Carbon account collaborative optimization method and device, computer equipment and storage medium

Abstract

The invention relates to the technical field of carbon emission monitoring, and discloses a carbon account collaborative optimization method and device, computer equipment and a storage medium, and the method comprises the steps: collecting multi-source heterogeneous data in real time; establishing different main carbon accounts based on the multi-source heterogeneous data, and determining reference carbon emissions and carbon quota elastic floating intervals of the different main carbon accounts; determining basic carbon points of different main body carbon accounts based on the actual carbon emission and the reference carbon emission; with minimization of total carbon emission of different main body carbon accounts and maximization of basic carbon points of the different main body carbon accounts as targets, a dynamic cooperation model of a preset constraint condition is constructed based on carbon quota elastic floating intervals of the different main body carbon accounts; according to the method and the device, the problem that in the prior art, due to the lack of a collaborative optimization strategy for multiple subjects of the park, collaborative optimization of different subjects of the park cannot be realized is effectively solved.

Description

Technical Field

[0001] The present invention relates to the field of carbon emission monitoring technology, and in particular to a carbon account collaborative optimization method, device, computer equipment and storage medium. Background Art

[0002] As concentrated areas of industrial activity and energy consumption, industrial parks face significant challenges in managing carbon emissions, making them a key component of green transformation. Carbon accounting, a key means for industrial parks to establish emission reduction mechanisms and stimulate public participation, holds significant significance. However, existing industrial park carbon management technologies face technical challenges in practical application, lacking a systematic approach encompassing the entire "monitoring-analysis-incentive" chain. This is particularly evident in the absence of a coordinated mechanism for cross-stakeholder emission reduction. Existing technologies primarily rely on one-way monitoring and lack collaborative optimization strategies across multiple stakeholders within the industrial park. These technologies are unable to generate industrial park management and incentive plans linked to carbon accounting based on real-time supply and demand and carbon emission fluctuations, hindering the coordinated optimization of diverse stakeholders within the industrial park. Summary of the Invention

[0003] In view of this, the present invention provides a carbon account collaborative optimization method, device, computer equipment and storage medium to solve the problem in the existing technology that the collaborative optimization strategy of multiple subjects in the park is lacking, resulting in the inability to achieve collaborative optimization of different subjects in the park.

[0004] In a first aspect, the present invention provides a carbon account collaborative optimization method, the method comprising: Real-time collection of multi-source heterogeneous data; Establish carbon accounts for different entities based on multi-source heterogeneous data, and determine the baseline carbon emissions and the elastic floating range of carbon quotas for different entities’ carbon accounts; Determine the basic carbon credits of different carbon accounts based on their actual carbon emissions and their benchmark carbon emissions; With the goal of minimizing the total carbon emissions of different carbon accounts and maximizing the basic carbon credits of different carbon accounts, a dynamic collaborative model with preset constraints is constructed based on the elastic floating range of carbon quotas of different carbon accounts. The park's carbon emissions are collaboratively optimized based on a dynamic collaborative model and preset carbon credit acquisition rules for different entities' carbon accounts.

[0005] The present invention provides a carbon account collaborative optimization method that collects multi-source heterogeneous data in real time. The data sources are comprehensive and highly real-time. It can not only capture carbon emissions from all scenarios such as enterprise production, public operations, and personal behavior in the park, but also dynamically reflect emission changes through real-time collection, providing high-precision data support for subsequent account calculations and quota formulation. By establishing carbon accounts of different entities and setting benchmark carbon emissions and carbon quota elastic floating intervals in a targeted manner, differentiated benchmarks and quotas are formulated according to the emission characteristics of carbon accounts of different entities, which not only ensures the scientific nature of the benchmarks, but also gives management flexibility through elastic floating intervals. The basic carbon credits are determined based on the actual carbon emissions and the benchmark carbon emissions, and the carbon credit maximization is incorporated into the optimization through a dynamic collaborative model. The goal is to directly link emission reduction behavior with quantitative incentives to form a positive cycle of emission reduction-credits-benefits, solve the pain point of weak emission reduction motivation caused by insufficient incentives in traditional carbon management, and conduct collaborative optimization based on dynamic collaborative models and preset carbon credit acquisition rules for different entities. It includes both quantitative mathematical models and differentiated credit rules designed for different entity carbon accounts. The quantitative model ensures that the optimization process is calculable and verifiable, avoiding subjective decision-making bias; differentiated rules adapt to the behavioral characteristics of different entities, effectively solving the problems of data lag, extensive management, insufficient incentives, system fragmentation, etc. in traditional park carbon management, as well as the lack of collaborative optimization strategies for multiple entities in the park in existing technologies, which makes it impossible to achieve collaborative optimization of different entities in the park.

[0006] In an optional embodiment, the multi-source heterogeneous data includes direct emission data, indirect emission data, integrated energy system data, and individual user energy consumption data; and the carbon account collaborative optimization method further includes: Use edge computing nodes to monitor and delete abnormal data on direct emission data, indirect emission data, integrated energy system data, and individual user energy consumption data; Build a carbon emission factor library for the park and convert different unit data in direct emission data, indirect emission data, integrated energy system data and individual user energy consumption data into standard carbon equivalent values.

[0007] The present invention provides a carbon account collaborative optimization method, which incorporates the carbon emission behaviors of enterprise clusters, park operators, individual users, etc. into the same model through unified coding of multi-source heterogeneous data and a carbon account point circulation mechanism, thereby realizing dynamic collaborative emission reduction across entities.

[0008] In an optional embodiment, establishing carbon accounts of different entities based on multi-source heterogeneous data includes: Establish corporate carbon accounts, operating entity carbon accounts and personal carbon accounts based on multi-source heterogeneous data.

[0009] The present invention provides a carbon account collaborative optimization method that, based on the principle of "whoever emits, whoever accounts", splits multi-source heterogeneous data according to the attribution subject (the production emissions of enterprises, the public service emissions of operating entities, and the transportation and office emissions of individuals are respectively recorded in corresponding accounts). This makes the carbon emission responsibility of each subject traceable and quantifiable, provides a clear responsible subject for subsequent quota allocation, assessment and rewards and punishments, and ensures that the pressure of emission reduction is accurately transmitted to specific objects. The method of establishing carbon accounts for each subject and accurately collecting emission data fundamentally solves the pain points of unclear responsibilities, inaccurate data, mismatched strategies, and inadequate incentives in park carbon management.

[0010] In an optional embodiment, determining the carbon quota elastic floating range of different subject carbon accounts includes: Generate the first monthly benchmark carbon emissions based on the previously acquired enterprise historical production capacity data and industry energy efficiency standards, and determine the first carbon quota elastic floating range based on the first monthly benchmark carbon emissions; Generate the second monthly benchmark carbon emissions based on the pre-acquired emission reductions of the operator's renewable energy facilities, and determine the second carbon quota elastic floating range based on the second monthly benchmark carbon emissions; The third monthly benchmark carbon emissions are generated based on the pre-acquired per capita carbon emissions in the park, and the third carbon quota elastic floating range is determined based on the third monthly benchmark carbon emissions.

[0011] The present invention provides a carbon account collaborative optimization method that generates a flexible quota floating range based on dynamic parameters such as the company's historical production capacity, industry energy efficiency coefficient, and renewable energy emission reduction, to avoid the problem of insufficient motivation for emission reduction due to excessively high quotas or reduced enthusiasm due to excessively low quotas. The determination of the baseline emissions is not a subjective setting, but is based on the actual data and objective standards of each entity, so that the quota is scientific from the source, emphasizes the flexible floating range of carbon quotas, rather than fixed quota values, and allows quotas to fluctuate up and down based on the baseline emissions. The quota can be dynamically tightened or relaxed according to the park's emission reduction targets, so that the management strategy is more in line with the actual needs of the park.

[0012] In an optional embodiment, determining the basic carbon credits of different carbon accounts based on the actual carbon emissions of different carbon accounts and the benchmark carbon emissions of different carbon accounts includes: Obtain the actual monthly carbon emissions of corporate carbon accounts, operating entity carbon accounts, and personal carbon accounts respectively; Determine the basic carbon credits of the enterprise's carbon account based on the actual carbon emissions of the enterprise's carbon account and the benchmark carbon emissions of the first month; Determine the basic carbon credits of the operating entity's carbon account based on the actual carbon emissions of the operating entity's carbon account and the second month's benchmark carbon emissions; The basic carbon points of the personal carbon account are determined based on the actual carbon emissions in the personal carbon account and the benchmark carbon emissions for the third month.

[0013] The present invention provides a carbon account collaborative optimization method that calculates basic carbon credits for the carbon accounts of enterprises, operating entities, and individuals. The actual emissions of each entity are defined differently from the baseline emissions, avoiding the inefficiency of a one-size-fits-all incentive. Enterprise emissions are strongly correlated with production activities, so their credit calculation must take into account production needs. Operating entity emissions are related to public services, so credits must recognize the necessity of their public services. Individual emissions are affected by lifestyle habits, so credits must accommodate reasonable individual differences. Differentiated accounting makes credit incentives more tailored to the actual scenarios of each entity, avoiding the problem of high-emission entities being left flat and low-emission entities being undermotivated due to unified standards.

[0014] In an optional embodiment, the preset constraints include energy supply and demand balance constraints and production demand constraints; With the goal of minimizing the total carbon emissions of different carbon accounts and maximizing the basic carbon credits of different carbon accounts, a dynamic collaborative model with preset constraints is constructed based on the elastic floating range of carbon quotas of different carbon accounts, including: Obtain the control parameters, comprehensive adjustment parameters and dynamic load adjustment response capacity parameters in the carbon quota elastic floating range of different subject carbon accounts; With the goal of minimizing the total carbon emissions of different carbon accounts and maximizing the basic carbon credits of different carbon accounts, a dynamic collaborative model including energy supply and demand balance constraints and production demand constraints is constructed based on the control parameters, comprehensive adjustment parameters and dynamic load adjustment response capabilities in the elastic floating range of carbon quotas of different carbon accounts.

[0015] The present invention provides a carbon account collaborative optimization method with the goal of minimizing total carbon emissions and maximizing basic carbon credits. It combines carbon quota elastic floating interval parameters with energy supply and demand and production demand constraints to construct a dynamic collaborative model design. It balances total carbon emissions and credit incentives through weight coefficients. Its core advantage lies in achieving a deep balance between emission reduction targets, system feasibility, and subject initiative, while also taking into account the model's scientific nature, practicality, and collaborative nature. The model introduces multiple parameters, including carbon quota elastic floating interval control parameters, comprehensive adjustment parameters, and dynamic load adjustment response capabilities, to accommodate the differentiated needs of different industrial parks. By adjusting these parameters, the model can be adapted to various scenarios without reconstruction, improving its versatility.

[0016] In an optional embodiment, the preset carbon credits of different subject carbon accounts include carbon credits of corporate subject carbon accounts, carbon credits of operating subject carbon accounts, and carbon credits of personal carbon accounts. The preset carbon credits of different subject carbon accounts are obtained in the following manner: Obtain carbon credits from the enterprise's carbon account based on the basic carbon credits of the enterprise's carbon account, the corresponding number of dynamic load adjustments, the optimized amount of carbon emission intensity per unit of output value, and the offset amount; Obtain carbon credits from the operating entity's carbon account based on the basic carbon credits of the operating entity's carbon account, low-carbon transportation management, and deployment of energy-saving and carbon reduction management systems; Obtain personal carbon account carbon points based on personal carbon account basic carbon points, green behavior incentives and daily emission reduction behavior records.

[0017] The present invention provides a carbon account collaborative optimization method, which presets the method of obtaining carbon credits for carbon accounts of different entities. By designing multi-dimensional credit rules based on the differentiated characteristics of enterprises, operating entities and individuals, a three-dimensional incentive system for basic emission reduction and special actions is constructed. Its core advantages lie in the accuracy of incentives, the diversity of emission reduction paths, the enthusiasm of entity participation and the synergy of system emission reduction, thus realizing the construction of a cross-entity carbon credit system.

[0018] In a second aspect, the present invention provides a carbon account collaborative optimization device, which includes: Data acquisition module, used to collect multi-source heterogeneous data in real time; The subject account establishment and carbon quota allocation module is used to establish different subject carbon accounts based on multi-source heterogeneous data, and determine the baseline carbon emissions and carbon quota elastic floating range of different subject carbon accounts; A subject account basic carbon credit determination module is used to determine the basic carbon credits of different subject carbon accounts based on the actual carbon emissions of different subject carbon accounts and the benchmark carbon emissions of different subject carbon accounts; The model building module is used to build a dynamic collaborative model with preset constraints based on the elastic floating range of carbon quotas of different carbon accounts, with the goal of minimizing the total carbon emissions of different carbon accounts and maximizing the basic carbon credits of different carbon accounts; The collaborative optimization module is used to collaboratively optimize the park's carbon emissions based on a dynamic collaborative model and preset carbon credit acquisition rules for different entities' carbon accounts.

[0019] In a third aspect, the present invention provides a computer device comprising: a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, and the processor executing the carbon account collaborative optimization method of the first aspect or any corresponding embodiment thereof by executing the computer instructions.

[0020] In a fourth aspect, the present invention provides a computer-readable storage medium having computer instructions stored thereon, the computer instructions being used to enable a computer to execute the carbon account collaborative optimization method of the first aspect or any corresponding embodiment thereof.

[0021] In a fifth aspect, the present invention provides a computer program product comprising computer instructions for causing a computer to execute the carbon account collaborative optimization method of the first aspect or any corresponding embodiment thereof. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the specific embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0023] Figure 1 is a flow chart of a carbon account collaborative optimization method according to an embodiment of the present invention; Figure 2 is a flow chart of another carbon account collaborative optimization method according to an embodiment of the present invention; Figure 3 is a flow chart of another carbon account collaborative optimization method according to an embodiment of the present invention; Figure 4 is a structural block diagram of a carbon account collaborative optimization device according to an embodiment of the present invention; Figure 5 Schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0024] To make the purpose, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making creative efforts shall fall within the scope of protection of the present invention.

[0025] Existing technologies mainly rely on one-way monitoring and lack collaborative optimization models involving multiple park entities, such as enterprise clusters, park operations, and users. They are unable to generate park management and incentive plans linked to carbon accounts based on real-time supply and demand and carbon emission changes.

[0026] An embodiment of the present invention provides a carbon account collaborative optimization method, which generates a flexible quota range based on dynamic parameters such as the company's historical production capacity, industry energy efficiency coefficient, and renewable energy emission reduction, to avoid the problem of insufficient motivation for emission reduction due to excessively high quotas or reduced enthusiasm due to too low quotas. The method aims to minimize the total carbon emissions of carbon accounts of different entities and maximize the basic carbon points of carbon accounts of different entities. A dynamic collaborative model with preset constraints is constructed based on the elastic floating range of carbon quotas of carbon accounts of different entities to collaboratively optimize the carbon emissions of the park, thereby effectively solving the problems of data lag, extensive management, insufficient incentives, system fragmentation, etc. in traditional park carbon management, as well as the lack of collaborative optimization strategies for multiple entities in the park in the existing technology, which makes it impossible to achieve collaborative optimization of different entities in the park.

[0027] According to an embodiment of the present invention, an embodiment of a carbon account collaborative optimization method is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0028] In this embodiment, a carbon account collaborative optimization method is provided, which can be used in industrial parks (hereinafter referred to as parks). Figure 1 is a flow chart of a carbon account collaborative optimization method according to an embodiment of the present invention. Figure 1 As shown, the process includes the following steps: Step S101: collect multi-source heterogeneous data in real time.

[0029] Specifically, IoT terminals are deployed in the industrial park, including smart electricity meters, heat meters, gas sensors, material flow meters, on-board OBD terminals (On Board Diagnostics, vehicle automatic diagnostic system), and GPS facilities to realize real-time collection of multi-source heterogeneous data in the industrial park.

[0030] Multi-source heterogeneous data collected in real time may include: 1) Direct emissions data: fuel combustion type and consumption, mainly including coal, coke, oil, natural gas, coal gas, etc.; main carbon emission sources and process parameters of industrial processes, determined by the type and process of enterprises in the park, including fuel consumption generated by vehicles within the park.

[0031] 2) Indirect emissions data: consumption of purchased electricity, heat, or steam, including charging capacity and mileage of vehicles within the park.

[0032] 3) Integrated energy system data: electricity and power data of rooftop photovoltaic and other photovoltaic building integrated facilities, electrochemical energy storage systems, charging piles, etc.

[0033] 4) Personal user energy consumption data such as fuel or electricity consumption of personal vehicles, electricity consumption of office facilities, etc.

[0034] Step S102: establishing carbon accounts of different entities based on multi-source heterogeneous data, and determining the baseline carbon emissions of the carbon accounts of different entities and the elastic floating range of carbon quotas of the carbon accounts of different entities.

[0035] Specifically, carbon accounts of different entities within the park are established based on multi-source heterogeneous data, such as corporate carbon accounts, park operating entity carbon accounts, and personal carbon accounts.

[0036] Baseline carbon emissions are set for different entities (companies, operating entities, individuals, etc.) as a "baseline" or "reference value" to measure the rationality of their carbon emissions. They are the basic standard for determining whether emissions behavior meets emission reduction targets. For example, a company's monthly baseline carbon emissions are based on its historical production capacity data and industry energy efficiency standards (for example, a chemical company's monthly baseline emissions are 500 tons of CO2); an individual's monthly baseline carbon emissions are based on the average per capita carbon emissions of the industrial park (for example, the monthly baseline emissions per person is 0.5 tons of CO2).

[0037] The carbon quota elastic floating range sets upper and lower limits for different entities based on their baseline carbon emissions. This means actual emissions can fluctuate within a certain percentage of the baseline value, preserving compliance margin while also guiding emissions reductions through the use of these limits. For example, if a company's monthly baseline emissions are 500 tons and the elastic range is set at ±10%, its permitted emissions range is 450-550 tons. If the company improves energy efficiency through technological transformation, the range may be tightened to ±5% (475-525 tons).

[0038] Step S103 : determining basic carbon credits of the carbon accounts of different entities based on the actual carbon emissions of the carbon accounts of different entities and the benchmark carbon emissions of the carbon accounts of different entities.

[0039] Specifically, obtaining actual carbon emissions is the core link in the carbon management system to ensure data accuracy, fairness and effectiveness. Targeted quantification methods must be adopted based on the emission characteristics and activity types of different entities (enterprises, operating entities, individuals).

[0040] For example, an enterprise's carbon emissions mainly come from energy consumption in the production process (such as fossil fuel combustion), process emissions (such as chemical reactions), purchased energy (such as electricity and heat), etc., and the acquisition method is mainly direct monitoring + indirect accounting.

[0041] 1. Direct emission data collection: Direct emission data is collected through online monitoring systems (such as CEMS, Continuous Emission Monitoring System) installed at key nodes such as production equipment, boilers, and exhaust gas treatment facilities. The CO2 emissions generated by the combustion of fossil fuels (coal, natural gas, diesel, etc.) are recorded in real time and the data is directly uploaded to the enterprise carbon account management platform.

[0042] For process emissions (such as carbonate decomposition in cement production and CH4 leakage in chemical synthesis), they need to be calculated based on process parameters such as raw material consumption and reaction conversion rate, combined with standard formulas in relevant documents (such as emissions = raw material consumption × emission factor × conversion rate).

[0043] 2. For indirect emissions data, such as carbon emissions from purchased electricity and heat, the calculation is based on the company's monthly electricity bill and heat purchase volume, combined with the average emission factor of the regional power grid (such as the annual regional power grid emission factor) (Emissions = Purchased Electricity × Power Grid Emission Factor).

[0044] Other indirect emissions: such as carbon emissions from the production process of purchased raw materials, can be estimated based on carbon emission reports provided by suppliers or industry average data.

[0045] Carbon credits, based on the carbon accounts of different entities, are initial credits generated through quantitative rules based on the comparison of each entity's (enterprises, operating entities, and individuals) actual carbon emissions with a preset baseline carbon emissions level within the carbon management system. These credits serve as a core indicator for measuring an entity's basic emissions reduction performance. Based on the core principle of "using the baseline as a yardstick and actual emissions as a basis," they directly reflect whether an entity has achieved or exceeded the emission reduction baseline within a specific period (e.g., a month) and serve as the basis for the subsequent accrual of credits for other emission reduction activities.

[0046] For example: 1. Basic carbon credits for an enterprise: calculated based on the comparison of the enterprise's actual monthly carbon emissions with the enterprise's benchmark carbon emissions (such as the industry average emission level or the historical best emission level). For example: If the actual emissions are less than the baseline emissions, the basic credits will increase as the emission reduction increases; If the actual emissions are ≥ the benchmark emissions, the basic credits may be zero or deducted proportionally.

[0047] 2. Operating entity basic carbon credits: This is calculated based on the comparison of the actual carbon emissions of the operating entity (such as a park or property) (such as public facility energy consumption and transportation emissions) with the operating benchmark (such as regional average operating emissions and planned emission reduction targets), reflecting the emission reduction achievements in its basic operations.

[0048] 3. Individual Basic Carbon Credits: This is calculated based on a comparison of an individual's actual monthly carbon emissions (such as household energy use and travel emissions) with a personal benchmark (such as the regional average per capita emissions and personalized emission reduction targets), reflecting whether an individual's daily emissions meet the low-carbon baseline.

[0049] Step S104 , with the goal of minimizing the total carbon emissions of different subject carbon accounts and maximizing the basic carbon credits of different subject carbon accounts, a dynamic collaborative model with preset constraints is constructed based on the elastic floating range of carbon quotas of different subject carbon accounts.

[0050] Specifically, the dynamic coordination model of carbon accounts of different entities is the decision-making center of the carbon management system. Its core is to express the dual goals of "minimizing total carbon emissions" and "maximizing basic carbon credits" through mathematical expression, and to link them with the elastic floating range of carbon quotas of each entity, preset constraints (energy supply and demand balance, production demand) and key parameters to achieve systematic optimization of carbon emissions in the entire park.

[0051] Step S105 , collaboratively optimizing the park's carbon emissions based on the dynamic collaborative model and the preset carbon credit acquisition rules of different subject carbon accounts.

[0052] Specifically, the park's carbon emissions are collaboratively optimized based on a dynamic collaborative model and pre-set carbon credit earning rules for different entities' carbon accounts. This closed-loop logic uses model objectives to guide behavior, credit rules to incentivize specific actions, and constraints to ensure feasibility. This transforms the dispersed emission reduction behaviors of enterprises, operators, and individuals within the park into systematic, collaborative actions, ultimately achieving the dual goals of "minimizing total carbon emissions and maximizing carbon credits." The core of this approach is to enable each entity's carbon account to adapt to the model's optimization goals by adjusting their own behaviors (such as production plans, facility construction, and daily habits) under clear rules, while also reinforcing behavioral sustainability through credit incentives.

[0053] The carbon account collaborative optimization method provided in this embodiment collects multi-source heterogeneous data in real time. The data sources are comprehensive and real-time. It can not only capture carbon emissions from all scenarios such as enterprise production, public operations, and personal behavior in the park, but also dynamically reflect emission changes through real-time collection, providing high-precision data support for subsequent account calculations and quota formulation. By establishing carbon accounts for different entities and setting benchmark carbon emissions and carbon quota elastic floating ranges in a targeted manner, differentiated benchmarks and quotas are formulated according to the emission characteristics of carbon accounts of different entities, which not only ensures the scientific nature of the benchmarks, but also gives management flexibility through elastic floating ranges. The basic carbon credits are determined based on actual carbon emissions and benchmark carbon emissions, and the carbon credit maximization is incorporated into the optimization through a dynamic collaborative model. The goal is to directly link emission reduction behavior with quantitative incentives to form a positive cycle of emission reduction-credits-benefits, solve the pain point of weak emission reduction motivation caused by insufficient incentives in traditional carbon management, and conduct collaborative optimization based on dynamic collaborative models and preset carbon credit acquisition rules for different entities. It includes both quantitative mathematical models and differentiated credit rules designed for different entity carbon accounts. The quantitative model ensures that the optimization process is calculable and verifiable, avoiding subjective decision-making bias; differentiated rules adapt to the behavioral characteristics of different entities, effectively solving the problems of data lag, extensive management, insufficient incentives, system fragmentation, etc. in traditional park carbon management, as well as the lack of collaborative optimization strategies for multiple entities in the park in existing technologies, which makes it impossible to achieve collaborative optimization of different entities in the park.

[0054] In this embodiment, a carbon account collaborative optimization method is provided, which can be used in industrial parks (hereinafter referred to as parks). Figure 2 is a flow chart of a carbon account collaborative optimization method according to an embodiment of the present invention. Figure 2 As shown, the process includes the following steps: Step S201: Real-time collection of multi-source heterogeneous data. Multi-source heterogeneous data includes direct emission data, indirect emission data, integrated energy system data, and individual user energy consumption data. Figure 1 Step S101 of the illustrated embodiment will not be described in detail here.

[0055] In step S202, edge computing nodes are used to monitor abnormal data of direct emission data, indirect emission data, integrated energy system data, and individual user energy consumption data, and to delete abnormal data; a park carbon emission factor library is constructed, and different unit data in direct emission data, indirect emission data, integrated energy system data, and individual user energy consumption data are converted into standard carbon equivalent values.

[0056] Specifically, edge computing nodes are deployed to detect abnormal data and perform real-time filtering processing, where μ is the mean of the sampled data and σ is the standard deviation of the sampled data. When the absolute value of the sampled data and the mean μ is greater than 3σ, it is considered to be abnormal data, and manual confirmation is performed and the abnormal data is deleted.

[0057] Unified coding of heterogeneous data: Build a carbon emission factor library for the park and convert data from different units in the park into standard carbon equivalent values ​​according to the carbon accounting calculation formula. , for the Carbon emissions from various emission sources (i.e., standard carbon equivalent values) , the formula is as follows: (1); in: Indicates the The carbon emission factor of each emission source, that is, the carbon emission per unit emission source, Indicates the calculated amount of the corresponding carbon emission source.

[0058] Step S203: establishing carbon accounts of different entities based on multi-source heterogeneous data, and determining the baseline carbon emissions of the carbon accounts of different entities and the elastic floating range of carbon quotas of the carbon accounts of different entities.

[0059] Specifically, the monthly baseline carbon emissions are also called monthly carbon quotas. Step S203 includes: Step S2031: Establishing corporate carbon accounts, operating entity carbon accounts, and personal carbon accounts based on multi-source heterogeneous data.

[0060] Specifically, based on multi-source heterogeneous data, carbon accounts for different entities in the park are established, including corporate carbon accounts, operating carbon accounts, and personal carbon accounts. After the carbon accounts for different entities are established, they are associated with carbon emissions. C1 is set to represent the carbon emissions of the corporate entity and C2 is set to represent the carbon emissions of the operating entities in the park. C1 and C2 are obtained through the steps of multi-source heterogeneous data. C1 is the sum of the direct emission data and indirect emission data attributable to the enterprise, that is, the sum of the carbon emissions of the carbon accounts of each corporate entity in the park. C2 is the sum of the direct emission data and indirect emission data attributable to the operating entities in the park, that is, the sum of the carbon emissions of the carbon accounts of each operating entity in the park. C3 is the sum of the carbon emissions generated by the energy consumption of personal transportation and personal office facilities, that is, the sum of the carbon emissions of personal carbon accounts.

[0061] Step S2032: Generate a first monthly benchmark carbon emissions based on the pre-acquired enterprise historical production capacity data and industry energy efficiency standards, and determine a first carbon quota elastic floating range based on the first monthly benchmark carbon emissions.

[0062] Specifically, the first monthly benchmark carbon emissions are the monthly benchmark carbon emissions of the corporate entity’s carbon account, and the first carbon quota elastic floating range is the carbon quota elastic floating range of the corporate entity’s carbon account.

[0063] For example, for enterprises in the park Establish a dynamic carbon emissions baseline: Generate an adjustable monthly baseline emission for the corporate carbon account based on the company's historical production capacity data and industry energy efficiency standards , and determine the monthly carbon quota based on the first month's baseline carbon emissions The formulas for the elastic floating range are as follows: (2); (3); in, Indicates the adjustable ratio of the monthly benchmark carbon emissions of the corporate carbon account. For enterprises The first step of extracting historical production capacity data Monthly historical carbon emissions, is the industry energy efficiency coefficient, is the industry benchmark value, that is, the standard carbon equivalent value, Represents monthly carbon allowance.

[0064] Step S2033: Generate a second monthly benchmark carbon emissions based on the pre-acquired emission reduction of the renewable energy facilities of the operating entity, and determine a second carbon quota elastic floating range based on the second monthly benchmark carbon emissions.

[0065] Specifically, the second monthly benchmark carbon emissions are the monthly benchmark carbon emissions of the operating entity's carbon account, and the second carbon quota elastic floating range is the carbon quota elastic floating range of the operating entity's carbon account.

[0066] For example, for the operating entities in the park Establish a dynamic carbon emissions baseline and generate adjustable monthly benchmark carbon emissions , and determine the monthly quota based on the monthly baseline carbon emissions The formulas for the elastic floating range are as follows: (4); (5); in, Indicates the adjustable ratio of the monthly benchmark carbon emissions of the operating entity’s carbon account. For the operating entity in the park No. Emission reductions from renewable energy facilities in the month.

[0067] Step S2034: Generate a third monthly benchmark carbon emissions based on the pre-acquired per capita carbon emissions in the park, and determine a third carbon quota elastic floating range based on the third monthly benchmark carbon emissions.

[0068] Specifically, the third monthly benchmark carbon emissions are the monthly benchmark carbon emissions of the personal carbon account, and the third carbon quota elastic floating range is the carbon quota elastic floating range of the personal carbon account.

[0069] For example, for users in the park Establish a dynamic baseline for carbon emissions based on the per capita carbon emissions within the park. Set an adjustable monthly baseline carbon emissions reduction , and determine the monthly quota based on the monthly benchmark carbon emissions of personal carbon accounts The formulas for the elastic floating range are as follows: (6); (7); in, Indicates the adjustable ratio of the monthly baseline carbon emissions of a personal carbon account.

[0070] Step S204: determining basic carbon credits of carbon accounts of different entities based on actual carbon emissions and benchmark carbon emissions of carbon accounts of different entities.

[0071] Specifically, a carbon account points system is constructed to realize cross-entity value transfer. The above step S204 includes: Step S2041 , respectively obtaining the actual monthly carbon emissions of the corporate carbon account, the actual carbon emissions of the operating entity carbon account, and the actual carbon emissions of the individual carbon account.

[0072] Specifically, 1. Obtaining the actual monthly carbon emissions from the corporate carbon account: Corporate carbon emissions primarily stem from production activities, which are characterized by being centralized, highly intensive, and measurable. The acquisition process must cover three aspects: energy consumption, process, and indirect emissions: Corporate carbon emissions are strongly linked to production, have concentrated emission sources, and have measurable data. They must cover both direct emissions (fuel combustion, process) and indirect emissions (purchased energy). The specific process is as follows: 1. Data Source and Collection Method: (1) Direct emission data (expressed as Scope 1): Fuel combustion: Intelligent flow meters (such as natural gas flow meters and diesel fuel dispensers) installed on boilers, generators, and other equipment collect real-time consumption of coal, natural gas, and fuel oil. The data is uploaded to the park's carbon management platform every 15 minutes. Process emissions: Based on the company's production process (such as chemical industry or metallurgy), raw material input (such as limestone and coke) and reaction products (such as CO2 by-products) are recorded through sensors or production records. For example, steel companies need to collect coke consumption and blast furnace gas emissions.

[0073] (2) Indirect emissions data (expressed as Scope 2): Purchased electricity / heat: Monthly electricity consumption (kWh) and heat consumption are collected through the company's dedicated electricity and heat meters, and the data is synchronized with the settlement statements of the power grid / heating company; Traffic emissions within the park: If the company owns its own freight vehicles, the fuel consumption or charging amount (kWh) is collected through the on-board OBD terminal and the carbon emissions are calculated based on the mileage.

[0074] 2. Calculation formula: Monthly actual carbon emissions = direct emissions + indirect emissions.

[0075] 2. Obtaining the actual carbon emissions from the operating entity’s carbon account: The carbon emissions of operating entities (such as park management committees and property management companies) come from public services, covering public facilities, public transportation, waste disposal, etc., and the boundaries with corporate / individual emissions must be clearly defined (only emission sources directly managed by the operating entity are included).

[0076] 1. Data collection: (1) Energy consumption of public facilities: Electricity: Install smart meters (with 4G modules) at the main incoming lines of public facilities, upload electricity consumption (kWh) every hour, and distinguish between subsystems such as lighting, air conditioning, and power; Gas / heat: Monthly readings of gas meters and heating heat meters in public buildings, and check with the supplier's settlement statement.

[0077] (2) Public transportation: Fuel shuttle buses: Monthly fuel consumption is recorded through the on-board fuel tank level sensor and verified with the refueling invoice; Electric shuttle buses: The charging pile smart meter records the charging amount (kWh), distinguishing between photovoltaic direct charging and grid charging (the photovoltaic direct charging part is not included in indirect emissions).

[0078] (3) Waste disposal: Domestic waste: Monthly waste removal orders signed with the waste removal company (including waste weight in tons), recording the landfill / incineration ratio; Sewage treatment: Sewage treatment station operation report (monthly treated water volume in m³, sludge production in tons).

[0079] 2. Calculation formula: Monthly actual carbon emissions = public facility emissions + public transportation emissions + waste disposal emissions.

[0080] 3. Obtaining actual monthly carbon emissions from personal carbon accounts: Personal carbon emissions are decentralized, behavior-driven, and data-fragmented, and need to be indirectly calculated through multi-source data fusion (smart devices + behavior records).

[0081] 1. Data Source and Collection Method: (1) Household energy consumption: Monthly electricity consumption (kWh) and gas consumption (m³) are collected through household smart meters and gas meters, and the data is bound to the residents (e.g., by room number); (2) Transportation: Private cars: mileage (km) recorded by the ETC in the park parking lot, or the owner manually enters the amount of fuel in the APP; public transportation: park shuttle bus card swipe record (number of times × single mileage), shared bicycle APP riding distance (km); (3) Office and consumption: The office smart socket records the electricity consumption (kWh) of personal computers and air conditioners, and the frequency of use of disposable items in the consumption records of the park supermarket (such as the number of takeout orders).

[0082] 2. Calculation formula: Monthly actual carbon emissions = household energy emissions + transportation emissions + office consumption emissions.

[0083] Step S2042: determine the basic carbon credits of the corporate carbon account based on the actual carbon emissions of the corporate carbon account and the first monthly benchmark carbon emissions; determine the basic carbon credits of the operating entity carbon account based on the actual carbon emissions of the operating entity carbon account and the second monthly benchmark carbon emissions; and determine the basic carbon credits of the personal carbon account based on the actual carbon emissions of the personal carbon account and the third monthly benchmark carbon emissions.

[0084] Specifically, the formula for generating basic carbon credits in month k is as follows: (8); in, The actual carbon emission value of a certain subject's carbon account (including corporate carbon account, operating entity carbon account and personal carbon account). The monthly carbon quota obtained for a certain entity's carbon account (including corporate entity carbon account, operating entity carbon account and personal carbon account), that is, the monthly benchmark carbon emissions.

[0085] Step S205: With the goal of minimizing the total carbon emissions of different carbon accounts and maximizing the basic carbon credits of different carbon accounts, a dynamic collaborative model with preset constraints is constructed based on the elastic floating range of carbon allowances of different carbon accounts. Figure 1 Step S104 of the illustrated embodiment will not be described in detail here.

[0086] Step S206: Coordinated optimization of the park's carbon emissions based on the dynamic collaborative model and the preset carbon credit acquisition rules for different carbon accounts. Figure 1 Step S105 of the illustrated embodiment will not be described in detail here.

[0087] The carbon account collaborative optimization method provided in this embodiment incorporates the carbon emission behaviors of enterprise clusters, park operators, individual users, etc. into the same model through the unified coding of multi-source heterogeneous data and the carbon account credit circulation mechanism, thereby achieving dynamic collaborative emission reduction across entities. Based on dynamic parameters such as the company's historical production capacity, industry energy efficiency coefficient, and renewable energy emission reduction, a flexible quota floating range is generated to avoid the problem of insufficient motivation for emission reduction due to excessively high quotas or reduced enthusiasm due to excessively low quotas. Basic carbon credits are calculated separately for the carbon accounts of enterprises, operating entities, and individuals, and their actual emissions are defined differently from the baseline emissions to avoid the inefficiency of one-size-fits-all incentives.

[0088] In this embodiment, a carbon account collaborative optimization method is provided, which can be used in industrial parks (hereinafter referred to as parks). Figure 3 is a flow chart of a carbon account collaborative optimization method according to an embodiment of the present invention. Figure 3 As shown, the process includes the following steps: Step S301: Real-time collection of multi-source heterogeneous data. Figure 2 Step S201 of the illustrated embodiment will not be described in detail here.

[0089] Step S302: establish carbon accounts of different entities based on multi-source heterogeneous data, and determine the baseline carbon emissions and carbon quota elastic floating range of different entities' carbon accounts. Figure 2 Step S203 of the illustrated embodiment will not be described in detail here.

[0090] Step S303: Determine the basic carbon credits of different carbon accounts based on the actual carbon emissions of different carbon accounts and the benchmark carbon emissions of different carbon accounts. Figure 2 Step S204 of the illustrated embodiment will not be described in detail here.

[0091] Step S304 , with the goal of minimizing the total carbon emissions of different subject carbon accounts and maximizing the basic carbon credits of different subject carbon accounts, a dynamic collaborative model with preset constraints is constructed based on the elastic floating range of carbon quotas of different subject carbon accounts.

[0092] Specifically, the preset constraints include energy supply and demand balance constraints and production demand constraints; the above step S304 includes: Step S3041: Obtain control parameters, comprehensive adjustment parameters, and dynamic load adjustment response capability parameters in the carbon quota elastic floating range of different subject carbon accounts.

[0093] Specifically, the control parameters in the carbon quota elastic floating range of different subject carbon accounts are system-level control parameters, including the adjustable ratio of the carbon quota elastic floating range. 、 、 The control parameters directly affect the value of carbon credits U. If the carbon quota is too low, the main users in the park will not be able to meet the emission reduction requirements, carbon credits will be difficult to obtain, and the carbon account system function will not be able to play a role. If the quota is too high, all the main users in the park can meet the emission reduction requirements, and the motivation for emission reduction is insufficient.

[0094] Comprehensive adjustment parameters : The weight coefficient of carbon credits and actual carbon emissions in the objective function.

[0095] The dynamic load regulation response capability (i.e., number of responses / number of required responses) is combined with the park’s emission reduction incentives to achieve coordinated optimization of energy use and emission reduction.

[0096] Step S3042, with the goal of minimizing the total carbon emissions of different subject carbon accounts and maximizing the basic carbon credits of different subject carbon accounts, a dynamic collaborative model including energy supply and demand balance constraints and production demand constraints is constructed based on the control parameters, comprehensive adjustment parameters and dynamic load adjustment response capabilities in the carbon quota elastic floating range of different subject carbon accounts.

[0097] Specifically, the dual objective function formula for minimizing the total carbon emissions of the park and encouraging the park's emission reduction behavior is as follows: (9); Among them, C1 represents the sum of carbon emissions from the carbon accounts of all enterprises in the park, C2 represents the sum of carbon emissions from the carbon accounts of all operating entities in the park, C3 represents the sum of carbon emissions from personal carbon accounts, and U represents all carbon credits generated by the carbon management system in the park, including carbon credits U1 from corporate carbon accounts, carbon credits U2 from operating entity carbon accounts, and daily carbon credits U3 from personal carbon accounts. is the weight coefficient between carbon credits and actual carbon emissions.

[0098] The energy supply and demand balance constraints and production demand constraints of the objective function are described as follows: Energy supply and demand balance constraint: After the park load is adjusted, the energy supply and demand balance is guaranteed. The formula is as follows: (10); in, Powering the integrated energy system, Powering the grid, is the total load of the park.

[0099] Production demand constraints: After load adjustment, the minimum load requirement of the enterprise is met .

[0100] Step S305 , collaboratively optimizing the park's carbon emissions based on the dynamic collaborative model and the preset carbon credit acquisition rules of different subject carbon accounts.

[0101] Specifically, the preset carbon credits of different subject carbon accounts include carbon credits of corporate subject carbon accounts, carbon credits of operating subject carbon accounts, and carbon credits of personal carbon accounts. In step S305, the preset carbon credits of different subject carbon accounts are obtained in the following manner: Step S3051: Obtain the carbon credits of the enterprise's carbon account based on the basic carbon credits of the enterprise's carbon account, the corresponding number of dynamic load adjustments, the optimized amount of carbon emission intensity per unit output value, and the offset amount.

[0102] The carbon credits U1 of the corporate carbon account include: 1) The basic carbon credits of the corporate carbon account are: 1 carbon credit can be obtained for every ton of carbon emissions reduced.

[0103] 2) Dynamically adjust load points. The energy management and dispatching system in the park takes into account the forecast results of renewable energy power generation in the park and the production plans of each enterprise, and proposes enterprise adjustment plans to achieve coordinated optimization of energy use and emission reduction. The enterprise entity will receive 1 point for each response.

[0104] 3) Carbon emission intensity optimization points: Enterprises reduce carbon emission intensity per unit of output value through technological transformation, and points are converted according to the amount of emission reduction. One carbon point can be obtained for every ton of carbon emissions reduced.

[0105] 4) Enterprises that adopt China Certified Emission Reduction (CCER) or internationally recognized Certified Emission Reduction (CER) can directly obtain credits based on the amount of carbon emissions offset, with one carbon credit being obtained for every ton of carbon emissions reduced.

[0106] Step S3052: Obtain the carbon credits of the operating entity's carbon account based on the basic carbon credits of the operating entity's carbon account, low-carbon transportation management, and deployment of the energy-saving and carbon reduction management system.

[0107] The carbon credits U2 of the operating entity's carbon account include: 1) Basic carbon credits in the carbon account of the operating entity: 1 carbon credit can be obtained for every ton of carbon emissions reduced.

[0108] 2) Low-carbon transportation management: The baseline proportion of operating new energy vehicles is set at 50%, and 1 point is awarded for every 5% increase; 1 point is added for each infrastructure such as charging stations.

[0109] 3) 1 point will be awarded for adopting an energy-saving and carbon-reduction management system within the park.

[0110] Step S3053: Obtain personal carbon account carbon credits based on personal carbon account basic carbon credits, green behavior incentives, and daily emission reduction behavior records.

[0111] Specifically, personal carbon account carbon credits U3 include: 1) Basic carbon credits in personal carbon accounts: 1 carbon credit can be obtained for every ton of carbon emissions reduced.

[0112] 2) Green behavior incentives: Participate in low-carbon activities such as garbage sorting and energy conservation, and earn 0.1 points each time.

[0113] 3) Record your daily emission reduction behaviors through your personal carbon account, such as reducing the use of disposable items, and earn 0.1 points each time.

[0114] The carbon account collaborative optimization method provided in this embodiment aims to minimize total carbon emissions and maximize basic carbon credits. It combines carbon quota elastic floating range parameters with energy supply and demand and production demand constraints to construct a dynamic collaborative model design. This model balances total carbon emissions with credit incentives through weight coefficients. Its core advantage lies in achieving a deep balance between emission reduction targets, system feasibility, and subject motivation, balancing the model's scientificity, practicality, and synergy. The model incorporates multiple parameters, including carbon quota elastic floating range control parameters, comprehensive adjustment parameters, and dynamic load adjustment response capabilities, to accommodate the differentiated needs of different industrial parks. By adjusting these parameters, the model can be adapted to various scenarios without requiring reconstruction, enhancing its versatility. Different subject carbon accounts have pre-set carbon credit earning methods. By designing multi-dimensional credit rules tailored to the differentiated characteristics of enterprises, operating entities, and individuals, a three-dimensional incentive system for basic emission reduction and specialized actions is established. Its core advantages lie in the precision of incentives, the diversity of emission reduction paths, the enthusiasm of subject participation, and the synergy of system emission reduction, thus achieving a cross-subject carbon credit system.

[0115] As one or more specific application examples of the embodiments of the present invention, the carbon account collaborative optimization method provided by the present invention is further described in detail by taking a certain industrial park as an example, as follows: The specific contents of the main carbon account, traditional carbon quota and dynamic carbon quota of the method of the present invention of a certain industrial park are shown in Table 1 below: Table 1 Comparison of carbon quotas and flexibility mechanisms

[0116] As can be seen from Table 1, the carbon account collaborative optimization method provided in this embodiment realizes dynamic collaborative emission reduction across entities and achieves the dual goals of "reducing total carbon emissions + increasing carbon credits".

[0117] For example, the traditional fixed quota for Enterprise A may lead to insufficient motivation for emission reduction or production constraints. This invention sets a wider elastic range based on its historical high emissions, which not only ensures its initial development space but also guides its gradual emission reduction through interval constraints.

[0118] Enterprise B responds to technological improvements and obtains carbon account points. The model automatically reduces its subsequent quota baseline value, forcing Enterprise B to further optimize emission reductions to maintain carbon points, forming a positive cycle of "technical reform, point rewards, quota tightening, and re-innovation."

[0119] The traditional single quota ignores actual differences. The present invention sets a flexible range based on the regional per capita emission average to better meet the actual needs of different users such as commuting, thereby improving fairness and acceptability.

[0120] The comparison results of the objective functions during the test period are shown in Table 2 below: Table 2 Comparison of objective functions during the test period

[0121] As can be seen from Table 2 above, among the three scenarios (i.e., conservative strategy, balanced strategy, and radical strategy), the balanced strategy is the best, with the lowest carbon emissions, a 12.5% ​​decrease compared to the traditional plan of 61,400 tons before the technical transformation; the quota elasticity of the conservative strategy is too small, which puts great pressure on emission reduction. Although it reduces carbon emissions, it suppresses the motivation for emission reduction, resulting in the lowest carbon credits; the quota elasticity of the radical strategy is too large, which makes the emission reduction constraints loose, and the total carbon emissions increase compared to the balanced strategy. The above comparison also shows that better emission reduction effects can be achieved by optimizing models with different parameters.

[0122] This embodiment also provides a carbon account collaborative optimization device for implementing the aforementioned embodiments and preferred implementations. Details already described are omitted for clarity. As used below, the term "module" may refer to a combination of software and / or hardware that implements a predetermined function. While the devices described in the following embodiments are preferably implemented using software, implementation using hardware, or a combination of software and hardware, is also possible and contemplated.

[0123] This embodiment provides a carbon account collaborative optimization device, such as Figure 4 As shown, including: The data acquisition module 401 is used to acquire multi-source heterogeneous data in real time.

[0124] The subject account establishment and carbon quota allocation module 402 is used to establish different subject carbon accounts based on multi-source heterogeneous data, and determine the baseline carbon emissions of different subject carbon accounts and the carbon quota elastic floating range of different subject carbon accounts.

[0125] The subject account basic carbon credit determination module 403 is used to determine the basic carbon credits of different subject carbon accounts based on the actual carbon emissions of different subject carbon accounts and the benchmark carbon emissions of different subject carbon accounts.

[0126] The model building module 404 is used to build a dynamic collaborative model with preset constraints based on the elastic floating range of carbon quotas of different carbon accounts, with the goal of minimizing the total carbon emissions of different carbon accounts and maximizing the basic carbon credits of different carbon accounts.

[0127] The collaborative optimization module 405 is used to collaboratively optimize the carbon emissions of the park based on the dynamic collaborative model and the preset carbon credit acquisition rules of different subject carbon accounts.

[0128] In some optional implementations, the multi-source heterogeneous data includes direct emission data, indirect emission data, integrated energy system data, and individual user energy consumption data; and the carbon account collaborative optimization device further includes: The data preprocessing and standard carbon equivalent value calculation module is used to use edge computing nodes to monitor abnormal data of direct emission data, indirect emission data, integrated energy system data and individual user energy consumption data, and delete abnormal data; build a park carbon emission factor library, and convert different unit data in direct emission data, indirect emission data, integrated energy system data and individual user energy consumption data into standard carbon equivalent values.

[0129] In some optional implementations, the subject account establishment and carbon quota allocation module 402 includes: The subject account establishment unit is used to establish corporate subject carbon accounts, operating subject carbon accounts and personal carbon accounts based on multi-source heterogeneous data.

[0130] The first carbon quota and elastic floating range determination unit is used to generate the first monthly benchmark carbon emissions based on the pre-acquired enterprise historical production capacity data and industry energy efficiency standards, and determine the first carbon quota elastic floating range based on the first monthly benchmark carbon emissions.

[0131] The second carbon quota and elastic floating range determination unit is used to generate a second monthly benchmark carbon emissions based on the pre-acquired emission reduction of the operating entity's renewable energy facilities, and determine the second carbon quota elastic floating range based on the second monthly benchmark carbon emissions.

[0132] The third carbon quota and elastic floating range determination unit is used to generate the third monthly benchmark carbon emissions based on the pre-acquired per capita carbon emissions in the park, and determine the third carbon quota elastic floating range based on the third monthly benchmark carbon emissions.

[0133] In some optional implementations, the subject account basic carbon credit determination module 403 includes: The actual carbon emissions acquisition unit is used to obtain the monthly actual carbon emissions of the corporate carbon account, the actual carbon emissions of the operating entity carbon account and the actual carbon emissions of the personal carbon account.

[0134] The entity account basic carbon credit determination unit is used to determine the basic carbon credits of the enterprise entity carbon account based on the actual carbon emissions of the enterprise entity carbon account and the first monthly benchmark carbon emissions; determine the basic carbon credits of the operating entity carbon account based on the actual carbon emissions of the operating entity carbon account and the second monthly benchmark carbon emissions; and determine the basic carbon credits of the personal carbon account based on the actual carbon emissions of the personal carbon account and the third monthly benchmark carbon emissions.

[0135] In some optional implementations, the preset constraints include energy supply and demand balance constraints and production demand constraints; the model building module 404 includes: The parameter acquisition unit is used to obtain the control parameters, comprehensive adjustment parameters and dynamic load adjustment response capability parameters in the carbon quota elastic floating range of different subject carbon accounts.

[0136] The model building unit is used to minimize the total carbon emissions of different subject carbon accounts and maximize the basic carbon credits of different subject carbon accounts. Based on the control parameters, comprehensive adjustment parameters and dynamic load adjustment response capabilities in the carbon quota elastic floating range of different subject carbon accounts, a dynamic collaborative model that includes energy supply and demand balance constraints and production demand constraints is constructed.

[0137] In some optional implementations, the preset carbon credits of different subject carbon accounts include carbon credits of corporate subject carbon accounts, carbon credits of operating subject carbon accounts, and carbon credits of personal carbon accounts. The collaborative optimization module 405 includes: The carbon credit acquisition unit of the enterprise carbon account is used to obtain the carbon credits of the enterprise carbon account based on the basic carbon credits of the enterprise carbon account, the corresponding number of dynamic load adjustments, the optimized amount of carbon emission intensity per unit output value, and the offset amount.

[0138] The carbon credit acquisition unit of the operating entity's carbon account is used to obtain the carbon credits of the operating entity's carbon account based on the basic carbon credits of the operating entity's carbon account, low-carbon transportation management, and energy-saving and carbon reduction management system deployment.

[0139] The personal carbon account carbon credit acquisition unit is used to obtain personal carbon account carbon credits based on personal carbon account basic carbon credits, green behavior incentives and daily emission reduction behavior records.

[0140] The further functional description of each of the above modules and units is the same as that of the above corresponding embodiments and will not be repeated here.

[0141] The carbon account collaborative optimization device in this embodiment is presented in the form of a functional unit, where the unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that executes one or more software or fixed programs, and / or other devices that can provide the above functions.

[0142] The embodiment of the present invention also provides a computer device having the above Figure 4 The carbon account collaborative optimization device shown.

[0143] See also Figure 5 , Figure 5 is a structural diagram of a computer device provided by an optional embodiment of the present invention, such as Figure 5 As shown, the computer device includes: one or more processors 10, memory 20, and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. Various components utilize different buses to communicate with each other and can be installed on a common mainboard or installed in other ways as needed. The processor can process the instructions executed in the computer device, including instructions stored in the memory or on the memory to display the graphical information of the GUI on an external input / output device (such as, a display device coupled to the interface). In some optional embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Equally, multiple computer devices can be connected, and each device provides part of the necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system). Figure 5 A processor 10 is taken as an example.

[0144] The processor 10 may be a central processing unit, a network processor, or a combination thereof. The processor 10 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit, a programmable logic device, or a combination thereof. The programmable logic device may be a complex programmable logic device, a field programmable gate array, a general purpose array logic, or any combination thereof.

[0145] The memory 20 stores instructions that can be executed by at least one processor 10, so that the at least one processor 10 executes the method shown in the above embodiment.

[0146] The memory 20 may include a program storage area and a data storage area, wherein the program storage area may store an operating system and application programs required for at least one function; the data storage area may store data created based on the use of the computer device, etc. In addition, the memory 20 may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some optional embodiments, the memory 20 may optionally include a memory remotely located relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0147] The memory 20 may include a volatile memory, such as a random access memory; the memory may also include a non-volatile memory, such as a flash memory, a hard disk or a solid-state drive; the memory 20 may also include a combination of the above types of memory.

[0148] The computer device further includes an input device 30 and an output device 40. The processor 10, the memory 20, the input device 30 and the output device 40 may be connected via a bus or other means. Figure 5 The bus connection is taken as an example.

[0149] The input device 30 can receive input digital or character information and generate key signal input related to user settings and function control of the computer device. Examples include a touch screen, keypad, mouse, trackpad, touchpad, pointing stick, one or more mouse buttons, trackball, joystick, etc. The output device 40 may include a display device, auxiliary lighting devices (e.g., LEDs), and tactile feedback devices (e.g., vibration motors). Such display devices include, but are not limited to, liquid crystal displays, light emitting diodes, monitors, and plasma displays. In some optional embodiments, the display device may be a touch screen.

[0150] The embodiment of the present invention also provides a computer-readable storage medium. The above-mentioned method according to the embodiment of the present invention can be implemented in hardware, firmware, or implemented as a computer code that can be recorded in a storage medium, or implemented as a computer code that is originally stored in a remote storage medium or a non-temporary machine-readable storage medium and downloaded through a network and will be stored in a local storage medium, so that the method described herein can be stored in such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only storage memory, a random access memory, a flash memory, a hard disk or a solid-state drive, etc.; further, the storage medium can also include a combination of the above-mentioned types of memory. It can be understood that a computer, a processor, a microprocessor controller or programmable hardware includes a storage component that can store or receive software or computer code. When the software or computer code is accessed and executed by a computer, a processor or hardware, the method shown in the above embodiment is implemented.

[0151] A portion of the present invention may be applied as a computer program product, such as a computer program instruction, which, when executed by a computer, can call or provide the method and / or technical solution according to the present invention through the operation of the computer. Those skilled in the art should understand that the form in which the computer program instruction exists in a computer-readable medium includes, but is not limited to, a source file, an executable file, an installation package file, etc. Accordingly, the way in which the computer program instruction is executed by the computer includes, but is not limited to: the computer directly executes the instruction, or the computer compiles the instruction and then executes the corresponding compiled program, or the computer reads and executes the instruction, or the computer reads and installs the instruction and then executes the corresponding installed program. Here, the computer-readable medium may be any available computer-readable storage medium or communication medium that can be accessed by the computer.

[0152] Although the embodiments of the present invention have been described with reference to the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present invention. Such modifications and variations are all within the scope defined by the appended claims.

Claims

1. A carbon account collaborative optimization method, characterized in that: The method comprises: Real-time collection of multi-source heterogeneous data; Establishing carbon accounts of different entities based on the multi-source heterogeneous data, and determining the benchmark carbon emissions of the carbon accounts of different entities and the elastic floating range of carbon quotas of the carbon accounts of different entities; Determine the basic carbon credits of different carbon accounts based on their actual carbon emissions and benchmark carbon emissions; With the goal of minimizing the total carbon emissions of different carbon accounts and maximizing the basic carbon credits of different carbon accounts, a dynamic collaborative model with preset constraints is constructed based on the elastic floating range of carbon quotas of different carbon accounts. Based on the dynamic collaborative model and the preset carbon credit acquisition rules of different subject carbon accounts, the park's carbon emissions are collaboratively optimized.

2. The method according to claim 1, characterized in that The multi-source heterogeneous data includes direct emission data, indirect emission data, integrated energy system data, and individual user energy consumption data; the method further includes: Using edge computing nodes to monitor abnormal data of the direct emission data, indirect emission data, integrated energy system data, and individual user energy consumption data, and delete abnormal data; Construct a park carbon emission factor library, and convert different unit data in the direct emission data, indirect emission data, integrated energy system data and individual user energy consumption data into standard carbon equivalent values.

3. The method according to claim 1, characterized in that The establishing of carbon accounts of different entities based on the multi-source heterogeneous data includes: Based on the multi-source heterogeneous data, an enterprise carbon account, an operating entity carbon account and a personal carbon account are established.

4. The method according to claim 3, characterized in that Determine the flexible floating range of carbon quotas for different entities' carbon accounts, including: Generate the first monthly benchmark carbon emissions based on the previously acquired enterprise historical production capacity data and industry energy efficiency standards, and determine the first carbon quota elastic floating range based on the first monthly benchmark carbon emissions; Generate the second monthly benchmark carbon emissions based on the pre-acquired emission reductions of the operator's renewable energy facilities, and determine the second carbon quota elastic floating range based on the second monthly benchmark carbon emissions; The third monthly benchmark carbon emissions are generated based on the pre-acquired per capita carbon emissions in the park, and the third carbon quota elastic floating range is determined based on the third monthly benchmark carbon emissions.

5. The method according to claim 4, characterized in that The determination of basic carbon credits for different carbon accounts based on actual carbon emissions and benchmark carbon emissions of different carbon accounts includes: Obtain the actual monthly carbon emissions of corporate carbon accounts, operating entity carbon accounts, and personal carbon accounts respectively; Determine the basic carbon credits of the enterprise's carbon account based on the actual carbon emissions of the enterprise's carbon account and the benchmark carbon emissions of the first month; Determine the basic carbon credits of the operating entity's carbon account based on the actual carbon emissions of the operating entity's carbon account and the second month's benchmark carbon emissions; The basic carbon points of the personal carbon account are determined based on the actual carbon emissions in the personal carbon account and the benchmark carbon emissions for the third month.

6. The method according to claim 1, characterized in that The preset constraints include energy supply and demand balance constraints and production demand constraints; The goal is to minimize the total carbon emissions of different carbon accounts and maximize the basic carbon credits of different carbon accounts, and to build a dynamic collaborative model with preset constraints based on the elastic floating range of carbon quotas of different carbon accounts, including: Obtain the control parameters, comprehensive adjustment parameters and dynamic load adjustment response capacity parameters in the carbon quota elastic floating range of different subject carbon accounts; With the goal of minimizing the total carbon emissions of different carbon accounts and maximizing the basic carbon credits of different carbon accounts, a dynamic collaborative model including energy supply and demand balance constraints and production demand constraints is constructed based on the control parameters, comprehensive adjustment parameters and dynamic load adjustment response capabilities in the elastic floating range of carbon quotas of different carbon accounts.

7. The method according to claim 5, characterized in that The preset carbon credits for different carbon accounts include corporate carbon account carbon credits, operating carbon account carbon credits, and personal carbon account carbon credits. The preset carbon credits for different carbon accounts are obtained in the following ways: Obtain carbon credits from the enterprise's carbon account based on the basic carbon credits of the enterprise's carbon account, the corresponding number of dynamic load adjustments, the optimized amount of carbon emission intensity per unit of output value, and the offset amount; Obtain carbon credits from the operating entity's carbon account based on the basic carbon credits of the operating entity's carbon account, low-carbon transportation management, and deployment of energy-saving and carbon reduction management systems; Obtain personal carbon account carbon points based on personal carbon account basic carbon points, green behavior incentives and daily emission reduction behavior records.

8. A carbon account collaborative optimization device, characterized in that: The device comprises: Data acquisition module, used to collect multi-source heterogeneous data in real time; A subject account establishment and carbon quota allocation module, configured to establish different subject carbon accounts based on the multi-source heterogeneous data, and determine the baseline carbon emissions of different subject carbon accounts and the carbon quota elastic floating range of different subject carbon accounts; A subject account basic carbon credit determination module is used to determine the basic carbon credits of different subject carbon accounts based on the actual carbon emissions of different subject carbon accounts and the benchmark carbon emissions of different subject carbon accounts; The model building module is used to build a dynamic collaborative model with preset constraints based on the elastic floating range of carbon quotas of different carbon accounts, with the goal of minimizing the total carbon emissions of different carbon accounts and maximizing the basic carbon credits of different carbon accounts; The collaborative optimization module is used to collaboratively optimize the carbon emissions of the park based on the dynamic collaborative model and the preset carbon credit acquisition rules of different subject carbon accounts.

9. A computer device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the carbon account collaborative optimization method according to any one of claims 1 to 7 by executing the computer instructions.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute the carbon account collaborative optimization method according to any one of claims 1 to 7.

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