A zero-carbon park micro-emission reduction scene dynamic aggregation and CCER intelligent adaptation system

Abstract

This application provides a dynamic aggregation and intelligent CCER adaptation system for micro-emission reduction scenarios in zero-carbon industrial parks, relating to the field of carbon asset development technology. It includes: a unified mapping and coordination of park physical equipment, data flow, and CCER rules, centered on a digital twin platform. An edge sensing module automatically identifies scattered micro-emission reduction scenarios through multi-source sensing and intelligent algorithms; a blockchain module provides trusted evidence storage for scenario data and dynamically aggregates it into virtual project packages with development scale based on smart contracts; a methodological intelligent adaptation module performs compliance matching and automatic material generation for project packages based on a computable rule graph, and associates them with a blockchain evidence chain. These modules interact deeply through the digital twin platform, forming a closed loop from automatic carbon asset discovery and trusted aggregation to intelligent reporting, solving the problems of "invisible, incoherent, and inaccurate reporting" of micro-emission reduction scenarios.

Description

Technical Field

[0001] This application belongs to the field of carbon asset development technology, specifically involving a dynamic aggregation and CCER intelligent adaptation system for zero-carbon park micro-emission reduction scenarios. Background Technology

[0002] With the deepening implementation of the "dual-carbon" strategy, zero-carbon industrial parks have become a key vehicle for achieving regional carbon neutrality. These parks contain numerous dispersed, small-scale potential emission reduction scenarios, such as distributed photovoltaic systems, small-scale energy storage systems, and energy-saving retrofits of equipment. These "micro-emission reduction scenarios" have small individual emission reductions, high monitoring costs, and occur dynamically, making it difficult for traditional technologies to effectively identify and develop them into projects that meet the methodology requirements of the National Certified Emission Reductions (CCERs).

[0003] Currently, CCER project development mainly relies on manual on-site surveys, paper records, and post-event accounting, which presents the following prominent problems: First, it is impossible to achieve real-time and automated discovery and monitoring of a large number of small and scattered emission reduction behaviors, resulting in poor "visibility" of carbon assets and the omission of a large number of assets; Second, there is a lack of effective technical means to dynamically and reliably aggregate multiple micro-scenarios into a "project package" with economic development scale, and the development cost of a single project is too high; Third, the preparation of CCER application materials is highly dependent on expert experience, the rules of different methodologies are complex, manual matching and material preparation are inefficient and prone to errors, and it is difficult to adapt to the updating of methodologies and the dynamic adjustment of project boundaries. Summary of the Invention

[0004] This application provides a dynamic aggregation and CCER intelligent adaptation system for zero-carbon industrial park micro-emission reduction scenarios to solve one of the aforementioned technical problems.

[0005] The technical solution adopted in this application is as follows: This application provides a dynamic aggregation and CCER intelligent adaptation system for zero-carbon industrial park micro-emission reduction scenarios, including: A digital twin platform is used to build and run a dynamic carbon flow model for the park. The model maps and interacts in real time with the equipment operation at the physical layer, the information flow at the data layer, and the methodological logic at the rule layer. The edge perception and recognition module is communicatively connected to the digital twin base. It is used to collect device-level operating data through multi-source sensor nodes deployed in the park, and automatically identify unregistered decentralized potential emission reduction scenarios based on the preset device digital fingerprint database and time-series anomaly detection algorithm, and generate a dynamic list of micro emission reduction scenarios. The blockchain trusted aggregation module is connected to the edge sensing and identification module and the digital twin base. It is used to generate timestamped emission reduction data hash evidence for each scenario in the micro emission reduction scenario list, and dynamically merge the emission reduction data of multiple micro scenarios that meet the spatial adjacency or technical homogeneity based on the preset smart contract rules to form a virtual emission reduction project package that meets the preset scale threshold. The methodology intelligent adaptation module is connected to the digital twin base and the blockchain trusted aggregation module. It stores a computable rule graph formed by converting CCER methodology clauses. The module is used to match and reason with the technical features of the virtual emission reduction project package and the rule graph, automatically generate project boundary descriptions, monitoring plans and demonstration materials that meet the methodology requirements, and associate the relevant evidence stored in the blockchain trusted aggregation module as an attached evidence chain.

[0006] Preferably, in the edge sensing and recognition module, the multi-source sensing node integrates at least two of the following: a non-invasive power monitoring unit, an infrared thermal imaging unit, and a local environmental parameter acquisition unit; and the device digital fingerprint database contains multi-dimensional electrical and thermodynamic feature patterns under typical operating conditions of the device.

[0007] Preferably, in the blockchain trusted aggregation module, the smart contract rules include a dynamic boundary adjustment clause. The trigger condition for this clause is: when the newly added micro-emission reduction scenario and the formed virtual emission reduction project package meet a preset correlation threshold in the spatial location or technology type mapped by the digital twin base, the data of the newly added scenario is automatically merged into the project package, and the boundary information of the project package is updated.

[0008] Preferably, the methodology intelligent adaptation module further includes a text generation unit based on a large language model, which is configured to automatically generate the natural language description portion of the monitoring plan and argumentation materials based on the results of matching inference, a historical project application text library, and the review result mode of reinforcement learning feedback.

[0009] Preferably, the system further includes: an iterative optimization agent deployed in the digital twin base, used to simulate the review process of CCER project applications and provide feedback on the application results; the digital twin base is further configured to: dynamically adjust the recognition sensitivity of the edge perception and recognition module, the aggregation correlation threshold of the blockchain trusted aggregation module, and the rule matching strategy of the methodology intelligent adaptation module according to the feedback of the iterative optimization agent.

[0010] A method for dynamic aggregation and intelligent adaptation of zero-carbon industrial park micro-emission reduction scenarios to CCER (China Certified Emission Reduction) is applied to the above system. The method includes: A dynamic model of carbon flow in the park is established and operated through the aforementioned digital twin base; The edge sensing and recognition module automatically identifies scattered micro-emission reduction scenarios within the park and generates a dynamic inventory. The blockchain trusted aggregation module performs trusted storage and dynamic aggregation of micro-emission reduction scenario data in the list to form a virtual emission reduction project package. The methodology intelligent adaptation module intelligently matches the virtual emission reduction project package with the CCER methodology rule map and automatically generates compliant application materials.

[0011] Preferably, the automatic identification through the edge perception and recognition module specifically includes: calculating the device's operating feature vector in real time based on the data stream of the multi-source sensing nodes; comparing the feature vector with the benchmark pattern in the device's digital fingerprint database; and using the time-series anomaly detection algorithm to identify operating states that deviate from the benchmark pattern and represent emission reduction potential, marking them as unregistered micro-emission reduction scenarios.

[0012] Preferably, after the methodological intelligent adaptation module intelligently matches the virtual emission reduction project package with the CCER methodology rule map and automatically generates compliant application materials, the methodological intelligent adaptation module further includes: using the iterative optimization agent to simulate the review of the generated application materials and obtain simulated feedback; based on the simulated feedback, inversely optimizing the identification logic, aggregation rules, and methodology matching parameters of the micro-emission reduction scenario in the digital twin base.

[0013] A second aspect of this application provides a computer-readable storage medium having a program stored thereon that, when executed by a processor, implements the steps described in the method.

[0014] A third aspect of this application provides an electronic device including a memory, a processor, and a program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the method as described.

[0015] Due to the adoption of the above technical solution, the beneficial effects achieved by this application are as follows: This application enables automated and intelligent identification of micro-emission reduction scenarios. By integrating edge perception and digital fingerprint comparison technologies, it can automatically and in real time detect unregistered, dynamically occurring micro-emission reduction behaviors within the park, greatly improving the "visibility" of carbon assets and solving the problem of "invisibility".

[0016] A trustworthy and dynamic emission reduction aggregation mechanism has been established. Blockchain technology is used to provide tamper-proof evidence for each micro-scenario data, and smart contracts are used to realize the automatic and dynamic aggregation of emission reduction scenarios to form project packages with development scale, thereby reducing the development cost of individual projects and solving the problem of "not being able to combine".

[0017] It provides intelligent adaptation of CCER methodology and automatic material generation capabilities. By transforming textual methodology into computable rule graphs and automatically matching them with project package features, it generates application materials in conjunction with a large language model, significantly improving the accuracy and efficiency of applications and solving the pain point of "inaccurate reporting".

[0018] It has formed a closed-loop feedback and continuous optimization system capability. By integrating full-process data and models through a digital twin base and introducing iterative optimization agent simulation audit feedback, the system can learn and optimize identification, aggregation and adaptation strategies, thereby improving the system's adaptability and long-term effectiveness.

[0019] This solution enhances the efficiency and value of carbon asset management in zero-carbon parks by integrating discrete technical processes into a collaborative and organic whole. It achieves full-process automation and intelligence from carbon asset discovery and integration to compliant monetization, uncovering new value growth points for parks and contributing to the achievement of carbon neutrality goals. Attached Figure Description

[0020] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings: Figure 1 A flowchart illustrating a method for dynamic aggregation and intelligent adaptation of zero-carbon park micro-emission reduction scenarios to CCER, provided in an embodiment of this application; Figure 2 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application.

[0021] Figure label: 810, Processor; 820, Communication interface; 830, Memory; 840, Communication bus. Detailed Implementation

[0022] To more clearly illustrate the overall concept of this application, a detailed explanation is provided below with reference to the accompanying drawings.

[0023] Many specific details are set forth in the following description to provide a thorough understanding of this application. However, this application may also be implemented in other ways different from those described herein. Therefore, the scope of protection of this application is not limited to the specific embodiments disclosed below. It should be noted that, unless otherwise specified, the embodiments of this application and the features thereof can be combined with each other.

[0024] In this application, unless otherwise expressly specified and limited, the "above" or "below" of the second feature can mean that the first and second features are in direct contact, or that the first and second features are in indirect contact through an intermediate medium. In the description of this specification, references to terms such as "an embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described can be combined in any suitable manner in one or more embodiments or examples.

[0025] To make the objectives, technical solutions, and advantages of this invention clearer, the embodiments of this invention will be further described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0026] Example 1 This embodiment provides a dynamic aggregation and CCER intelligent adaptation system for zero-carbon industrial park micro-emission reduction scenarios. At the core of this system is a digital twin platform. This platform is not simply a data visualization platform, but a complex system capable of unified modeling, simulation, and real-time interaction of physical entities within the park (such as photovoltaic panels, air conditioning units, and charging piles), data flows (energy consumption, power generation), and business rules (CCER methodology). It establishes a full-element, full-process mapping relationship between physical and digital spaces, serving as the central hub for the collaborative work of all modules in this solution.

[0027] The digital twin base establishes bidirectional data and instruction connections with the edge sensing and identification module, the blockchain trusted aggregation module, and the methodology intelligent adaptation module.

[0028] The edge sensing and recognition module is responsible for the "last mile" of carbon asset discovery. It deploys lightweight, multi-source sensing nodes at key nodes throughout the park. Each node integrates at least two types of sensors; for example, one configuration is a "non-invasive power monitoring unit + infrared thermal imaging unit" to monitor the energy consumption and operating temperature of motor equipment; the other configuration is a "power monitoring unit + local illumination / temperature sensing unit" to monitor the operating status of distributed photovoltaic systems. These nodes continuously collect high-frequency data and extract features (such as current harmonic distortion rate, power drop slope at specific times, and deviation of the plate temperature-power generation relationship) to form equipment operating feature vectors. The module internally maintains a digital fingerprint database of equipment, storing feature vectors for various types of equipment under standard operating conditions or high-energy-consumption baseline modes. A time-series anomaly detection algorithm (such as LSTM-based prediction error analysis) is used to compare the real-time feature vectors with the baseline patterns in the fingerprint database. When a feature vector is detected to continuously deviate from the baseline pattern, and this deviation points to improved energy efficiency or renewable energy generation (e.g., a smooth and declining motor power curve, or photovoltaic panels outputting higher power than historical benchmarks under specific irradiance), the algorithm identifies the device or group of devices as a new "micro-emission reduction scenario" and adds its device ID, location, emission reduction type, estimated emission reduction, and other information to a dynamically updated list of micro-emission reduction scenarios. This process enables the automatic capture of "device-driven energy-saving behavior" or "green electricity production not included in statistics."

[0029] The blockchain trusted aggregation module addresses the credibility and scalability issues of micro-emission reduction data. This module generates a unique data certificate for each micro-emission reduction scenario in the list. The certificate contains core data of the emission reduction process (such as time intervals and measurement values) and a timestamp, hashed and recorded on a permissioned blockchain to ensure data immutability and traceability. The module pre-configures several smart contract rules, one key rule being the "dynamic boundary aggregation contract." The triggering logic for this contract is provided by the digital twin platform: the platform analyzes in real-time the correlation between newly generated micro-emission reduction scenarios and existing virtual emission reduction project packages in terms of spatial topology (e.g., belonging to the same power distribution branch) or technology type (e.g., both being "rooftop solar power"). When the correlation exceeds a preset threshold (e.g., spatial distance <100 meters and the same technology type), the smart contract automatically executes, merging the certificate of the new scenario with the certificate of the project package and recalculating the total emission reduction of the project package. If the total emission reductions reach the minimum scale threshold for the economic development of a CCER project (e.g., an average annual emission reduction exceeding 5,000 tons of CO2 equivalent), a notification is triggered, indicating that a "virtual emission reduction project package" with development value is ready.

[0030] The methodology intelligent adaptation module is responsible for automating compliance. Its core is a computable rule graph, which uses natural language processing and knowledge extraction technologies to transform the normative provisions of the CCER methodology (such as applicable conditions, project boundary definitions, baseline scenario selection rules, and monitoring procedures) into structured logical judgment nodes and relational edges. When a virtual emission reduction project package is generated, the module extracts all technical features of the package (project type, geographical location, involved technologies, monitoring data frequency, etc.) from the digital twin base and the blockchain trusted aggregation module as input, and performs traversal and reasoning matching in the rule graph. For example, the matching process might be: "Technical feature A (photovoltaics) → Trigger methodology M (renewable energy grid-connected power generation) → Check condition C1 (project located in a specific area) → Satisfy → Determine monitoring plan P1 (requiring monitoring of power generation and grid-connected power)". After matching is complete, the material generation unit (built based on a large language model) automatically generates a complete and linguistically standardized draft of the Project Design Document (PDD), monitoring plan, and other key application materials based on the matched rule nodes, specific parameters of the project package, and a database of historically successful application cases. Simultaneously, it automatically links to blockchain-based evidence to support the credibility of the monitoring data.

[0031] The iterative optimization agent, deployed as a software intelligence agent within the digital twin platform, works as follows: It periodically retrieves application materials generated by the methodology intelligent adaptation module, conducts simulated reviews based on a built-in review rule model (built by learning from historical review opinions), and generates "simulated review opinions." For example, a simulated opinion might indicate "insufficient additionality argument for a certain distributed scenario." This feedback is sent back to the digital twin platform. The platform analyzes the root cause: Is the edge perception module insufficiently extracting the recognition features of the scenario? Or has the blockchain aggregation module failed to aggregate the scenario with another related scenario that could enhance the additionality argument? Or is there a deviation in the rule matching weights of the methodology adaptation module? The platform then automatically generates adjustment instructions, dynamically fine-tuning the parameters of relevant modules (such as increasing the recognition sensitivity of a certain type of scenario, relaxing the aggregation correlation threshold of a certain type of technology, and adjusting the trigger weights of additionality argument rules in the rule graph), thereby achieving self-learning and continuous optimization of the system.

[0032] Example 2 This embodiment further illustrates multi-source sensing nodes. Besides the combination method mentioned in Embodiment 1, other feasible embodiments exist: For building energy conservation scenarios, the sensing node can integrate "non-intrusive power monitoring unit + ultrasonic flow meter + indoor temperature and humidity sensor" to monitor the energy consumption of air conditioning unit and water pump and the relationship between water flow and temperature difference, and identify non-standard energy-saving operation strategies in combination with indoor environment.

[0033] For small-scale biomass utilization scenarios, nodes can integrate "weighing sensors + flue gas composition analysis units (such as CO2 concentration sensors)" to estimate emission reductions by monitoring fuel consumption rates and emission gas composition.

[0034] The above embodiments illustrate that the overarching concept of "multi-source sensing nodes" can be used to monitor the consumption and emission reduction of various forms of energy, such as electricity, heat, and gas, through different combinations of sensors. Its core lies in non-invasive, multi-dimensional data acquisition and feature extraction, rather than being limited to a few specific sensors.

[0035] Example 3 This embodiment provides a detailed explanation of the generation of computable rule graphs and large language models. The construction of rule graphs is not unique: Based on structured rules: For a certain "grid-connected photovoltaic power generation" methodology, its clauses are decomposed into the following decision tree diagram: Root node: Is the project activity grid-connected photovoltaic power generation? (Yes / No) Sub-node 1: Is the project's installed capacity less than 15MW? (Yes → Simplified method applies; No → Standard method applies) Sub-node 2 (using simplified method): Is the project on the list of specific provinces and cities? (Yes → Baseline is the regional power grid emission factor; No → Demonstration required) Sub-node 3: Monitoring Requirements → (Power generation: continuous monitoring; Power to grid: monthly readings; Meter accuracy: not less than 0.5).

[0036] The system inputs the project package features into this map, automatically matches them along the path, and obtains conclusions and requirements.

[0037] Semantic Relationship Graph: Key concepts in the methodology (such as "baseline," "leakage," and "monitoring plan") are treated as entities, and "includes," "requires," and "depends on" are treated as relationships, constructing a semantic network. Matching is performed using graph neural networks, which can handle more complex and unstructured rule logic.

[0038] After obtaining the structured output of the spectrum matching (e.g., "A simplified method is required, the baseline factor is EF_grid,CM, monitoring power generation and grid-connected power"), the material generation unit calls the large language model and combines it with the specific data of the project package (e.g., location, capacity) to generate the following natural language paragraph: "This project is a distributed photovoltaic power generation project located in XX City, XX Province, with an installed capacity of XX kW. According to Article X of Methodology XXXX, it is applicable to projects below 15MW and adopts a simplified baseline method. The baseline emission factor is the average power supply emission factor EF_grid,CM (0.XXXX tCO2 / MWh) of the power grid in the project area in the most recent year. The monitoring plan requires the installation of meters with an accuracy of not less than 0.5 to continuously monitor the project's power generation and record the grid-connected power monthly..." This demonstrates the role of the large language model in transforming structured rules into compliant and fluent text.

[0039] Example 4 like Figure 1 As shown, this embodiment provides a method for dynamic aggregation and intelligent adaptation of zero-carbon park micro-emission reduction scenarios to the above system, including the following steps: S610: Building and running a digital twin platform This step is the cornerstone of the system's operation. In practice, a comprehensive physical information collection of the park is required first, including but not limited to: GIS coordinates and 3D models of all buildings; installation locations, models, rated parameters, and affiliations (workshops or buildings to which they belong) of major energy-consuming equipment (air conditioners, air compressors, motors, etc.) and power generation equipment (photovoltaic panels, wind turbines); and the energy network topology (power distribution lines, heating pipelines). This static data forms the "skeleton" of the digital twin. Secondly, various real-time data streams, such as smart meter readings, distributed generation monitoring system data, and building automation system data, are accessed through the park's IoT platform or data interfaces, forming the "blood" of the digital twin. Finally, a business rule layer is established within the base, abstracting the key decision logic in the CCER methodology (such as project boundary definition and additionality argumentation steps) into callable rule models or functions. During base operation, changes in the physical layer's state (such as a photovoltaic array starting to generate electricity) drive real-time updates in the data layer (increase in power generation value) and may trigger logical judgments in the rule layer (whether the power generation can be included in a certain aggregated project package). The core of this step is to establish a unified virtual model that can reflect the physical world in real time, carry data flow, and encapsulate business logic, providing a "sandbox" for collaborative calculations for all subsequent intelligent operations.

[0040] S620: Automatically identifies minor emission reduction scenarios This step enables the "automated discovery" of carbon assets. The specific workflow is as follows: Data Acquisition and Feature Extraction: Edge sensing nodes deployed on the device side continuously collect raw data (such as current and voltage waveforms, device surface temperature, and ambient light intensity). The built-in algorithm of the node calculates a set of "feature vectors" from the raw data in real time. For example, for a motor, the feature vector may include: current load rate, three-phase current imbalance, vibration amplitude in a specific frequency band, and the difference between operating temperature and ambient temperature.

[0041] Fingerprint comparison: The system maintains a "device digital fingerprint database" that stores the standard feature vector ranges of similar devices under various typical operating conditions (full load, half load, standby, fault). The feature vectors calculated in real time are compared with the standard ranges in the fingerprint database in a multi-dimensional similarity comparison.

[0042] Anomaly Detection and Emission Reduction Judgment: Temporal anomaly detection algorithms (such as Isolation Forest or prediction-based residual analysis) are used to analyze the time series of feature vectors. When the algorithm detects that a feature vector continuously and significantly deviates from its historical baseline pattern or standard fingerprint, and the deviation points towards energy efficiency improvement or clean energy output, a judgment is triggered. For example: If the power consumption curve of an air compressor shows a stable, step-like decrease under the same air production demand, and the operating temperature characteristics are normal, it may be determined that it has undergone energy-saving optimization (such as installing a frequency converter or optimizing the start-stop strategy), and is identified as an "energy efficiency improvement micro-scenario".

[0043] If the current characteristics of a building's roof show reverse flow (powering to the grid) during periods of good sunlight, and no photovoltaic equipment has been registered at that location before, it may be determined that a new "distributed photovoltaic micro-scenario" has been added.

[0044] List Generation: The judgment results, including equipment identification, geographical location, scenario type (such as "motor energy saving" or "self-consumption photovoltaic"), start time, and estimated real-time emission reduction / power generation, are added to a dynamically updated "micro-emission reduction scenario list". The key innovation of this step lies in combining "anomaly detection" technology with "emission reduction behavior characteristics" to automatically filter out anomaly patterns with carbon asset value from massive amounts of equipment data, rather than all anomalies.

[0045] S630: Trusted Evidence Storage and Dynamic Aggregation This step addresses the issues of "credibility" and "scalability" of microdata.

[0046] Trusted Evidence Storage: For each micro-emission reduction scenario in the list, the system extracts its key immutable data (such as the metering start timestamp, end timestamp, cumulative emission reduction / power generation value, and metering device ID), calculates its hash value, and writes this hash value, along with the timestamp and other information, into a distributed ledger (such as the Fabric consortium blockchain) by calling the blockchain network interface. This generates a unique, time-verified, and tamper-proof "digital ID card" (evidence storage) for the emission reduction data of each micro-scenario.

[0047] Dynamic aggregation: Correlation Calculation: The digital twin platform continuously analyzes the correlation between newly generated micro-emission reduction scenarios and existing "virtual emission reduction project packages." The correlation calculation is based on multiple rules, such as: spatial correlation (whether they are in the same factory area, the same building, or the same power distribution circuit); technological correlation (whether they are both photovoltaic power generation or both air compressor energy saving); and temporal correlation (whether they occur continuously within a similar time period).

[0048] Smart contract execution: The preset "dynamic boundary aggregation smart contract" defines aggregation rules (such as "automatic aggregation of scenarios with spatial distance < 50 meters and the same technology type"). When the digital twin base determines that the correlation between a new scenario and an existing project package exceeds a threshold, the smart contract is automatically triggered.

[0049] Aggregation operation: The execution of smart contracts logically "binds" or "merges" the blockchain evidence of the new scenario with the evidence of the project package, records this attribution relationship on the ledger, and recalculates and updates the total emission reduction data of the project package. This process is automatic, requires no human intervention, and has a clear audit trail.

[0050] Scale Assessment: The system continuously monitors the total emission reductions of each project package. Once the estimated annualized emission reductions of a project package reach the feasibility threshold for CCER project development (e.g., greater than a certain tonnage of CO2 equivalent), the system marks the package as "developable." The essence of this step lies in using blockchain to ensure the credibility of the data source and using smart contracts to achieve flexible, automatic, and rule-based expansion of project boundaries, thereby combining scattered value points into commercially valuable asset packages.

[0051] S640: Intelligent Adaptation and Material Generation This step enables "intelligent compliance" in the development of the CCER project.

[0052] Rule Graph Matching: The "Computable Rule Graph" in the methodology adaptation module has structured the textual methodology. For example, for the "Renewable Energy Grid-Connected Power Generation" methodology, the graph may contain the following node chain: Project Type: Photovoltaic -> Applicable Conditions: Installed Capacity < 15MW -> Baseline Selection: Grid Baseline Factor of the Project Area -> Monitoring Requirements: Power Generation (Continuous Monitoring), Grid-Connected Power (Monthly Records). The system inputs the characteristics of the "Virtual Emission Reduction Project Package" (Technology Type: Photovoltaic; Total Capacity: XX kW; Location: XX Grid) into the graph for traversal matching, automatically deriving the applicable specific methodology clauses, baseline scenarios, and monitoring requirements.

[0053] Materials are automatically generated: Structured population: For highly structured content such as monitoring plan tables and project boundary maps, the system directly populates the data based on the matching results and specific data of the project package (equipment list, coordinates, monitoring point parameters).

[0054] Natural Language Generation: For text sections requiring discussion, such as "Additionality Argumentation" and "Environmental Impact Analysis," the "Large Language Model Text Generation Unit" is responsible. This unit uses matched rule nodes, project package feature data, and a vast amount of historical successful application texts as input and reference to generate argumentative paragraphs that conform to professional context and logic. For example, it automatically generates text similar to "This project is located in a non-priority development area, uses non-mainstream technologies, and faces financing obstacles, therefore it has investment additionality..."

[0055] Evidence chain linkage: In the generated application materials, key data (such as the emission reduction calculation process) will directly reference or attach its evidence ID (hash value) on the blockchain. The reviewer can use this ID to verify the authenticity and integrity of the original data on the blockchain. The core value of this step lies in transforming the complex rule judgments and paperwork that rely on expert experience into an automated process based on knowledge graphs and AI, greatly improving accuracy and efficiency.

[0056] S650: Iterative Optimization This step gives the system the ability to "evolve itself".

[0057] Simulated Review: "Iterative Optimization Agent" is a built-in, trained review model. It simulates the perspective of an examiner, conducting multiple rounds of "stress tests" on the application materials generated in step S640, attempting to identify logical loopholes, data contradictions, or weaknesses in the argumentation, and generating "simulated review opinions."

[0058] Root Cause Analysis and Feedback: The digital twin platform receives these simulated feedbacks and traces back to the root cause of the problem. For example, if the review comments point out that "the baseline annual energy consumption data for a certain energy-saving scenario is insufficiently estimated," the platform will analyze: Did the edge sensing module fail to fully collect its historical operating data before the modification as a comparison benchmark when identifying the scenario? Or, could similar equipment scenarios with continuous historical data be prioritized for aggregation during the aggregation process to form a more complete baseline? Parameter tuning: Based on root cause analysis, the base automatically generates adjustment instructions. For example: Adjust the algorithm parameters of the edge sensing module to increase the depth of historical data backtracking for certain types of devices.

[0059] Adjust the smart contract rules of the blockchain aggregation module, and modify the correlation threshold or priority aggregation strategy.

[0060] Adjust the rule graph matching weights of the methodology adaptation module to enhance the depth of argumentation material generation for certain clauses that are prone to raising questions.

[0061] Closed-loop formation: After optimization, the system runs the S620-S640 process again, generating new materials and undergoing a new round of simulation review. Through multiple iterations, the strategies for collaborative work among various parts of the system are continuously optimized, resulting in increasingly higher quality materials and a sustained improvement in the robustness and success rate of the entire carbon asset development process. This step demonstrates the system's advanced intelligent form, enabling it to move from "automated execution" to "intelligent learning and optimization," proactively adapting to changes in external rules and internal data environments.

[0062] In summary, the five steps in Example 4 are interconnected, forming a complete and closed-loop intelligent carbon asset development workflow that extends from physical world perception to data value extraction, compliance encapsulation, and finally self-optimization.

[0063] Example 5 like Figure 2 As shown, the system of the present invention can be deployed in a distributed computing environment consisting of servers, edge computing gateways, and terminal devices. A typical electronic device includes at least one processor, at least one memory, and a computer program stored in the memory. When the processor executes the program, it implements the steps of the method described in Embodiment 4. The computer-readable storage medium can be any tangible medium containing or storing a program, such as ROM / RAM, disk, optical disk, etc., which implements the above-described method when executed by the processor.

[0064] For any parts not mentioned in this application, existing technologies may be used or referenced.

[0065] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

[0066] The above description is merely an embodiment of this application and is not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A zero-carbon park micro-emission reduction scene dynamic aggregation and CCER intelligent adaptation system, characterized in that, include: A digital twin platform is used to build and run a dynamic carbon flow model for the park. The model maps and interacts in real time with the equipment operation at the physical layer, the information flow at the data layer, and the methodological logic at the rule layer. The edge perception and recognition module is communicatively connected to the digital twin base. It is used to collect device-level operating data through multi-source sensor nodes deployed in the park, and automatically identify unregistered decentralized potential emission reduction scenarios based on the preset device digital fingerprint database and time-series anomaly detection algorithm, and generate a dynamic list of micro emission reduction scenarios. The blockchain trusted aggregation module is connected to the edge sensing and identification module and the digital twin base. It is used to generate timestamped emission reduction data hash evidence for each scenario in the micro emission reduction scenario list, and dynamically merge the emission reduction data of multiple micro scenarios that meet the spatial adjacency or technical homogeneity based on the preset smart contract rules to form a virtual emission reduction project package that meets the preset scale threshold. The methodology intelligent adaptation module is connected to the digital twin base and the blockchain trusted aggregation module. It stores a computable rule graph formed by converting CCER methodology clauses. The module is used to match and reason with the technical features of the virtual emission reduction project package and the rule graph, automatically generate project boundary descriptions, monitoring plans and demonstration materials that meet the methodology requirements, and associate the relevant evidence stored in the blockchain trusted aggregation module as an attached evidence chain.

2. The system of claim 1, wherein, In the edge sensing and recognition module, the multi-source sensing node integrates at least two of the following: a non-intrusive power monitoring unit, an infrared thermal imaging unit, and a local environmental parameter acquisition unit. The device digital fingerprint database contains multi-dimensional electrical and thermodynamic feature patterns under typical operating conditions of the device.

3. The system according to claim 1, characterized in that, In the blockchain trusted aggregation module, the smart contract rules include a dynamic boundary adjustment clause. The trigger condition for this clause is: when the newly added micro-emission reduction scenario and the existing virtual emission reduction project package meet a preset correlation threshold in the spatial location or technology type mapped by the digital twin base, the data of the newly added scenario is automatically merged into the project package, and the boundary information of the project package is updated.

4. The system according to claim 1, characterized in that, The methodology intelligent adaptation module also includes a text generation unit based on a large language model. This unit is configured to automatically generate the natural language description portion of the monitoring plan and argumentation materials based on the results of matching inference, a historical project application text library, and the review result mode of reinforcement learning feedback.

5. The system according to claim 1, characterized in that, The system also includes: An iterative optimization agent, deployed in the digital twin base, is used to simulate the review process of CCER project applications and provide feedback on the application results; The digital twin base is further configured to dynamically adjust the recognition sensitivity of the edge perception and recognition module, the aggregation correlation threshold of the blockchain trusted aggregation module, and the rule matching strategy of the methodology intelligent adaptation module based on the feedback from the iterative optimization agent.

6. A method for dynamic aggregation and intelligent adaptation of zero-carbon industrial park micro-emission reduction scenarios, characterized in that, The method, applied to the system as described in any one of claims 1-5, comprises: A dynamic model of carbon flow in the park is established and operated through the aforementioned digital twin base; The edge sensing and recognition module automatically identifies scattered micro-emission reduction scenarios within the park and generates a dynamic inventory. The blockchain trusted aggregation module performs trusted storage and dynamic aggregation of micro-emission reduction scenario data in the list to form a virtual emission reduction project package. The methodology intelligent adaptation module intelligently matches the virtual emission reduction project package with the CCER methodology rule map and automatically generates compliant application materials.

7. The method according to claim 6, characterized in that, The automatic identification via the edge perception and recognition module specifically includes: Based on the data stream from the multi-source sensor nodes, the operating feature vector of the device is calculated in real time. The feature vector is compared with the baseline pattern in the device digital fingerprint database, and the timing anomaly detection algorithm is used to identify the operating status that deviates from the baseline pattern and represents emission reduction potential, and it is marked as an unregistered micro-emission reduction scenario.

8. The method according to claim 6, characterized in that, After the method intelligently matches the virtual emission reduction project package with the CCER methodology rule map through the methodology intelligent adaptation module and automatically generates compliant application materials, it also includes: The generated application materials are simulated and reviewed using the iterative optimization agent, and simulated feedback is obtained. Based on the simulated feedback, the identification logic, aggregation rules, and methodological matching parameters of the micro-emission reduction scenario are optimized in reverse within the digital twin base.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps of the method as described in any one of claims 6 to 8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the steps of the method as described in any one of claims 6 to 8.

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