Carbon neutralization path planning method and device

Abstract

The invention discloses a carbon neutralization path planning method and device, and relates to the technical field of carbon neutralization, and the method comprises the following steps: S1, generating an initial scheme; s2, carbon flow tracing and multi-dimensional emergency risk early warning are carried out; s3, cross-stage collaborative optimization is carried out; and S4, optimizing scheme synchronization. According to the carbon neutralization path planning method and device, various sudden risk signals of environments, supply chains, policies and the like can be captured in real time by building a multi-dimensional sudden risk early warning mechanism, the impact degree of risks on a carbon neutralization target is quantified in advance, and the defect that traditional planning lacks risk early warning is overcome; when a risk triggers an adjustment instruction, by means of a cross-stage collaborative optimization algorithm and a full-life-cycle emission reduction measure library, an emission reduction gap of an emission reduction potential compensation problem stage of a non-core stage can be flexibly called, an initial scheme failure caused by the risk is avoided, it is guaranteed that a carbon neutralization path can still be stably propelled in a dynamic change scene, and the method is simple and convenient. The toughness and the flexibility achieved by a carbon neutralization target are obviously improved.

Description

Technical Field

[0001] This invention relates to the field of carbon neutrality technology, specifically to a carbon neutrality pathway planning method and apparatus. Background Technology

[0002] As the global carbon neutrality goal is further advanced, carbon emission management of products and projects has become a core focus of the industry. Carbon neutrality path planning is a systematic emission reduction and carbon sequestration action plan formulated by economies, industries or organizations to achieve carbon neutrality goals within a specific time frame. However, existing carbon neutrality pathway plans only consider conventional factors such as technological iteration and carbon price fluctuations, but do not cover unforeseen scenarios such as extreme weather (e.g., typhoons causing green electricity supply disruptions), supply chain disruptions (e.g., supply chain disruptions), and emergency policy adjustments (e.g., sudden "dual control" orders). Such risks can directly lead to a sudden increase in carbon emissions or a delay in emission reduction progress. Traditional plans lack early warning and cross-stage adjustment mechanisms. Once a risk occurs, the initial plan immediately becomes invalid, and enterprises or projects face problems such as penalties for exceeding carbon emission standards and uncontrolled costs. Summary of the Invention

[0003] To address the shortcomings of existing technologies, this invention provides a carbon neutrality pathway planning method and apparatus, which solves the problems mentioned in the background section.

[0004] To achieve the above objectives, the present invention provides the following technical solution: a carbon neutrality pathway planning method, comprising the following steps: S1. Generate the initial scheme: Based on baseline data from each stage of the product and project lifecycle, combined with the emission reduction potential and initial constraints at each stage, a carbon emission calculation model is constructed using a full lifecycle carbon optimization algorithm, which calculates carbon emissions from each stage of raw material acquisition, production and manufacturing, transportation and sales, use and maintenance to waste disposal. Carbon emission constraints and optimization objectives are set, and intelligent optimization algorithms such as particle swarm optimization are used to solve the model. After multiple iterations, the carbon emissions and economic costs at each stage are weighed to determine the initial plan. S2, Carbon Flow Tracing and Multidimensional Emergency Risk Warning: Set carbon flow monitoring indicators for each stage, collect carbon emission data in real time during the design, production, consumption and recycling stages, and use blockchain technology to ensure that the carbon flow data is stored in an immutable manner. The specific carbon flow monitoring indicators for each stage are as follows: Design stage includes: material carbon footprint, proportion of recyclable materials, and energy-saving design coefficient of product structure; Production stage includes: carbon emissions per unit product energy consumption, carbon emissions from production process losses, and proportion of green electricity or green certificates used; Consumption stage includes: carbon emissions from product use energy consumption, correlation coefficient between usage frequency and carbon consumption, and carbon emissions during idle periods; Recycling stage includes: carbon emissions from recycling processes, resource recycling rate, carbon emissions from waste disposal, and deployment of IoT sensing devices. Set up emergency risk classification standards, collect risk-related data in real time, calculate the impact of risks on carbon neutrality targets at each stage through a risk impact quantification model, and trigger a scheme adjustment instruction when the impact exceeds the preset risk threshold. S3, Cross-stage collaborative optimization: Based on the triggered risk type and the problem node located by carbon flow tracing, the carbon emission deviation is calculated through a risk quantification model. The calculation of the carbon emission deviation requires the determination of the following key parameters: historical carbon emission data: collect carbon emission data over a period of time; carbon emission target value: clearly define the carbon emission limit under the set carbon neutrality target; risk impact factors: determine the risk factors affecting carbon emissions based on industry characteristics and production activity characteristics, and assign corresponding impact factors. Then, a risk quantification model was constructed, specifically using regression analysis to build the model, which assumed carbon emissions. With multiple risk factors Correlation, establish regression equation ,in, For regression coefficients, To define the error term, the regression coefficients are estimated using historical data to determine the model parameters; Calculate carbon emission deviations with a lag; analyze the calculated carbon emission deviations and, in conjunction with risk impact factors, determine the main sources of risk; Based on the magnitude of carbon emission deviation and the sources of risk, corresponding countermeasures are formulated, such as adjusting production plans and optimizing energy structure, to reduce carbon emission risks and ensure the achievement of carbon neutrality goals; non-core stage emission reduction measures are adjusted through cross-stage collaborative algorithms, such as compensating for emission reduction gaps at the production end by increasing recycling rates at the recycling end and reducing carbon consumption by extending product lifespan at the consumption end, thereby generating an optimized full life cycle carbon neutrality path scheme. S4. Optimization plan synchronization: The optimization plan will be synchronized to the implementing entities at each stage of the entire life cycle, and the thresholds of carbon flow monitoring indicators and risk warning parameters will be updated to enter the next cycle of carbon flow tracing, risk warning, and optimization adjustment to ensure that the total carbon emissions throughout the entire life cycle meet the standards.

[0005] Furthermore, in step S1, during the stage of constructing the carbon emission calculation model, a phased carbon emission calculation model is first established based on the LCA life cycle assessment standard: Raw material acquisition:

[0006] in, For the first Energy consumption in the production of various raw materials The carbon emission factor corresponding to energy consumption; Manufacturing:

[0007] in, Energy consumption in the production process The carbon emission coefficient for the process; Transportation and Sales:

[0008] in, For transportation distance, For the load capacity of the transport vehicle, Carbon emission intensity per unit of transportation; Usage and maintenance:

[0009] in, Energy consumption during product operation. The corresponding carbon emission factor for the time period; Waste disposal:

[0010] in, Waste disposal volume Carbon emission coefficient for the treatment method; Total carbon emissions over the entire lifecycle:

[0011] Carbon accounting is broken down into five core stages throughout the entire life cycle, including raw material acquisition and production. The calculation logic and parameters for carbon emissions at each stage are clarified, making carbon emission accounting more accurate and traceable, and providing a standardized and refined data foundation for subsequent risk assessment and cross-stage optimization.

[0012] Furthermore, the constraints include policy constraints: setting regional or industry-specific carbon emission quota caps. ,Right now Economic constraints: Controlling the entire lifecycle cost budget ,in This includes the costs of raw materials, production, transportation, use, and waste disposal. This represents the pre-set cost budget ceiling; by using the carbon emission quota ceiling (policy constraint) and the full-cycle cost budget ceiling (economic constraint), it avoids the plan from focusing only on emission reduction without regard to compliance or controlling carbon emissions while ignoring costs, ensuring that the initial plan not only meets the requirements of industry and regional policies, but also has the economic feasibility for actual implementation.

[0013] Furthermore, the particle swarm optimization algorithm is used to solve the model: Initialize the particle swarm: randomly generate M particles, each particle's position representing a possible solution, and its velocity representing the direction of parameter adjustment; Individual and global optimum updates: calculate the fitness of each particle and update its historical best position. and global optimal position ; Speed ​​and position updates:

[0014]

[0015] in, Inertial weight; , For learning factors; , It is a random number; In order to be in At that moment, the The particle in the first Dimensional speed; Then it is At that moment, the The particle in the first The speed after the update; In order to be in At that moment, the The particle in the first The position of the dimension; In order to be in At that moment, the The particle in the first The updated position; For the first The particle in the first The best historical position of an individual on the dimension; For the particle in the first The globally optimal position in the dimension; This process is iteratively optimized and repeated until the maximum number of iterations is reached or the accuracy requirements are met. After multiple iterations of the algorithm, the output includes the emission reduction targets for each stage, the carbon flow monitoring node deployment plan, the emission reduction measures list, and the initial plan for the entire life cycle cost allocation. Through intelligent algorithm iteration and optimization, the system efficiently balances carbon emissions and economic costs at each stage, quickly finds the optimal combination of solutions, solves the problems of low efficiency and inaccurate trade-offs in traditional manual planning, and ensures the scientific nature and optimality of the initial plan.

[0016] Furthermore, in step S2, the classification criteria for sudden risks include environmental risks (extreme weather, natural disasters); supply chain risks (disruption of core raw material supply, production stoppage of upstream enterprises); and policy risks (emergency emission reduction directives, sudden changes in carbon accounting standards). Real-time collection of risk-related data includes weather warnings, supply chain status, and policy documents. The three core categories of sudden risks—environmental, supply chain, and policy—are clearly defined, and the scope of risk data collection (weather, supply chain status, etc.) is standardized to make risk identification more comprehensive and focused, providing clear classification criteria and data support for subsequent risk quantification and early warning.

[0017] Furthermore, in step S2, the impact of risks on the carbon neutrality target at each stage is calculated using a risk impact quantification model, employing the following specific calculation formula: Carbon emission increment calculation: Assuming that extreme weather causes a disruption in green electricity supply, the originally planned green electricity consumption is... The actual amount of non-green electricity (such as thermal power) used during the outage was: The carbon intensity per unit of non-green electricity is Then the increase in carbon emissions The calculation formula is:

[0018] in, The carbon emission intensity per unit of green electricity is ideally 0 or a very small value. Calculation of emission reduction lag: Assume that the reduction in production capacity due to supply chain disruption is... The emission reduction per unit of production capacity is The duration of the supply disruption is Then the emission reduction lag amount The calculation formula is:

[0019] Impact assessment: Taking into account the increase in carbon emissions and the lag in emission reductions, the total impact is calculated. Using a weighted summation method, the weight of the carbon emission increment is set as follows: The weight of the emission reduction lag is ,but:

[0020] in, This refers to the carbon emission control targets for this phase. The emission reduction target to be achieved in this phase; when Exceeding the preset risk threshold At that time, a scheme adjustment instruction is triggered; By using specific formulas for carbon emission increments and emission reduction lags, abstract risks are transformed into quantifiable impact values, avoiding subjective risk assessments, making early warning thresholds more precise, and ensuring timely and accurate adjustment of plans when risks occur.

[0021] Furthermore, in step S3, when calculating the carbon emission deviation, the current risk factor values ​​are substituted into the constructed risk quantification model to obtain the predicted carbon emission. Using predicted carbon emissions Subtract carbon emission target value That is, carbon emission deviation ; Intuitively quantifying the degree of carbon emission deviation caused by risks provides clear adjustment targets for cross-stage collaborative optimization, making subsequent countermeasures more targeted.

[0022] Furthermore, When the predicted carbon emissions exceed the target value, there is a risk of exceeding the carbon emission standard; when When this occurs, it indicates that the predicted carbon emissions are lower than the target value; Quickly identify the risk of carbon emission exceeding standards ( ) and carbon emission surplus ( There are two states to avoid wasting optimization resources. For cases where the standard is exceeded, cross-stage adjustments are initiated to improve the efficiency and accuracy of solution optimization.

[0023] An apparatus for applying the above-described carbon neutrality pathway planning method, the apparatus comprising: The full life cycle initial solution generation module is used to connect to the product design PLM system to obtain material carbon footprint data, the production MES system to obtain energy consumption data, the consumer IoT platform to obtain usage carbon consumption data, and the recycling traceability system to obtain recycling data. Combined with industry emission reduction targets and cost constraints, it generates a full life cycle initial carbon neutrality solution. The full-chain carbon flow traceability module includes an IoT sensing interface to connect energy consumption sensors, smart metering instruments, and carbon emission detectors; and a blockchain storage unit to store carbon flow data at each stage, enabling real-time visibility and traceability of carbon flow throughout its entire lifecycle. Multi-dimensional emergency risk early warning module: Integrates meteorological early warning platform, supply chain management system and policy database, calculates the degree of risk impact through risk impact quantification unit, and generates early warning information and adjustment instructions when the threshold is exceeded; Cross-stage collaborative optimization module: It has a built-in library of emission reduction measures throughout the entire life cycle, risk and measure matching templates, and cost optimization algorithms. After receiving early warning instructions, it automatically generates cross-stage optimization solutions.

[0024] This invention provides a carbon neutrality pathway planning method and apparatus, which have the following beneficial effects: 1. This carbon neutrality pathway planning method and device, by establishing a multi-dimensional emergency risk early warning mechanism, can capture various emergency risk signals such as environmental, supply chain, and policy risks in real time, and quantify the impact of risks on carbon neutrality targets in advance, thus solving the deficiency of traditional planning in lacking risk early warning. When risks trigger adjustment instructions, by leveraging cross-stage collaborative optimization algorithms and a full life cycle emission reduction measure library, it can flexibly call upon the emission reduction potential of non-core stages to compensate for the emission reduction gaps in problem stages, avoiding the failure of the initial plan due to risks, ensuring that the carbon neutrality pathway can still be steadily promoted in dynamically changing scenarios, and significantly improving the resilience and flexibility of achieving carbon neutrality targets.

[0025] 2. This carbon neutrality pathway planning method and device effectively solves the problem of fragmented carbon flow management at each stage in traditional carbon neutrality planning by constructing a carbon flow traceability system covering the entire process of product or project design, production, consumption, and recycling. It enables real-time visualization and correlation analysis of carbon flows throughout the entire lifecycle, avoiding carbon emission imbalances across the entire chain caused by optimization at a single stage. At the same time, relying on blockchain technology to immutably store carbon flow data at each stage ensures the authenticity and integrity of carbon emission data from collection to storage. This not only provides reliable data support for pathway optimization but also meets compliance requirements such as carbon information disclosure and carbon trading verification, reducing compliance risks caused by data issues and helping to accurately achieve the goal of carbon neutrality throughout the entire lifecycle. Attached Figure Description

[0026] Figure 1 This is a schematic diagram of the steps of a carbon neutrality path planning method according to the present invention. Detailed Implementation

[0027] The embodiments of the present invention will be described in further detail below with reference to the accompanying drawings and examples. The following examples are for illustrative purposes only and should not be construed as limiting the scope of the invention.

[0028] like Figure 1 As shown, the present invention provides a technical solution: a carbon neutrality pathway planning method, comprising the following steps: S1. Generate the initial scheme: Based on baseline data from each stage of the product and project lifecycle (material carbon footprint in the design stage, energy consumption carbon emissions in the production stage, carbon consumption during consumption, and carbon emissions from disposal in the recycling stage), and combined with the emission reduction potential at each stage (such as material substitution in the design stage, process optimization in the production stage, energy-saving use in the consumption stage, and improved recycling rate in the recycling stage) and initial constraints (cost budget, technology maturity, and policy requirements), a carbon emission calculation model is constructed using a full lifecycle carbon optimization algorithm, which calculates carbon emissions from each stage of raw material acquisition, production and manufacturing, transportation and sales, use and maintenance to waste disposal. Carbon emission constraints and optimization objectives are set, and intelligent optimization algorithms such as particle swarm optimization are used to solve the model. After multiple iterations, the carbon emissions and economic costs at each stage are weighed to determine the initial solution. In the stage of constructing the carbon emission calculation model, a phased carbon emission calculation model is first established based on the LCA (Life Cycle Assessment) standard: Raw material acquisition:

[0029] in, For the first Energy consumption in the production of various raw materials The carbon emission factor corresponding to energy consumption; Manufacturing:

[0030] in, Energy consumption in the production process The carbon emission coefficient for the process; Transportation and Sales:

[0031] in, For transportation distance, For the load capacity of the transport vehicle, Carbon emission intensity per unit of transportation; Usage and maintenance:

[0032] in, Energy consumption during product operation. The corresponding carbon emission factor for the time period; Waste disposal:

[0033] in, Waste disposal volume Carbon emission coefficient for the treatment method; Total carbon emissions over the entire lifecycle:

[0034] The constraints include policy constraints: setting caps on carbon emission quotas for specific regions or industries. ,Right now Economic constraints: Controlling the entire lifecycle cost budget ,in This includes the costs of raw materials, production, transportation, use, and waste disposal. This represents the preset upper limit of the cost budget; The model was solved using the particle swarm optimization algorithm. Initialize the particle swarm: randomly generate M particles, each particle's position representing a possible solution, and its velocity representing the direction of parameter adjustment; Individual and global optimum updates: calculate the fitness of each particle and update its historical best position. and global optimal position ; Speed ​​and position updates:

[0035]

[0036] in, Inertial weight; , For learning factors; , It is a random number; In order to be in At that moment, the The particle in the first Dimensional speed; Then it is At that moment, the The particle in the first The speed after the update; In order to be in At that moment, the The particle in the first The position of the dimension; In order to be in At that moment, the The particle in the first The updated position; For the first The particle in the first The best historical position of an individual on the dimension; For the particle in the first The globally optimal position in the dimension; This process is iteratively optimized and repeated until the maximum number of iterations is reached or the accuracy requirements are met. After multiple iterations of the algorithm, the output includes the emission reduction targets for each stage, the carbon flow monitoring node deployment plan, the emission reduction measures list, and the initial plan for the entire life cycle cost allocation. S2, Carbon Flow Tracing and Multidimensional Emergency Risk Warning: Set carbon flow monitoring indicators for each stage, collect carbon emission data in real time during the design, production, consumption and recycling stages, and use blockchain technology to ensure that the carbon flow data is stored in an immutable manner. The specific carbon flow monitoring indicators for each stage are as follows: The design stage includes: material carbon footprint (e.g., carbon emissions per ton of aluminum), proportion of recyclable materials, and energy-saving design coefficient of product structure; the production stage includes: carbon emissions per unit product energy consumption (e.g., carbon emissions per kilowatt-hour of electricity), carbon emissions from production process losses, and the proportion of green electricity or green certificates used; the consumption stage includes: carbon emissions from product usage energy consumption (e.g., carbon consumption per kilometer of electric vehicle charging), the correlation coefficient between usage frequency and carbon consumption, and carbon emissions during idle periods (e.g., standby energy consumption of equipment); the recycling stage includes: carbon emissions from recycling processes (e.g., energy consumption from dismantling waste batteries), resource recycling rate (e.g., metal recovery rate), carbon emissions from waste disposal (e.g., carbon emissions from incineration or landfill), and the deployment of IoT sensing devices (e.g., energy consumption sensors at the production end, smart metering instruments at the consumer end, and carbon emission detectors at the recycling end). Set up emergency risk classification standards (environmental: extreme weather, natural disasters; supply chain: core raw material supply disruptions, upstream enterprise shutdowns; policy: emergency emission reduction directives, sudden changes in carbon accounting standards), collect risk-related data in real time (weather warnings, supply chain status, policy documents), calculate the impact of risks on carbon neutrality targets at each stage through a risk impact quantification model (such as the carbon emission increase due to green electricity supply disruptions caused by extreme weather, and the emission reduction lag due to capacity reduction caused by supply chain disruptions), and trigger a scheme adjustment directive when the impact exceeds the preset risk threshold; The impact of risks on the carbon neutrality target at each stage is calculated using a risk impact quantification model, with the following specific calculation process and formula: Carbon emission increment calculation: Assuming that extreme weather causes a disruption in green electricity supply, the originally planned green electricity consumption is... The actual amount of non-green electricity (such as thermal power) used during the outage was: The carbon intensity per unit of non-green electricity is Then the increase in carbon emissions The calculation formula is:

[0037] in, The carbon emission intensity per unit of green electricity is ideally 0 or a very small value. Calculation of emission reduction lag: Assume that the reduction in production capacity due to supply chain disruption is... The emission reduction per unit of production capacity is The duration of the supply disruption is Then the emission reduction lag amount The calculation formula is:

[0038] Impact assessment: Taking into account the increase in carbon emissions and the lag in emission reductions, the total impact is calculated. Using a weighted summation method, the weight of the carbon emission increment is set as follows: The weight of the emission reduction lag is ,but:

[0039] in, This refers to the carbon emission control targets for this phase. The emission reduction target to be achieved in this phase; when Exceeding the preset risk threshold At that time, a scheme adjustment instruction is triggered; S3, Cross-stage collaborative optimization: Based on the triggered risk type and the problem node located by carbon flow tracing, carbon emission deviation is calculated through a risk quantification model. The calculation of carbon emission deviation requires determining the following key parameters: historical carbon emission data: collecting carbon emission data over a past period, such as the past 1, 3, or 5 years. This data can be obtained from enterprise energy consumption records, production activity data, carbon emission monitoring equipment, etc.; carbon emission target value: clearly defining the carbon emission limit under the carbon neutrality target, which may be determined by the enterprise's own strategic planning, government policy requirements, etc.; risk impact factors: identifying risk factors affecting carbon emissions based on industry characteristics and production activity characteristics, and assigning corresponding impact factors. For example, factors such as energy price fluctuations and unstable production processes have different degrees of impact on carbon emissions. The impact factor for each factor is determined through expert evaluation, historical data analysis, and other methods. Then, a risk quantification model was constructed, specifically using regression analysis to build the model, which assumed carbon emissions. With multiple risk factors Correlation, establish regression equation ,in, For regression coefficients, The error term is used to estimate the regression coefficients using historical data to determine the model parameters; The carbon emission deviation is calculated with a lag. First, the current risk factor values ​​are substituted into the constructed risk quantification model to obtain the predicted carbon emissions. Using predicted carbon emissions Subtract carbon emission target value That is, carbon emission deviation , When the predicted carbon emissions exceed the target value, there is a risk of exceeding the carbon emission standard; when When this occurs, it indicates that the predicted carbon emissions are lower than the target value; Analyze the calculated carbon emission deviation and combine it with risk impact factors to determine the main sources of risk. For example, if the risk impact factor caused by energy price fluctuations is large and the carbon emission deviation is positive, it indicates that rising energy prices are an important factor leading to excessive carbon emissions. Based on the magnitude of carbon emission deviation and the sources of risk, corresponding countermeasures are formulated, such as adjusting production plans and optimizing energy structure, to reduce carbon emission risks and ensure the achievement of carbon neutrality goals. Through cross-stage collaborative algorithms, emission reduction measures in non-core stages are adjusted (such as compensating for emission reduction gaps at the production end by increasing recycling rates at the recycling end and reducing carbon consumption by extending product lifespan at the consumption end), generating an optimized full life-cycle carbon neutrality path scheme. S4. Optimization plan synchronization: The optimization plan will be synchronized to all implementing entities at each stage of the entire life cycle (design team, production enterprise, consumer users, recycling organization), and the carbon flow monitoring indicator thresholds and risk warning parameters will be updated (such as improving the sensitivity of green electricity supply risk warning during seasons with frequent extreme weather). The cycle of carbon flow tracing, risk warning, and optimization adjustment will then begin to ensure that the total carbon emissions throughout the entire life cycle meet the standards.

[0040] An apparatus that applies the aforementioned carbon neutrality pathway planning method includes: The full lifecycle initial solution generation module is used to connect to the product design PLM system (to obtain material carbon footprint data), the production MES system (to obtain energy consumption data), the consumer IoT platform (to obtain usage carbon consumption data), and the recycling traceability system (to obtain recycling data), and generate a full lifecycle initial carbon neutrality solution by combining industry emission reduction targets and cost constraints. The full-chain carbon flow traceability module includes an IoT sensing interface (connecting to energy consumption sensors, smart metering instruments, and carbon emission detectors) and a blockchain evidence storage unit (used to store carbon flow data at each stage), enabling real-time visibility and traceability of carbon flow throughout its entire lifecycle. Multi-dimensional emergency risk early warning module: integrates meteorological early warning platform (to obtain environmental risk data), supply chain management system (to obtain inventory or enterprise status data), and policy database (to obtain emergency policy documents), calculates the degree of risk impact through risk impact quantification unit, and generates early warning information and adjustment instructions when the threshold is exceeded; Cross-stage collaborative optimization module: It has a built-in library of emission reduction measures throughout the entire life cycle (categorized by design, production, consumption, and recycling), risk and measure matching templates (such as matching recycling end optimization templates with supply chain disruptions), and cost optimization algorithms. After receiving early warning instructions, it automatically generates cross-stage optimization solutions.

[0041] Based on the above description, this invention, by establishing a multi-dimensional emergency risk early warning mechanism, can capture various emergency risk signals such as those related to the environment, supply chain, and policies in real time, and quantify the impact of risks on carbon neutrality goals in advance, thus solving the deficiency of traditional planning in lacking risk early warning. When a risk triggers an adjustment instruction, by leveraging a cross-stage collaborative optimization algorithm and a full life-cycle emission reduction measure library, it can flexibly utilize the emission reduction potential of non-core stages to compensate for emission reduction gaps in problem stages, avoiding the failure of the initial plan due to risks, and ensuring that the carbon neutrality path can still be stably advanced in dynamically changing scenarios, significantly improving the resilience and flexibility of achieving carbon neutrality goals; it also constructs a comprehensive This comprehensive carbon flow traceability system, covering the entire process of product or project design, production, consumption, and recycling, effectively addresses the fragmented carbon flow management at each stage of traditional carbon neutrality planning. It enables real-time visibility and correlation analysis of the entire carbon flow lifecycle, preventing imbalances in carbon emissions across the entire chain caused by optimization at a single stage. Furthermore, leveraging blockchain technology for tamper-proof storage of carbon flow data at each stage ensures the authenticity and integrity of carbon emission data from collection to storage. This not only provides reliable data support for pathway optimization but also meets compliance requirements such as carbon information disclosure and carbon trading verification, reducing compliance risks arising from data issues and facilitating the precise implementation of the full lifecycle carbon neutrality goal. The embodiments of the present invention are given for illustrative and descriptive purposes only, and are not intended to be exhaustive or to limit the invention to the forms disclosed. Many modifications and variations will be apparent to those skilled in the art. The embodiments were chosen and described in order to better illustrate the principles and practical application of the invention, and to enable those skilled in the art to understand the invention and to design various embodiments with various modifications suitable for a particular purpose.

Claims

1. A carbon neutrality pathway planning method, characterized in that: Includes the following steps: S1. Generate the initial scheme: Based on baseline data at each stage of the product or project's entire life cycle, combined with the emission reduction potential and initial constraints at each stage, a carbon emission calculation model is constructed using a life-cycle carbon optimization algorithm. Carbon emission constraints and optimization targets are set, and the model is solved using an intelligent optimization algorithm. The carbon emissions and economic costs at each stage are weighed to determine the initial plan. S2, Carbon Flow Tracing and Multidimensional Emergency Risk Warning: Set carbon flow monitoring indicators for each stage, collect carbon emission data for each stage of the entire life cycle in real time and store it through blockchain technology; set emergency risk classification standards, collect risk-related data in real time, calculate the impact of risks on the carbon neutrality target through a risk impact quantification model, and trigger a scheme adjustment instruction when the risk exceeds the preset risk threshold. S3, Cross-stage collaborative optimization: Based on the triggered risk type and the problem node located by carbon flow tracing, the carbon emission deviation is calculated through the risk quantification model, and after analyzing the risk sources, countermeasures are formulated. The emission reduction measures in non-core stages are adjusted through cross-stage collaborative algorithms to generate an optimized full life cycle carbon neutrality path scheme. S4. Optimization plan synchronization: The optimization plan will be synchronized to the implementing entities at each stage of the entire life cycle, and the thresholds of carbon flow monitoring indicators and risk warning parameters will be updated to enter the next cycle of carbon flow tracing, risk warning, and optimization adjustment to ensure that the total carbon emissions throughout the entire life cycle meet the standards.

2. The carbon neutrality pathway planning method according to claim 1, characterized in that: In step S1, during the stage of constructing the carbon emission calculation model, a phased carbon emission calculation model is first established based on the LCA life cycle assessment standard: Raw material acquisition: in, For the first Energy consumption in the production of various raw materials The carbon emission factor corresponding to energy consumption; Manufacturing: in, Energy consumption in the production process The carbon emission coefficient for the process; Transportation and Sales: in, For transportation distance, For the load capacity of the transport vehicle, Carbon emission intensity per unit of transportation; Usage and maintenance: in, Energy consumption during product operation. The corresponding carbon emission factor for the time period; Waste disposal: in, Waste disposal volume Carbon emission coefficient for the treatment method; Total carbon emissions over the entire lifecycle: 。 3. The carbon neutrality pathway planning method according to claim 2, characterized in that: The constraints include policy constraints: setting caps on carbon emission quotas for specific regions or industries. ,Right now Economic constraints: Controlling the entire lifecycle cost budget ,in This includes the costs of raw materials, production, transportation, use, and waste disposal. This represents the preset upper limit of the cost budget.

4. The carbon neutrality pathway planning method according to claim 3, characterized in that: The model was solved using the particle swarm optimization algorithm. Initialize the particle swarm: randomly generate M particles, each particle's position representing a possible solution, and its velocity representing the direction of parameter adjustment; Individual and global optimum updates: calculate the fitness of each particle and update its historical best position. and global optimal position ; Speed ​​and position updates: in, Inertial weight; , For learning factors; , It is a random number; In order to be in At that moment, the The particle in the first Dimensional speed; Then it is At that moment, the The particle in the first The speed after the update; In order to be in At that moment, the The particle in the first The position of the dimension; In order to be in At that moment, the The particle in the first The updated position; For the first The particle in the first The best historical position of an individual on the dimension; For the particle in the first The globally optimal position in the dimension; This process is iteratively optimized and repeated until the maximum number of iterations is reached or the accuracy requirements are met. After multiple iterations of the algorithm, the output includes the emission reduction targets for each stage, the deployment plan for carbon flow monitoring nodes, the list of emission reduction measures, and the initial plan for the entire life cycle cost allocation.

5. The carbon neutrality pathway planning method according to claim 1, characterized in that: In step S2, the sudden risk classification criteria include environmental factors: extreme weather and natural disasters; supply chain factors: supply disruptions of core raw materials and production stoppages of upstream enterprises; policy factors: emergency emission reduction directives and sudden changes in carbon accounting standards; and real-time collection of risk-related data includes weather warnings, supply chain status, and policy documents.

6. The carbon neutrality pathway planning method according to claim 1, characterized in that: In step S2, the impact of risks on the carbon neutrality target at each stage is calculated using a risk impact quantification model, employing the following specific calculation formula: Carbon emission increment calculation: Assuming that extreme weather causes a disruption in green electricity supply, the originally planned green electricity consumption is... The actual amount of non-green electricity (such as thermal power) used during the outage was: The carbon intensity per unit of non-green electricity is Then the increase in carbon emissions The calculation formula is: in, The carbon emission intensity per unit of green electricity is ideally 0 or a very small value. Calculation of emission reduction lag: Assume that the reduction in production capacity due to supply chain disruption is... The emission reduction per unit of production capacity is The duration of the supply disruption is Then the emission reduction lag amount The calculation formula is: Impact assessment: Taking into account the increase in carbon emissions and the lag in emission reductions, the total impact is calculated. Using a weighted summation method, the weight of the carbon emission increment is set as follows: The weight of the emission reduction lag is ,but: in, This refers to the carbon emission control targets for this phase. The emission reduction target to be achieved in this phase; when Exceeding the preset risk threshold At that time, a scheme adjustment command is triggered.

7. The carbon neutrality pathway planning method according to claim 1, characterized in that: In step S3, when calculating the carbon emission deviation, the current risk factor values ​​are substituted into the constructed risk quantification model to obtain the predicted carbon emissions. Using predicted carbon emissions Subtract carbon emission target value That is, carbon emission deviation .

8. The carbon neutrality pathway planning method according to claim 7, characterized in that: When the predicted carbon emissions exceed the target value, there is a risk of exceeding the carbon emission standard; when When the predicted carbon emissions are lower than the target value, it indicates that the predicted carbon emissions are lower than the target value.

9. An apparatus that applies the carbon neutrality pathway planning method according to any one of claims 1-8, characterized in that: The device includes: The full life cycle initial solution generation module is used to connect to the product design PLM system to obtain material carbon footprint data, the production MES system to obtain energy consumption data, the consumer IoT platform to obtain usage carbon consumption data, and the recycling traceability system to obtain recycling data. Combined with industry emission reduction targets and cost constraints, it generates a full life cycle initial carbon neutrality solution. The full-chain carbon flow traceability module includes an IoT sensing interface to connect energy consumption sensors, smart metering instruments, and carbon emission detectors; and a blockchain storage unit to store carbon flow data at each stage, enabling real-time visibility and traceability of carbon flow throughout its entire lifecycle. Multi-dimensional emergency risk early warning module: Integrates meteorological early warning platform, supply chain management system and policy database, calculates the degree of risk impact through risk impact quantification unit, and generates early warning information and adjustment instructions when the threshold is exceeded; Cross-stage collaborative optimization module: It has a built-in library of emission reduction measures throughout the entire life cycle, risk and measure matching templates, and cost optimization algorithms. After receiving early warning instructions, it automatically generates cross-stage optimization solutions.

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