385 results about "Emission intensity" patented technology

The invention discloses an active power distribution network interactive carbon reduction decision-making agent construction method based on carbon flow distribution, and relates to the technical field of active power distribution network low-carbon scheduling. According to the method, a carbon flow distribution dynamic tracking model is constructed, node-level carbon emission intensity is quantified, an intelligent agent framework based on a Markov decision process is designed, a state space including real-time carbon flow, new energy output and load fluctuation and action spaces including unit adjustment, an energy storage strategy and the like are defined, and the real-time carbon flow, new energy output and load fluctuation are determined. And optimizing a carbon emission minimization target through a reward function, training an intelligent agent by adopting a reinforcement learning algorithm fusing physical constraints and historical data, and finally generating a multi-target collaborative optimization decision by combining offline pre-training and online fine tuning strategies, so as to synchronously optimize new energy consumption, line loss and economy. According to the invention, the carbon emission intensity of the system is effectively reduced, and a technical support is provided for constructing a novel low-carbon and high-elasticity power system.

The invention discloses a carbon emission real-time regulation and control method and system based on multi-source data fusion and AI decision, and relates to the field of data processing systems or methods specially suitable for administrative, commercial, financial, management, supervision or prediction purposes, in the method, real-time production data is collected through Internet of Things equipment, and the real-time carbon emission intensity is calculated in combination with a carbon accounting engine. And constructing an AI optimization layer comprising a process knowledge graph, a long-short-term memory network and a reinforcement learning agent, converting process constraints into mathematical boundary conditions, predicting energy demands, carrying out iterative optimization by taking carbon emission intensity minimization as a target, generating a dynamic scheduling strategy, and realizing real-time adjustment of operation parameters of production equipment. The method and the device are used for improving the accuracy of carbon emission monitoring data on production scheduling and reducing the risk of high-carbon-intensity production caused by improper time period selection of enterprises.

The invention provides a remote sensing-based land utilization carbon emission evaluation method, device and equipment and a medium, and the method comprises the steps: carrying out the radiometric calibration and spatial registration of obtained multi-source remote sensing data, and obtaining a standardized raster data set; generating a land utilization classification grid map based on the standardized grid data set; generating a land change vector layer through a continuous multi-stage land utilization classification grid map; generating a dynamic evaluation unit vector boundary according to the land utilization classification grid map and the land change vector layer; correcting a vegetation carbon absorption coefficient and an industrial emission coefficient in real time based on remote sensing inversion data, and constructing a spatial carbon transfer model; and inputting the land utilization classification grid map, the dynamic evaluation unit vector boundary and the spatial carbon transfer model into a layered mixed carbon emission evaluation model to obtain a carbon emission intensity distribution map. By adopting the scheme, the carbon emission under different land utilization modes can be accurately evaluated, and the scientificity and policy applicability of regional carbon balance evaluation are improved.

The invention provides a construction carbon emission dynamic simulation and optimization decision-making method based on BIM and digital twinning, relates to the technical field of building construction carbon emission management and control, and solves the problem of limitation of an existing management and control process in the aspects of accounting precision, response speed, emission reduction effect and economical efficiency. The method comprises the following steps: firstly, establishing a multi-dimensional BIM model, constructing a digital twinborn body linked with a physical construction site in real time based on the model, and mapping real-time energy consumption and material data; and then monitoring and calculating the real-time carbon emission intensity of the whole construction process in combination with a dynamic carbon emission factor library, further performing multi-target optimization solution by adopting a mixed integer programming algorithm based on the intensity value and an optimization decision, outputting a low-carbon construction optimization scheme, and guiding field execution. Through BIM, digital twinning and dynamic optimization full-chain technology integration, real-time monitoring, accurate accounting, intelligent optimization and closed-loop management and control of building construction carbon emission are smoothly realized.

The present invention provides: a steel sheet which contains Ni, Cu and Sn, and has excellent chemical conversion treatability; and a component which includes said steel sheet.　A steel sheet and a component including the same according to the present invention are characterized by having a chemical composition containing, in mass %, 0.010-1.000% of Ni, 0.010-1.000% of Cu, and 0.003-1.000% of Sn, and by the emission intensities of Ni, Cu, and Sn in the surface layer of the steel sheet as measured by high-frequency glow discharge optical emission spectrometry satisfying the following relationships.　Ni: Surface layer maximum intensity / Bulk intensity ≤ 1.5 Cu: Surface layer maximum intensity / Bulk intensity ≤ 1.3 Sn: Surface layer maximum intensity / Bulk intensity ≤ 15

The invention discloses a regional carbon emission prediction method and system based on carbon emission monitoring and electronic equipment, and relates to the technical field of carbon emission prediction, and the method comprises the steps: collecting regional discrete point emission data through a multi-modal sensing network, and generating a regional-level continuous flow field; performing feature decoupling and quantization on different emission source types to generate a source stream feature vector matrix; reconstructing a continuous space-time emission field based on the source stream feature vector matrix, identifying a conduction path, and dynamically binding with field features along the path to generate a dynamic field data set; wherein the field characteristics comprise emission intensity, diffusion rate and regional coupling coefficient; constructing a field domain succession rule model based on the dynamic field data set, identifying a dominant evolution path based on an evolution baseline library of the model, injecting external disturbance along a conduction path of the dominant evolution path, and generating a multi-branch evolution scene tree; wherein the external disturbance comprises policy regulation and control intensity and a meteorological anomaly index. According to the invention, accurate prediction of regional carbon emission is realized.

The invention relates to the technical field of electric power system planning, in particular to a carbon emission evaluation method, system and device for multi-type adjusting power supply and new energy collaborative planning and a storage medium. The method comprises the following steps: acquiring power system equipment parameters, constructing a hierarchical planning platform architecture, and establishing a parameterized model library through data collection and governance; based on the parameterized model library, various adjusting power supply operation characteristic parameters are extracted, a collaborative optimization objective function is constructed in combination with new energy output characteristics, and a preliminary collaborative planning scheme is generated; formulating an active support strategy according to a power supply configuration result, determining a scheduling plan and generating an energy management and control instruction sequence, constructing typical and extreme weather scenes for collaborative operation simulation, and obtaining power supply output and power flow distribution; the node carbon emission intensity and the total system carbon emission amount are calculated through tidal current tracking in combination with the carbon emission factors, and carbon emission space-time distribution is generated; and comprehensively evaluating the collaborative planning scheme through a multi-dimensional evaluation index, and generating a visual decision support report.

The invention relates to the technical field of carbon footprint management, in particular to a carbon footprint accounting method considering a whole life cycle, which comprises the following steps: analyzing life cycle path assessment influence and compressing to generate a path serial number sequence, reconstructing, assessing and transmitting identification abnormity according to serial numbers and reconstructing, identifying turning matching factors to obtain an adaptive set, and calculating a carbon footprint accounting result according to the adaptive set. And positioning heat loss inversion critical contrast monitoring to obtain accounting verification information, and collecting, layering and summarizing the carbon amount according to verification conversion. According to the method, through comprehensive utilization of flow path information, measurement of emission intensity and activeness and path screening are achieved, analysis of node carbon transfer characteristics is achieved, the accuracy of carbon flow judgment is improved, matching of operation sections and factor conditions is combined, identification and truncation of heat loss characteristics in an energy link are adopted, the reasonability of an inversion range is enhanced, and the accuracy of carbon flow judgment is improved. Through comparison of an emission monitoring result and energy input, verification of accounting data is enhanced, cross-level conversion and collection are combined, and consistency and integrity of carbon emission mapping are guaranteed.

The invention discloses a carbon emission prediction and emission reduction strategy optimization method and system, and belongs to the field of power data processing, and the method comprises the steps: firstly building an accurate carbon flow incidence relation through the power grid topology and power flow data through employing an upstream apportionment principle, and generating carbon flow intensity time series data of spatial and temporal distribution; and fusing operation data of the power system, constructing a multi-dimensional time sequence data set, and extracting spatial-temporal characteristics of the carbon emission intensity by using deep learning. A multi-time-scale nonlinear mapping relation network is established based on the characteristics, and accurate carbon emission intensity prediction from the minute level to the day level is achieved. And finally, by taking a prediction result as a constraint, constructing an optimization model with minimum total carbon emission and minimum operation cost as targets, and generating an optimal power generation scheduling and load adjustment strategy. According to the method, accurate tracing and multi-time scale prediction of carbon emission are realized, and the accuracy and efficiency of low-carbon optimization operation decision making of the power grid are effectively improved.

The invention belongs to the field of power system operation and control, and particularly relates to an operation control method for a wind-light hydrogen storage multi-energy complementary system, which comprises the following steps: performing empirical mode decomposition on an original output sequence to obtain an IMF component, training parameters by combining a hidden Markov chain model and a forward-backward algorithm and solving an optimal state sequence, and fusing a predicted value by using Bayesian estimation to obtain an optimal state sequence; outputting a wind and light output and load demand global prediction value; by taking the minimum comprehensive operation cost, the minimum carbon emission intensity and the minimum power supply shortage rate as targets, obtaining equipment, power balance and hydrogen storage capacity constraints through a constraint verification algorithm, and constructing a multi-target optimization model; a scene trigger function identifies four types of typical scenes, a chaos particle swarm optimization algorithm is improved to solve a model, and density peak clustering is carried out to obtain a global scheduling scheme containing output of an electrolytic cell and a fuel cell and energy storage charge and discharge power. According to the invention, through scene adaptive optimization and intelligent clustering screening, the randomness and volatility of wind and light output are dealt with.

The invention discloses a carbon quantity prediction method and device for coal-fired unit carbon emission, and a medium, and relates to the technical field of energy data intelligent analysis, and the method comprises the steps: calculating a carbon emission intensity prediction value and a confidence interval boundary value based on a real-time coal quality fusion operation parameter set and a device health index, and generating a dynamic carbon emission prediction package; comparing the dynamic carbon emission prediction packet with CEMS real-time monitoring data, calculating a prediction error rate, extracting a multi-dimensional error feature according to the prediction error rate, and generating a multi-dimensional error feature vector; and based on the multi-dimensional error feature vector, dynamically adjusting an equipment health index correlation factor and a coal quality confidence weight parameter, generating a dynamic correction instruction set, and generating a carbon emission prediction report in combination with a dynamic carbon emission prediction packet. According to the method, dynamic noise reduction, enhancement and feature quantization of the coal flow multispectral image are realized, the high-fidelity coal quality feature vector is directly generated, the problem of coal quality data lag is solved, and the coal quality sudden change response capability is improved.

The present invention provides a steel sheet containing Ni, Cu, and Sn, which has excellent chemical conversion treatability, and a part including the steel sheet. The steel sheet and parts including the steel sheet of the present invention have a chemical composition containing, in mass%, Ni: 0.010 to 1.000%, Cu: 0.010 to 1.000%, and Sn: 0.003 to 1.000%, and are characterized in that the emission intensities of Ni, Cu, and Sn in the surface layer of the steel sheet, measured by high-frequency glow discharge optical emission spectroscopy, respectively satisfy the following relationships: Ni: Maximum surface strength / bulk strength ≦ 1.5 Cu: Maximum surface strength / bulk strength ≦ 1.3 Sn: Maximum surface strength / bulk strength ≦ 15

The invention discloses a carbon emission flow analysis method for carbon emission evaluation in multiple application scenes, belongs to the technical field of power system carbon emission modeling and evaluation, constructs a connection relationship among nodes, branches and a power supply in a power system, and collects output data and type information of a power generation power supply and a power flow result in multiple operation conditions. The method comprises the following steps: calculating carbon emission of a power generation node, distributing the carbon emission to a load node according to a power flow path, identifying a carbon emission source of the load node by adopting a power tracking method, and calculating carbon emission intensity and a carbon potential index; a transmission structure of carbon emission in a power grid is defined, step-by-step distribution and flow direction tracking of the carbon emission are realized, and the defect that a carbon emission path is unobservable in the prior art is overcome; the identification precision of the carbon emission composition is improved, and a basis is provided for the priority division of energy-saving and emission-reducing measures; a carbon emission path mapping matrix and a transition probability matrix are constructed, dynamic identification of path changes is realized, and discovery of key paths and nodes is facilitated.

The invention relates to the field of power system carbon emission flow analysis, in particular to a closed-loop power grid-oriented electric energy tracking and power flow tracing coupled carbon emission flow analysis method, which is based on power flow distribution of an actual power grid and comprehensively utilizes an electric energy tracking method and a power flow tracing method. According to the invention, fine allocation and full-link physical traceability of nodes, branches and loads in a closed-loop power grid structure by carbon emission are realized. By establishing a power flow distribution matrix, a unit injection matrix, a load distribution matrix and a carbon emission intensity vector, the carbon potential of each node, the branch carbon flow rate and the load carbon flow rate are calculated in a recursive manner, an electric energy tracking method and power flow traceability are coupled, and node allocation is iteratively optimized, so that the actual flow path and spatial distribution characteristics of the carbon flow in a power grid are disclosed. The method can adapt to scenes such as complex multi-source access and a looped network structure, the scientificity and fairness of closed-loop power grid carbon emission management are improved, and theoretical and method support is provided for development of low-carbon scheduling, carbon accounting and low-carbon economy.

The invention relates to a photovoltaic low-carbon park smart energy management system and method based on digital twinning. Accurate mapping and simulation analysis of a park energy system are realized by constructing a digital twinborn model, dynamic optimization scheduling of energy equipment is realized by adopting a multi-agent cooperative control algorithm, an energy optimization strategy is formulated by taking low carbon as a target, the health state of the equipment is predicted by utilizing machine learning, and an intelligent operation and maintenance plan is generated. And decision support is provided through energy big data analysis. The system integrates multi-source data, and efficient utilization and low-carbon operation of park energy are realized through the steps of digital twin modeling, intelligent agent collaboration, low-carbon scheduling, equipment prediction, big data analysis and the like. Practical application shows that the system can improve the photovoltaic efficiency by 8%, reduce the energy cost by 18%, reduce the carbon emission intensity by 30%, and significantly improve the park energy management level and the low-carbon degree.

The invention relates to the technical field of power distribution network dispatching, in particular to a power distribution network dispatching optimization method and device. Constructing a dynamic digital twin of the power distribution network to be dispatched, and reversely correcting a line impedance parameter and an equipment efficiency coefficient in combination with a twin simulation result; multiple scheduling strategies are rehearsed in parallel, and the optimal scheduling strategy is selected through weighted scoring; generating a multi-energy flow collaborative scheduling instruction set according to the optimal scheduling strategy; dynamically distributing a control task by utilizing an edge node; and performing differentiated load response in combination with the power consumption behavior classification of the user. Space-time synchronous acquisition of electricity-heat-gas data is realized by deploying a multi-energy-flow sensor group, a multi-energy-flow collaborative scheduling instruction is generated by combining reverse parameter correction and scene rehearsal optimization of a dynamic digital twin, and dynamic resource allocation is realized based on an edge node dynamic allocation control task and load classification response. The new energy consumption rate capability is obviously improved, and the carbon emission intensity is reduced.

The invention relates to the technical field of industrial digitization and carbon monitoring, in particular to an industrial park carbon emission dynamic monitoring and evaluation system based on digital twinning, which comprises a digital twinning construction module, a carbon emission analog calculation module, a monitoring task configuration module, a data enhancement processing module, a situation fusion generation module and a dynamic evaluation output module. Data are collected through a sensor network to drive digital twinborn evolution, carbon emission space-time distribution is simulated, a hot spot area is identified, a monitoring task of the sensor network is dynamically configured accordingly, and self-adaptive focusing of monitoring resources is achieved. And the system executes the task set to obtain high-resolution data, after enhancement processing, the high-resolution data and a simulation result are subjected to cross-space-time fusion, a carbon emission situation map is generated, and the carbon emission intensity and the change track are evaluated according to the carbon emission situation map. According to the system, dynamic optimization of a monitoring strategy and deep fusion of virtual and real data are realized, and the accurate efficiency of monitoring and the comprehensive accuracy of evaluation are improved.

The invention relates to the technical field of carbon emission prediction, and discloses a multi-source data driven automobile carbon emission characteristic space-time prediction method and system, and the method comprises the steps: collecting multi-source data, carrying out the preprocessing, and constructing a multi-factor carbon emission data set; performing factor analysis on the data set, and extracting three principal component factors; a CNN-LSTM mixed deep learning model containing an attention mechanism is constructed to predict carbon emission; calculating carbon emission intensity of different spatial scales; based on a Gaussian diffusion theory, constructing a carbon emission diffusion model under a multi-wind-speed condition, and calibrating a diffusion coefficient by adopting measured data of an air monitoring station; modeling is carried out on the fused spatio-temporal data, and a carbon emission intensity prediction value of a grid scale in a specified time period in the future is output; performing visual display on the prediction result, and triggering early warning and generating a corresponding ecological driving control strategy based on the prediction value; the problem that an existing carbon emission prediction mode is low in prediction precision is solved.

The invention relates to the technical field of remote monitoring, and discloses an urban energy carbon emission monitoring system and method, and the system comprises a data table construction module which is used for constructing a structured data table of corresponding energy consumption original data, environmental parameters and spatial basic data of a to-be-monitored city; the carbon emission intensity analysis module is used for outputting a carbon emission intensity matrix of the city to be monitored; the early warning list determination module is used for generating a gridding thermodynamic diagram of the city digital twin base so as to determine a high emission early warning list of the city to be monitored; the strategy set construction module is used for constructing an executable strategy set of the city to be monitored; and the carbon emission monitoring module is used for calculating the absolute emission reduction, the carbon intensity reduction rate and the renewable energy permeability of the to-be-monitored city by the executable strategy set so as to execute energy carbon emission monitoring of the to-be-monitored city. According to the invention, the scientificity and intelligent level of urban carbon emission management can be improved.

The invention discloses a new energy low-carbon operation scheduling method and system for an island microgrid, and the method comprises the steps: generating a day-ahead scheduling plan with the minimization of the weighted average carbon emission intensity of a system as a core target through an upper-layer day-ahead optimization model based on load prediction data and renewable energy power prediction data; according to the day-ahead scheduling plan, generating an adjustment instruction of output of each unit through a lower-layer intra-day rolling optimization model; based on the adjustment instruction, calculating the wind and light abandoning power of the renewable energy source, and optimizing the renewable energy source consumption through a cost punishment mechanism; according to the optimized unit output and renewable energy consumption scheme, coordinating a charging and discharging strategy of an energy storage system to obtain an optimized scheduling instruction; and executing the scheduling instruction, and updating the system state through a closed-loop feedback mechanism. According to the embodiment of the invention, power balance and low-carbon operation of the island microgrid under a complex fluctuation working condition can be realized, and the utilization rate of renewable energy sources and the overall environmental protection property of the system are improved.

The invention discloses a power grid low-carbon scheduling method and device, terminal equipment and a storage medium, and belongs to the technical field of low-carbon scheduling, and the method comprises the steps: calculating the marginal carbon intensity of a node according to the output of a thermal power generating unit, the carbon emission intensity of the thermal power generating unit, the load power of the node and the sensitivity of the node to the thermal power generating unit; nodes with the marginal carbon intensity exceeding a preset marginal carbon intensity threshold value are screened out to serve as standard exceeding nodes; the method comprises the following steps: performing output reduction analysis on a related thermal power generating unit of a standard exceeding node, generating an output reduction constraint to solve a low-carbon scheduling model, performing low-carbon scheduling on a power grid according to model output, realizing reduction of the output of the related thermal power generating unit, so as to pertinently reduce the carbon emission of the corresponding standard exceeding node, and realizing accurate control of the carbon emission. The problem of space misalignment in the prior art is solved.

The invention relates to the technical field of industrial enterprise carbon data management, and discloses an industrial enterprise supply chain carbon footprint economic accounting and management method for a dual-carbon target, and the method comprises the steps: selecting and obtaining a public high-frequency economic agent signal based on a technical premise that core factors for driving industrial product price and carbon emission intensity have homology, dynamically correcting the static reference emission factor through a relevance conduction model; according to the method, a public economic signal is utilized to avoid a traditional mode of relying on static lagging data, the technical problem that the real dynamic state of the carbon intensity of the supply chain cannot be sensed is solved, and the real dynamic state of the carbon intensity of the supply chain can be sensed. The almost real-time carbon risk situation sensing ability of an enterprise is improved, and passive bookkeeping after carbon management is engaged is converted into active intervention fused into daily business.

The invention relates to the technical field of energy internet and intelligent micro-grids, and discloses a dynamic energy-carbon coupling modeling and collaborative optimization method for a wind and light storage and charging system, which comprises the following steps of: a, acquiring operation data of the wind and light storage and charging system; b, calculating to obtain the equipment-level dynamic real-time carbon potential of the output port of the energy conversion equipment; step c, updating the virtual total carbon load by using the state data of the energy storage unit and the real-time carbon potential; step d, constructing and solving a rolling horizon optimization problem to obtain an optimal control strategy covering the prediction horizon; and step e, issuing and executing a control instruction, and repeating the step a to the step e. According to the method, the physical working condition and the real efficiency of the equipment are dynamically mapped, the carbon emission intensity and the equipment operation state are associated in real time, and then static emission factors are replaced, so that the influence of the equipment on the carbon flow is accurately and dynamically reflected, and the precision and the real-time performance of microscopic carbon accounting are improved.

A galvannealed steel sheet includes: a cold-rolled steel sheet having a chemical composition containing Si in an amount of 0.1 mass % or more and 3.0 mass % or less; and a galvannealed layer formed on one or both surfaces of the cold-rolled steel sheet. An Fe-based electroplating layer is absent between the galvannealed layer and the cold-rolled steel sheet. In an emission intensity profile, a value of (ISi,Fe) / (ISi,bulk) is 0.90 or less, where (ISi,Fe) denotes an average Si intensity in a range from an interface between the galvannealed layer and the cold-rolled steel sheet to +0.5 μm towards the cold-rolled steel sheet, and (ISi,bulk) denotes an average Si intensity in the cold-rolled steel sheet. An average value of C concentration in a range of 10 μm to 20 μm in a thickness direction from the interface between the galvannealed layer and the cold-rolled steel sheet is 0.10 mass % or less.

The invention discloses a power distribution network multi-type flexible resource low-carbon scheduling method based on carbon emission flow, and relates to the technical field of power system low-carbon scheduling, and the method comprises the steps: constructing a scheduling time period set of a discrete time domain, and building an equivalent lossless network model; power flow calculation is carried out, and a node-branch carbon flow mapping relation is established; according to the carbon emission intensity of the unit, the carbon flow rate and node carbon emission flow of each branch are calculated; performing differential approximate calculation on the node injection power to obtain a carbon marginal coefficient of each node; generating a time-varying carbon price signal of each node in combination with an external carbon price reference; constructing a weighted combination objective function based on the node time-varying carbon price signal; and solving a weighted combination objective function and constraint conditions by adopting a distributed multi-agent collaborative optimization strategy. According to the invention, collaborative scheduling of multiple types of flexible resources under a low-carbon target is realized, and the carbon emission reduction effect is improved.

The invention discloses an electric power and carbon management collaborative optimization method and system for a multi-industry symbiotic zero-carbon park, and the method comprises the steps: constructing a mixed integer programming model which comprises an electric power dispatching sub-model, a carbon collection sub-model and a source-sink matching sub-model, and defining a coupling relation between an electric power dispatching variable and a carbon management variable; designing a multi-objective optimization function, and bringing coal consumption cost, start-stop cost, carbon transaction cost, wind curtailment penalty cost, deep peak regulation cost, peak regulation compensation benefit and transportation cost into a unified optimization framework; and setting time-space coordination constraint conditions, including power balance constraint, output upper and lower limit constraint, climbing rate constraint, capture-transport volume balance constraint and carbon sink capacity limitation constraint, to realize coordination optimization of power production and carbon logistics in time and space dimensions. According to the method, the total cost of the system is effectively reduced, the utilization efficiency of carbon sink resources is improved, real-time controllability of carbon emission intensity is ensured, and the problem of global optimum deficiency caused by a traditional two-stage optimization method is solved.

The invention discloses a carbon emission prediction and optimization method for a campus functional group. The technical problem that in the prior art, when carbon emission of grouped buildings is predicted, specific analysis and processing are not carried out on functions, forms and regional characteristics of campus groups, and consequently the prior art cannot be completely suitable for predicting carbon emission of the campus groups is solved. The building carbon emission intensity rapid prediction based on the spatial form parameters of the functional group is realized by utilizing the spatial form and the building power consumption characteristic of the functional group of the cold region campus and the regional characteristic and the climate characteristic of the cold region city and combining a performance simulation method and a machine learning method. And the spatial morphological parameters of the functional group are automatically optimized under the carbon reduction target, so that an architect is effectively assisted to carry out low-carbon design of the functional group of the campus in the cold region in a limited design period. The method is mainly used for predicting carbon emission of campus functional groups.

The invention discloses a node marginal carbon emission intensity calculation method and device based on incomplete information, and belongs to the technical field of low-carbon electric power technologies. The method comprises the following steps: determining a node marginal electricity price, a line blocking shadow price and a unit clearing amount only by means of incomplete clearing information under the condition that a power system network topology, a unit quotation and a network power flow distribution result are unknown; determining a marginal unit according to the node marginal electricity price and the unit clearing amount; a linear equation used for describing the relation between the node marginal electricity price and the line blocking shadow price is constructed, and key network parameters are reversely analyzed by solving the linear equation; and reversely analyzing a generator-line distribution factor (GLDF) matrix by using network parameters, jointly constructing an equivalent system model only containing a marginal unit in combination with a node loss distribution factor, and solving the equivalent system model to obtain the node marginal carbon emission intensity. According to the method, the precision and efficiency of calculating the node marginal carbon emission intensity can be improved.

The invention provides a product full production cycle carbon footprint tracing evaluation method and system, and relates to the technical field of carbon footprint tracing, and the method comprises the steps: obtaining production activity data of each link of a product full life cycle, constructing a directed weighted network model, and executing path traversal, extracting a traversal path of which the path carbon intensity meets a preset path carbon intensity condition as a first carbon flow path, obtaining the node net carbon emission of each node, and determining the apportionment amount of each node to the third-range indirect emission; and matching the production activity data of the link corresponding to each node with the emission parameters in the carbon emission database to obtain the link carbon emission intensity of each link, and determining the link carbon emission of each link based on the link carbon emission intensity and the node net carbon emission. According to the method and the device, a small-flow high-carbon emission intensity path can be identified, and the range three-emission accounting accuracy is improved to the level of tracing to a specific supply chain node through a double-layer mapping relation of link level microscopic accounting and node level macroscopic tracing.

The invention discloses a high-precision carbon emission online monitoring method and system based on multi-source data fusion, and relates to the technical field of carbon emission monitoring. The method comprises the following steps: constructing a multi-source heterogeneous carbon emission data acquisition layer, and acquiring carbon emission original data of a target area from different sources in real time; performing data preprocessing and feature extraction on the carbon emission original data to obtain a standard carbon emission feature data set; establishing a dynamic adaptive weight distribution mechanism, and distributing target fusion weights for standard carbon emission characteristic data sets from different sources; inputting the standard carbon emission characteristic data set configured with the target fusion weight into a pre-trained carbon emission inversion model, and calculating and generating the spatial gridding carbon emission intensity of the target area; and carrying out trend analysis on the spatial gridding carbon emission intensity, and executing abnormity alarm according to an analysis result. The technical problem of insufficient reliability of the carbon emission monitoring result in the prior art is solved, and the technical effect of improving the reliability of the carbon emission monitoring result is achieved.