1186 results about "Carbon footprint" patented technology

The invention relates to the technical field of enterprise carbon emission analysis, and discloses an enterprise carbon emission analysis method and system based on an ESG comprehensive evaluation model, and the system comprises a data collection layer, an analysis processing layer and a decision output layer. The data acquisition layer acquires carbon emission related data in the whole production and operation process of an enterprise in real time through multiple devices; the analysis processing layer constructs a credible data processing environment, and realizes data cleaning verification, secure transmission, classified storage and dynamic synchronization of data and a model; and the decision output layer establishes a multi-dimensional associated ESG comprehensive evaluation model by using a mode recognition technology and an integrated learning algorithm, and realizes carbon emission intensity calculation, supply chain carbon footprint tracing and emission reduction path optimization decision. The method and the system can comprehensively and accurately analyze the carbon emission condition of the enterprise, provide scientific decision support for low-carbon management of the enterprise, and assist in realizing a carbon neutralization target.

The present invention relates to the technical field of carbon footprint calculation. Disclosed are a method and system for calculating a carbon footprint of a high-energy-consuming product on the basis of electro-carbon coupling. The method comprises: collecting data of electricity consumption and data of a carbon emission factor for electricity generation in the production process of a high-energy-consuming product; constructing an electro-carbon coupling model for describing the relationship between electricity consumption and carbon emission, and performing iterative optimization; using the electro-carbon coupling model combined with the collected electricity consumption data and the carbon emission factor for electricity within a corresponding time period to calculate carbon emissions of energy directly consumed for electricity generation during each phase; and analyzing and calculating indirect carbon emissions of raw materials and energy consumed during the other indirect stages in the production of the product, and verifying the result. Thus, the relationship between electricity consumption and carbon emission is more accurately reflected, the impact of time and place changes on carbon emission factors can be taken into consideration, and a method for comprehensively evaluating an overall carbon footprint of a product is provided, thereby facilitating more comprehensive understanding and management of the environmental impact of the product.

The invention relates to the technical field of smart ports and low-carbon environmental protection, and discloses a green low-carbon port monitoring and evaluation system, which comprises an Internet of Things data acquisition module for acquiring multi-dimensional environmental data, carbon emission data and energy consumption data in real time through multi-type sensor equipment deployed at a port; the data processing and analysis module is used for cleaning and preprocessing the collected data, and mining the relationship between energy use and carbon emission by using a big data analysis technology and an artificial intelligence algorithm; the carbon footprint calculation module is used for calculating the carbon emission of the port activity through an accurate model according to the processed data; by deploying multiple types of sensors, multi-dimensional data in port operation can be acquired in real time, a comprehensive carbon emission evaluation system is formed, the limitation that a traditional method can only monitor a single parameter is overcome through the multi-dimensional data comprehensive acquisition mode, and the problem of information fragmentation is effectively solved; and a solid foundation is provided for subsequent data analysis and decision making.

The invention discloses an intelligent building energy-saving optimization platform and method based on carbon footprint tracking, and relates to the technical field of building energy saving and carbon emission management. The method is used for solving the problems of extensive carbon emission evaluation, rigid quota distribution and insufficient energy-carbon collaboration. A three-dimensional carbon density map is constructed by collecting people flow, equipment energy consumption and environment data in real time, and carbon emission hotspots are dynamically identified. And analyzing the association between the power grid and the renewable energy source through a carbon flow tracking model, and correcting a weight output contribution matrix. The characteristics of equipment energy efficiency, building material hidden carbon emission and the like are fused to construct a carbon emission gene entropy, a quota migration strategy is generated in combination with a game algorithm, and oriented transfer from high carbon to low carbon buildings is promoted. A double-ring collaborative framework is constructed, an inner ring chaos search optimization device starts and stops to suppress carbon density fluctuation, an outer ring carbon price mapping adjusts energy storage scheduling, accurate carbon emission tracing, quota dynamic allocation and energy-carbon deep collaboration are achieved, building low-carbon transformation is supported, and the building cluster carbon emission reduction efficiency is improved.

The invention discloses a full-life-cycle carbon footprint intelligent detection system and method, belongs to the technical field of carbon emission monitoring, and aims to solve the problems of insufficient standardization of full-life-cycle carbon emission data, inaccurate abnormal link identification and delayed carbon footprint risk early warning. Carbon emission data of a production link in a full life cycle of a target product is collected and transmitted to a block chain, and standardized coding is realized through a hierarchical coding rule; detecting the collected data based on a multi-dimensional triggering rule to judge whether carbon footprint updating is triggered or not, if so, configuring an anchoring reference of a quantization range according to a production mode, and screening the data to construct a quantization data set; after the quantitative data set is corrected, the total carbon emission amount of each production link is calculated, and a carbon emission report is generated; the abnormal links are identified based on the carbon emission and proportion of the production links in the report, the abnormal source is positioned through standardized coding, historical carbon emission data are tracked to predict the emission, and carbon footprint grade change risk early warning is realized.

Predictive Maintenance Digital Twin with Carbon Footprint Optimization System, consisting of: a digital twin framework for real-time asset monitoring, performance simulation, and failure prediction; an IoT-enabled data acquisition layer with sensors to collect operational and environmental data; an edge computing module for low-latency anomaly detection and diagnostics; a cloud-based AI engine that uses deep learning for predictive maintenance and remaining useful life (RUL) estimation; a carbon footprint optimization engine for tracking energy consumption, emissions, and sustainability metrics; an LLM-supported NLP interface for analyzing maintenance data, generating prescriptive recommendations, and optimizing CO2 efficiency; a predictive analytics engine for early fault detection, energy optimization, and automatic alerts; an interactive dashboard for visualizing asset health, predictive insights, and tracking carbon impacts; a hybrid cloud-edge architecture for secure, scalable, and efficient real-time processing.

The invention relates to the technical field of carbon footprint evaluation, and discloses a carbon footprint optimization method and system based on LCA-GLSS coupling. The carbon footprint optimization method comprises the following steps: calculating carbon footprints of all stages of a product in a full life cycle, carbon footprint contribution ratios corresponding to materials, energy sources, resources and wastes involved in all process units in the full life cycle, and carbon footprint sensitivities corresponding to the materials, the energy sources, the resources and the wastes; a high-carbon emission link found in the carbon footprint evaluation process is optimized, waste and variation in the carbon emission process are recognized and eliminated, a carbon footprint prediction model is established, the carbon emission trend in the future is predicted and simulated, and the prediction accuracy is improved through model optimization and parameter adjustment; the evaluation result is comprehensively analyzed and explained, main contribution factors and key links of the carbon footprint are identified, and emission reduction measures and optimization suggestions are formulated. The method has both quantitative analysis and process improvement, and can effectively improve the efficiency and accuracy of carbon footprint management.

The invention relates to a building material mining life cycle process carbon emission improvement potential quantification method comprising the following steps: constructing a full life cycle carbon footprint digital model covering mining, transportation, processing and restoration, and integrating a digital twinning and BIM technology dynamic correlation carbon emission factor library; key carbon emission influence factors are identified through Monte Carlo simulation and regression analysis; establishing a multi-objective optimization model, generating an optimal process parameter combination by adopting an intelligent algorithm, and quantifying the emission reduction potential; model parameters are dynamically calibrated in combination with real-time data, and a closed-loop optimization mechanism is formed; space-time distribution visualization and block chain evidence storage technologies are introduced, and accurate monitoring, intelligent decision making and credible tracing of carbon emission are achieved. According to the method, the problems of data lag and single optimization in a traditional method are solved, the accuracy of carbon emission and the feasibility of an emission reduction scheme are remarkably improved, meanwhile, carbon sink offset evaluation is supported, and a whole-process technical support is provided for mine green transformation.

The invention discloses a micro-grid intelligent economic regulation and control system and method based on carbon emission optimization, and relates to the technical field of economic regulation and control. The method comprises the following steps: deploying an Internet of Things sensor to collect photovoltaic generating capacity, energy storage SOC, load demand and power grid carbon intensity data in real time, and transmitting the data to an edge computing node through a 5G / optical fiber hybrid communication network; preprocessing the data by adopting wavelet transform, and establishing a time sequence prediction model of photovoltaic output / load demand; constructing a dynamic carbon flow tracking matrix; performing decision optimization according to the output dynamic carbon emission spectrum and the power carbon flow traceability data; and the edge node issues a control instruction through a Modbus-TCP protocol to carry out economic regulation and control. According to the method, multi-source data are collected in real time, a minute-level carbon emission equation is established based on a dynamic carbon flow tracking model, dynamically-changed energy carbon emission factors and line transmission loss are fused, and the micro-grid carbon footprint is accurately quantified.

The invention discloses a highway full life cycle carbon emission statistical accounting method, and relates to the technical field of carbon emission accounting, and the method comprises the steps: dividing the highway full life cycle into a plurality of stages, and dynamically calibrating the accounting boundary of each stage based on the space-time dimension; setting all types of carbon emission accounting indexes, and collecting multi-modal data corresponding to each stage; establishing a hierarchical computing architecture based on an edge-cloud collaborative technology, checking the carbon emission of each stage, and verifying a carbon emission checking result by using a virtual road model capable of online learning and updating; and drawing a carbon footprint thermodynamic diagram based on a carbon emission accounting result, and displaying a space-time evolution process. According to the invention, the construction, operation and demolition stages of the whole life cycle of the expressway are covered, and the comprehensiveness and systematicness of an accounting result are ensured; a plurality of data sources and collection methods are adopted, and a corresponding accounting method is combined for calculation, so that the accuracy and reliability of an accounting result are ensured, and a scientific basis is provided for low-carbon construction and operation of the expressway.

The invention discloses a block chain-based carbon emission factor data traceability management system, particularly relates to the technical field of cross-supply-chain carbon data management, and is used for solving the problem that cross-enterprise / cross-industry effective association of carbon emission data under an existing single-chain architecture cannot be realized. The problems that the whole life cycle carbon emission data of the same product is repeatedly calculated and accounting boundaries are overlapped are solved; the method comprises the following steps: acquiring a carbon emission factor data packet of each participant through a data acquisition module; the block evidence storage module writes the data packet into a block chain to generate a non-tampering block; the cross-chain identification module creates a cross-chain traceability identifier containing a product batch number and an arrangement stage mark for the block; the coherence detection module analyzes data coherence during cross-chain request, and if the data coherence is lower than a threshold value, cross-chain verification based on matching of the mark and the batch number is triggered; the batch verification module executes repeatability verification on data in different stages of the same batch after verification is passed; and the traceability integration module integrates the verification data to generate a global carbon footprint traceability record and stores the global carbon footprint traceability record to a cross-chain shared account book.

The invention relates to the technical field of sewage plant-oriented carbon neutralization regulation and control scheme design, in particular to a sewage plant-oriented carbon neutralization regulation and control method and a sewage plant-oriented carbon neutralization regulation and control system. The method comprises the steps of collecting water quality, energy consumption and gas emission data in real time through a multi-source sensor, inputting the data into a space-time diagram convolutional network to predict a carbon emission trend after edge computing node cleaning, and dynamically optimizing an aeration rate and a sludge reflux ratio based on a fuzzy PID controller. A whole-process carbon footprint is tracked by adopting a life cycle evaluation method, a multi-target compensation strategy is generated in combination with an improved NSGA-III algorithm, and photovoltaic, biogas and green power resources are scheduled preferentially. A digital twin platform and a block chain evidence storage module are innovatively integrated, the former calibrates model parameters in real time, and the latter generates a non-tampering carbon neutralization voucher. According to the method, the problems of one-sided data, regulation and control lag and insufficient credibility of a traditional method are solved, and minute-level sensing, prediction and offset closed-loop control of carbon emission in the sewage treatment process is realized.

The invention relates to the technical field of data processing, and discloses a ship enterprise-oriented building and structure carbon footprint fine management and accounting method and a ship enterprise-oriented building and structure carbon footprint fine management and accounting system. The method comprises the steps of establishing a ship enterprise building full life cycle carbon footprint accounting framework, collecting and sorting carbon emission factor data, constructing a matrix mathematical model, developing an accounting tool and performing actual project verification, finally forming a carbon footprint accounting process, a monitoring scheme and an emission reduction strategy, and outputting a management manual and a measure library. And the whole-process carbon emission fine management is realized. According to the invention, on the premise that the calculation accuracy is guaranteed, carbon emission full life cycle management, dynamic monitoring, early warning response and emission reduction decision support are realized.

The present invention relates to the technical field of carbon footprint accounting. Disclosed are a carbon footprint accounting method and system for a power grid material product based on life cycle assessment (LCA). The method comprises: on the basis of an LCA method, building a product carbon footprint model for a power grid material product; collecting power grid material product data, and on the basis of the collected data, accounting the carbon emission of the power grid material product from the raw material acquisition stage to the scrap recovery stage; during carbon footprint accounting, selecting power carbon emission factors of different grades in each stage, and accounting the power carbon emission; and on the basis of the accounted data, performing high-precision real-time carbon footprint accounting of the power grid material product in the whole life cycle. The environmental impacts of the product are assessed more accurately. Moreover, real-time accounting can reflect any change or improvement effect in time. By means of the comprehensive carbon footprint analysis, detailed carbon footprint accounting can reveal high-emission activities, provide data support for optimization of the product design and production process, and reduce the overall carbon emission.

The invention provides an AI-based carbon footprint and supply chain carbon emission collaborative management method and platform, and the method comprises the steps: collecting various types of carbon-related data from each link of a supply chain in real time, and carrying out the standardization processing to form a unified data flow. An intelligent sensing technology is combined with a dynamic reference model, and sudden carbon emission fluctuation generated in production operation is automatically recognized. The propagation influence and potential risk of these carbon emission fluctuations in the entire supply chain are analyzed. The risk propagation condition and the processing capacity of each node are comprehensively considered, and an optimal carbon emission distribution processing scheme is formulated through an optimization algorithm. A distributed account book technology and an automatic execution mechanism are utilized, and an excitation system is combined to ensure that each participant completes a corresponding carbon processing task according to an optimal scheme. According to the method, the intelligent level and the overall effect of supply chain carbon management can be effectively improved, and a technical support is provided for an enterprise to realize a carbon emission reduction target.

The invention provides a multi-dimensional quality evaluation method, device and equipment for carbon footprint data and a storage medium, and the method comprises the steps: obtaining multi-source heterogeneous data of a product, the multi-source heterogeneous data comprising basic information of the product and carbon footprint related data of the whole life cycle of the product; generating carbon footprint data of the product based on the multi-source heterogeneous data; and performing multi-dimensional quality evaluation on the carbon footprint data based on a pre-constructed carbon footprint knowledge graph, a time dimension detection model, a space dimension detection model and a cross-dimension detection rule. According to the invention, the carbon footprint knowledge graph, the time dimension detection model, the space dimension detection model and the cross-dimension detection rule are introduced to realize semantic, time, space and cross-dimension multi-dimensional quality evaluation of the carbon footprint data, and the detection dimension can cover the multi-dimensional features required by the carbon footprint calculation. Therefore, it is ensured that the carbon footprint data quality evaluation result can reflect the influence on the carbon footprint calculation reliability more accurately.

The invention relates to the technical field of carbon footprint dynamics, in particular to a railway traffic carbon footprint dynamic tracking system and method based on a block chain and edge calculation. The data acquisition unit is used for acquiring train operation state, environment parameters and traction energy consumption data; the edge calculation unit fuses the multi-source data acquired by the data acquisition unit through adaptive Kalman filtering to obtain standardized data, converts a train operation state into carbon emission based on an instantaneous power model, and introduces a dynamic emission factor to calculate and obtain accumulated carbon emission; and the block chain network unit is used for compressing the accumulated carbon emission data and then generating a Merkle tree root hash upper chain, and storing the carbon emission data to a block chain. Incremental uplink and rapid verification of the data are realized, the on-chain data processing efficiency and the query traceability are improved, and the problems of high storage pressure and low verification efficiency of a traditional on-chain evidence storage mode in a high-frequency track data writing scene are solved.

The invention provides an industrial carbon footprint dynamic prediction method fusing multi-source data, which comprises the following steps: step 1, collecting multi-source data, core data including electricity consumption, converted standard coal consumption, key industrial process product yield and carbon emission, and three types of extended data including new energy consumption data, process upgrading data and external data; 2, performing dynamic modeling according to the multi-source data obtained in the step 1, including dynamic carbon emission factor calculation and a process-environment coupling equation; 3, an industrial carbon-energy-economy multi-objective optimization model of dynamic coupling new energy consumption is constructed, minimization of carbon emission and operation cost is taken as an objective function, and constraint conditions comprise production requirements, energy supply and policy compliance; and 4, performing optimization solution on the target function of the multi-target optimization model by adopting an NSGA-II algorithm, and outputting a carbon emission-cost tradeoff curve.

The invention discloses a three-stage trigger type carbon emission factor dynamic updating system and method, particularly relates to the technical field of carbon emission management in an industrial flexible manufacturing process, and is used for solving the problem of confusion and distortion of carbon emission data of mixed products in an existing fixed period updating mode. Accurate product-level carbon accounting is realized through a three-level trigger mechanism. Firstly, energy consumption data are aggregated in real time based on a spatial geographic boundary to generate a spatial dimension carbon emission factor; when the space factor change rate exceeds a dynamic threshold value, triggering a product data redirection operation; detecting a product type change event to obtain a material code and a process route; constructing a virtual data acquisition domain and generating a dynamic membership matrix; shunting the redirected data to a virtual domain according to a matrix, and freezing abnormal data at the same time; and finally, outputting a dynamic carbon emission factor through event-triggered emergency calculation and periodic iteration double-track updating, and realizing accurate traceability of the product-grade carbon footprint in a flexible manufacturing environment.

The invention provides a carbon footprint calculation-oriented multi-source heterogeneous data intelligent fusion method and device, and the method comprises the steps: obtaining the multi-source heterogeneous data of a product, the multi-source heterogeneous data comprising the basic information of the product and the carbon footprint data of the whole life cycle of the product; analyzing the data type of the multi-source heterogeneous data, and selecting an algorithm matched with the data type to convert the multi-source heterogeneous data into structured data to obtain a structure standardized data set; based on the loaded carbon footprint domain ontology, performing semantic extraction on the structure standardized data set to obtain a semantic standardized triple data set; performing quality evaluation on the semantic standardized triple data set, and constructing an initial carbon footprint data set including quality information based on a quality evaluation result; and performing conflict detection and conflict ablation on the initial carbon footprint data set to obtain a final carbon footprint data set of the product. Through the method disclosed by the invention, effective fusion of multi-source heterogeneous data in carbon footprint calculation can be realized.

The invention relates to the technical field of data center interaction, and particularly discloses an information interaction method and system for computing power and electric power collaborative scheduling of a data center, which integrates multi-source heterogeneous data and constructs a space-time correlation model to realize accurate prediction and dynamic modeling of resource demands of the data center. The real-time learning ability of the DRL and the discrete decision advantage of the MIP are utilized to realize multi-objective synchronous optimization, the energy efficiency and economy of the data center are significantly improved, the multi-objective optimization can realize "computing power-electric power-heating power" coupling, the block chain technology ensures that data cannot be tampered and is transparent and credible, the security and auditing performance of the system are improved, and the system performance is improved. Carbon footprint tracking, green power authentication and PUE index dynamic energy efficiency optimization are supported, green power use is directly stimulated, the carbon footprint and operation cost is reduced, thermal data is used for cooling control, power data is used for task scheduling, resource conflicts are avoided, the limitation of traditional single resource scheduling is broken through, and the response speed is increased.

The invention discloses a carbon footprint tracking method and device, a storage medium and a computer program product, and relates to the technical field of carbon emission accounting, and the method comprises the steps: obtaining a carbon footprint evaluation target inputted by a user, and screening a to-be-tracked core product according to the carbon footprint evaluation target; drawing a life cycle boundary diagram according to the supply chain hierarchy diagram of the to-be-tracked core product, and marking high-emission key nodes in the life cycle boundary diagram; collecting carbon emission activity data and emission factors of each node in the life cycle boundary diagram; and performing carbon footprint calculation according to the carbon emission activity data and the emission factor to obtain a carbon footprint calculation result, and generating a carbon footprint tracking report according to the carbon footprint calculation result and the high-emission key node. The accuracy of carbon footprint tracking is improved by drawing the life cycle boundary diagram, so that emission reduction can be performed in a targeted manner according to the carbon footprint tracking report.

The invention provides an Internet of Things system rigid-flex board full life cycle carbon footprint accounting method and device, and the method comprises the steps: carrying out the feature extraction according to carbon emission activity data, and generating carbon emission activity features; according to different life cycle stages, in combination with a carbon emission factor method, through a pre-trained carbon emission prediction model, prediction is carried out according to the carbon emission activity characteristics, and carbon emission prediction results corresponding to the life cycle stages are generated; and according to the carbon emission prediction result and the global warming potential value corresponding to the life cycle stage, generating a carbon footprint accounting result corresponding to the life cycle stage, thereby generating a full-life-cycle carbon footprint accounting report, and realizing accurate accounting of the full-life-cycle carbon footprint of the rigid-flex board. The carbon footprint is scientifically managed, the accuracy of carbon emission data is improved, meanwhile, the efficiency of carbon footprint accounting is improved, a real-time monitoring and early warning function is provided, and a transportation route is optimized to reduce carbon emission in the transportation process.

The invention discloses a privacy protection and verifiable product carbon footprint evaluation method based on a block chain. The method mainly comprises the steps that a chain owner enterprise defines a purpose and a range; a chain owner enterprise requests carbon emission list data from an upstream raw material supplier according to a product material list (BOM), the raw material supplier performs homomorphic encryption on the carbon emission list data and then sends the data to a cloud computing platform of the chain owner enterprise, and identity verification is performed on a data source in combination with an RSA digital signature algorithm; the cloud platform completes carbon footprint calculation in a ciphertext state, and finally, a ciphertext result is returned to an upstream supplier; and the chain owner enterprise integrates the raw material supplier data through the cloud platform, completes final product carbon footprint calculation, verifies the validity of the calculation process based on a zero-knowledge proof technology, realizes automatic verification in combination with an intelligent contract, and finally explains and explains the whole process. According to the method, privacy protection of the carbon emission list data is realized, the evaluation process is verifiable, and the evaluation result is more accurate.

A power plant full life cycle carbon footprint monitoring method based on an intelligent carbon tube platform relates to the field of electric digital data processing, and comprises the following steps: acquiring unit load data and fuel consumption data, and detecting desulfurization outlet gas according to a multi-layer infrared detection structure to obtain gas concentration data; integrating the gas concentration data, the unit load data and the fuel consumption data to obtain a synchronous data set; and inputting the synchronous data set into a carbon emission dynamic measurement and calculation model to obtain a carbon emission trend prediction result of a preset future time period, inputting the synchronous data set into a multi-target optimization model, and carrying out optimization calculation by taking carbon emission intensity minimization, carbon quota utilization rate optimization and power generation cost minimization as targets to obtain an optimization scheme. And generating a carbon emission monitoring result based on the carbon emission trend and the optimization scheme, and performing unit start-stop and system load adjustment control according to the carbon emission monitoring result. By implementing the method, the monitoring precision of power plant carbon footprint monitoring can be improved.

The uncertainty of availability and risk associated with the cost of power procurement hinders the consumer from using new sources of supply / storage devices or taking part in the competitive power markets or procuring power from green energy resources. Present disclosure provides a method and a system for optimal scheduling of power sources for meeting electricity demand of enterprise. In particular, the system performs portfolio optimization formulation for cost, risk and carbon emission minimization for the enterprise. The portfolio optimization formulation simulates market scenario and guides in providing effective power purchase strategy for enterprise along with real-time adjustments. In particular, the level of risk to be considered along with cost of power procurement and carbon footprint determine portfolio allocation for each time block in portfolio optimization formulation. Thus, enterprise may choose most appropriate terms of contract, best installation size of renewable resources or batteries, and supply sources.

The invention discloses a multi-source data fusion accounting method for a full-life-cycle carbon footprint, and belongs to the technical field of carbon footprint accounting, and the method specifically comprises the steps: inputting a purchase order number set of a target product, and automatically associating a logistics fuel consumption record, a production process energy consumption log and a supplier indirect emission declaration form according to the purchase order number; constructing a dynamic traceability tree taking a purchase order as a root node, and generating a trunk path through matching of an order material code and a workpiece batch number of a process energy consumption log; when the difference between the logistics real-time energy consumption data and the supplier declaration form exceeds a set threshold value, activating a dispute data branch reconstruction algorithm, and creating parallel branches to temporarily replace dispute data segments while keeping trunk continuity; finally, outputting a full-life-cycle carbon footprint data graph with dispute marks, and reserving original association paths of all data sources; according to the invention, reliable support is provided for accurately tracing the full-life-cycle carbon emission of the product and improving the carbon footprint accounting quality.

The invention relates to the technical field of carbon emission, in particular to a full-life-cycle carbon footprint tracking management platform for power equipment. The platform collects carbon emission parameters in the whole process from raw material input to scrap recovery in real time through a monitoring equipment network and a sensor array to generate an original monitoring data stream; performing multi-source fusion and deviation correction on the data stream to generate a standardized carbon footprint sequence; a carbon emission trend model is constructed based on sequence extraction correlation features, and a dynamic adjustment strategy is generated when an integration fracture risk or emission abnormity is detected; transmitting an abnormal signal through a mobile terminal interface, receiving an adjustment instruction, and generating a closed-loop control signal; storing the extended carbon footprint data to construct a full life cycle file; estimating emission fluctuation to generate a stability strengthening strategy; and a monitoring-analysis-control-prediction four-order optimization link is formed. According to the invention, accurate tracking and dynamic optimization of carbon emission are realized, data integrity and management timeliness are improved, and technical support is provided for green manufacturing of power equipment.

The invention relates to the technical field of data processing, in particular to an electric power carbon emission intelligent analysis system based on a dual-carbon target, which comprises a quantum security acquisition engine, a dynamic privacy calculation fusion device, a quantum and classical mixed twin modeling module, a multi-agent quantum decision engine and a cross-modal causal auditing platform. The dynamic privacy calculation fusion device aggregates the distributed carbon intensity feature vectors in a ciphertext state, and dynamically adjusts the Laplace noise amplitude based on a polarization key fragmentation strategy; the quantum decision engine optimizes and maps the unit commitment to an Isin model to output a Pareto solution set, and generates dynamic block chain contract terms; a cross-modal auditing platform monitors a carbon footprint boundary, and when a prediction deviation exceeds a threshold value, quantum branch parameters are calibrated through an anti-fact gradient and contract terms are reconstructed. According to the system, privacy security is maintained through frequency binding of quantum noise amplitude and channel bit error rate, prediction time lag caused by source-load coupling is solved through anti-fact gradient closed-loop calibration, and the low-carbon scheduling precision of the power system is improved.

The invention relates to the technical field of carbon emission monitoring and environment perception, and discloses a user-side carbon emission real-time monitoring and tracing system, which comprises a multi-source data integration module, which is used for acquiring microenvironment parameters related to carbon emission in real time; the microenvironment feature analysis module is used for analyzing the feature parameters of the traffic road section through a microenvironment perception algorithm and establishing a microenvironment feature library; the environment relation modeling module is used for constructing a vehicle performance and microenvironment relation model and calculating an environment adjustment coefficient; the dynamic carbon emission calculation module is used for generating a carbon emission estimated value; the abnormal recognition and early warning module is used for recognizing an abnormal emission mode under a specific microenvironment combination and constructing an early warning mechanism; the carbon emission map building module is used for building an urban microenvironment carbon emission influence map; the carbon footprint analysis and report module generates a refined carbon footprint report through a microenvironment factor contribution degree quantitative decomposition algorithm; according to the method, through microenvironment influence decomposition and visual display, a carbon emission result has good interpretability.