrical level;

[0022] Among them, the initial period base Customized by the user, each retrieved In the process of analyzing the characteristics of building energy consumption anomalies, based on the current... Iterate over the current Used when retrieving Proceed to the next step The pursuit of.

[0023] Furthermore, the building carbon emission tracking operation is as follows:

[0024] The building energy consumption anomaly feature analysis logic is applied to the building energy consumption parameters stored in each sub-storage interval in the cloud data to analyze the energy consumption anomaly features of the equipment groups corresponding to each sub-storage interval.

[0025] The energy consumption anomaly feature analysis logic of the equipment groups corresponding to each sub-storage interval is formula (1) or formula (2):

[0026] ;

[0027] After the energy consumption anomaly features of the equipment groups corresponding to each sub-storage interval are analyzed, the equipment groups corresponding to each analysis result are arranged in descending order based on the anomaly feature values.

[0028] Further, the energy consumption parameters of the equipment groups corresponding to each sub-storage interval are accumulated and summed, and the equipment groups to which the energy consumption parameters based on the accumulated sum results belong are arranged in descending order.

[0029] Further, when the energy consumption parameters of the equipment groups corresponding to each sub-storage interval are accumulated and summed, the energy consumption parameters are converted and processed, and then the accumulation sum operation is performed.

[0030] The energy consumption parameter conversion processing logic is:

[0031] ;

[0032] In the formula, is the carbon emission corresponding to the electricity consumption accumulation result in the sub-storage interval; is the electricity consumption accumulation in the sub-storage interval; is the power carbon emission factor; is the carbon emission corresponding to the gas consumption accumulation result in the sub-storage interval; is the gas consumption accumulation in the sub-storage interval; is the gas carbon emission factor;

[0033] Among them, according to the energy consumption parameter accumulation stored in the sub-storage interval, formula (3) or formula (4) in the above formula is selected to convert and process the corresponding energy consumption parameter.

[0034] Further, the building carbon emission tracking operation result is the descending order arrangement result of the two groups of equipment groups and 、 ;

[0035] The selection logic of the continuous tracking target is:

[0036] Select two groups of equipment groups in the descending order queue, and record them as equipment group set one and equipment group set two.

[0037] Further, the operation of configuring the tracking period for the continuous tracking target is configured for the first tracking target equipment group and the second tracking target equipment group.

[0038] The tracking period configured for the first tracking target equipment group and the second tracking target equipment group is customized by the user end, and the tracking period configured for the first tracking target equipment group and the second tracking target equipment group is always less than the original tracking period, and the tracking period configured for the first tracking target equipment group is always less than the tracking period configured for the second tracking target equipment group.

[0039] In a second aspect, a device for tracking carbon emissions of a building, the processing device comprises a memory, a processor and a computer program stored on the memory and executable on the processor, when the computer program is executed by the processor, the execution steps of a method for tracking carbon emissions of a building are realized.

[0040] A storage medium for tracking carbon emissions of a building, the storage medium stores a computer program, when the computer program is executed by a processor, the execution steps of a method for tracking carbon emissions of a building are realized.

[0041] Compared with the known prior art, the technical scheme provided by the present application has the following beneficial effects:

[0042] The present application provides a method, device and storage medium for tracking carbon emissions of a building. In the execution process, the method provides whole and distributed tracking logic for tracking carbon emissions of a building by comprehensively collecting building energy consumption parameters and constructing a building energy consumption device distribution topology. When carbon emission abnormalities are found based on whole tracking, the distributed tracking logic can capture the source of carbon emission abnormalities, effectively improving the comprehensiveness and pertinence of building carbon emission management, ensuring long-term health of building carbon emissions, and realizing continuous operation of buildings with lower carbon emissions and lower costs. BRIEF DESCRIPTION OF DRAWINGS

[0043] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiment or prior art description. Obviously, the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor on the basis of these drawings.

[0044] Figure 1 This is a flowchart illustrating a method for tracking carbon emissions from buildings. Detailed Implementation

[0045] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, 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, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0046] The present invention will be further described below with reference to embodiments.

[0047] Example:

[0048] This embodiment provides a method for tracking carbon emissions from buildings, such as... Figure 1 As shown, it includes:

[0049] A device for tracking carbon emissions from buildings, the device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program, when executed by the processor, implementing the execution steps of a method for tracking carbon emissions from buildings;

[0050] A storage medium for tracking carbon emissions from buildings stores a computer program. When the computer program is executed by a processor, it implements the execution steps of a method for tracking carbon emissions from buildings.

[0051] Collect building energy consumption parameters, establish a cloud database, and accumulate and store the building energy consumption parameters based on the cloud database;

[0052] Building energy consumption parameters include: daily cumulative electricity consumption and daily cumulative gas consumption. During the initial collection phase of building energy consumption parameters, the distribution information of the building's internal power distribution lines and gas delivery lines are uploaded simultaneously. Based on the power distribution line distribution information and gas delivery line distribution information, the power distribution line topology and gas delivery line topology are constructed. The power distribution line topology and gas delivery line topology are then forwarded to the cloud database. Differentiated storage intervals are created for each node location on the power distribution line topology and gas delivery line topology stored in the cloud database. The energy consumption parameters generated by the equipment deployed at each node location on the power distribution line topology and gas delivery line topology are stored in the differentiated storage interval where the corresponding node is located.

[0053] When the number of building energy consumption parameters stored in the cloud database is no less than three in each storage area, the analysis operation of building energy consumption variation characteristics is performed.

[0054] The logical representation of the building energy consumption anomaly characteristics analysis is as follows:

[0055] ;

[0056] In the formula: These are the characteristic values ​​of energy consumption variation at the building electrical level. This represents the total number of nodes in the power distribution line topology. To distinguish the total daily cumulative electricity consumption parameters stored in the storage area; , This refers to the electricity consumption on day j and day j+1. The earliest set of cumulative average electricity consumption stored in the storage interval corresponding to each node in the power distribution line topology; These are the characteristic values ​​of energy consumption variation at the building gas level. This represents the total number of nodes in the gas delivery route topology. To distinguish the total daily cumulative gas consumption parameter stored in the storage area; This refers to the gas consumption on day p and day p+1. The earliest cumulative gas consumption average stored in the storage interval corresponding to each node in the gas delivery route topology; These are characteristic values ​​of building energy consumption variation. , As weight;

[0057] Among them, weight , The sum is 1, and the weights are... , All are positive numbers, weights , The value is defined by the system user, and the weight... , Initially set to 0.5, 0.5, characteristic value of building energy consumption variation. The larger the value, the more likely it is to be used to obtain the characteristic value of building energy consumption variation. The more unstable the building energy consumption is during the corresponding period of the parameter source, the better; conversely, the more unstable the building energy consumption is during the same period, the more stable the building energy consumption becomes. The more stable the building's energy consumption is during the period corresponding to the parameter source;

[0058] The above logical formula is used to calculate the characteristic values ​​of building energy consumption variation in a digital form, providing further execution data support for the subsequent steps of the method in the above embodiments.

[0059] Traverse the accumulated building energy consumption parameters stored in the cloud database, and analyze the characteristics of building energy consumption anomalies based on the building energy consumption parameters;

[0060] The trigger threshold for building carbon emission tracking operations is defined by the system user.

[0061] The analysis of building energy consumption anomalies is performed continuously based on adaptive logic.

[0062] The adaptive logic is represented as:

[0063] ;

[0064] In the formula: The cycle of analysis operations for the characteristics of building energy consumption anomalies; This serves as the initial periodic base. Compared to The previously obtained characteristic value of energy consumption variation at the building power level; These are the latest obtained characteristic values ​​of energy consumption anomalies at the building electrical level;

[0065] Among them, the initial period base Customized by the user, each retrieved In the process of analyzing the characteristics of building energy consumption anomalies, based on the current... Iterate over the current Used when retrieving Proceed to the next step The pursuit;

[0066] By using the above logical formula, the cycle of analysis operations for building energy consumption anomaly characteristics is limited, effectively improving the effectiveness of the parameters used in the analysis of building energy consumption anomaly characteristics.

[0067] Set a trigger threshold for building carbon emission tracking operations. Based on the comparison between the trigger threshold and the analysis results of building energy consumption anomaly characteristics, execute the building carbon emission tracking operation when the analysis results of building energy consumption anomaly characteristics meet the trigger threshold.

[0068] The building carbon emission tracking operation is as follows:

[0069] The logic for analyzing building energy consumption anomalies is obtained by using the building energy consumption parameters stored in the storage intervals of each area in the cloud data to analyze the energy consumption anomaly characteristics of the corresponding equipment groups in each storage interval.

[0070] The logic for analyzing the energy consumption variation characteristics of the equipment groups corresponding to the storage areas in each region is Equation (1) or Equation (2):

[0071] ;

[0072] After analyzing the energy consumption variation characteristics of the corresponding equipment groups in each storage area, the equipment groups corresponding to each analysis result are sorted in descending order based on the magnitude of the variation characteristic values.

[0073] Further, the energy consumption parameters of the equipment groups corresponding to the storage areas of each district are cumulatively summed, and the equipment groups to which the energy consumption parameters from the cumulative summation result belong are sorted in descending order;

[0074] When the energy consumption parameters of the corresponding equipment groups in each storage area are accumulated and summed, the energy consumption parameters are simultaneously converted and then the accumulation and summation operation is performed.

[0075] Energy consumption parameter conversion processing logic:

[0076] ;

[0077] In the formula; To differentiate the carbon emissions corresponding to the cumulative electricity consumption in the storage area; To differentiate the cumulative electricity consumption within the storage area; Carbon emission factor for electricity; To differentiate the carbon emissions corresponding to the cumulative gas consumption in the storage area; To differentiate the accumulated gas consumption in the storage area; Carbon emission factors from fuel gas;

[0078] Among them, based on the cumulative energy consumption parameters of the storage in the different storage areas, the corresponding energy consumption parameters are converted by formula (3) or formula (4) in the above formula.

[0079] By setting the above logic formula, the logic for analyzing the energy consumption variation characteristics of the equipment group corresponding to each storage zone and the logic for normalizing energy consumption parameters are limited, ensuring that the method in the above embodiments provides logical support for determining the continuous carbon emission tracking target.

[0080] It should be noted that:

[0081] The values ​​for electricity carbon emission factors and gas carbon emission factors need to balance authoritativeness, regionality, and dynamic adaptability. Specifically, they can be determined according to the following logic: Prioritize official data released by national or local environmental authorities, such as the regional average electricity carbon emission factor (e.g., a weighted calculation value after differentiating between thermal power generation and renewable energy generation) specified in the annually updated "Guidelines for the Compilation of Provincial Greenhouse Gas Inventories" issued by the Ministry of Ecology and Environment, or the gas consumption carbon emission accounting coefficients issued by local housing and construction departments for the building sector, ensuring that the basic data complies with policy accounting standards; if industry-specific standards exist (e.g., factor requirements under specific systems such as green building evaluation and LEED certification), the recommended value under the corresponding system can be selected based on the building's application scenario (e.g., commercial building, residential building); for areas where no official direct data is currently available... In certain situations, regionally adapted data from authoritative industry databases (such as the default values ​​in the IPCC's National Greenhouse Gas Inventory Guidelines and the building carbon emission accounting database published by the China Academy of Building Research) can be used, along with fine-tuning based on the energy structure of the building's location (such as the proportion of clean energy sources like hydropower and wind power in the local power grid, and differences in gas supply sources like natural gas and liquefied petroleum gas). Simultaneously, an annual update mechanism should be established for factor values, iterating each year based on the latest official data or changes in the regional energy structure (such as the addition of new photovoltaic power plants or replacement of gas types) to ensure the accuracy and timeliness of carbon emission conversion results. Furthermore, all values ​​must record the data source, acquisition time, and correction basis, storing them in the parameter description module of the cloud database corresponding to the storage area for easy subsequent traceability and verification.

[0082] Based on the results of building carbon emission tracking operations, select targets for continuous tracking;

[0083] Obtain the selected continuous tracking target, configure the tracking period for the continuous tracking target, and perform continuous carbon emission tracking on the continuous tracking target based on the configured tracking period;

[0084] The results of building carbon emission tracking operations, namely the descending order of the two equipment groups and , ;

[0085] The selection logic for continuously tracking targets is as follows:

[0086] Select the first half of the device groups in the two descending queues, denoted as Device Group Set 1 and Device Group Set 2. The device group pointed to by the intersection of Device Group Set 1 and Device Group Set 2 is taken as the first-level tracking target device group, and the remaining device groups are taken as the second-level device groups.

[0087] To continuously track targets, the operational targets configured for the tracking cycle are the primary tracking target device group and the secondary tracking target device group;

[0088] The tracking period configured for the primary tracking target device group and the secondary tracking target device group is customized by the user. The tracking period configured for the primary tracking target device group and the secondary tracking target device group is always less than the original tracking period, and the tracking period configured for the primary tracking target device group is always less than the tracking period configured for the secondary tracking target device group.

[0089] In this embodiment, the implementation of the above method effectively improves the building carbon emission management effect. When there are abnormal problems in building carbon emissions, the source of the abnormality can be found more quickly based on this method, which is conducive to the long-term safety and stability of buildings.

[0090] In summary, the methods described in the above embodiments, through the comprehensive collection of building energy consumption parameters and the construction of a distributed topology for building energy consumption equipment, provide both overall and distributed tracking logics for building carbon emission tracking. This enables timely capture of the source of carbon emission anomalies based on distributed tracking logic when anomalies are detected based on overall tracking, effectively improving the comprehensiveness and targeting of building carbon emission management, ensuring the long-term health of building carbon emissions, and enabling buildings to operate continuously with lower carbon emissions and lower costs.

[0091] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions will not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for tracking carbon emissions from buildings, characterized in that, The application comprises the following steps: Collecting building energy consumption parameters, establishing a cloud database, and accumulating and storing building energy consumption parameters based on the cloud database; The building energy consumption parameters include daily cumulative electricity consumption, daily cumulative gas consumption, and building energy consumption parameter first collection stage. The building internal power distribution line distribution information and gas distribution line distribution information are uploaded synchronously. The power distribution line topology and gas distribution line topology are constructed based on the power distribution line distribution information and gas distribution line distribution information. The power distribution line topology and gas distribution line topology are further forwarded to the cloud database. The node positions on the power distribution line topology and gas distribution line topology stored in the cloud database are created to distinguish the storage intervals. The energy consumption parameters generated by the devices deployed at each node position on the power distribution line topology and gas distribution line topology are stored in the corresponding node position distinguished storage interval. Traverse the building energy consumption parameters accumulated and stored in the cloud database, and analyze the building energy consumption anomaly characteristics according to the building energy consumption parameters. The building energy consumption anomaly characteristic analysis logic is represented as: ； In the formula: is an energy consumption anomaly characteristic value of the building power level; is the total number of nodes in the power distribution line topology; is the total amount of daily cumulative electricity consumption parameters stored in the differentiated storage interval; , is the electricity consumption of the jth day and the j+1th day; is the earliest set of cumulative electricity consumption averages stored in the differentiated storage interval corresponding to each node in the power distribution line topology; is an energy consumption anomaly characteristic value of the building gas level; is the total number of nodes in the gas distribution line topology; is the total amount of daily cumulative gas consumption parameters stored in the differentiated storage interval; is the gas consumption of the pth day and the p+1th day; is the earliest set of cumulative gas consumption averages stored in the differentiated storage interval corresponding to each node in the gas distribution line topology; is an energy consumption anomaly characteristic value of the building; , is a weight; wherein the weights , are 1, and the weights , are positive numbers, the values of the weights , are defined by the user of the system, and the weights , are initially set to 0.5, 0.5; Setting a building carbon emission tracking operation trigger threshold, comparing the trigger threshold with the building energy consumption anomaly characteristic analysis result, and executing the building carbon emission tracking operation when the building energy consumption anomaly characteristic analysis result meets the trigger threshold. According to the building carbon emission tracking operation result, select the continuous tracking target; the building carbon emission tracking operation is: Obtain the building energy consumption anomaly characteristic analysis logic, and apply the building energy consumption parameters stored in each distinguished storage interval in the cloud data to the building energy consumption anomaly characteristic analysis logic to analyze the energy consumption anomaly characteristics of the equipment group corresponding to each distinguished storage interval. The energy consumption anomaly characteristic analysis logic of the equipment group corresponding to each distinguished storage interval is formula (1) or formula (2): ； After the energy consumption anomaly characteristics of the equipment group corresponding to each distinguished storage interval are analyzed, the equipment groups corresponding to each analysis result are arranged in descending order based on the anomaly characteristic value size; Further, the energy consumption parameters belonging to the equipment group corresponding to each distinguished storage interval are accumulated and summed, and the equipment groups to which the energy consumption parameters of the summed results belong are arranged in descending order based on the accumulated summation results; When the energy consumption parameters of the equipment group corresponding to each distinguished storage interval are accumulated and summed, the energy consumption parameters are simultaneously converted and processed, and then the accumulation summation operation is performed; The energy consumption parameter conversion processing logic is: ； In the formula, To distinguish the carbon emission corresponding to the cumulative result of electricity consumption in the storage interval; To distinguish the cumulative electricity consumption in the storage interval; To distinguish the carbon emission factor of electricity; To distinguish the carbon emission corresponding to the cumulative result of gas consumption in the storage interval; To distinguish the cumulative gas consumption in the storage interval; To distinguish the carbon emission factor of gas; Among them, according to the energy consumption parameter accumulation stored in the distinguished storage interval, formula (3) or formula (4) in the above formula is selected to convert and process the corresponding energy consumption parameter; Obtain the selected continuous tracking target, configure a tracking period for the continuous tracking target, and continuously track the continuous tracking target based on the tracking period configured for the continuous tracking target; The building carbon footprint tracking operation results, i.e., the two groups of device population descending order arrangement results, and , ; The selection logic of the continuous tracking target is: Select the equipment group in the first half of the front bit in the two descending order queues, and mark it as equipment group set one and equipment group set two. The equipment group pointed to by the intersection of equipment group set one and equipment group set two is the first tracking target equipment group, and the remaining equipment group is the second equipment group. The operation target for configuring the tracking period for the continuous tracking target is the first tracking target equipment group and the second tracking target equipment group. The tracking period configured for the primary tracking target device group and the secondary tracking target device group is defined by the user end, and the tracking period configured for the primary tracking target device group and the secondary tracking target device group is always less than the original tracking period, and the tracking period configured for the primary tracking target device group is always less than the tracking period configured for the secondary tracking target device group.

2. A method of tracking carbon emissions for a building according to claim 1, wherein, When the number of stored building energy consumption parameters in each sub-storage interval in the cloud database is not less than three groups, the building energy consumption anomaly feature analysis operation is performed; Building energy consumption anomaly characteristic value The greater, the building energy consumption anomaly characteristic value The more unstable the building energy consumption, the corresponding parameter source period, the building energy consumption anomaly characteristic value The more stable the building energy consumption, the corresponding parameter source period.

3. A method of tracking carbon emissions for a building according to claim 1, wherein, The building carbon emission tracking operation trigger threshold is defined by the system end user; The building energy consumption anomaly feature analysis operation is continuously performed based on adaptive logic; The adaptive logic is represented as: ； In the formula: is the period of the analysis operation of the building energy consumption anomaly characteristics; is the initial period base; is the last calculated building power level energy consumption anomaly characteristic value of the building; is the last calculated building power level energy consumption anomaly characteristic value of the building; is the last calculated building power level energy consumption anomaly characteristic value of the building; Wherein, the initial cycle base Defined by the user end, each obtained In the application of building energy consumption anomaly characteristics analysis operation process, to the current Iterative current Sought when used , the next Obtained.

4. An apparatus for tracking carbon emissions for a building, the apparatus comprising: a carbon footprint calculator configured to calculate a carbon footprint for the building; and a carbon footprint monitor configured to monitor the carbon footprint for the building. The processing device includes a memory, a processor, and a computer program stored on the memory and executable on the processor, and when the computer program is executed by the processor, the execution steps of the method for tracking carbon emissions of a building according to any one of claims 1-3 are realized.

5. A storage medium for tracking carbon emissions for a building, the storage medium comprising: The storage medium stores a computer program, and when the computer program is executed by the processor, the execution steps of the method for tracking carbon emissions of a building according to any one of claims 1-3 are realized.

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