A carbon reduction scheme automatic generation method and device based on carbon emission genes and a medium

Abstract

The present application relates to a kind of carbon reduction scheme automatic generation method, device and medium based on carbon emission gene, wherein, a kind of carbon reduction scheme automatic generation method, equipment and system based on carbon emission gene are disclosed, to solve the problems that existing carbon reduction scheme relies on artificial, matching is not accurate, poor implementability, method includes, collection enterprise production operation, energy consumption and carbon emission data and is arranged to obtain effective data;Analysis effective data identifies carbon emission key node, generates enterprise special carbon emission gene label;Based on the label matching carbon reduction technology and policy requirement applicable to enterprise;Generation executable carbon reduction implementation scheme, quantitatively calculate the emission reduction of each scheme and economic benefit and assess;Screening output preferred scheme.The present application realizes carbon reduction scheme accurate matching by carbon emission gene label, replaces artificial screening, improves scheme individualization and implementability, reduces enterprise carbon reduction decision cost, helps enterprise to efficiently implement carbon reduction target.

Description

Technical Field

[0001] This invention relates to the technical field of carbon emission reduction, and in particular to a method, apparatus and medium for automatically generating carbon reduction schemes based on carbon emission genes. Background Technology

[0002] Against the backdrop of the national "dual-carbon" strategy, energy-saving and carbon-reduction transformation and carbon asset development have become routine management requirements for industrial enterprises. Currently, the formulation of corporate carbon reduction plans mainly relies on manual research and screening of technical paths and manual calculation of emission reduction and economic benefits. This is not only inefficient, but also the quality of the plans is highly dependent on personnel experience, making it difficult to guarantee their adaptability and scientific validity.

[0003] Most existing energy consumption and emission management systems only have data statistics and basic accounting functions, and can only statically display enterprise emissions, failing to automatically generate targeted carbon reduction implementation plans. Some plan recommendation tools rely solely on industry-standard templates for technical matching, without considering the unique characteristics of each enterprise, such as its production conditions, emission node distribution, and emission intensity, resulting in poor plan targeting. Furthermore, existing technologies generally suffer from problems such as a disconnect between plan generation and policy compliance requirements, a simplistic approach to calculating emission reductions and economic benefits, and a lack of quantitative assessment and optimization mechanisms. The generated plans struggle to balance emission reduction effectiveness, economic benefits, and policy compatibility, and lack sufficient integration with engineering implementation and carbon asset development processes, resulting in low practical executability.

[0004] In summary, there is currently a lack of a complete technical means to automatically and personalizedly generate carbon reduction solutions based on a company's own emission characteristics, and to complete quantitative assessment and optimization, which cannot meet the actual needs of companies to carry out carbon reduction work efficiently, accurately and compliantly. Summary of the Invention

[0005] The main objective of this invention is to provide a method, apparatus, and medium for automatically generating carbon reduction schemes based on carbon emission genes. By using carbon emission gene tags, carbon reduction schemes can be accurately matched, replacing manual screening and improving the personalization and feasibility of the schemes.

[0006] To achieve the above objectives, this invention provides an automatic method for generating carbon reduction schemes based on carbon emission genes, comprising the following steps: Collect enterprises' energy consumption data, carbon emission data, and basic production and operation data to identify key carbon emission nodes and form corresponding enterprise carbon emission gene tags. Based on the carbon emission gene tag, carbon reduction technologies and corresponding policy requirements suitable for the enterprise are matched from the pre-established carbon reduction technology library and carbon policy library; Based on the aforementioned carbon reduction technologies and policy requirements, at least one executable corporate carbon reduction implementation plan will be automatically generated. For the aforementioned carbon reduction implementation plan, the carbon emission reduction and economic benefits are quantitatively calculated to obtain the corresponding plan evaluation results; Based on the evaluation results of the proposed schemes, the various carbon reduction implementation schemes are ranked and screened, and the preferred carbon reduction schemes are output.

[0007] Furthermore, the steps of collecting enterprises' energy consumption data, carbon emission data, and basic production operation data to identify key carbon emission nodes and form corresponding enterprise carbon emission genetic tags include: Collect basic production operation data from each production stage of the enterprise, filter and organize the collected data, remove invalid and abnormal data, and obtain valid data; The effective data was analyzed to identify key carbon emission nodes where enterprises had high carbon emission concentrations and large emission volumes. Based on the type and emission intensity of the key carbon emission nodes, corresponding corporate carbon emission gene tags are generated.

[0008] Furthermore, the step of generating corresponding corporate carbon emission genomic tags based on the type and emission intensity of the key carbon emission nodes includes: Classify the identified key carbon emission nodes and clarify the emission types corresponding to each key node; The emission intensity level of each key node is quantified, and the "key node type + emission intensity level" is combined and coded to generate a unique corresponding corporate carbon emission gene tag. The corporate carbon emission gene tag is directly associated with the carbon reduction technology matching logic.

[0009] Furthermore, the step of matching carbon reduction technologies and corresponding policy requirements suitable for the enterprise from a pre-established carbon reduction technology library and carbon policy library based on the carbon emission genotype includes: Based on the matched carbon reduction technologies and the industry to which the enterprise belongs, the corresponding carbon reduction support policies and compliance requirements are retrieved from the carbon policy database; The retrieved carbon reduction technologies and policy requirements are screened for suitability to determine the carbon reduction technologies and corresponding policy requirements applicable to the enterprise.

[0010] Furthermore, the step of automatically generating at least one executable corporate carbon reduction implementation plan based on the aforementioned carbon reduction technologies and policy requirements includes: Determine the basic parameters of the carbon reduction implementation plan, including the implementation period, expected investment budget, and emission reduction targets; Based on the matched carbon reduction technologies, clarify the application stages, implementation steps, and dosage of each carbon reduction technology; By aligning with relevant policy requirements and adjusting the details of the plan, at least one executable corporate carbon reduction implementation plan can be automatically generated.

[0011] Furthermore, the step of quantitatively calculating the carbon emission reduction and economic benefits of the aforementioned carbon reduction implementation scheme to obtain the corresponding scheme evaluation results includes: Determine the calculation benchmarks for each carbon reduction implementation plan, including the enterprise's current carbon emission benchmark, energy consumption benchmark, and basic cost benchmark; Based on the application effects of carbon reduction technologies, the carbon emission reduction that can be achieved by each scheme is quantitatively calculated; Calculate the implementation cost, operating cost, and expected revenue of each plan to obtain the economic benefit data for each plan; By integrating the carbon emission reduction data and economic benefit data, an evaluation result is generated for each carbon reduction implementation plan.

[0012] Furthermore, the step of ranking and screening the various carbon reduction implementation schemes based on the scheme evaluation results, and outputting the selected preferred carbon reduction scheme, includes: Set sorting and screening criteria, including priority of carbon emission reduction, priority of economic benefits, and priority of policy compatibility; Based on the aforementioned ranking and screening criteria, the evaluation results of each carbon reduction implementation plan are comprehensively scored and ranked. Select the top-scoring proposals that meet the company's preset emission reduction targets and investment budgets, and eliminate proposals that do not meet the requirements. The selected carbon reduction solutions are compiled and optimized, the core implementation points of each solution are clarified, and the results are output to the user terminal.

[0013] This invention also provides an automatic generation device for carbon reduction schemes based on carbon emission genes, comprising: The data collection and processing module is used to collect basic production operation data, energy consumption data, and carbon emission data of various production links of the enterprise, and to filter and process the collected data, removing invalid and abnormal data to obtain valid data. The key node identification module is used to analyze the effective data and identify key carbon emission nodes with high carbon emission concentration and large emission volume for enterprises. The carbon emission gene tag generation module is used to generate corresponding corporate carbon emission gene tags based on the type and emission intensity of the key carbon emission nodes. The technology policy matching module is used to match and screen the carbon reduction technologies and corresponding policy requirements applicable to the enterprise from a pre-established carbon reduction technology library and carbon policy library based on the carbon emission gene tag. The scheme generation module is used to determine basic parameters and adjust scheme details based on the matched carbon reduction technologies and policy requirements, and automatically generate at least one executable corporate carbon reduction implementation plan. The quantitative assessment module is used to determine the calculation benchmark for each carbon reduction implementation plan, quantify the carbon emission reduction and economic benefit data of each plan, and integrate them to form the plan assessment results. The sorting and filtering output module is used to set sorting and filtering criteria, comprehensively score, sort, and filter various carbon reduction implementation plans, and organize and output the best carbon reduction plans to the user terminal.

[0014] The present invention also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, characterized in that the processor executes the computer program to implement the steps of the automatic generation method for carbon reduction scheme based on carbon emission genes as described above.

[0015] The present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the automatic generation method for carbon reduction schemes based on carbon emission genes as described above. Attached Figure Description

[0016] Figure 1 This is a flowchart of an automatic generation method for carbon reduction schemes based on carbon emission genes in one embodiment of the present invention; Figure 2 This is a structural block diagram of an automatic carbon reduction scheme generation device based on carbon emission genes in one embodiment of the present invention; The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0018] Reference Figure 1 This is a flowchart illustrating an automatic generation method for carbon reduction schemes based on carbon emission genes proposed in this invention, including the following steps: S1 collects energy consumption data, carbon emission data and basic production operation data of enterprises, identifies key carbon emission nodes of enterprises, and forms corresponding enterprise carbon emission gene tags. S2, Based on the carbon emission gene tag, match the carbon reduction technologies and corresponding policy requirements applicable to the enterprise from the pre-established carbon reduction technology library and carbon policy library; S3, based on the aforementioned carbon reduction technologies and policy requirements, automatically generate at least one executable corporate carbon reduction implementation plan; S4. For the carbon reduction implementation plan, perform quantitative calculations on carbon emission reduction and economic benefits respectively to obtain the corresponding plan evaluation results; S5. Based on the evaluation results of the proposed scheme, sort and screen the various carbon reduction implementation schemes, and output the preferred carbon reduction scheme after screening.

[0019] As described in step S1 above, the system collects basic production operation data, energy consumption data, and carbon emission data from each production stage of the enterprise. Data sources cover various types of real business data, including enterprise production ledgers, energy consumption metering equipment, and carbon emission accounting records, ensuring data coverage of the entire production process and complete statistical period. The system standardizes and organizes the collected data, eliminating missing, duplicate, or out-of-range invalid and abnormal data through data verification rules, retaining only valid data that accurately reflects the enterprise's production and emission status. Based on the valid data, the system conducts multi-dimensional analysis, calculating carbon emission concentration and cumulative emission scale by production stage, identifying core stages with high carbon emission concentrations and large total emissions, and recognizing these as key carbon emission nodes. The system integrates the emission attributes and emission intensity levels of these key nodes to form a unique carbon emission gene tag corresponding to the enterprise's emission characteristics, providing precise and personalized basis for subsequent technology and policy matching.

[0020] As described in step S2 above, the system uses the enterprise's carbon emission genetic label as the matching basis to conduct precise searches in a pre-set carbon reduction technology library. This library contains various mature energy-saving and carbon reduction technologies, covering suitable technology types for different emission nodes. The system automatically filters out carbon reduction technologies that highly align with key carbon emission nodes. Combining the matched carbon reduction technology types with the enterprise's industry category, the system searches the carbon policy library for corresponding industry and technology carbon reduction support policies, subsidy conditions, and carbon asset development compliance requirements. The policy library contains the latest local and industry carbon reduction policy provisions and can be dynamically updated. The system performs a dual matching and screening of the retrieved carbon reduction technologies and policy requirements, eliminating technologies and policies that are incompatible with the enterprise's production conditions or cannot meet compliance requirements. Ultimately, it determines suitable solution elements that combine technical feasibility and policy compliance, laying the foundation for subsequent solution generation.

[0021] As described in step S3 above, the system combines the company's current production and operation status with its carbon reduction needs to determine the core basic parameters of the carbon reduction implementation plan, clarifying the implementation cycle, expected investment budget range, and phased emission reduction targets. The parameter settings are tailored to the company's actual operating capabilities and carbon reduction plan. The system matches the completed carbon reduction technologies with the company's key carbon emission nodes, clarifying the specific application stages, on-site implementation steps, and equipment or material usage for each carbon reduction technology, detailing the implementation details of the technology. The system compares the retrieved and screened policy compliance requirements and subsidy application conditions, adjusting and optimizing the implementation process, technical parameters, record requirements, and other details of the plan to ensure that the plan complies with policy regulations throughout. The system integrates basic parameters, technical implementation details, and policy adaptation content to automatically generate at least one enterprise carbon reduction implementation plan with on-site implementation conditions. The plan content is complete and can be directly used for subsequent evaluation and implementation.

[0022] As described in step S4 above, the system uses the enterprise's actual production data before the transformation as a basis to determine a unified calculation benchmark for each carbon reduction implementation plan, including carbon emission benchmark values, energy consumption benchmark values, and basic production and operation cost benchmarks, ensuring that the evaluation criteria for different plans remain consistent. The system combines the actual application efficiency of carbon reduction technologies and historical emission data at key carbon emission nodes to quantify the carbon emission reductions achievable after the implementation of each plan, with the calculation process closely aligned with the enterprise's actual production conditions. The system calculates the initial implementation costs, subsequent operation and maintenance costs, carbon reduction benefits, and policy subsidy benefits for each plan, forming complete economic benefit data. The system integrates the quantified carbon emission reduction data with the economic benefit data, conducting a comprehensive analysis from the two core dimensions of emission reduction effect and economic benefit, forming a unique evaluation result for each carbon reduction implementation plan, providing objective quantitative support for plan selection.

[0023] As described in step S5 above, the system incorporates three core ranking and screening criteria: carbon emission reduction, economic benefits, and policy suitability. Enterprises can determine the weighting of each criterion according to their own needs, adapting to different enterprises' carbon reduction decision-making preferences. Based on the set ranking and screening criteria, the system quantitatively scores and comprehensively ranks the evaluation results of various carbon reduction implementation plans, intuitively reflecting the superiority or inferiority of each plan. The system compares the scored and ranked plans with the enterprise's preset emission reduction targets and investment budget limits, selecting the top-ranked plans that fully meet the enterprise's constraints and automatically eliminating those that do not meet the requirements. The system organizes and refines the selected optimal carbon reduction plans, clarifying key aspects such as core technologies, implementation points, and expected effects, and outputs the final optimal plan to the user terminal, providing direct and implementable reference for enterprise carbon reduction decisions.

[0024] The system's database adopts a fixed three-level architecture: industry classification, emission node type, and technical parameters. This architecture is fully mapped to the coding rules of the carbon emission gene tag of this invention. Each technical entry is assigned a unique technical identification code, and the coding rules are completely consistent with those of the carbon emission gene tag (GB / T4754 industry code - emission type code - emission intensity level code - technical serial number). The core mandatory fields for each technical entry include: unique technical identification code, applicable industry category, applicable emission node type, applicable emission intensity level, technical principle, engineering implementation process, measured emission reduction efficiency range, unit capacity investment cost, standard implementation cycle, applicable operating condition boundary conditions, annual operation and maintenance cost coefficient, equipment adaptation requirements, authoritative verification source, and engineering case data. Among them, the emission type code is fixed in three categories: 01 = fuel combustion emission, 02 = process emission, and 03 = indirect emission from electricity generation; the emission intensity level code is fixed in three categories: 01 = high emission intensity, 02 = medium emission intensity, and 03 = low emission intensity, corresponding 1:1 to the coding rules of the carbon emission gene tag. The database has been pre-classified according to the GB / T4754 National Economic Industry Classification, covering 31 mainstream industrial sectors such as equipment manufacturing, steel, chemical, building materials, textile, papermaking, and food processing. Each sector is further classified into secondary and tertiary levels according to emission type and production process to ensure complete matching with the classification rules for key carbon emission nodes of enterprises.

[0025] The database has built-in fixed field-level matching logic. After reading the enterprise's carbon emission gene tag, the system first decomposes the industry code, emission type code, and emission intensity level code in the tag, and then performs precise matching with the unique identification code of the technology item. It prioritizes outputting technology items with completely matching codes, and then supplements similar technology items in the same industry and emission type to form a preliminary search list. Precise matching can be completed without any manual intervention.

[0026] The database is configured with an annual full update and a monthly incremental update mechanism. Every year, the entire database is iterated in conjunction with the latest technology promotion catalog released by the official authorities, and outdated technologies that have been explicitly eliminated by the state are removed. Every month, new industrialization technologies that have been verified by authoritative organizations are added. All new technology entries must be accompanied by emission reduction efficiency test reports issued by authoritative third-party institutions and at least one engineering case data that has been implemented. Before being added to the database, industry adaptability and operating condition boundary verification must be completed to ensure that all technologies added to the database are engineering-applicable.

[0027] The carbon policy library is a structured policy database with real-time updates, clear hierarchies and strong adaptability, which serves as the core foundation for ensuring the compliance and policy adaptability of carbon reduction schemes. Its complete construction rules are as follows: All policy entries in the database are derived from currently valid documents officially released by the government. The database adopts a three-level fixed architecture of regional hierarchy - industry category - applicable technology. Each policy entry is assigned a unique policy identification code, and the coding rule is regional hierarchy code - industry code - policy type code - policy serial number, where the regional hierarchy codes are: 01=national level, 02=provincial level, 03=municipal level, 04=district and county level; the policy type codes are: 01=compliance control requirements, 02=subsidy support policies, 03=carbon asset development specifications. The core mandatory fields of each policy entry include: unique policy identification code, issuing entity, effective period, applicable region, applicable industry, applicable carbon reduction technology type, core policy content, compliance control boundary, subsidy application conditions, subsidy accounting standards, declaration process and material requirements, emission reduction accounting and verification rules, and invalidation status. The database realizes monthly dynamic updates through public data interfaces on the official websites of national and local governments, synchronizing newly added policies in real time, revising policy contents, and marking invalid policies, so as to ensure the timeliness and accuracy of policy contents. After each update, the association and binding between policy entries and technical entries in the carbon reduction technology library is completed synchronously, to clarify the applicable policy entries corresponding to each technology, and realize the linkage matching between technologies and policies. The database has a built-in policy compliance check logic, which can automatically identify the applicable boundaries of policies. For policies with restrictions on regions, industries and technology types, it can accurately filter out non-adaptable entries, and only output policy content that fully corresponds to the enterprise's region, industry and matched technologies, ensuring that subsequent scheme generation fully meets the policy compliance requirements.

[0028] In one embodiment, the step S1 of collecting the enterprise's energy consumption data, carbon emission data and basic production operation data, identifying the key nodes of the enterprise's carbon emission, and forming the corresponding enterprise carbon emission gene tag, includes: S11: Multi-source production and carbon emission basic data collection of the enterprise; S12: Raw data screening and invalid and abnormal data elimination; S13: Analysis and identification of key nodes of enterprise carbon emission; S14: Generation of enterprise carbon emission gene tag.

[0029] In the specific implementation process, the system completes the entire process data collection through three types of standardized interfaces, including: ① Industrial Internet interface, which connects to the enterprise's on-site intelligent energy consumption metering instruments, production equipment PLC control systems, energy management systems (EMS), and DCS systems to collect equipment operating parameters and hourly energy consumption data in real time; ② System interface, which connects to the enterprise's ERP system, financial system, and carbon emission management system to collect the enterprise's production ledgers, energy procurement settlement documents, annual carbon emission accounting reports, and equipment asset ledgers; ③ Manual upload interface, which supports enterprises to upload supplementary data such as equipment maintenance records, production shift plans, and historical technical improvement data. The core indicators of the collected content are fixed as follows: capacity load of each production process, product output, equipment service life, operating parameters, various energy consumption data such as electricity / heat / gas / coal consumption, greenhouse gas emission accounting data of each process, production shift arrangements, and equipment maintenance plans. The data statistics period is fixed to the normal production period of the enterprise for 12 consecutive months, automatically excluding periods of complete shutdown such as the Spring Festival and statutory holidays, to ensure that the data can completely and accurately reflect the emission characteristics of the enterprise under normal production conditions.

[0030] The system performs four levels of standardized cleaning and verification on the collected raw data: ① Integrity verification: removing invalid data with a field missing rate exceeding 30% or duplicate entries; ② Logical verification: removing data that does not conform to production logic, such as abnormal data where output is 0 but energy consumption is not; ③ Outlier verification: using box plots to identify and remove outliers that deviate from the data mean by more than 3 standard deviations; ④ Operating condition verification: excluding energy consumption and emission data generated under abnormal production conditions such as equipment maintenance, temporary shutdowns, and abnormal fluctuations in operating conditions. Finally, valid data that conforms to the company's regular production patterns is retained, and standardized classification and storage are performed according to production processes, providing a unified and accurate data foundation for subsequent analysis.

[0031] The system uses production processes as the smallest accounting unit, breaking down effective data for calculation of four core indicators: ① the total cumulative carbon emissions of each process and its proportion of the company's total emissions; ② carbon emission intensity per unit product; ③ carbon emission concentration per unit time; and ④ overall energy consumption ratio. The system pre-sets fixed thresholds for identifying key carbon emission nodes: production processes with a cumulative carbon emission ratio ≥3% and a carbon emission intensity per unit product higher than the industry average officially published by the relevant industry authority are identified as key carbon emission nodes for the company. Simultaneously, the system records basic information such as emission sources, production conditions, equipment parameters, and accounting boundaries for each key node, ensuring that the identification results accurately address the company's emission pain points.

[0032] The system performs standardized classification and coding on the identified key carbon emission nodes. First, it clarifies the emission type corresponding to each key node, fixing it into three categories: fuel combustion emissions, process emissions, and indirect power generation emissions, corresponding to codes 01, 02, and 03, respectively. Then, based on the total carbon emission proportion of each key node, it quantifies the emission intensity level, where an emission proportion ≥10% is high emission intensity (code 01), 3% ≤ emission proportion <10% is medium emission intensity (code 02), and emission proportion <3% is low emission intensity (code 03). Finally, it uses a fixed rule of "GB / T4754 industry code - emission type code - emission intensity level code" to combine and encode the characteristics of each key node, generating a unique carbon emission gene tag corresponding to the enterprise's emission characteristics. This tag forms a direct mapping with the technology identification code in the carbon reduction technology library, which can be directly used for accurate retrieval and matching of subsequent carbon reduction technologies.

[0033] Taking a general equipment manufacturing enterprise (GB / T4754 industry code C34) as an example, after data collection and calculation, the carbon emissions of the enterprise's casting process account for 42%, the emission type is process emission (02), and the emission intensity is high (01); the carbon emissions of the heat treatment process account for 28%, the emission type is fuel combustion emission (01), and the emission intensity is high (01); the carbon emissions of the machining process account for 8%, the emission type is indirect power emission (03), and the emission intensity is medium (02). The resulting enterprise carbon emission gene tags are "C34-02-01, C34-01-01, C34-03-02", which fully cover the core emission characteristics of the enterprise and can be directly matched with the carbon reduction technology library.

[0034] In one embodiment, step S2, which matches the carbon reduction technologies and corresponding policy requirements applicable to the enterprise from a pre-established carbon reduction technology library and carbon policy library based on the carbon emission genotype, includes: S21, Search for carbon reduction technologies based on carbon emission gene tags; S22, Search for industry-specific carbon reduction policies and compliance requirements; S23, Screening for the compatibility of carbon reduction technologies with policy content; S24, Determining the applicable carbon reduction technologies and policies for enterprises.

[0035] In its implementation, the system breaks down a company's carbon emission genetic label into three core feature fields: industry code, emission type code, and emission intensity level code. These are used as search keywords to perform a three-level precise matching search within the carbon reduction technology database. The first level matches the industry code, filtering out technology entries that perfectly match the company's industry. The second level matches the emission type code, filtering out technology entries consistent with the emission types at key carbon emission nodes. The third level matches the emission intensity level code, filtering out technology entries that match the emission intensity at those nodes. Through this three-level matching, a preliminary list of carbon reduction technologies highly aligned with the company's carbon emission genetic characteristics is selected. Simultaneously, core parameters such as emission reduction efficiency, investment cost, and implementation conditions are extracted for each technology entry, ensuring that the retrieved technologies accurately correspond to the company's emission pain points.

[0036] Based on the initial list of carbon reduction technologies, the system simultaneously extracts four basic pieces of information about the enterprise: industry category, registered region, enterprise size, and production capacity. It then performs a four-level targeted search within the carbon policy database. The first level matches applicable regions, filtering out currently valid policies in the enterprise's registered location. The second level matches applicable industries, filtering out policies relevant to the enterprise's industry. The third level matches suitable technology types, filtering out policies corresponding to the initially retrieved carbon reduction technologies. The fourth level matches policy validity status, removing expired policies. Through this four-level search, the system ultimately selects policy entries that perfectly match the enterprise and its suitable technology, clarifying the policy's applicable conditions, subsidy standards, compliance requirements, application procedures, and other core information.

[0037] The system conducts a dual-adaptability screening of the carbon reduction technologies and policies initially retrieved. The first dimension is technical feasibility screening, which verifies the applicable operating conditions, equipment compatibility requirements, and unit investment costs of carbon reduction technologies based on the company's existing equipment operating conditions, production capacity load, workshop layout, and annual technological upgrading investment budget. Technologies with excessively high technical requirements, requiring large-scale production shutdowns for renovation, incompatible with the company's existing equipment, or exceeding the company's budget for a single technology investment are eliminated. The second dimension is policy compliance screening, which selects technology and policy combinations that meet the policy compliance conditions and can apply for relevant subsidies or carbon asset development qualifications, based on the retrieved policy requirements. Technologies that are out of touch with policy requirements and cannot enjoy policy support are eliminated. Finally, technology and policy combinations that simultaneously meet both technical feasibility and policy compliance are retained.

[0038] The system integrates the results of the suitability screening to form a list of carbon reduction technologies and policies tailored to each enterprise. It clarifies the application nodes, implementation points, emission reduction potential of each carbon reduction technology, as well as the supporting policy content, compliance requirements, and benefit acquisition paths, providing clear technical and policy basis for the subsequent generation of carbon reduction solutions.

[0039] Taking the aforementioned equipment manufacturing enterprise as an example, its carbon emission gene tags are C34-02-01, C34-01-01, and C34-03-02. After a three-level matching search, three technologies were initially selected: energy-saving retrofit of medium-frequency furnaces, waste heat recovery of heat treatment furnaces, and energy-saving retrofit of servo motors. Combining the enterprise's registered location and industry attributes, a four-level search was conducted, which matched two core policies of the enterprise's registered location: "Management Measures for Subsidies for Industrial Energy-Saving Technology Transformation Projects" and "Methodology for Voluntary Greenhouse Gas Emission Reduction Projects: Utilization of Industrial Waste Heat and Pressure." After a double-fit screening, the servo motor energy-saving retrofit technology, which requires large-scale replacement of machining equipment and investment exceeding the enterprise's annual technological transformation budget, was eliminated. Finally, the two applicable technologies, energy-saving retrofit of medium-frequency furnaces and waste heat recovery of heat treatment furnaces, were determined, along with the corresponding subsidy application conditions and CCER project development compliance requirements.

[0040] In one embodiment, step S3, which automatically generates at least one executable corporate carbon reduction implementation plan based on the aforementioned carbon reduction technologies and policy requirements, includes: S31, Determination of the implementation cycle, budget and targets for carbon reduction plans; S32 clarifies the application stages and implementation details of carbon reduction technologies; S33, Detailed adjustments to the policy compliance-oriented plan; S34, Enterprise carbon reduction implementation plan is automatically generated.

[0041] In the specific implementation process, the system combines the company's annual production plan, equipment routine maintenance window, annual energy conservation and carbon reduction special fund amount, and low-carbon transformation work deployment to determine the implementation cycle of the carbon reduction plan. Priority is given to arranging the transformation and construction period during the company's off-season and equipment maintenance period. The transformation time of a single process is fully matched with the company's routine equipment maintenance time to avoid affecting normal production. Referring to the unit investment cost of similar technologies in the carbon reduction technology library, and combining the company's implementation scale and number of equipment, the expected investment budget range of the plan is accurately defined, with the budget fluctuation ratio not exceeding 10%. In accordance with the company's carbon emission control indicators, local emission reduction task requirements, and carbon quota compliance gap, the phased carbon emission reduction quantitative targets of the plan are set. The target value is not lower than the company's preset minimum emission reduction requirements. All parameters are in line with the company's actual operating capacity and there are no unreasonable settings that are divorced from production reality.

[0042] The system will select and identify carbon reduction technologies and link them one by one with the key carbon emission nodes of the enterprise, clarifying the specific application procedures and on-site implementation locations for each carbon reduction technology. Combining the technical engineering implementation process, equipment selection standards, and construction specifications in the carbon reduction technology library, the system will refine the details of the entire process of technology implementation. The core includes: equipment selection list, material usage and specifications, construction procedures and connection plans, implementation cycle of single procedures, construction personnel configuration, production continuity assurance plan, equipment commissioning process, and completion acceptance standards. This ensures that the application of the technology is fully compatible with the enterprise's existing production conditions, workshop layout, and equipment status, and will not interfere with the enterprise's normal production order.

[0043] The system compares and matches the completed policy compliance requirements, subsidy application conditions, and carbon asset development accounting rules, and makes targeted adjustments and optimizations to the details of the plan. Regarding the policy requirement that emission reductions be measurable and verifiable, it supplements the installation and configuration plans for carbon emission metering instruments and energy consumption monitoring equipment for corresponding processes, clarifying the accuracy level, installation location, data collection frequency, storage requirements, and reporting procedures for the metering equipment. Regarding subsidy application requirements, it supplements the comparison of equipment parameters before and after the modification, energy consumption data comparison, process data retention nodes, and archiving requirements. Regarding carbon asset development requirements, it adjusts the implementation boundaries, accounting cycle, and monitoring plan of the plan to ensure that the plan fully complies with policy regulations and has the basic conditions for applying for policy subsidies and carrying out carbon asset development.

[0044] The system integrates all elements of information, including implementation cycle, investment budget, emission reduction targets, technology implementation details, policy compliance requirements, safety assurance measures, and risk prevention and control plans. Following a standardized template of "Project Overview - Implementing Entity and Scope - Implementation Cycle and Schedule - Investment Budget Details - Emission Reduction Targets - Core Technology Implementation Details - Policy Compliance and Benefit Plan - Acceptance Criteria - Safety Assurance Measures - Risk Prevention and Control Plan," the system automatically generates at least one executable corporate carbon reduction implementation plan. The system can generate multiple differentiated plans based on the technology implementation sequence and investment scale, including single-technology phased implementation plans, multi-technology simultaneous implementation plans, and phased technological upgrading implementation plans, for subsequent evaluation and selection by the enterprise.

[0045] Taking the aforementioned equipment manufacturing enterprise as an example, the system, considering the enterprise's production characteristics—July-August being the off-season and the routine equipment maintenance window being 15 days—determines the implementation period for the plan to be 6 months (July to December), with an investment budget range of 800,000-1,000,000 yuan and an annual carbon emission reduction target of 120 tons of CO2 equivalent. The energy-saving retrofit of the intermediate frequency furnace is linked to the casting process, specifying the replacement of two 1.5MW energy-saving intermediate frequency furnaces with the existing equipment that has been in service for over 8 years. Construction will be carried out in two batches, with one furnace being retrofitted in each batch. The retrofit cycle for each furnace is 15 days, completely coinciding with the equipment maintenance window. During construction, a backup intermediate frequency furnace will be used to ensure production continuity. The waste heat recovery of the heat treatment furnace is linked to the heat treatment process, specifying the installation of a shell-and-tube waste heat recovery heat exchanger, laying 30 meters of seamless heat exchange pipeline, and connecting to the exhaust ports of the two existing heat treatment furnaces. Construction will be carried out during periods other than... During peak daily production hours, construction time does not exceed 4 hours per day, ensuring uninterrupted normal production of the heat treatment process. In accordance with local energy-saving subsidy policies requiring "the installation of metering and monitoring equipment with an accuracy of no less than 0.5 and the retention of records throughout the entire renovation process," and the CCER development standard of "measurable and verifiable emission reductions," two additional carbon emission metering instruments with an accuracy of 0.5 were installed (corresponding to the two renovation processes respectively). The requirements for daily data collection and monthly archiving were clarified, and a weekly comparison and recording of equipment operating parameters and energy consumption data before and after the renovation was added. Ultimately, two executable plans were automatically generated: a "prioritize medium-frequency furnace renovation and implement waste heat recovery in stages" plan and a "simultaneous implementation of both technologies" plan. These plans clearly define the schedule, material list, division of responsibilities, and acceptance standards for each step of the construction, allowing enterprises to directly refer to the plans to organize on-site construction.

[0046] In one embodiment, step S4, which involves quantifying the carbon emission reduction and economic benefits of the carbon reduction implementation scheme to obtain the corresponding scheme evaluation results, includes: S41, The unified calculation benchmark for the scheme is determined; S42, Quantitative calculation of carbon emission reduction in the plan; S43, Accounting for the economic benefits of the implementation and operation of the plan; S44, The comprehensive evaluation results of the plan are formed.

[0047] In the specific implementation process, the system uses the normal production and operation data of the enterprise for 12 consecutive months before the transformation as the basis to determine the unified calculation benchmark for each carbon reduction implementation plan. The core fixed indicators include: the benchmark value of carbon emissions for each process of the enterprise, the benchmark value of energy consumption per unit product, the benchmark unit price of various energy purchases, the benchmark cost of routine operation and maintenance of equipment, the benchmark monthly average transaction price of the national carbon market, and the benchmark rate of return of the corresponding industry. All benchmark data are taken from the enterprise's actual production ledger, energy settlement documents, industry accounting guidelines issued by the Ministry of Ecology and Environment, and publicly available transaction data of the national carbon market, to ensure that different plans are calculated under the same reference standard and to completely avoid the assessment bias caused by differences in data caliber.

[0048] The system is based on the industry-specific greenhouse gas emission accounting guidelines issued by the Ministry of Ecology and Environment. It adopts a universally applicable carbon emission reduction quantification formula and combines it with the key carbon emission nodes corresponding to the enterprise's carbon emission genotype and the measured emission reduction efficiencies from the carbon reduction technology database to conduct accurate accounting. The general calculation formula is: ER = In the formula, ER represents the cumulative carbon emission reduction over the entire project lifecycle, expressed in tons of CO2 equivalent. The annual baseline carbon emissions for the i-th critical carbon emission node are taken from the enterprise's measured data in step S1, and the unit is tons of carbon dioxide equivalent per year. The measured emission reduction efficiency of the corresponding carbon reduction technology is taken from the median range of emission reduction efficiency of the corresponding technology entry in the carbon reduction technology database, and the unit is % (%). The dynamic correction coefficient for enterprise production conditions has a fixed range of 0.8-1.2. It is determined by weighting four sub-parameters: the service life of equipment at key enterprise nodes, actual capacity load rate, production shift stability, and continuity of operating conditions, with weighting percentages of 40%, 30%, 20%, and 10%, respectively. The policy compliance adjustment coefficient is determined based on the official carbon emission accounting guidelines and carbon asset development methodology. It is set to 1.0 only when the scheme meets the policy accounting boundary requirements, and is adjusted downward according to policy requirements when it does not. The calculation period for the plan is in years; This represents the number of key carbon emission nodes covered by the scheme. During the calculation process, the system automatically eliminates interference from abnormal operating conditions such as equipment maintenance and temporary shutdowns, ensuring that the calculation results closely reflect the actual production status of enterprises and fully comply with official carbon emission accounting rules. The calculation results can be directly used for carbon quota compliance, carbon asset development, and subsidy application.

[0049] The system calculates the full-cycle costs and comprehensive benefits of each carbon reduction scheme, generating complete economic benefit data with a unified accounting standard across the industry. Cost dimensions include: one-time implementation costs such as energy-saving equipment procurement fees, construction and installation fees, commissioning fees, and testing fees; and annual operating costs such as routine equipment inspection fees, replacement fees for vulnerable parts, and maintenance labor costs. All cost parameters are taken from standardized cost indicators in the carbon reduction technology database. Benefit dimensions include: energy-saving and cost-reduction benefits from annual energy consumption reductions; one-time / annual policy subsidies; carbon asset trading revenue; and energy consumption tax and fee reduction benefits. All benefit parameters are taken from subsidy standards, actual energy unit prices for enterprises, and the average transaction price in the national carbon market, all from the carbon policy database.

[0050] By comparing income and expenditure, the three core economic indicators of the plan are calculated, and the general calculation formula is as follows: Annual net income = + - Static investment payback period = / In the formula The total cost of implementing the plan in one go. To generate annual energy savings and cost reduction benefits, For available policy subsidies; Annual carbon asset trading revenue; The system calculates the annual operation and maintenance cost of the solution. It systematically integrates the quantified carbon emission reduction indicators with economic benefit indicators to form a comprehensive evaluation result that includes four dimensions: emission reduction effect, economic feasibility, policy compatibility, and implementation difficulty. Each dimension corresponds to a clear quantitative indicator, clearly presenting the core value and advantages and disadvantages of each solution, and providing objective and traceable quantitative support for subsequent solution ranking and selection.

[0051] Taking the simultaneous implementation of two technologies by the aforementioned equipment manufacturing enterprise as an example, the enterprise uses the annual carbon emissions of 1000 tons of CO2 equivalent for the casting and heat treatment processes before the upgrade as a benchmark. The benchmark emissions for the casting process are 600 tons of CO2 equivalent per year, and for the heat treatment process, they are 400 tons of CO2 equivalent per year. The emission reduction efficiency of the medium-frequency furnace upgrade technology is 8%, and the emission reduction efficiency of the heat treatment waste heat recovery technology is 5%, both taken from the carbon reduction technology library. The operating condition correction coefficient, after weighted calculation, is 0.95, and the policy compliance correction coefficient is 1.0. The calculation period is one year. Substituting these values ​​into the formula, the annual carbon emission reduction of the plan is calculated as follows: (600 × 8% + ...) (400 × 5%) × 0.95 × 1.0 × 1 = 123.5 tons of carbon dioxide equivalent; the total one-time implementation cost of the plan is 850,000 yuan, the annual energy saving and cost reduction benefit is 220,000 yuan, the policy subsidy benefit is 170,000 yuan, the annual operation and maintenance cost is 30,000 yuan, the calculated annual net benefit is 360,000 yuan, and the static investment payback period is 2.36 years; the final evaluation results clearly indicate the core indicators of the plan such as emission reduction, investment payback period, policy compatibility, and implementation difficulty, and all data are traceable and verifiable.

[0052] In one embodiment, step S5, which involves ranking and screening various carbon reduction implementation schemes based on the scheme evaluation results and outputting the selected preferred carbon reduction scheme, includes: S51, Setting the sorting and filtering criteria for the schemes; S52, Comprehensive scoring and ranking of the solutions; S53, Selection of the preferred solution that meets the constraints; S54, optimal solution organization and user terminal output.

[0053] In the specific implementation process, the system incorporates four core ranking and screening dimensions: carbon emission reduction, economic benefits, policy compatibility, and implementation difficulty. Each dimension corresponds to clear quantitative assessment indicators. Specifically, the carbon emission reduction dimension includes two indicators: annual emission reduction and emission reduction target completion rate; the economic benefits dimension includes two indicators: static investment payback period and annual net income; the policy compatibility dimension includes two indicators: subsidy application compliance and carbon asset development feasibility; and the implementation difficulty dimension includes two indicators: implementation cycle and duration of production stoppage impact. Enterprises can flexibly adjust the weighting of each dimension according to their core carbon reduction needs, with the total weight fixed at 100%. If an enterprise prioritizes short-term carbon quota compliance, it can increase the weighting of the carbon emission reduction dimension; if an enterprise prioritizes cost control and return on investment, it can increase the weighting of the economic benefits dimension. The weighting settings are perfectly aligned with the actual decision-making preferences of enterprises.

[0054] The system uses standardized quantitative scoring to evaluate the results of various carbon reduction implementation plans based on predefined ranking and weighting criteria. Each indicator corresponds to a fixed score range of 0-100 points, with higher scores for better indicators. For example, an annual emission reduction completion rate of ≥100% earns 100 points, 80%-100% earns 80 points, and below 80% earns no points; a static investment payback period of ≤3 years earns 100 points, 3-5 years earns 70 points, and over 5 years earns no points. The system calculates a weighted sum of scores for each plan and then ranks them from highest to lowest, visually presenting the differences in merit between the plans. The entire scoring process is traceable, ensuring the objectivity and fairness of the ranking results without human intervention.

[0055] After sorting, the system compares the core indicators of each plan with the hard constraints preset by the enterprises one by one. The constraints are fixed and include the upper limit of investment budget, the minimum annual emission reduction target, the longest implementation period, and the maximum duration of production stoppage impact, all of which are taken from the actual business plan and carbon reduction needs of the enterprises. The system automatically eliminates the plans that exceed any of the constraints and only retains the plans that meet all the constraints and have the highest comprehensive scores, ensuring that the selected plans have emission reduction effects, economic feasibility, and operability.

[0056] The system systematically organizes and refines the selected optimal carbon reduction solutions, clarifying the core implementation points, expected emission reduction effects, investment and return, policy compliance points, implementation schedule, and risk control measures for each solution. This results in a well-structured and concise master document for the optimal solution. At the same time, the corresponding quantitative accounting data, evaluation results, technical and policy basis, and construction drawings are also organized as supplementary attachments. Finally, the system is output through user terminals such as web, mobile, and WeChat for enterprises, providing direct and actionable reference for enterprises' carbon reduction decisions.

[0057] Taking the aforementioned equipment manufacturing enterprise as an example, the enterprise set the following screening criteria with weights: carbon emission reduction 40%, economic benefits 40%, policy compatibility 15%, and implementation difficulty 5%. The preset hard constraints were: an investment budget cap of 1 million yuan, an annual carbon emission reduction target of no less than 100 tons of CO2 equivalent, an implementation period of no more than 8 months, and zero production stoppage impact. The system scored three candidate schemes. The simultaneous implementation scheme of two technologies scored 92 points, with an annual emission reduction of 123.5 tons, an investment of 850,000 yuan, an implementation period of 6 months, and no production stoppage impact, fully meeting the constraints. The phased implementation scheme for the intermediate frequency furnace renovation scored 88 points, with an annual emission reduction of 80 tons, failing to meet the minimum emission reduction target and thus being eliminated. The two technology upgrade schemes scored 85 points, with an investment of 1.12 million yuan, exceeding the budget cap and thus being eliminated. Finally, the simultaneous implementation scheme of the two technologies with the highest comprehensive scores was selected as the preferred scheme. After being compiled, the scheme was output to the enterprise's user terminal. The document clearly marked the scheme's construction plan, acceptance standards, subsidy application process, and carbon asset development path, allowing the enterprise to directly refer to and implement it.

[0058] To demonstrate that this invention can generate efficient, accurate, and compliant carbon reduction solutions in various industries and enterprises with significantly different production conditions, achieving the technical effects of this invention, the following three complete verification examples from different industries are provided, covering three major categories of industries with significantly different operating conditions: discrete manufacturing, process chemicals, and high-energy-consuming building materials: A fine chemical enterprise (GB / T4754 industry code C26) has core production processes of synthesis reaction, distillation purification, and drying molding, which are continuous process production conditions, significantly different from the discrete production conditions of equipment manufacturing enterprises. After data collection and analysis in step S1, the carbon emissions from the synthesis reaction process were identified as 55% (fuel combustion emissions, high emission intensity) and the carbon emissions from the distillation and purification process were identified as 27% (process emissions, high emission intensity), generating carbon emission gene tags "C26-01-01, C26-02-01". Step S2, a three-level matching search, identified two suitable technologies: energy-saving combustion retrofit of the synthesis reactor and waste heat recovery from the distillation tower. Correspondingly, local chemical industry energy-saving retrofit subsidy policies and CCER waste heat utilization project methodologies were retrieved. Step S3 generated two executable solutions, with implementation scheduled during the company's annual overhaul window, an investment budget of 1.8 million yuan, and an annual emission reduction target of 300 tons of CO2 equivalent. Step S4, quantitative calculation, showed an annual carbon emission reduction of 320 tons of CO2 equivalent and a static investment payback period of 2.8 years. Step S5, the two technologies with the highest comprehensive scores were selected for simultaneous implementation as the preferred solution. After the enterprise implemented the technology, the actual annual emission reduction deviated from the calculated value by less than 8%, fully achieving the expected technical effect and verifying the applicability of the invention in process-type production conditions.

[0059] A cement production enterprise (GB / T4754 industry code C30) has core production processes of raw material grinding, clinker calcination, and cement grinding, which are 24-hour continuous high-energy-consuming production conditions, which are very different from the working conditions of other industries. After data collection and analysis in step S1, it was identified that the carbon emission share of the clinker calcination process was 82% (process emission, high emission intensity) and the carbon emission share of the raw meal grinding process was 10% (indirect emission from electricity, medium emission intensity), generating carbon emission gene tags "C30-02-01, C30-03-02". After three-level matching and retrieval in step S2, two suitable technologies were selected: waste heat and pressure recovery from clinker calcination kilns and energy-saving retrofit of raw meal vertical mills. Correspondingly, national cement industry carbon emission control standards and local pollution reduction and carbon reduction collaborative transformation support policies were retrieved. After step S3, an executable plan was generated, with the implementation period scheduled during the off-season for cement sales and off-peak production, with an investment budget of 3.2 million yuan and an annual emission reduction target of 1,200 tons of CO2 equivalent. After quantitative calculation in step S4, the annual carbon emission reduction of the plan was 1,350 tons of CO2 equivalent, with a static investment payback period of 3.2 years. After screening in step S5, the optimal plan was output. After implementation, the company successfully met its annual carbon emission quota compliance target and enjoyed environmental tax reduction and exemption benefits, verifying the applicability of this invention in high-energy-consuming industries.

[0060] A food processing company (GB / T4754 industry code C14) operates on intermittent production processes, including cooking and sterilization, freezing and refrigeration, and packaging. Production load fluctuates significantly with the seasons. Step S1, data collection and analysis, identified cooking and sterilization as accounting for 48% of carbon emissions (fuel combustion emissions, high emission intensity) and freezing and refrigeration as accounting for 35% (indirect electricity emissions, high emission intensity), generating carbon emission gene tags "C14-01-01, C14-03-01". Step S2, a three-level matching search, identified two suitable technologies: low-NOx energy-saving retrofit of gas-fired boilers and frequency conversion energy-saving retrofit of cold storage refrigeration systems. Corresponding local green manufacturing support policies for the food industry were also found. Step S3 generated an executable plan, scheduled for implementation during the off-season, with an investment budget of 650,000 yuan and an annual emission reduction target of 150 tons of CO2 equivalent. Step S4, quantitative calculation, showed an annual carbon emission reduction of 168 tons of CO2 equivalent and a static investment payback period of 2.5 years. Step S5, a selection of the optimal solution was identified. After the invention was implemented in the enterprise, the actual energy saving rate reached 18%, and the enterprise successfully applied for local green manufacturing special funds, which verified the versatility of the invention in intermittent production conditions.

[0061] Reference Figure 2 Here is a structural block diagram of an automatic carbon reduction scheme generation device based on carbon emission genes in one embodiment of the present invention, comprising: The data collection and processing module is used to collect basic production operation data, energy consumption data, and carbon emission data of various production links of the enterprise, and to filter and process the collected data, removing invalid and abnormal data to obtain valid data. The key node identification module is used to analyze the effective data and identify key carbon emission nodes with high carbon emission concentration and large emission volume for enterprises. The carbon emission gene tag generation module is used to generate corresponding corporate carbon emission gene tags based on the type and emission intensity of the key carbon emission nodes. The technology policy matching module is used to match and screen the carbon reduction technologies and corresponding policy requirements applicable to the enterprise from a pre-established carbon reduction technology library and carbon policy library based on the carbon emission gene tag. The scheme generation module is used to determine basic parameters and adjust scheme details based on the matched carbon reduction technologies and policy requirements, and automatically generate at least one executable corporate carbon reduction implementation plan. The quantitative assessment module is used to determine the calculation benchmark for each carbon reduction implementation plan, quantify the carbon emission reduction and economic benefit data of each plan, and integrate them to form the plan assessment results. The sorting and filtering output module is used to set sorting and filtering criteria, comprehensively score, sort, and filter various carbon reduction implementation plans, and organize and output the best carbon reduction plans to the user terminal.

[0062] For the specific implementation of each module in the above device example, please refer to the above method embodiments, which will not be repeated here.

[0063] In summary, this invention, driven by carbon emission genes, automates the generation, evaluation, and optimization of corporate carbon reduction solutions. It closely aligns with actual production conditions and boasts strong feasibility for implementation. The system collects production operation, energy consumption, and carbon emission data from each stage of the enterprise's production process. After filtering and cleaning to remove invalid and abnormal data, it analyzes and identifies key carbon emission nodes. Based on the emission characteristics of these nodes, it generates a unique carbon emission gene tag for the enterprise. Using this tag, the system accurately searches for suitable technologies in a pre-set carbon reduction technology library, simultaneously searching for carbon reduction policies and compliance requirements corresponding to the enterprise's industry. After adaptation and screening, it determines the appropriate combination of technologies and policies. Based on these factors, the system sets implementation parameters according to the enterprise's funds and production plans, clarifies the details of technology implementation, adjusts the policy adaptation of the solution, automatically generates an executable carbon reduction implementation plan, sets a unified accounting benchmark for each plan, quantifies the carbon emission reduction and full-cycle economic benefits, and integrates them to form a comprehensive evaluation result. Finally, it sets screening criteria based on the enterprise's core needs, quantitatively scores and ranks the solutions, eliminates solutions that do not meet constraints such as budget and emission reduction targets, and compiles the optimal solutions and outputs them to the user terminal. Taking equipment manufacturing enterprises as an example, the system accurately generates optimal solutions such as energy-saving retrofits for medium-frequency furnaces through the above process, clarifying implementation details and expected effects. Enterprises can directly refer to these solutions for implementation, effectively achieving the precision, automation, and feasibility of carbon reduction solutions, and helping enterprises efficiently implement carbon reduction goals.

[0064] An embodiment of the present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-described method. It is understood that the computer-readable storage medium in this embodiment can be a volatile readable storage medium or a non-volatile readable storage medium.

[0065] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the present invention and embodiments can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual-rate SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM, etc.

[0066] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, apparatus, article, or method that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, apparatus, article, or method. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, apparatus, article, or method that includes that element.

[0067] The above description is only a preferred embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structural or procedural transformations made based on the content of the present invention specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. A method for automatically generating carbon reduction schemes based on carbon emission genes, characterized in that, Includes the following steps: Collect enterprises' energy consumption data, carbon emission data, and basic production and operation data to identify key carbon emission nodes and form corresponding enterprise carbon emission gene tags. Based on the carbon emission gene tag, carbon reduction technologies and corresponding policy requirements suitable for the enterprise are matched from the pre-established carbon reduction technology library and carbon policy library; Based on the aforementioned carbon reduction technologies and policy requirements, at least one executable corporate carbon reduction implementation plan will be automatically generated. For the aforementioned carbon reduction implementation plan, the carbon emission reduction and economic benefits are quantitatively calculated to obtain the corresponding plan evaluation results; Based on the evaluation results of the proposed schemes, the various carbon reduction implementation schemes are ranked and screened, and the preferred carbon reduction schemes are output.

2. The method for automatically generating carbon reduction schemes based on carbon emission genes according to claim 1, characterized in that, The steps of collecting energy consumption data, carbon emission data, and basic production operation data from enterprises, identifying key carbon emission nodes, and forming corresponding enterprise carbon emission genetic tags include: Collect basic production operation data from each production stage of the enterprise, filter and organize the collected data, remove invalid and abnormal data, and obtain valid data; The effective data was analyzed to identify key carbon emission nodes where enterprises had high carbon emission concentrations and large emission volumes. Based on the type and emission intensity of the key carbon emission nodes, corresponding corporate carbon emission gene tags are generated.

3. The method for automatically generating carbon reduction schemes based on carbon emission genes according to claim 2, characterized in that, The step of generating corresponding corporate carbon emission genetic tags based on the type and emission intensity of the key carbon emission nodes includes: Classify the identified key carbon emission nodes and clarify the emission types corresponding to each key node; The emission intensity level of each key node is quantified, and the "key node type + emission intensity level" is combined and coded to generate a unique corresponding corporate carbon emission gene tag. The corporate carbon emission gene tag is directly associated with the carbon reduction technology matching logic.

4. The method for automatically generating carbon reduction schemes based on carbon emission genes according to claim 1, characterized in that, The step of matching the carbon emission gene tag with the carbon reduction technologies and corresponding policy requirements applicable to the enterprise from a pre-established carbon reduction technology library and carbon policy library includes: Based on the matched carbon reduction technologies and the industry to which the enterprise belongs, the corresponding carbon reduction support policies and compliance requirements are retrieved from the carbon policy database; The retrieved carbon reduction technologies and policy requirements are screened for suitability to determine the carbon reduction technologies and corresponding policy requirements applicable to the enterprise.

5. The method for automatically generating carbon reduction schemes based on carbon emission genes according to claim 1, characterized in that, The step of automatically generating at least one executable corporate carbon reduction implementation plan based on the aforementioned carbon reduction technologies and policy requirements includes: Determine the basic parameters of the carbon reduction implementation plan, including the implementation period, expected investment budget, and emission reduction targets; Based on the matched carbon reduction technologies, clarify the application stages, implementation steps, and dosage of each carbon reduction technology; By aligning with relevant policy requirements and adjusting the details of the plan, at least one executable corporate carbon reduction implementation plan can be automatically generated.

6. The method for automatically generating carbon reduction schemes based on carbon emission genes according to claim 1, characterized in that, The steps for quantifying carbon emission reduction and economic benefits of the aforementioned carbon reduction implementation scheme to obtain corresponding scheme evaluation results include: Determine the calculation benchmarks for each carbon reduction implementation plan, including the enterprise's current carbon emission benchmark, energy consumption benchmark, and basic cost benchmark; Based on the application effects of carbon reduction technologies, the carbon emission reduction that can be achieved by each scheme is quantitatively calculated; Calculate the implementation cost, operating cost, and expected revenue of each plan to obtain the economic benefit data for each plan; By integrating the carbon emission reduction data and economic benefit data, an evaluation result is generated for each carbon reduction implementation plan.

7. The method for automatically generating carbon reduction schemes based on carbon emission genes according to claim 1, characterized in that, The step of ranking and screening various carbon reduction implementation schemes based on the evaluation results of the schemes, and outputting the selected preferred carbon reduction schemes, includes: Set sorting and screening criteria, including priority of carbon emission reduction, priority of economic benefits, and priority of policy compatibility; Based on the aforementioned ranking and screening criteria, the evaluation results of each carbon reduction implementation plan are comprehensively scored and ranked. Select the top-scoring proposals that meet the company's preset emission reduction targets and investment budgets, and eliminate proposals that do not meet the requirements. The selected carbon reduction solutions are compiled and optimized, the core implementation points of each solution are clarified, and the results are output to the user terminal.

8. An automatic carbon reduction scheme generation device based on carbon emission genes, characterized in that, include: The data collection and processing module is used to collect basic production operation data, energy consumption data, and carbon emission data of various production links of the enterprise, and to filter and process the collected data, removing invalid and abnormal data to obtain valid data. The key node identification module is used to analyze the effective data and identify key carbon emission nodes with high carbon emission concentration and large emission volume for enterprises. The carbon emission gene tag generation module is used to generate corresponding corporate carbon emission gene tags based on the type and emission intensity of the key carbon emission nodes. The technology policy matching module is used to match and screen the carbon reduction technologies and corresponding policy requirements applicable to the enterprise from a pre-established carbon reduction technology library and carbon policy library based on the carbon emission gene tag. The scheme generation module is used to determine basic parameters and adjust scheme details based on the matched carbon reduction technologies and policy requirements, and automatically generate at least one executable corporate carbon reduction implementation plan. The quantitative assessment module is used to determine the calculation benchmark for each carbon reduction implementation plan, quantify the carbon emission reduction and economic benefit data of each plan, and integrate them to form the plan assessment results. The sorting and filtering output module is used to set sorting and filtering criteria, comprehensively score, sort, and filter various carbon reduction implementation plans, and organize and output the best carbon reduction plans to the user terminal.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the automatic generation method for carbon reduction schemes based on carbon emission genes as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the automatic generation method for carbon reduction schemes based on carbon emission genes as described in any one of claims 1 to 7.

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