650 results about "Emission intensity" 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 embodiment of the invention relates to the technical field of computers, in particular to an energy conservation and emission reduction scheme generation method and system based on a digital factory, and the method comprises the steps: obtaining real-time energy consumption data streams and emission monitoring signals of a plurality of pieces of production equipment in the digital factory; extracting dynamic energy consumption characteristics from the real-time energy consumption data stream, and identifying periodic emission parameters from the emission monitoring signal; a target energy consumption prediction algorithm is called to process the dynamic energy consumption characteristics and the periodic emission parameters, and energy consumption prediction results and emission intensity distribution of multiple time periods are generated; and generating an equipment-level optimization regulation and control scheme and a production line-level scheduling strategy according to the energy consumption prediction result and the emission intensity distribution in combination with preset energy conservation and emission reduction constraint conditions. Therefore, collaborative optimization of equipment-level real-time regulation and control and production line-level flexible scheduling can be realized.

The invention discloses an intelligent scheduling and optimization method for an industrial electrical automation system, and the method comprises the following steps: deploying a distributed sensor network to collect electrical parameters, an equipment vibration spectrum and production work order data in real time, and constructing a unified feature vector based on time-space alignment and confidence weighting; a production system-energy management-external power grid three-layer interaction model is established, and a dynamic carbon emission calculation engine and process deadlock detection module is embedded; an improved NSGA-III algorithm is adopted to solve a multi-target Pareto leading edge, and energy consumption, productivity and carbon emission target priorities are adjusted in real time in combination with a dynamic weight mechanism; distributed optimization is executed through an edge-cloud federated architecture, cross-system instruction synchronization is achieved, and closed-loop dynamic feedback is formed. According to the method, the limitation of traditional single system optimization is broken through, the energy consumption is reduced by 15%-30%, the carbon emission intensity is reduced by 12%-18%, the abnormal response speed is increased to 3 seconds, and intelligent decision making and green transformation in a complex industrial scene are supported.

The invention relates to an urban road carbon emission prediction and optimization control method and system, and belongs to the technical field of road traffic carbon emission monitoring, and the method comprises the steps: obtaining real-time monitoring data of a to-be-detected road, and carrying out the preprocessing; performing space-time alignment on the preprocessed real-time monitoring data, extracting dynamic traffic features and static road features, and generating a space-time feature matrix; inputting the spatial-temporal characteristic matrix into a pre-trained carbon emission prediction model to obtain a prediction result matrix of the to-be-detected road; on the basis of the prediction result matrix, identifying a road grid of which the carbon emission intensity prediction value exceeds a preset threshold value as a high emission area; acquiring real-time traffic state data of the high emission area; and based on the real-time traffic state data and the prediction result matrix of the high emission area, constructing a multi-objective optimization function and carrying out solving to obtain an optimization control strategy of the high emission area. According to the invention, traffic carbon emission optimization control and real-time traffic condition synchronization can be realized.

The invention discloses a carbon neutralization target-oriented electricity-carbon-green certificate multi-target scheduling optimization method and system, and relates to the technical field of carbon neutralization control, and the method comprises the following steps: obtaining first data of a target energy storage unit, constructing a constraint generation network, and dynamically updating an energy storage operation constraint domain based on a dynamic time warping algorithm; based on the updated operation constraint domain, establishing an electricity-carbon coupling model, extracting a nonlinear mapping relation between an electric energy output behavior and carbon emission intensity, and outputting a dynamic incidence matrix through a multi-dimensional time sequence predictor; constructing a carbon flow distribution topological graph according to the dynamic incidence matrix, and obtaining an optimal carbon flow path through a Dijkstra algorithm; based on the operation constraint domain and the optimal carbon flow path, establishing a multi-subject collaborative decision-making mechanism, solving a multi-target unit equilibrium solution by adopting a distributed alternating direction multiplier method, and generating a global scheduling instruction set; according to the method, a carbon flow topology and dynamic constraint domain cooperation mechanism is constructed, so that efficient solving of a multi-main-body carbon response optimization scheduling problem is realized.

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 provides a carbon emission accounting method and system based on an artificial intelligence technology, and belongs to the field of carbon emission. According to the technical scheme, the method comprises the following steps: constructing a sensor network of an equipment operation state, obtaining real-time operation data, and preprocessing the real-time operation data through an edge computing node; establishing a dynamic prediction model of emission factor boundary parameters based on the real-time operation data; according to the dynamic prediction model, constructing a carbon emission propagation map, analyzing the spatial and temporal distribution trend of carbon emission intensity, and identifying the position of an abnormal emission source; according to the abnormal emission source position identification result, carbon emission trend prediction is executed, and emission early warning response is triggered in combination with a preset threshold value. The method has the beneficial effects that dynamic prediction of carbon emission factors, identification of emission abnormal sources and trend early warning response are realized, the real-time modeling and space-time propagation analysis capabilities of the system on carbon emission behaviors are enhanced, and the intelligent level and response efficiency of emission management are effectively improved.

A power transmission network carbon emission accounting method based on a full life cycle theory. The method comprises: acquiring a bill of materials required in a production stage of a power transmission network device and carbon emission factors of materials corresponding to the bill of materials, constructing a carbon emission accounting model and calculating carbon emissions; acquiring transportation modes and carbon emission coefficients of related materials in the transportation and construction stages, constructing a model and calculating carbon emissions; establishing a model, which takes into consideration indirect carbon emissions generated due to line losses and carbon emissions generated during a replacement process related to conventional consumables and device failure, and calculating carbon emissions; constructing a carbon emission accounting model in the decommissioning and recycling stages, and calculating carbon emissions and carbon emission reductions generated in the stages; and performing accounting on carbon emissions throughout the full life cycle of a power transmission network, and calculating the carbon emission intensity. A carbon emission reduction effect of device recycling in a decommissioning stage is taken into consideration, the carbon emission intensity throughout the full life cycle of the power transmission network is calculated by means of a net power transmission capacity, and practical significance is given to a carbon emission target.

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 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 discloses a source load distributed scheduling system based on medium and low voltage power distribution network carbon flow tracking, and relates to the technical field of power distribution network scheduling. The source-load distributed scheduling system comprises a data acquisition module, a carbon flow tracking module, a load prediction module, a distributed power source scheduling module, a source-load matching module, a collaborative scheduling module and a carbon emission monitoring module, and all the modules are connected through electric signals. The data acquisition module is used for collecting operation data of each node and branch in the middle and low voltage power distribution network. By monitoring the carbon emission condition of each distributed power supply and combining load prediction and a source-load matching strategy, a low-carbon scheduling scheme is accurately formulated, output adjustment of the power supply is considered, load reduction and transfer are also considered, collaborative optimization of the source side and the load side is realized, and the scheduling efficiency of the distributed power supply can be improved according to the carbon emission intensity of each power supply. And the overall carbon emission of the power distribution network is effectively reduced while the load requirement is met.

The invention relates to the technical field of environmental pollutant detection, in particular to a straw open-air incineration pollutant emission quantitative method based on multi-source data fusion, which comprises the following steps: based on air pollutant monitoring data, calling a sensor network to obtain PM2.5, CO and NOX concentrations, analyzing fire point heat radiation intensity, collecting meteorological data, and calculating the concentration of PM2.5, CO and NOX; and obtaining a pollution source data set by combining the time sequence change of the fire points and the meteorological parameters. By integrating an environment monitoring sensor, satellite remote sensing and ground meteorological data, the comprehensiveness and accuracy of pollution source monitoring are improved, the combination of fire point intensity and pollutant concentration time sequence data is optimized, the evaluation of fire point and pollution hot spot areas and the comprehensive analysis of wind directions and meteorological conditions are optimized, the accuracy of a pollutant diffusion model is improved, and the pollution source diffusion efficiency is improved. The pollutant release amount is dynamically adjusted, and the diffusion model is corrected by using a geographic information system, so that the scientificity of pollution prediction and emission intensity evaluation is enhanced, and more accurate pollution prevention and control decision support is provided.

The invention discloses an energy planning method for a low-carbon economic comprehensive energy system based on multi-agent deep reinforcement learning. The method comprises the following steps: constructing a comprehensive energy system structure including multiple energy forms such as wind power, photovoltaic, electricity-to-gas, combined heat and power generation, energy storage and the like; establishing a carbon emission intensity model of each subsystem based on an energy input and output relationship, and constructing a segmented carbon tax function to quantify the carbon cost; a double-layer two-stage Nash optimization model is designed, and the game relationship between collaboration between subsystems and external energy interaction is considered; the optimization problem is converted into a Markov decision process, and a state, an action and a reward function are defined; a multi-agent TD3 algorithm model is constructed and trained, and strategy stability and robustness are improved through a differential evolution mechanism; and deploying the trained strategy model in an actual system to realize self-adaptive energy planning and scheduling for the carbon economic target. The method has the characteristics of high adaptability, high carbon benefit and excellent intelligent cooperation capability.

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 relates to the technical field of sewage treatment, in particular to a method and a system for quickly tracing sewage. According to the technical scheme, the system comprises a data acquisition and processing module, a data analysis module and a result display and decision support sub-module. According to the method, the sewage pollution source can be quickly positioned, the discharge intensity can be quickly estimated, and the time interval from pollution discovery to pollution source determination is shortened; according to the method, the position of the pollution source and the emission intensity are accurately determined through comprehensive learning and analysis of multiple factors on the basis of an algorithm combining a convolutional neural network and a long-short-term memory network in deep learning, and the traceability accuracy is remarkably improved; through model training and optimization, the system has the characteristic of data driving, model overfitting is effectively prevented by adopting a regularization technology, the generalization ability of the model is enhanced, meanwhile, the model is continuously evaluated and adjusted according to evaluation indexes, and high traceability accuracy and reliability are kept.

The invention relates to a collaborative optimization scheduling method and system based on low-carbon demand response, and the method comprises the following steps: constructing a source-network-load-carbon storage emission flow metering model, and obtaining the carbon emission data of each part during the operation of a power system; obtaining a dynamic carbon emission factor according to the carbon emission data of each part during the operation of the power system, issuing the dynamic carbon emission factor to a user side, guiding a user to change the power utilization habit, and achieving the dynamic transfer of the power utilization load; a stepped carbon transaction mechanism is formulated according to the power utilization load transfer condition of the user, and the user is stimulated to optimize the carbon emission cost through carbon quota distribution and carbon price grading; and constructing a multi-objective optimization problem with minimum carbon emission intensity and minimum total cost of the power system, and solving the multi-objective optimization problem through a near-end strategy optimization PPO algorithm to obtain an optimal scheduling strategy.

The invention discloses a regional integrated energy system two-stage optimization method considering # imgabs0 # efficiency and stepped carbon transaction. The method comprises the following steps: constructing a regional integrated energy system model based on an energy concentrator model; establishing a multi-objective optimization model considering the efficiency of the equipment # imgabs1 #, and incorporating the multi-objective optimization model into a stepped carbon transaction mechanism; constructing an RIES two-stage optimization model which comprises an upper-layer capacity configuration model and a lower-layer scheduling model; solving the established RIES two-stage optimization model, solving a Pareto leading edge of equipment capacity configuration by an upper-layer capacity configuration model by adopting an NSGA-III algorithm, and generating a plurality of groups of candidate schemes; the lower-layer scheduling model obtains an optimal scheduling strategy corresponding to each candidate scheme through a solver; and objective weighting is carried out based on an entropy weight method to determine index weight coefficients such as economy, carbon emission intensity and # imgabs2 efficiency, a TOPSIS method is adopted to carry out full-dimension quantitative evaluation on a Pareto leading-edge solution set, and an optimal capacity configuration scheme and optimal scheduling data of the system are selected. According to the method, the system energy efficiency can be improved, the carbon emission intensity is reduced, and the equipment configuration redundancy is optimized.

The invention relates to the technical field of energy management, and discloses an energy efficiency grading and carbon emission evaluation method for a communication base station, and the method comprises the steps: collecting real-time operation data through an Internet of Things sensor disposed in the communication base station; dynamic weight configuration parameters are generated based on the base station type and the climate characteristics, a digital twinborn model is constructed based on the verified dynamic weight configuration parameters and the real-time operation data, and multi-dimensional energy consumption characteristic curves under different load rates are simulated through the digital twinborn model; performing space-time matching on the multi-dimensional energy consumption characteristic curve and a regional power grid dynamic carbon emission factor to generate a carbon emission intensity matrix of the communication base station; and inputting the multi-dimensional energy consumption characteristic curve and the carbon emission intensity matrix into a pre-trained energy efficiency grading model to output an energy efficiency grading result and a carbon emission evaluation report of the communication base station, so that the accuracy of energy efficiency grading and carbon emission evaluation of the communication base station can be 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 invention relates to a production workshop carbon flow twinborn mapping method, and belongs to the technical field of carbon emission optimization. The method comprises the steps of collecting production workshop data in real time to perform multi-modal data fusion; constructing a carbon flow dynamic accounting mechanism model, and calculating the real-time carbon emission intensity of the process; constructing a carbon flow intensity tensor model, extracting multi-granularity carbon flow features based on the carbon flow intensity tensor model, and realizing real-time digital twinborn deduction of carbon flow propagation by using an intelligent prediction algorithm; establishing a differential equation to describe double-flow real-time interaction of the carbon flow and the value flow, constructing a carbon value incidence matrix, and performing carbon flow value analysis; and on the basis of the carbon value incidence matrix, a space-time carbon chain-oriented cooperative adjustment strategy is dynamically generated through a multi-objective optimization algorithm, a double-layer topological optimization model is constructed, and a closed-loop feedback mechanism is introduced to drive the economic sustainability of the control strategy in an industrial environment. The workshop carbon footprint can be displayed in real time, the workshop carbon emission condition can be truly reflected, and the carbon emission data can be analyzed and optimized in time.

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 zero-carbon park inter-industry information fusion operation and maintenance system, particularly relates to the technical field of carbon emission dynamic management and control, and is used for solving the problem of carbon accounting distortion caused by state deviation of an existing static carbon emission model and a physical system. The method comprises the following steps: establishing an initial carbon emission model containing energy efficiency reference parameters in a design stage, adopting multi-working-condition dynamic calibration in a construction stage to obtain an operation boundary characteristic spectrum and synchronously acquiring a thermodynamic trace, and identifying topological fragile nodes based on Lyapunov exponential stability analysis and optical flow method thermal gradient analysis; quantizing a carbon emission cascade conduction effect by combining an equipment energy topology network, generating a dynamic carbon emission model correction instruction to update an energy efficiency parameter, and finally executing double-instruction collaborative management and control in an operation and maintenance stage; a complete-cycle carbon data flow closed loop is realized, and through multi-source information fusion and conduction chain modeling, the effect of continuously reducing the carbon emission intensity of the park is achieved.

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 provides a non-point source pollutant emission flux prediction and coastal risk identification method and system, and belongs to the technical field of water environment pollution big data estimation and risk early warning. The method comprises the following steps: acquiring water quality pollution data and space-time auxiliary characteristic data of a research area, and constructing a database; analyzing a causal relationship of the candidate planar driving elements to water quality pollution by using a CCM causal analysis model; extracting features of the key planar driving elements, and converting and generating feature vectors by utilizing supervised learning; performing layer-by-layer calculation on the feature vectors based on a Transs-GRU coupling model, outputting Transform branch features and GRU branch features, performing weighted coupling, and outputting a non-point source pollution flux prediction value through a prediction layer; and obtaining the emission intensity and the emission trend of the pollutants according to the predicted value, and further dividing risk grades to realize coastal risk identification and early warning. The method effectively improves the prediction precision of the emission flux of non-point source pollutants and the precise recognition of the position and time period of a coastal risk region.

The invention provides a carbon emission data quality analysis method which comprises the following steps: acquiring various activity level data influencing product / enterprise carbon emission, calculating carbon emission intensity, longitudinally comparing the carbon emission intensity with historical data and transversely comparing the carbon emission intensity with an industry reference value, and acquiring first quality evaluation data of the carbon emission activity level data; according to the production / business logic of the enterprise, analyzing the correlation of the activity level data in the production process, and obtaining second quality evaluation data of the carbon emission activity level data; constructing a multiple regression model, performing multiple regression analysis on correlation between molecules and denominators of the carbon emission intensity indexes and noise influence on time, places, processes / technologies and quality of the carbon emission intensity indexes, and obtaining third quality evaluation data of the carbon emission activity level data; and the authenticity of the carbon emission data is judged according to the first, second and third quality evaluation data, so that the carbon emission data quality can be improved, and disordered filling, wrong filling and counterfeiting of the data are avoided.

The invention relates to a power distribution network partition and transformer substation power-carbon collaborative planning method considering multi-terminal uncertainty. The method comprises the following steps: extracting a representative wind-light output curve and a load curve; constructing an electrical coupling degree index, a load carbon flux index and a load balance degree index, calculating a comprehensive division index, and performing dynamic partitioning; and planning the transformer substation capacity which meets the main transformer N-1 verification, considers the influence of the load coincidence rate and the load prediction coefficient, and can meet the maximum load demand in the network element. The node carbon flux clustering index is combined with the traditional electrical coupling degree and load balance in the partition index, the limitation that traditional partition only depends on the electrical topological relation is broken through, the real-time carbon emission intensity of the load nodes is accurately quantified, and balanced partition of the power distribution network elements is achieved; carbon loss can be obviously reduced, and joint optimization of low carbon emission and high safety in the planning process of the transformer substation is realized; a typical scene expected value curve is used as input, and the influence of uncertainty on power distribution network planning is remarkably reduced.

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 discloses an electric carbon tracking method based on electric power flow topological grid modeling, and solves the problems that the electric power transmission path is not clear and the electric carbon factor calculation is not refined enough in various voltage level electric power grids. The method comprises the following steps: based on a regional power grid architecture, establishing an electrical load flow calculation model of an electrical carbon tracking standard grid; inputting power supply output, transformer substation and line power flow, generating a unit injection matrix, a branch power flow distribution matrix and a load distribution matrix, generating a node adjacency shunt matrix, calculating the proportion of outflow power line power flow of each node, and generating a node adjacency shunt coefficient matrix; calling a depth-first search algorithm for the nodes, calculating directed line paths from the starting node to the tail end node, forming shunting coefficients contributed by all paths from power flow generated by the starting node to the tail end node, obtaining a power distribution matrix in combination with the power injected into the starting node by the corresponding unit, and obtaining a power distribution matrix according to the carbon emission intensity of each power supply; a node carbon potential matrix is obtained through calculation, and electric carbon tracking of a power supply layer, a power grid layer and a load layer is achieved.

The invention belongs to the technical field of carbon emission determination, and discloses a block chain-based carbon emission reduction comprehensive determination method and system, and the method comprises the steps: building a hierarchical data collection architecture, introducing a carbon emission description language, carrying out the collection and structural unification of internal heterogeneous data through a protocol converter, and obtaining multi-source heterogeneous data; the method comprises the following steps: establishing an intelligent oracle machine network, dynamically accessing an external data source, and aggregating multi-source heterogeneous data by adopting a BFT consensus mechanism to form a basic credible carbon data set; constructing a double-chain hybrid block chain architecture comprising a main chain and a side chain, and automatically calculating the carbon emission intensity based on a basic credible carbon data set in combination with a carbon right pledge mechanism; marking the data of which the carbon emission intensity is greater than a preset carbon emission intensity threshold value as high carbon emission behavior data, and triggering an account freezing operation; the scientificity, effectiveness and transparency of carbon emission reduction management are improved, and powerful technical support is provided for achieving the purpose of carbon emission reduction.

The invention provides a method and a system for calculating carbon emission in a building operation stage, and relates to the field of carbon emission metering. The method comprises the following steps: acquiring model driving data in real time, wherein the model driving data comprises meteorological data in a building operation stage, heating system operation data, building use state data and power grid carbon emission intensity data; the model driving data are input into a third-order RC model to predict the dynamic energy consumption in the building operation stage and obtain energy consumption prediction result data, and the third-order RC model is a multi-region coupled model established according to the partition characteristics of the building structure and comprises indoor environment nodes, enclosure structure nodes, indoor air nodes and internal heat quality nodes; constructing a dynamic differential equation set according to a thermal network theory; and according to the energy consumption prediction result data and the dynamically updated carbon emission factor database, calculating to obtain corresponding carbon emission data. According to the technical scheme, accurate and real-time calculation of the carbon emission in the operation stage of the building can be achieved with low cost.