890 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 discloses a carbon emission intensity inversion and traceability method and system based on numerical simulation and a reduced-order model. The method comprises the following steps: constructing a full-size three-dimensional map; numerical simulation of pollutant diffusion is carried out according to map information and known environment conditions; reducing the order of the analog data through an intrinsic orthogonal decomposition method; establishing a carbon emission simulation database under the multi-working-condition variable environment based on the reduced-order model; the carbon dioxide concentration and meteorological data are measured in real time and on site, a simulation database is used as a supplement, and the intensity of a known emission source and the intensity and position of an unknown emission source are calculated by adopting a source item estimation method, a particle swarm algorithm and the like. According to the invention, full-size reduction of a limited area and high-precision small-scale carbon emission monitoring can be realized, and effective emission source tracing, intensity analysis, potential emission source prediction and the like can be carried out according to a small amount of field actual measurement data.

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.

The invention discloses a thin film surface defect detection method and system, and particularly relates to the technical field of thin film defect detection. Two groups of light sources in different directions and a multi-frequency light source are introduced into a detection system, reflection intensity and response difference data of a film surface are acquired from multiple angles by combining a camera, and emission intensity fluctuation characteristics and light source response deviation characteristics are extracted and analyzed, so that the accuracy of the detection system for identifying real defects is evaluated; accuracy is divided into a high grade, a middle grade and a low grade, dynamic processing is performed according to the grades, especially for the middle accuracy grade, the false texture false alarm rate is remarkably reduced by predicting the accuracy change trend and adjusting the light source intensity and angle in real time, the detection reliability is improved, and the false alarm problem of a detection system caused by complex backgrounds and light changes is reduced; and efficient and stable detection under different production conditions is realized.

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.

The invention relates to the field of carbon emission management, energy distribution and optimization regulation and control, in particular to an industrial enterprise carbon emission allocation method based on regional energy and carbon platform supervision data. The method comprises the following steps: step 1, data acquisition and preprocessing, including data acquisition, data preprocessing and data analysis; 2, initial carbon quota distribution, wherein a multi-level decision-making model is established, and the initial carbon quota of an enterprise is calculated; 3, a dynamic adjustment mechanism is provided, wherein the dynamic adjustment mechanism comprises the steps of obtaining power data feedback, calculating a quota adjustment coefficient and dynamically adjusting a quota; step 4, accumulation of double-control index data, including accumulation of the double-control index data and accumulation of carbon emission intensity data; and 5, continuously optimizing the model. According to the method, the flexibility of carbon quota distribution is improved, normal production of enterprises is ensured, meanwhile, the total carbon emission control target is not exceeded, and the overall efficiency of carbon emission management is improved by introducing a multi-level decision model and a feedback mechanism.

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.

A display device whose change in chromaticity is small and grayscale controllability is high is provided. The display device can turn on and turn off a light-emitting device by PAM and PWM control (a pulse width control involving changes in amplitude). The display device writes the same input signal (data potential) to a first node and a second node to turn on the light-emitting device in accordance with a potential of the first node and generate a pulse signal in accordance with a potential of the second node. The potential of the first node can be reset in accordance with the generated pulse signal. Therefore, the light-emitting device can emit light for a desired period with a desired emission intensity.

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 power systems, in particular to an indirect carbon emission method considering new energy and green power transactions, which comprises the following steps: S1, load flow calculation and network loss consideration: determining load flow distribution and forming a matrix required by calculation; s2, unit carbon emission intensity calculation and node carbon potential determination: determining the carbon emission property and intensity of the connected unit; s3, carbon emission responsibility affiliation and green power transaction allocation: distinguishing carbon emission responsibilities of contract power and non-contract power; s4, carbon emission property right distribution and time weighted calculation: after obtaining the carbon emission flow rate allocated by the power supply and the load of each unit, multiplying the carbon emission flow rate by a preset time scale to obtain the carbon emission property right limit of each part of each power grid; according to the method, the accuracy of carbon emission accounting is improved, the definition of the carbon emission distribution characteristics of the power system is enhanced, and the method is suitable for analysis of a lossy network and carbon emission management.

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 low-carbon building carbon emission optimization system, which relates to the technical field of building energy conservation, and comprises the following steps: collecting energy consumption data and environmental parameters of a building energy system, preprocessing the collected data, extracting data features, and identifying a typical mode of the building energy system; based on the typical mode of the building energy system, constructing a multi-objective optimization model including renewable energy power generation prediction, load demand prediction and carbon emission prediction, and determining constraint conditions and objective functions of the multi-objective optimization model; and generating an energy optimization scheduling strategy and a carbon capture control scheme execution optimization scheduling strategy considering carbon emission intensity according to an objective function of the multi-objective model, collecting an abnormal condition execution recovery strategy, and performing effect evaluation and strategy dynamic adjustment in real time. According to the method, economy, environmental protection and reliability of the system are taken into consideration, effective control over carbon emission is achieved through the improved multi-objective optimization algorithm, and the operation reliability and adaptability of the system are enhanced.

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.

A method for calculating carbon emission of a boiler is provided. First, a test boundary and emission sources are determined, and the emission sources in the test boundary are divided into direct carbon emission sources and indirect carbon emission sources. Then, combined with a boiler thermal performance test, the carbon emission of each emission source is tested and calculated, so as to obtain a total carbon emission of the boiler per hour in a test period. Finally, a carbon emission intensity of the boiler per unit output heat is calculated according to the total carbon emission of the boiler per hour in the test period. Different test boundaries and emission sources are fully considered, so that quantification requirements of carbon emission of different boiler systems can be met, and meanwhile, the accurate quantification of carbon emission of a boiler is realized.