Carbon emission measurement method and device based on smart park operation and management scenarios

Abstract

The present disclosure relates to the field of energy technology, and provides a carbon emission measurement method and device based on a smart park operation and management scenario. The method includes: determining the scenario category of the operation and management scenario; collecting a feature data set related to the carbon emission behavior corresponding to the scenario category; classifying the feature data set to obtain multiple classification data subsets, each classification data subset including at least one classification data; assigning a carbon emission conversion coefficient to each classification data in each classification data subset; if it is determined that a preset carbon emission measurement model library contains a target carbon emission measurement model that matches the carbon emission behavior category and carbon emission conversion coefficient of multiple classification data subsets, then the target carbon emission measurement model is retrieved; according to the target carbon emission measurement model, the total carbon emissions under the operation and management scenario are calculated, which can effectively and flexibly calculate carbon emission measurement under different daily management and operation scenarios.

Description

Technical Field

[0001] The present disclosure relates to the field of energy technology, and in particular to a carbon emission measurement method and device based on a smart park operation and management scenario. Background Art

[0002] At present, most of the carbon emission measurement technologies focus on calculating carbon emission factors for energy consumption scenarios such as different fossil fuels, measuring carbon emission factors during the production and use of different building materials, or measuring carbon emissions during the use and operation of energy consumption equipment. However, there is almost no mention of energy consumption measurement and carbon emission statistical conversion involved in daily management and business activities.

[0003] Because energy consumption currently accounts for a significant portion of carbon emissions, the industry's carbon emission technologies primarily focus on this area. However, as the use of green and clean energy sources such as wind and hydropower gradually increases, these technologies and calculation rules will gradually become obsolete or weakened, while the proportion of carbon emissions from daily operations and management will increase significantly, even becoming the majority.

[0004] However, existing carbon emission measurement technology relies solely on material type and energy consumption meters to calculate carbon emissions. This single calculation method is too rigid and cannot be directly applied to the carbon emission measurement needs in various daily management and operation scenarios. Therefore, there is an urgent need to provide an effective method that can flexibly calculate carbon emissions in different daily management and operation scenarios. Summary of the Invention

[0005] In view of this, the embodiments of the present disclosure provide a carbon emission measurement method and device based on a smart park operation and management scenario, so as to provide an effective method for flexibly calculating carbon emission measurements in different daily management and operation scenarios.

[0006] A first aspect of the embodiments of the present disclosure provides a carbon emission measurement method based on a smart park operation and management scenario, including:

[0007] Determine the scenario categories for operations management scenarios;

[0008] Collect feature datasets related to carbon emission behaviors corresponding to scenario categories;

[0009] Classifying the feature data set to obtain multiple classification data subsets, each classification data subset including at least one classification data;

[0010] Assign a carbon emission conversion coefficient to each piece of classified data in each classified data subset;

[0011] If it is determined that the preset carbon emission measurement model library contains a target carbon emission measurement model that matches the carbon emission behavior category and carbon emission conversion coefficient of the multiple classification data subsets, the target carbon emission measurement model is retrieved;

[0012] Based on the target carbon emission measurement model, the total carbon emissions under the operation and management scenario are calculated.

[0013] A second aspect of the embodiments of the present disclosure provides a carbon emission metering device based on a smart park operation and management scenario, including:

[0014] A scenario determination module, configured to determine a scenario category of an operation management scenario;

[0015] A data collection module is configured to collect feature data sets related to carbon emission behaviors corresponding to the scenario categories;

[0016] A data classification module is configured to perform data classification on the feature data set to obtain a plurality of classified data subsets, each of which includes at least one piece of classified data;

[0017] A coefficient allocation module is configured to allocate a carbon emission conversion coefficient to each piece of classified data in each classified data subset;

[0018] The model retrieval module is configured to retrieve the target carbon emission measurement model if it is determined that the preset carbon emission measurement model library contains a target carbon emission measurement model that matches the carbon emission behavior category and the carbon emission conversion coefficient of the multiple classification data subsets;

[0019] The carbon emission measurement module is configured to calculate the total carbon emissions under the operation management scenario based on the target carbon emission measurement model.

[0020] According to a third aspect of an embodiment of the present disclosure, an electronic device is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above method when executing the computer program.

[0021] According to a fourth aspect of the embodiments of the present disclosure, a computer-readable storage medium is provided, which stores a computer program. When the computer program is executed by a processor, the steps of the above method are implemented.

[0022] Compared with the prior art, the beneficial effects of the disclosed embodiment include at least the following: determining the scenario category of the operation and management scenario; collecting a feature data set related to the carbon emission behavior corresponding to the scenario category; classifying the feature data set to obtain multiple classified data subsets, each classified data subset including at least one classified data; assigning a carbon emission conversion coefficient to each classified data in each classified data subset; if it is determined that the preset carbon emission measurement model library contains a target carbon emission measurement model that matches the carbon emission behavior category and carbon emission conversion coefficient of multiple classified data subsets, then the target carbon emission measurement model is retrieved; according to the target carbon emission measurement model, the total carbon emissions under the operation and management scenario are calculated, which can effectively and flexibly calculate the carbon emission measurement under different daily management and operation scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the technical solutions in the embodiments of the present disclosure, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present disclosure. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0024] Figure 1 This is a flow chart of a carbon emission measurement method based on a smart park operation and management scenario provided by an embodiment of the present disclosure;

[0025] Figure 2 This is a flowchart of a specific application example of the carbon emission measurement method based on the smart park operation and management scenario provided by the embodiment of the present disclosure;

[0026] Figure 3 This is a structural diagram of a carbon emission metering device based on a smart park operation and management scenario provided by an embodiment of the present disclosure;

[0027] Figure 4 It is a structural diagram of an electronic device provided by an embodiment of the present disclosure. DETAILED DESCRIPTION

[0028] In the following description, specific details such as specific system structures and techniques are provided for purposes of illustration rather than limitation to facilitate a thorough understanding of the embodiments of the present disclosure. However, it will be apparent to those skilled in the art that the present disclosure may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid obscuring the description of the present disclosure with unnecessary detail.

[0029] A carbon emission measurement method and device based on a smart park operation and management scenario according to an embodiment of the present disclosure will be described in detail below with reference to the accompanying drawings.

[0030] In recent years, there has been a huge market demand for carbon emissions and related statistical methods. As a comprehensive business entity, the park, with its centralized personnel, equipment, production, and management activities, faces the need to clarify and formulate statistical rules for carbon emissions from various daily activities.

[0031] Generally speaking, parks can be roughly divided into different categories such as industrial parks, chemical parks, science and technology parks, financial parks and comprehensive parks based on the park's operating methods and the types of companies that move in.

[0032] Although the proportion of carbon emissions from business management behaviors is not as high as that of energy and material carbon emissions at the current stage, with the gradual upgrading of alternative behaviors such as new energy and green electricity, daily management behaviors will become the focus of refined management of carbon emissions in the future. In addition, with the popularization of information technology, digitalization, big data, AI (artificial intelligence) and 5G, and the implementation of various fields of smart cities, the daily data collection, acquisition, classification, and analysis of daily operations and management behaviors have a technical basis and refined capabilities. The embodiments of this disclosure mainly focus on some common scenarios in the above-mentioned parks, such as office management, traffic management, security management, and smart operations, and conduct in-depth research on carbon emission measurement.

[0033] Figure 1 This is a flow chart of a carbon emission measurement method based on a smart park operation and management scenario provided by an embodiment of the present disclosure. Figure 1 As shown in the figure, the carbon emission measurement method based on the smart park operation and management scenario includes:

[0034] Step S101: Determine the scenario category of the operation management scenario.

[0035] Operational management scenarios primarily refer to those related to the daily operational management of the aforementioned parks. These scenarios typically arise from factors such as the park's "people, land, finance, materials, events, and organization." Specifically, different carbon emission scenarios can be categorized based on the park's actual operational management practices and operational management data.

[0036] In some embodiments, the scenario category of the operation management scenario may be determined according to the following steps:

[0037] First, the management area attribute information related to the operation management scenario is obtained; then, the scenario category of the operation management scenario is determined based on the management area attribute information.

[0038] Among them, the management area attribute information usually refers to the area type (for example, office area, passage area, security area, smart space area, etc.), area function (for example, office, passage, security, smart space, etc.) and other information of the management area related to carbon emission behavior (that is, energy consumption behavior) in the operation management scenario.

[0039] As an example, the daily operations management of the park can be divided into various management scenarios based on the management area attribute information of the daily operations management of the park. Among them, various management scenarios include but are not limited to general scenarios such as office management, access management, security management, and smart operations. Each scenario can be defined as a corresponding scenario category. For example, the scenario category of office management can be defined as "office management scenario", the scenario category of access management can be defined as "access management scenario", the scenario category of security management can be defined as "security management scenario", and the scenario category of smart operations can be defined as "smart operations management scenario".

[0040] In some embodiments, the aforementioned general scenarios can be further divided into multiple sub-scenarios based on their specific functionalities. For example, the "Smart Operations Management Scenario" can be further subdivided into: Conference Room Space Management, Rental Space Management, and Public Space Management based on the functional zoning of the space. The scenario corresponding to the Conference Room Space Management functional zoning can be categorized as the Conference Room Space Management Scenario; the scenario corresponding to the Rental Space Management functional zoning can be categorized as the Rental Space Management Scenario; and the scenario corresponding to the Public Space Management functional zoning can be categorized as the Public Space Management Scenario.

[0041] The above sub-scenarios can be further divided into multiple sub-scenarios based on the various aspects of daily operations and management. For example, the rental space management scenario can be further divided into external rental space management and internal rental space management. The public space management scenario can be further divided into parking lot management, public health venue management, public hall management, etc.

[0042] Step S102: collecting a feature data set related to the carbon emission behavior corresponding to the scenario category.

[0043] Carbon emission behaviors refer to behaviors / activities that involve energy consumption in daily operations and management. These behaviors / activities typically include administrative behaviors / activities, consumption behaviors / activities, and safety management behaviors / activities.

[0044] Feature datasets related to carbon emission behaviors refer to data related to behaviors and activities involving energy consumption in daily operations and management. These include, but are not limited to, park administrative data (such as data related to administrative activities), consumption data (such as paper consumption), and safety data (such as data related to park security management). These operational management behaviors primarily refer to those related to carbon emissions (such as electricity, water, and paper usage).

[0045] In order to better characterize and quantify the carbon emissions of the above-mentioned behaviors / activities, the above-mentioned behavior / activity data can be further subdivided. For example, for loss data, it can be further subdivided into the type of paper (such as paper made from recycled materials or paper made from XX trees, etc.), the amount of paper used, etc. For administrative data and security data, their behaviors can be further refined based on their actual operational management steps or the corresponding SOP (standard operating procedures). Where possible, the granularity of the refinement can be drilled down to a single behavioral operation mode, for example, the on / off operation of energy-consuming equipment (such as air conditioners, etc.) (such as automatic on-automatic off, automatic on-manual off, manual on-automatic off, manual on-manual off), etc.

[0046] Furthermore, this feature dataset may also include the data sources for various types of behavior / activity data. Data sources typically refer to the sources and methods of obtaining operational management behavior data. These sources and methods include, but are not limited to, big data, cloud servers, edge servers, intelligent building management systems (IBMS), mobile devices, and IoT devices.

[0047] As an example, assuming that the scenario category determined according to the above step S101 is a conference room space management scenario under the smart space management scenario, the conference room space management scenario includes air conditioners, lighting equipment, projection equipment, power strips and other energy-consuming equipment (electrical equipment) related to carbon emission behavior, and these devices can establish a communication connection with the carbon emission metering terminal (or server) through the Internet of Things technology. At this time, the carbon emission metering terminal (or server) can collect characteristic data such as energy consumption behavior (such as on / off), on / off usage time, on / off usage frequency, on / off mode (such as automatic on-automatic off mode, automatic on-manual off mode, manual on-automatic off mode, manual on-manual off mode) of these electrical devices in this scenario, thereby obtaining a characteristic data set related to carbon emission behavior in this scenario.

[0048] Preferably, the feature data set may include multiple pieces of feature data, where one piece of feature data may be data characterizing the energy usage behavior of an energy-consuming device. For example, for an air conditioner in a conference room space management scenario, relevant parameters such as the air conditioner's type, model, quantity, capacity, ventilation volume, usage time, and set temperature may be collected and recorded to form one piece of feature data.

[0049] In practical applications, various intelligent monitoring devices, IoT terminals, and mobile tools can be pre-installed in smart space management locations to facilitate the collection of data related to carbon emissions. This can also enable intelligent reservation, usage, and resource release operations, helping to improve smart space utilization and reduce energy consumption and emissions. For example, during scheduled usage periods, energy-consuming devices in a conference room can be turned on; during periods when the conference room is not scheduled to be used, energy-consuming devices in the conference room can be kept in standby or off.

[0050] Step S103 , classifying the feature data set to obtain a plurality of classified data subsets, each of which includes at least one piece of classified data.

[0051] As an example, the feature data in the feature data set may be classified according to the data source to obtain multiple classified data subsets.

[0052] Specifically, the feature data set can be classified according to the following steps to obtain multiple classified data subsets: first, determine the data source of each feature data in the feature data set; then, based on the data source of each feature data in the feature data set, divide the feature data in the feature data set to obtain multiple classified data subsets, and one classified data subset corresponds to one type of data source.

[0053] In one exemplary embodiment, data sources can be divided into five categories: "item consumption," "device usage," "behavior trajectory," "AIOT data," and "neutralization data." "Item consumption" refers to paper consumption, for example; "device usage" refers to the use of energy-consuming equipment in a specific scenario, including turning it on and off; "behavior trajectory" refers to behaviors / activities involving carbon emissions; "AIOT data" is an abbreviation of AI (artificial intelligence) and IOT (Internet of Things), referring to the artificial intelligence Internet of Things, which collects massive amounts of data from various dimensions through the Internet of Things, stores it in the cloud and at the edge, and then uses big data analysis and AI technologies to achieve the digitization and intelligent interconnection of all things; and "neutralization data" refers to data related to carbon neutrality, such as data on the use of new energy or energy recycling.

[0054] First, five categorized data subsets corresponding to "item consumption," "equipment usage," "behavior trajectory," "AIOT data," and "neutralization data" can be pre-set. Then, the data source of each feature data item in the feature data set is extracted, and each feature data item is classified into the categorized data subset corresponding to the corresponding data source. For example, if the data source of the feature data recording the type, model, quantity, capacity, ventilation volume, usage time, set temperature, and other related parameters of the air conditioner is equipment usage, then this feature data item can be classified into the categorized data subset corresponding to "equipment usage."

[0055] In a preferred embodiment, before classifying the feature data set, the feature data in the feature data set may be first verified to eliminate invalid data. Specifically, it may be determined whether each feature data in the feature data set meets the preset qualification conditions. If the preset qualification conditions are met, the feature data is determined to be valid data, otherwise the feature data is determined to be invalid data. The qualification conditions may be to set corresponding verification conditions for different types of feature data. For example, for the feature data used in air conditioning, the qualification conditions may be: the data is not a null value, and it needs to include the on / off parameters, usage duration parameters, and usage frequency parameters of the equipment. For the feature data of paper loss, the qualification conditions may be: the data is not a null value, and it needs to include the type of paper and the number of sheets used.

[0056] Performing data verification before classifying the feature data set can eliminate invalid data, save memory space occupied by invalid data, and reduce the interference of invalid data on subsequent carbon emissions calculations, thereby ensuring the accuracy and reliability of subsequent carbon emissions calculation results.

[0057] In one exemplary embodiment, the categorized data within the aforementioned categorized data subsets of "item consumption," "equipment usage," "behavior trajectories," "AIOT data," and "neutralization data" can be further categorized to yield datasets corresponding to direct carbon emissions, indirect carbon emissions, carbon emission materials, and carbon sink reduction behaviors. For example, categorized data related to "item consumption" can be categorized into the "carbon emission materials" category; categorized data related to "equipment usage," "behavior trajectories," and "AIOT data" can be further categorized into the "indirect carbon emission" and / or "direct carbon emission" categories based on their carbon emission methods. categorized data related to "neutralization data" can be categorized into the "carbon sink reduction" category.

[0058] Step S104 : assigning a carbon emission conversion coefficient to each piece of classified data in each classified data subset.

[0059] In an exemplary embodiment, a carbon emission conversion coefficient may be assigned to each piece of categorical data in each categorical data subset according to the following steps:

[0060] First, the regional attribute information and the field attribute information corresponding to each classified data in the classified data subset are obtained; then, a corresponding carbon emission conversion coefficient is assigned to each classified data in the classified data subset according to the regional attribute information and the field attribute information.

[0061] Typically, for the same carbon emission behavior / activity, the corresponding carbon emission conversion coefficients may be different in different regions / areas and / or different fields / industries, such as different policy regulations (or industry standards) in different regions / areas. For example, for the fuel activity of fuel vehicles, the carbon emission conversion coefficient (i.e., carbon emission factor) in region A is F1, and the carbon emission conversion coefficient (i.e., carbon emission factor) in region B may be F2, where F1 is not equal to F2. Therefore, we can first obtain the regional attribute information (i.e., which region / area it belongs to) and its field attribute information (i.e., which field / industry it belongs to) corresponding to each classified data in the classified data subset. Then, based on the carbon emission conversion coefficients corresponding to the carbon emission activities published by each region / area and field / industry, we assign a corresponding carbon emission conversion coefficient to each classified data, thereby ensuring the accuracy and reliability of the subsequent carbon emission calculation results.

[0062] In step S105 , if it is determined that the preset carbon emission measurement model library contains a target carbon emission measurement model that matches the carbon emission behavior categories and carbon emission conversion coefficients of the plurality of classification data subsets, the target carbon emission measurement model is retrieved.

[0063] In one embodiment, the carbon emission measurement model library includes a plurality of carbon emission measurement models.

[0064] The carbon emission measurement model can be a deep learning network (e.g., a neural network) trained using characteristic datasets from various daily business management scenarios to quantify and calculate carbon emissions in each scenario. Each management scenario corresponds to a carbon emission measurement model.

[0065] The above step S105 may specifically include the following steps:

[0066] Extract the carbon emission behavior type and carbon emission conversion factor of the carbon emission behavior characteristic data corresponding to each carbon emission measurement model in the carbon emission measurement model library;

[0067] Traversing the carbon emission behavior type of the carbon emission behavior characteristic data corresponding to each carbon emission measurement model in the carbon emission measurement model library, and finding at least one candidate carbon emission measurement model whose carbon emission behavior type of the carbon emission behavior characteristic data matches the carbon emission behavior category of the multiple classification data subsets;

[0068] A carbon emission measurement model whose carbon emission conversion factor in at least one candidate carbon emission measurement model matches the carbon emission conversion coefficient of each classified data in the classified data subset is determined as the target carbon emission measurement model, and the target carbon emission measurement model is retrieved.

[0069] Carbon emission behavior types and categories include direct carbon emission behavior, indirect carbon emission behavior, carbon emission consumption materials and carbon sink emission reduction behavior.

[0070] Direct carbon emission behaviors generally refer to actions within a business's operations and management that directly generate CO2 emissions, such as the direct charging of energy-consuming devices like mobile phones, devices, and electric vehicles. Indirect carbon emission behaviors generally refer to actions within a business's operations and management that indirectly generate CO2 emissions, such as the on / off operation of an IoT meter, smart power strip, or smart air conditioner. Carbon emission consumable materials include the amount of paper consumed or garbage generated within a specific management scenario. Carbon sink emission reduction behaviors generally refer to actions that help reduce CO2 emissions within a specific management scenario, such as the use of new energy, energy recycling, the amount of green space in a region, and new energy vehicles used for parking and traffic.

[0071] As an example, assume that the current carbon emission measurement model library stores five carbon emission measurement models, designated as carbon emission measurement models 01, 02, 03, 04, and 05. The carbon emission behavior types of the carbon emission behavior characteristic data for carbon emission measurement models 01, 02, 03, 04, and 05 can be extracted. Assume that the carbon emission behavior types for carbon emission measurement models 01, 02, and 03 all include direct carbon emission behavior, indirect carbon emission behavior, carbon emission material consumption, and carbon sink reduction behavior; that the carbon emission behavior types for carbon emission measurement model 04 include direct carbon emission behavior and indirect carbon emission behavior; and that the carbon emission behavior types for carbon emission measurement model 05 include direct carbon emission behavior, indirect carbon emission behavior, and carbon emission material consumption. The carbon emission behavior categories for multiple categorized data subsets include direct carbon emission behavior, indirect carbon emission behavior, carbon emission material consumption, and carbon sink reduction behavior. Therefore, carbon emission measurement models 01, 02, and 03 with carbon emission behavior types consistent with the carbon emission behavior categories for the multiple categorized data subsets can be identified as candidate carbon emission measurement models.

[0072] Next, for the same carbon emission behavior type (carbon emission behavior category), the carbon emission conversion factors of the carbon emission behavior characteristic data of carbon emission measurement models 01, 02, and 03 are compared with the carbon emission conversion coefficients of the classification data in multiple classification data subsets to see if they are the same. For example, for direct carbon emission behavior, the carbon emission conversion factors of the carbon emission behavior characteristic data of direct carbon emission behavior of carbon emission measurement models 01, 02, and 03 can be compared with the carbon emission conversion coefficients of each classification data in multiple classification data subsets to see if they are the same.

[0073] For example, assume that there are three pieces of categorical data for direct carbon emissions in multiple categorical data subsets, with corresponding carbon emission conversion coefficients S1, S2, and S3, respectively. Carbon emission accounting model 01 also contains three pieces of carbon emission behavior characteristic data for direct carbon emissions, with corresponding carbon emission conversion factors S1, S2, and S3, respectively. Therefore, it can be determined that the carbon emission conversion factors for direct carbon emissions in carbon emission accounting model 01 match the carbon emission conversion coefficients for each piece of categorical data for direct carbon emissions in the categorical data subsets. Similarly, referring to the above method, the carbon emission conversion factors for the carbon emission behavior characteristic data for indirect carbon emissions, carbon emission consumption materials, and carbon sink reduction behaviors in carbon emission accounting model 01 can be compared with the carbon emission conversion coefficients for each piece of categorical data for indirect carbon emissions, carbon emission consumption materials, and carbon sink reduction behaviors in multiple categorical data subsets to see if they are identical. If they are identical, carbon emission accounting model 01 can be determined as the target carbon emission accounting model.

[0074] In some embodiments, if, according to the above steps, it is determined that the carbon emission conversion factors in carbon emission accounting models 01, 02, and 03 do not fully match the carbon emission conversion coefficients of each piece of classification data in the classification data subset, then the scenarios corresponding to the multiple classification data subsets may be determined to be newly added scenarios. In this case, a new carbon emission accounting model corresponding to the newly added scenario can be obtained by training using the multiple classification data subsets through machine learning or other methods, and the new carbon emission accounting model can be added to the carbon emission accounting model library.

[0075] In some embodiments, the carbon emission measurement models for different management scenarios can also be graded to determine the importance level of the carbon emission measurement model for each management scenario. This operation is mainly to deal with future emergency plans. For example, when there are local policies and regional indicators for power or carbon restrictions, or when a sudden power outage causes the UPS power supply (i.e., uninterruptible power supply) to start, which level of scene will be shut down first and which level of scene will be retained as the focus, so as to ensure the normal and orderly operation of daily operations and management in the park.

[0076] Step S106: Calculate the total carbon emissions under the operation and management scenario according to the target carbon emission measurement model.

[0077] In some embodiments, the above step S106 may specifically include the following steps:

[0078] For the first classification data subset whose carbon emission behavior category is direct carbon emission behavior, the direct carbon emissions under the operation and management scenario are calculated based on the first classification data subset and its carbon emission conversion coefficient;

[0079] For the second classification data subset whose carbon emission behavior category is indirect carbon emission behavior, the indirect carbon emissions under the operation and management scenario are calculated based on the second classification data subset and its carbon emission conversion coefficient;

[0080] For the third classification data subset whose carbon emission behavior category is carbon emission consumable materials, the carbon emissions of consumable materials in the operation and management scenario are calculated based on the third classification data subset and its carbon emission conversion coefficient;

[0081] For the fourth classification data subset whose carbon emission behavior category is carbon sink emission reduction behavior, the carbon sink emission reduction amount under the operation management scenario is calculated based on the fourth classification data subset and its carbon emission conversion coefficient;

[0082] The total carbon emissions under the operation and management scenario are calculated based on direct carbon emissions, indirect carbon emissions, carbon emissions from consumed materials and carbon sink emission reductions.

[0083] To calculate direct carbon emissions, we can directly multiply the energy consumption value (such as electricity consumption value) corresponding to these direct carbon emission behaviors by the electricity-carbon factor specified by the corresponding industry and region to calculate the direct carbon emissions under the operation and management scenario.

[0084] The calculation of indirect carbon emissions may specifically include the following steps:

[0085] determining a carbon emission activity type corresponding to each second classification data in the second classification data subset;

[0086] Determine the average carbon energy consumption level corresponding to each second classification data according to the type of carbon emission activity;

[0087] Calculate the indirect carbon emission level corresponding to each second-classification data based on the average carbon energy consumption level, energy consumption frequency, and energy consumption duration corresponding to each second-classification data;

[0088] The indirect carbon emission levels corresponding to each second classification data are superimposed to obtain the indirect carbon emissions under the operation and management scenario.

[0089] The types of carbon emission activities may include fuel oil (including various types of fuel oil), gas (including various types of gas), electricity consumption (including various types of electricity), etc.

[0090] Specifically, the indirect carbon emissions in the operation and management scenario can be calculated according to the following formula (1).

[0091]

[0092] Among them, E 间接 It represents the total amount of carbon dioxide emissions generated by the indirect activities of corporate operations and management.

[0093] AD x Indicates the average energy consumption level of the xth activity, such as the average electricity consumption level, in megawatt-hours (MWh); EF x represents the carbon emission factor of the x-th activity, in tCO2 / MWh, and n represents the number of second-category data in the second-category data subset.

[0094] Taking the xth activity as the use of electrical equipment as an example, AD x = Average power consumption level of the equipment used in the xth activity × usage time × switching frequency. The average power consumption level can be obtained from smart monitoring equipment, for example, data can be collected through IoT meters, smart socket strips, or smart circuit breakers. x These are the national standard factor values ​​for various industries and regions. For example, the average power supply emission factor for the East China region is 0.928 kg / kWh.

[0095] AD 热 and EF 热 Represents the fuel energy consumption level and fuel carbon emission factor (if any) involved in the scenario, with units of million kilojoules (GJ) and tCO2 / GJ, respectively. For example, some corporate operations and management activities involve turning on and off heat-consuming equipment such as boilers. If such equipment exists, it will be taken into account; if not, it will not be considered.

[0096] Carbon emissions from material consumption can be calculated by multiplying the amount of material consumed in a given scenario, such as the amount of paper and weight of garbage, by the carbon dioxide equivalent of the corresponding paper (garbage). The unit of carbon emissions is tons of carbon dioxide equivalent (tCO2e).

[0097] Carbon sink emission reductions can be calculated by multiplying the amount of new energy or recycled energy in the scenario by the corresponding carbon dioxide equivalent (CO2e). The unit is tons of carbon dioxide equivalent (tCO2e).

[0098] Finally, the total carbon emissions under the operation and management scenario are calculated according to the following formula (2).

[0099] E=E 直接 +E 间接 +E 材料 -E碳汇 (2).

[0100] Where E represents the total carbon emissions in this scenario; E 直接 Indicates direct carbon emissions; E 间接 Indirect carbon emissions; E 材料 Indicates the carbon emissions of consumed materials; E 碳汇 Represents carbon sink emission reduction.

[0101] The technical solution provided by the embodiment of the present disclosure is achieved by determining the scenario category of the operation and management scenario; collecting a feature data set related to the carbon emission behavior corresponding to the scenario category; classifying the feature data set to obtain multiple classification data subsets, each classification data subset including at least one classification data; assigning a carbon emission conversion coefficient to each classification data in each classification data subset; if it is determined that the preset carbon emission measurement model library contains a target carbon emission measurement model that matches the carbon emission behavior category and carbon emission conversion coefficient of the multiple classification data subsets, then the target carbon emission measurement model is retrieved; according to the target carbon emission measurement model, the total carbon emissions in the operation and management scenario are calculated, which can effectively and flexibly calculate the carbon emission measurement in different daily management and operation scenarios.

[0102] Figure 2 This is a flowchart of a specific application example of the carbon emission measurement method based on the smart park operation and management scenario provided by the embodiment of the present disclosure. Figure 2 As shown, the carbon emission measurement method includes the following steps:

[0103] Step 1: Determine the scenario category of the operation management scenario.

[0104] Step 2: Collect feature data sets related to carbon emission behaviors corresponding to scenario categories.

[0105] Step 3: Perform data verification on the feature data in the feature data set.

[0106] Step 4: If the verification passes, the feature data is classified to obtain the classified data subsets of "item consumption", "equipment use", "behavior trajectory", "AIOT data", and "neutralization data"; if the verification fails, the feature data set is re-collected.

[0107] Step 5. Reclassify the classified data subsets of "item consumption", "equipment use", "behavioral trajectory", "AIOT data", and "neutralization data" to obtain data sets of direct carbon emission behavior, indirect carbon emission behavior, carbon emission consumption materials, and carbon sink reduction behavior.

[0108] Step 6: Configure corresponding carbon emission conversion coefficients for each piece of data in the direct carbon emission behavior, indirect carbon emission behavior, carbon emission consumption materials and carbon sink reduction behavior data sets.

[0109] Step 7: Classify the scenarios based on the direct carbon emission behavior, indirect carbon emission behavior, carbon emission consumption materials and carbon sink reduction behavior data sets and their carbon emission conversion coefficients to determine whether there is a matching target carbon emission measurement model in the current carbon emission measurement model library.

[0110] Step 8. If there is a matching target carbon emission measurement model in the carbon emission measurement model library, the target carbon emission measurement model is retrieved, the model parameters of the target carbon emission measurement model are imported, and the total carbon emissions under the operation and management scenario are calculated; if there is no matching target carbon emission measurement model in the carbon emission measurement model library, the corresponding new carbon emission measurement model is obtained through machine learning training, and the new carbon emission measurement model is stored in the carbon emission measurement model library.

[0111] Step 9: Classify the carbon emission measurement models in the carbon emission measurement model library, determine the warning level corresponding to each carbon emission measurement model and recommend warning processing methods.

[0112] All of the above optional technical solutions can be combined in any way to form optional embodiments of the present application, and will not be described in detail here.

[0113] The following are embodiments of the apparatus disclosed herein, which can be used to implement the method embodiments disclosed herein. For details not disclosed in the apparatus embodiments disclosed herein, please refer to the method embodiments disclosed herein.

[0114] Figure 3 This is a schematic diagram of a carbon emission metering device based on a smart park operation and management scenario provided by an embodiment of the present disclosure. Figure 3 As shown in the figure, the carbon emission metering device based on the smart park operation and management scenario includes:

[0115] A scenario determination module 301 is configured to determine a scenario category of an operation management scenario;

[0116] The data collection module 302 is configured to collect feature data sets related to carbon emission behaviors corresponding to the scenario categories;

[0117] The data classification module 303 is configured to perform data classification on the feature data set to obtain multiple classification data subsets, each classification data subset including at least one classification data;

[0118] The coefficient allocation module 304 is configured to allocate a carbon emission conversion coefficient to each piece of classified data in each classified data subset;

[0119] The model retrieval module 305 is configured to retrieve the target carbon emission measurement model if it is determined that the preset carbon emission measurement model library contains a target carbon emission measurement model that matches the carbon emission behavior category and carbon emission conversion coefficient of the multiple classification data subsets;

[0120] The carbon emission measurement module 306 is configured to calculate the total carbon emissions in the operation management scenario according to the target carbon emission measurement model.

[0121] In some embodiments, the carbon emission measurement model library includes multiple carbon emission measurement models. The model retrieval module 305 includes:

[0122] An extraction unit is configured to extract the carbon emission behavior type and carbon emission conversion factor of the carbon emission behavior characteristic data corresponding to each carbon emission measurement model in the carbon emission measurement model library;

[0123] a traversal unit configured to traverse the carbon emission behavior type of the carbon emission behavior characteristic data corresponding to each carbon emission measurement model in the carbon emission measurement model library, and find at least one candidate carbon emission measurement model whose carbon emission behavior type of the carbon emission behavior characteristic data matches the carbon emission behavior category of the multiple classification data subsets;

[0124] The retrieval unit is configured to determine a carbon emission measurement model whose carbon emission conversion factor in at least one candidate carbon emission measurement model matches the carbon emission conversion coefficient of each classified data in the classified data subset as a target carbon emission measurement model, and retrieve the target carbon emission measurement model.

[0125] In some embodiments, the above carbon emission behavior categories include direct carbon emission behavior, indirect carbon emission behavior, carbon emission consumption material and carbon sink reduction behavior. The above carbon emission measurement module 306 includes:

[0126] The first calculation unit is configured to calculate, for a first classified data subset whose carbon emission behavior category is direct carbon emission behavior, the direct carbon emissions in the operation management scenario based on the first classified data subset and its carbon emission conversion coefficient;

[0127] The second calculation unit is configured to calculate the indirect carbon emissions in the operation management scenario based on the second classified data subset and its carbon emission conversion coefficient for the carbon emission behavior category of the second classified data subset;

[0128] The third calculation unit is configured to calculate the carbon emission of consumable materials in the operation management scenario based on the third classified data subset and its carbon emission conversion coefficient for the third classified data subset whose carbon emission behavior category is carbon emission consumable materials;

[0129] a fourth calculation unit configured to calculate, for a fourth classification data subset whose carbon emission behavior category is carbon sink emission reduction behavior, a carbon sink emission reduction amount under an operation management scenario based on the fourth classification data subset and its carbon emission conversion coefficient;

[0130] The fifth calculation unit is configured to calculate the total carbon emissions in the operation management scenario based on direct carbon emissions, indirect carbon emissions, carbon emissions from consumed materials and carbon sink emission reductions.

[0131] In some embodiments, the second computing unit may be specifically configured as follows:

[0132] determining a carbon emission activity type corresponding to each second classification data in the second classification data subset;

[0133] Determine the average carbon energy consumption level corresponding to each second classification data according to the type of carbon emission activity;

[0134] Calculate the indirect carbon emission level corresponding to each second-classification data based on the average carbon energy consumption level, energy consumption frequency, and energy consumption duration corresponding to each second-classification data;

[0135] The indirect carbon emission levels corresponding to each second classification data are superimposed to obtain the indirect carbon emissions under the operation and management scenario.

[0136] In some embodiments, the data classification module 303 includes:

[0137] a source determination unit, configured to determine a data source of each feature data item in the feature data set;

[0138] The division unit is configured to divide the feature data in the feature data set according to the data source of each feature data in the feature data set to obtain multiple classification data subsets, and each classification data subset corresponds to a type of data source.

[0139] In some embodiments, the coefficient allocation module 304 includes:

[0140] an acquiring unit configured to acquire region attribute information and field attribute information corresponding to each classification data in the classification data subset;

[0141] The allocating unit is configured to allocate a corresponding carbon emission conversion coefficient to each classified data in the classified data subset according to the regional attribute information and the field attribute information.

[0142] In some embodiments, the scene determination module 301 includes:

[0143] An information acquisition unit configured to acquire management area attribute information related to the operation management scenario;

[0144] The scenario determination unit is configured to determine the scenario category of the operation management scenario according to the management area attribute information.

[0145] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present disclosure.

[0146] Figure 4 FIG. 4 is a schematic diagram of an electronic device 4 provided in an embodiment of the present disclosure. Figure 4 As shown, the electronic device 4 of this embodiment includes: a processor 401, a memory 402, and a computer program 403 stored in the memory 402 and executable by the processor 401. When the processor 401 executes the computer program 403, the steps of the above-described method embodiments are implemented. Alternatively, when the processor 401 executes the computer program 403, the functions of the modules / units in the above-described device embodiments are implemented.

[0147] The electronic device 4 may be a desktop computer, a notebook, a PDA, a cloud server or other electronic device. The electronic device 4 may include but is not limited to a processor 401 and a memory 402. Those skilled in the art will understand that Figure 4 This is merely an example of the electronic device 4 and does not limit the electronic device 4 . The electronic device 4 may include more or fewer components than shown in the figure, or different components.

[0148] The processor 401 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.

[0149] Memory 402 can be an internal storage unit of electronic device 4, such as a hard drive or memory of electronic device 4. Memory 402 can also be an external storage device of electronic device 4, such as a plug-in hard drive, a Smart Media Card (SMC), a Secure Digital (SD) card, a flash memory card, etc. equipped on electronic device 4. Memory 402 can also include both an internal storage unit of electronic device 4 and an external storage device. Memory 402 is used to store computer programs and other programs and data required by the electronic device.

[0150] Those skilled in the art will clearly understand that for the sake of convenience and brevity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The above-mentioned integrated units can be implemented in the form of hardware or in the form of software functional units.

[0151] If the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present disclosure implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and when the computer program is executed by the processor, it can implement the steps of the above-mentioned various method embodiments. The computer program may include computer program code, which may be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0152] The above embodiments are only used to illustrate the technical solutions of the present disclosure, rather than to limit them. Although the present disclosure has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present disclosure, and should all be included in the scope of protection of the present disclosure.

Claims

1. A carbon emission measurement method based on the smart park operation and management scenario, characterized in that: include: Determining a scenario category of an operation management scenario, where the scenario category is determined based on management area attribute information related to the operation management scenario, where the management area attribute information refers to the area type and area function of the management area related to carbon emission behavior involved in the operation management scenario; Collecting feature data sets related to carbon emission behaviors corresponding to the scenario categories; Classifying the feature data set to obtain a plurality of classified data subsets, each of the classified data subsets including at least one piece of classified data; Assigning a carbon emission conversion coefficient to each piece of classified data in each of the classified data subsets, respectively, wherein the carbon emission conversion coefficient is determined according to the regional attribute information and field attribute information corresponding to each piece of classified data, wherein the regional attribute information is the region or area corresponding to each piece of classified data, and the field attribute information is the field or industry corresponding to each piece of classified data; If it is determined that the preset carbon emission measurement model library contains a target carbon emission measurement model that matches the carbon emission behavior category and the carbon emission conversion coefficient of the multiple classification data subsets, then the target carbon emission measurement model is retrieved; The total carbon emissions under the operation and management scenario are calculated based on the target carbon emissions measurement model.

2. The method according to claim 1, characterized in that The carbon emission measurement model library includes multiple carbon emission measurement models; If it is determined that the preset carbon emission measurement model library contains a target carbon emission measurement model that matches the carbon emission behavior category and the carbon emission conversion coefficient of the multiple classification data subsets, then the target carbon emission measurement model is retrieved, including: Extracting the carbon emission behavior type and carbon emission conversion factor of the carbon emission behavior characteristic data corresponding to each carbon emission measurement model in the carbon emission measurement model library; Traversing the carbon emission behavior type of the carbon emission behavior characteristic data corresponding to each carbon emission measurement model in the carbon emission measurement model library, and searching for at least one candidate carbon emission measurement model whose carbon emission behavior type of the carbon emission behavior characteristic data matches the carbon emission behavior category of the multiple classification data subsets; A carbon emission measurement model whose carbon emission conversion factor in the at least one candidate carbon emission measurement model matches the carbon emission conversion coefficient of each classification data in the classification data subset is determined as a target carbon emission measurement model, and the target carbon emission measurement model is retrieved.

3. The method according to claim 1, characterized in that The carbon emission behavior categories include direct carbon emission behavior, indirect carbon emission behavior, carbon emission consumption materials and carbon sink emission reduction behavior; The total carbon emissions under the operation and management scenario are calculated based on the target carbon emissions measurement model, including: For the first classified data subset whose carbon emission behavior category is direct carbon emission behavior, the direct carbon emissions under the operation management scenario are calculated based on the first classified data subset and its carbon emission conversion coefficient; For the second classified data subset whose carbon emission behavior category is indirect carbon emission behavior, the indirect carbon emissions under the operation and management scenario are calculated based on the second classified data subset and its carbon emission conversion coefficient; For the third classification data subset whose carbon emission behavior category is carbon emission consumable materials, the carbon emission amount of consumable materials in the operation management scenario is calculated based on the third classification data subset and its carbon emission conversion coefficient; For the fourth classification data subset whose carbon emission behavior category is carbon sink emission reduction behavior, calculate the carbon sink emission reduction amount under the operation management scenario based on the fourth classification data subset and its carbon emission conversion coefficient; The total carbon emissions under the operation and management scenario are calculated based on the direct carbon emissions, indirect carbon emissions, carbon emissions from consumed materials and carbon sink emission reductions.

4. The method according to claim 3, characterized in that The indirect carbon emissions under the operation and management scenario are calculated based on the second classification data subset and its carbon emission conversion coefficient, including: determining a carbon emission activity type corresponding to each second classification data in the second classification data subset; Determine an average carbon energy consumption level corresponding to each second classification data according to the carbon emission activity type; Calculate the indirect carbon emission level corresponding to each piece of the second-classified data based on the average carbon energy consumption level, energy consumption frequency, and energy consumption duration corresponding to each piece of the second-classified data; The indirect carbon emission levels corresponding to each of the second classification data are superimposed to obtain the indirect carbon emissions in the operation and management scenario.

5. The method according to claim 1, wherein The feature data set is classified to obtain multiple classified data subsets, including: Determining the data source of each feature data in the feature data set; According to the data source of each piece of feature data in the feature data set, the feature data in the feature data set is divided to obtain a plurality of classified data subsets, where one classified data subset corresponds to one type of data source.

6. The method according to claim 1, characterized in that A carbon emission conversion coefficient is assigned to each piece of categorical data in each of the categorical data subsets, including: Obtaining regional attribute information and field attribute information corresponding to each classification data in the classification data subset; A corresponding carbon emission conversion coefficient is allocated to each classified data in the classified data subset according to the regional attribute information and the field attribute information.

7. The method according to claim 1, characterized in that Determine the scenario categories for operations management scenarios, including: Obtain management area attribute information related to operation management scenarios; The scenario category of the operation management scenario is determined according to the management area attribute information.

8. A carbon emission metering device based on the smart park operation and management scenario, characterized in that: include: a scenario determination module configured to determine a scenario category of an operation management scenario, wherein the scenario category is determined based on management area attribute information related to the operation management scenario, wherein the management area attribute information refers to the area type and area function of the management area related to carbon emission behavior involved in the operation management scenario; a data collection module configured to collect a feature data set related to the carbon emission behavior corresponding to the scenario category; a data classification module configured to perform data classification on the feature data set to obtain a plurality of classified data subsets, each of the classified data subsets including at least one piece of classified data; a coefficient allocation module configured to allocate a carbon emission conversion coefficient to each piece of classified data in each of the classified data subsets, wherein the carbon emission conversion coefficient is determined based on regional attribute information and field attribute information corresponding to each piece of classified data, wherein the regional attribute information is the region or area corresponding to each piece of classified data, and the field attribute information is the field or industry corresponding to each piece of classified data; a model retrieval module configured to retrieve the target carbon emission measurement model if it is determined that a preset carbon emission measurement model library contains a target carbon emission measurement model that matches the carbon emission behavior category and the carbon emission conversion coefficient of the multiple classification data subsets; The carbon emission measurement module is configured to calculate the total carbon emissions in the operation management scenario according to the target carbon emission measurement model.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

Images