17 results about "Carbon density" patented technology

The invention relates to the technical field of land data planning, and discloses a reasonable land planning system based on machine learning carbon storage, and the system comprises a multi-source data fusion module, a carbon storage prediction module, a three-generation coordination discrimination module, a land use intelligent optimization module, a scene interaction deduction module, and a planning result generation module. By integrating data such as standardized land utilization and carbon density, a machine learning model is utilized to predict the dynamic state of the carbon density in real time, and the functional coordination degree of the three plants is quantitatively evaluated; and then, by taking the improvement of the carbon reserves and the coordination of the three plants as targets, carrying out spatial simulation and iterative optimization in combination with multi-objective optimization and a cellular automaton, comparing different policy carbon sink effects through scene deduction, and finally outputting an optimization scheme, thereby realizing the real-time closed loop of evaluation and optimization, and synergistically improving the carbon sink benefits and the three-plant function.

The present disclosure relates to a method for determining carbon storage information, a storage medium, an electronic device, and a program product. The method includes: obtaining reference images of a target area before and after the construction of a hydropower project; determining land use change information within the target area based on the reference images; determining the carbon density of different types of land under the land use change information; and determining the carbon storage change information within the target area under the influence of the hydropower project according to the land use change information and the carbon density. In this way, the accuracy of the obtained carbon storage change information can be ensured, the impact of the hydropower project on the carbon storage change in the target area can be accurately quantified, and thus strong support can be provided for environmental protection and sustainable development.

The invention relates to the technical field of forestry monitoring, in particular to a forestry carbon sink dynamic monitoring method and system based on multi-source image data, and the method comprises the steps: collecting multi-source data to form a multi-source data set; inverting vegetation biomass and carbon density, initial reserve of organic carbon in surface soil and annual input of litter; calculating the decomposition rate and residual quantity of the litter according to the meteorological data; based on the soil initial carbon reserve, the litter input quantity and the microbial respiratory capacity, calculating the soil carbon library variation; integrating the dynamic process of the carbon library, and calculating the vegetation carbon variation and the total carbon sequestration amount per month; repeating the steps S1 to S4 periodically, updating data and parameters, and outputting a periodic carbon sequestration amount; and accumulating data to generate an annual carbon sink dynamic curve and a spatial distribution map. The dynamic carbon sequestration process is completely reflected, and the deviation with the actual carbon sequestration capacity is eliminated.

This invention relates to the field of carbon density measurement and sampling technology, specifically a rapid carbon density measurement and sampling device for Dahurian larch trunks. The device includes a drive unit, a sampling rod connected to the output end of the drive unit, a retaining seat connected between the sampling rod and the drive unit, a protective cover slidably connected to the outer side of the sampling rod, a housing fixed to one side of the drive unit, a telescopic component fixed to the inner side of the housing, an impeller connected to one side of the meshing wheel, a filter on the air inlet side of the impeller, and two gear sets coaxially connected to the outer side of the output end of the drive unit. The impeller, by utilizing the force of the drive unit driving the sampling rod to rotate and drill, generates pressure to draw in cold air from the outside. After filtering out carbon-containing gases through the filter, the air is sent into the inner side of the sampling rod to cool it. Carbon-free gases are discharged from inside the sampling rod and flow out through the gap between the sampling rod and the trunk to the inner side of the protective cover, creating positive pressure inside the protective cover.

This invention discloses a method for predicting carbon storage in regional forest ecosystems, belonging to the field of climate change analysis technology. The method includes: predicting future land use patterns using a land use change simulation model; obtaining current vegetation carbon storage values ​​using a biomass remote sensing inversion model, and extrapolating the increase in vegetation carbon storage in newly added forest areas and unchanged forest areas using the average biomass method and allometric growth model, thereby predicting the vegetation carbon storage of the forest ecosystem; obtaining current soil carbon storage values ​​based on carbon density data of different soil types in the target area, analyzing the increase in soil carbon storage in the target area using carbon sequestration rate analysis, thereby predicting the soil carbon storage of the forest ecosystem; and combining the predicted vegetation carbon storage values ​​and the predicted soil carbon storage values ​​to determine the predicted carbon storage value of the forest ecosystem in the target area. This invention solves the technical problems of incomplete coverage and low accuracy in related methods for predicting carbon storage in regional forest ecosystems.

This application relates to a method, apparatus, computer equipment, and medium for quantifying the carbon footprint of electric vehicles. The method includes: acquiring distribution network energy data from charging stations and user behavior characteristic data of electric vehicles; based on the distribution network energy data, weighted fusion of carbon emission factors from multiple nodes to construct a real-time carbon status analysis model and output a dynamic curve of carbon density changing over time; time-series matching of charging periods in the user behavior characteristic data with the dynamic curve of carbon density to establish a correlation between charging periods and corresponding carbon densities, and calculating the quantified carbon footprint value throughout the charging process based on the correlation and charging power data; creating a user carbon account based on the quantified carbon footprint value, determining the optimal low-carbon charging period according to the dynamic curve of carbon density, and adjusting the incentive coefficient associated with user rights. This method enables high-granularity carbon density monitoring and deep coupling with user incentives to improve measurement accuracy.

The invention discloses an intelligent regulation and control method for solid waste collaborative utilization in a calcium carbide production process, and relates to the technical field of collaborative regulation and control, and the method comprises the following steps: collecting solid waste carbon spectral density original data of solid waste entering a batching bin in the calcium carbide production process, and carrying out clustering calculation on carbon content distribution and activation energy of different types of waste plastics to obtain a joint feature vector; establishing a solid waste carbonaceous feature matrix according to the joint feature vector; the gas recovery and residue separation process is adjusted through optimization control parameters of a digital twinborn model, and whole-process operation data is obtained by combining gas reutilization and calcium solid waste backflow; based on the whole process operation data, the change trend of the key variables is extracted, a working condition evolution rule is established, a control strategy network is trained through a reinforcement learning algorithm, and a working condition optimization instruction adaptive to the current production state is generated. Through the reinforcement learning algorithm, changes in the production environment can be adaptively coped with, the carbon conversion efficiency is improved, the energy consumption is reduced, and automatic optimization of production is achieved.

The invention discloses a reservoir carbon sink dynamic accounting method and system based on multi-source data fusion, and relates to the technical field of clean energy carbon emission. Multi-source data is collected for incoming water flow and reservoir water level prediction, power generation power is calculated based on an incoming water flow prediction value, and regional power grid emission factors and power plant internal power consumption rates are combined, so that the power generation efficiency is improved. Calculating the hydroelectric power generation carbon emission reduction of the power station; based on the reservoir water level predicted value, combining vegetation data and topographic data, establishing an associated knowledge graph, carrying out dimension weight calculation and weighted fusion to obtain a comprehensive weight, and calculating a dynamically updated residual vegetation submerging area through the daily water level amplitude calculated based on the reservoir water level predicted value and the comprehensive weight; based on the submerged area, the carbon sink amount is calculated by combining the soil vegetation carbon density, the environmental parameters, the multi-scale time parameters and the organic carbon decomposition rate; and based on the carbon emission reduction amount and the carbon sink amount, calculating the economic value of reservoir carbon sink. According to the invention, reservoir carbon sink dynamic accounting is realized.

The invention discloses an urban carbon reserve estimation method based on fine-grained source domain alignment and causal constraint. Aiming at the problems of negative migration and lack of ecological mechanism constraints in carbon density prediction caused by overall alignment of source domains, an innovative scheme is proposed: firstly, extracting geographical environment characteristics of the source domain and a target domain, dividing the source domain into sub-regions according to geographical environment clustering, calculating the maximum mean difference distance between the target domain and each sub-region, and calculating the maximum mean difference distance between the target domain and each sub-region; on the basis of similarity distribution alignment weights, a fine-grained weighted domain alignment network is constructed for land utilization classification, so that high-similarity sub-regions participate in domain alignment, and negative migration is avoided. Secondly, constructing a layered carbon density prediction model, generating a factor importance label through factor disturbance analysis, adding importance prediction branches, and ensuring that the climate factor importance accords with an ecological law by utilizing self-supervised loss and causal constraint loss; and finally, calculating urban carbon reserve space distribution.

The invention discloses a soil carbon sequestration and recarburization optimization method based on artificial intelligence, and relates to the technical field of soil carbon sequestration and recarburization. The method comprises the following steps: acquiring a soil internal data set and a remote sensing image data set of soil in a target area; constructing a time prediction model based on an LSTM network, inputting a soil internal data set, and outputting a future soil carbon content change rate; constructing a spatial distribution prediction model, inputting a remote sensing data set, and outputting soil carbon density spatial distribution; combining the change rate with the spatial distribution by using a weighted fusion algorithm to calculate a soil carbon reserve prediction value, and further obtaining the soil carbon sequestration capability; determining a microbial respiration rate, calculating carbon content natural loss in combination with soil oxygen content, and then obtaining soil carbon sink potential in combination with carbon sequestration capacity; and grading the target area according to the carbon sink potential, and implementing carbon sequestration and recarburization measures for areas with different potential grades. By integrating multi-source data, soil carbon content coupling analysis is realized, and the pertinence of carbon sequestration and recarburization measures is optimized.

The application discloses a kind of soil carbon sequestration and carbon increase optimization method based on artificial intelligence, and the application relates to soil carbon sequestration and carbon increase technical field.The steps of the method include: obtaining the soil internal data set and remote sensing image data set of the target area soil;Based on LSTM network, a time prediction model is constructed, the soil internal data set is input, and the future soil carbon content change rate is output;A spatial distribution prediction model is constructed, the remote sensing data set is input, and the soil carbon density spatial distribution is output;The change rate and spatial distribution are combined using a weighted fusion algorithm to calculate the predicted value of soil carbon storage, and then the soil carbon sequestration capacity is obtained;Determine the microbial respiration rate, calculate the carbon content natural loss combined with the soil oxygen content, and then obtain the soil carbon sink potential combined with the carbon sequestration capacity;According to the carbon sink potential, the target area is classified, and carbon sequestration and carbon increase measures are implemented for different potential level areas.The application integrates multiple data sources to realize soil carbon content coupling analysis, and then optimizes the pertinence of carbon sequestration and carbon increase measures.

This invention discloses a method for analyzing and optimizing the impact of landscape patterns on county-level carbon storage, relating to the field of energy conservation and carbon reduction technology. It addresses the problems of weak quantitative foundations, inaccurate analysis of impact mechanisms, and a lack of differentiated optimization strategies in existing technologies for analyzing the correlation between carbon storage and landscape patterns. This solution includes constructing standardized land use data and a regionally adapted carbon density parameter library to quantify the landscape pattern index and county-level carbon storage; determining the spatial clustering characteristics of both through spatial autocorrelation analysis and hotspot identification; analyzing nonlinear interaction effects and identifying optimal thresholds for key factors using the XGBoost-SHAP model; analyzing spatial heterogeneity using the GWR model; and revealing the impact mechanisms based on the analysis results to formulate differentiated landscape pattern and land space optimization strategies. This invention also proposes a method and analysis system for enhancing carbon sequestration capacity, achieving accurate analysis of the correlation between carbon storage and landscape patterns. The optimization strategies are highly targeted and practical, providing scientific guidance for enhancing county-level carbon sequestration.

The invention relates to a multi-source remote sensing and artificial intelligence fused wetland system plant carbon reserve fine estimation method, which belongs to the technical field of wetland ecological monitoring and remote sensing inversion, and comprises the following steps: obtaining and preprocessing multi-source remote sensing image data; a two-stage progressive classification system is constructed in combination with land utilization types and vegetation community spatial distribution data acquired in the field, and vegetation fine class identification is realized based on a random forest and a graph convolutional neural network; the method comprises the following steps: fusing spectrum and texture features of multi-source remote sensing, combining actual measurement data of a biomass quadrat, and constructing a biomass regression estimation model through feature variable optimization; and based on the biomass estimation result and the carbon conversion coefficient corresponding to the vegetation subclass, calculating the carbon reserve and drawing a carbon density spatial distribution diagram. According to the method, the problems of low wetland vegetation classification precision, insufficient multi-source data collaborative utilization and poor biomass estimation model stability are effectively solved, and reliable technical support is provided for wetland ecosystem protection and dual-carbon strategy implementation.

The invention relates to the technical field of mining area data measurement and calculation, and discloses a subsidence ponding area water carbon reserve measurement and calculation method which comprises the following steps: acquiring geological mining condition data of a mining area where a subsidence ponding area is located, and calculating a subsidence data set of the subsidence ponding area; acquiring and preprocessing a remote sensing image data sequence of the subsidence ponding area to generate a ponding area data sequence; carrying out superposition processing on the ponding area data sequence and the subsidence data in a unified coordinate system, and obtaining a reservoir capacity time sequence; sampling at multiple points and calculating organic carbon density data of the water body; calculating water carbon reserves at a set moment; the remote sensing method and the probability integral method are fused, the reservoir capacity information of the ponding area is calculated, field sampling is carried out, the organic carbon density of the water body is measured through experiments, the water body carbon reserve is obtained, the water body carbon reserve of the subsidence ponding area is accurately calculated, reliable technical support is provided for treatment of the coal mining subsidence area, and a more reasonable and effective treatment scheme is provided.

The invention discloses an industrial park resource operation optimization method based on electric carbon space-time distribution characteristics, and relates to the technical field of energy low-carbon optimization, and the method comprises the steps: collecting energy carbon operation data of an industrial park, and carrying out the time synchronization, abnormality elimination and unit standardization processing, and obtaining a multi-source synchronous energy carbon data matrix; performing multi-objective collaborative optimization calculation on the park energy carbon density field distribution diagram to obtain a preliminary operation optimization scheme; carrying out extreme scene verification and nonlinear performance correction on the initial operation optimization scheme to obtain a carbon sensing operation optimization result; performing dynamic execution mapping and energy efficiency response calculation on the carbon sensing operation optimization result to generate an execution state data stream; and performing time sequence comparative analysis and energy-carbon benefit evaluation on the execution state data flow to generate a carbon peak clipping effect evaluation report. And the accuracy, reliability and sustainability of industrial park energy and carbon management are enhanced.

The invention discloses plant fuel charcoal prepared from wormwood residues and a preparation method of the plant fuel charcoal. The method comprises the following steps of: drying and crushing residues obtained after deep processing of wormwood serving as main raw materials, plasticizing, bonding and forming lignin of the residues under the high temperature of 150-250 DEG C and the high pressure of 10-30MPa under the condition of not adding any chemical adhesive, and carbonizing at low temperature in an oxygen-limited environment of 300-500 DEG C, thereby obtaining the product. The density of the obtained fuel carbon is 0.8-1.2 g / cm < 2 >, the compressive strength is 5-15 MPa, the combustion heat value is 4500-5800 kcal / kg, the ash content is 3%-8%, the volatile component content is 15%-25%, and the fixed carbon content is 70%-80%. High-valued utilization of wormwood waste is achieved, the obtained fuel charcoal emits fresh wormwood fragrance during combustion, has the auxiliary effects of repelling mosquitoes and purifying air, is little in combustion smoke and stable in heat value, and is suitable for various scenes such as family barbecue, surrounding furnace tea boiling, outdoor camping and atmosphere building. The method is simple in process, low in cost and easy to industrialize, and has good economic and social benefits.

The invention relates to the technical field of hydropower station operation management, and discloses a hydropower station operation period carbon sink effect dynamic evaluation method and system, and the method comprises the steps: carrying out the deep integration and probabilistic processing of basic data through a mixed probability distribution model and a kernel density estimation method, generating a probability distribution sequence including key parameters such as generating capacity, water level and land utilization conversion probability; the carbon emission reduction amount of hydroelectric power generation is calculated through the IPCC standard, the reservoir water surface carbon emission is calculated according to the climate type, and the vegetation carbon reserve loss of the submerged area is quantified based on the carbon density table, namely the net fixed carbon amount of the vegetation of the submerged area is dynamically monitored; gathering the four results into net carbon sink probability distribution of the hydropower station; the carbon sink economic value of the hydropower station in the whole life cycle is automatically aggregated, and a visual carbon sink effect time sequence curve is generated. The system comprises a probability distribution sequence acquisition module, a carbon sink quantity convergence module and a data visualization display module. According to the invention, an extensible technical framework is provided for carbon sink evaluation of the watershed scale.