38 results about "Carbon density" patented technology

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

The invention discloses an urban green land carbon sink dynamic accounting method and system based on multi-source data fusion and spatial network modeling. The method comprises the following steps: collecting urban green space carbon sink original data and preprocessing the data, and constructing a space * time * feature structure carbon sink data cube; identifying green land source plaques based on the spatial dimension data; taking the source ground plaque as an initial node, constructing a resistance surface by using a dynamic resistance factor, simulating a carbon flow in combination with a circuit theory, and generating a carbon flow gallery and a carbon flow density grid; inputting multi-source data into the carbon density dynamic calibration model, and outputting a dynamic grid; constructing a multi-scale prediction engine, and outputting carbon density prediction results under different time scales; and combining GIS and BIM technologies, inputting a prediction result and an urban space structure into a three-dimensional visual platform, generating a carbon density thermodynamic diagram and a carbon flow gallery evolution diagram, and realizing carbon sink dynamic accounting. According to the invention, the precision and space-time continuity of carbon sink accounting can be obviously improved.

The invention relates to the technical field of mine ecological restoration and carbon reserve estimation, in particular to a coal mining subsidence area ecological system carbon reserve estimation method and system.The method comprises the steps that S1, a coal mining subsidence area is divided according to land utilization types, and the range and area of each land utilization type are determined; s2, dividing the coal mining subsidence area according to a space state, and forming a land utilization grid table with a unique code with each land utilization type; s3, obtaining an initial carbon density parameter corresponding to the land utilization type, calculating the surface subsidence, and carrying out differential correction on the carbon density parameter of each subarea of the coal mining subsidence area; and S4, calculating the total carbon reserves of different collapse stages, analyzing the carbon reserves, the carbon sequestration amount and the spatial distribution of each land utilization type, and comprehensively analyzing the time change characteristics of the carbon reserves. According to the method, the carbon density change is analyzed in combination with land utilization and the collapse state, and the time-space evolution characteristics of the carbon reserves in the coal mining subsidence area are evaluated.

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 invention provides an urban green land carbon storage and sink quantification method based on remote sensing and field investigation, and relates to the technical field of environmental science and engineering, remote sensing technology and ecological assessment, and the method comprises the steps: obtaining a high-definition remote sensing image, combining a green land planning file, determining the distribution of various types of green lands, calculating an NDVI, and extracting an effective green land space; and generating a sampling point scheme according to the area proportion. Growing parameters of arbors and shrubs in the quadrat are investigated on site, biomass is calculated and converted into carbon reserves, an NDVI and carbon density model is established, and a carbon density spatial distribution diagram is generated. And constructing a carbon sink model based on the relationship between the age of the arbor and the carbon reserves, calculating the annual carbon sink, establishing a relationship model between the NDVI and the carbon sink, and generating a carbon sink spatial distribution diagram. And integrating the two diagrams to output carbon storage and sink capability evaluation results of different green land types, thereby providing a scientific basis for urban ecological planning.

The present invention provides a method for evaluating the carbon sink potential after ecological restoration of a quarry pit. First, ecological parameters of the quarry pit and the surrounding mountains are collected by using an unmanned aerial vehicle, and the measured carbon density values of the quarry pit and the surrounding mountains are obtained by using publicly released carbon density data. Secondly, the area in the surrounding mountains of the quarry pit that has not been disturbed by humans is used as a reference ecosystem, and the ecological parameters of the reference ecosystem are used as variable factors, and the measured carbon density value of the reference ecosystem is used as the model supervision value. The variable factors and the model supervision value form a data set for training a machine learning model. Finally, by using the trained qualified machine learning model, with the ecological parameters of the quarry pit as variable factors, the predicted carbon density value after ecological restoration of the quarry pit is output, and the carbon sink potential of the quarry pit is obtained by subtracting the measured carbon density value from the predicted carbon density value after ecological restoration. This method can meet the accuracy requirements for rapid estimation of the carbon sink potential after quarry pit restoration.

The invention provides a regional forest vegetation carbon sequestration capability assessment method, and belongs to the technical field of forest vegetation resource monitoring and carbon sink assessment. According to the method, 13 representative core sample plots serve as hubs, unmanned aerial vehicle multi-source remote sensing and satellite images are fused, 20-meter-resolution carbon density mapping is achieved, and a space-time coupling carbon sequestration index is constructed to evaluate the carbon reserve stability and the carbon sequestration potential. Arranging 13 20-meter core sample plots in a typical subarea in a structured manner, and measuring and calculating the carbon density of arbor, shrub and grass; extracting optical and laser features of the unmanned aerial vehicle to establish a subarea-level carbon density inversion model; training a star-sky-ground fusion estimation model by taking the sub-region unmanned aerial vehicle carbon density grid as a supervision label, and expanding the model to a region; and calculating a space-time coupling carbon sequestration index according to the carbon density time sequence and interference monitoring information. The method gives consideration to sampling efficiency and estimation precision, and is suitable for large-area forest carbon sink monitoring and management.

The application relates to a mangrove carbon storage calculation method and device based on an airborne laser radar point cloud, wherein the method comprises the following steps: extracting single-tree height and crown width parameters by using unmanned aerial vehicle laser radar point cloud data, calculating single-tree carbon storage by using corresponding tree species allometric growth equations, and establishing a function relationship between mangrove single-tree carbon density and single-tree height; filtering the laser radar point cloud data to obtain a crown height model, and substituting the crown height model into a mangrove carbon density equation between the single-tree carbon density and the single-tree height to obtain a mangrove region carbon storage. Therefore, the problems that the current mangrove carbon storage estimation precision is difficult to evaluate and the results lack theoretical explanation are solved.

The invention discloses a distributed energy station low-carbon planning method considering load energy carbon density optimization. The method comprises the following steps: establishing an energy flow model of each energy conversion device integrated in a distributed energy station and an energy flow concentrator model of the distributed energy station; establishing a carbon flow model of each energy conversion device, and establishing a distributed energy station carbon flow concentrator model considering carbon loss; according to the method, a distributed energy station planning objective function is constructed, corresponding planning constraint conditions and operation constraint conditions are established according to design requirements of a planned distributed energy station system and characteristics of a load supplied by the planned distributed energy station system, and the planning constraint conditions comprise device commissioning constraint, device type selection constraint and device constant volume constraint; the operation constraint conditions comprise a power balance constraint, an energy network power constraint, a refrigerating unit operation constraint, a heating unit operation constraint and an energy storage equipment operation constraint; and based on the distributed energy station carbon flow concentrator model, dividing loss carbon emission and load carbon emission, and constructing a load energy carbon density constraint in an operation constraint condition.

The invention provides a method for predicting carbon reserves and analyzing a driving mechanism of the carbon reserves, and the method comprises the following steps: firstly collecting land utilization data of two periods, and constructing a land utilization change and carbon reserve correlation model based on an RF-FLUS-InVEST model in combination with carbon density data; furthermore, a driving mechanism of natural and social economic factors on carbon reserve space differentiation is quantified through an optimal parameter geographic detector, which comprises the following steps of: checking and screening key driving factors based on interaction, optimizing parameter configuration of the geographic detector by utilizing a differentiation factor (q value), and revealing a non-linear influence rule of a multi-factor synergistic effect on carbon reserve change. According to the method, the limitation of subjective parameter setting in traditional drive analysis is overcome, high-precision spatial analysis of a carbon reserve evolution mechanism is realized, and a scientific basis is provided for regional ecological protection and carbon neutralization policy formulation.

The invention provides a coupling MCCA-inVEST model-based carbon reserve prediction method, and relates to the technical field of mixed land utilization prediction and carbon cycling, the method comprises the steps of obtaining influence factor data corresponding to a to-be-evaluated mixed land, and generating MCCA training data; according to the influence factor data and the MCCA training data, constructing an MCCA model, and based on the MCCA model and a Markov chain, obtaining a land type prediction demand in a preset scene; constructing a land utilization conversion rule based on a random forest model and simulating different types of mixed land spatial distribution; taking the secondary land utilization classification system as a target classification, and performing spatial superposition on the Chinese carbon density data set and the vegetation type distribution data to obtain secondary land utilization carbon density parameters; and inputting mixed land spatial distribution and secondary land utilization carbon density parameters into an inVEST model to obtain a regional carbon reserve prediction result and a carbon sink capacity distribution result. According to the invention, the regional applicability and precision of carbon reserve prediction can be improved.

The present invention discloses a control device and method for activated carbon in the all-slime cyanidation carbon-in-pulp gold extraction process, which relates to the technical field of all-slime cyanidation carbon-in-pulp gold extraction process. The present invention includes multiple groups of leaching and absorption tanks, a water pipe and a carbon powder pipe. The water pipe is connected to the leaching and absorption tanks through a make-up water pipe, and a flow control valve II is fixedly installed inside the make-up water pipe. The carbon powder pipe is connected to the leaching and absorption tanks through a make-up carbon pipe, and a flow control valve III is fixedly installed inside the make-up carbon pipe. A carbon extractor is fixedly installed inside the leaching and absorption tank, and the discharge port of the carbon extractor is connected to the next group of leaching and absorption tanks through a connecting pipe. Through the setting of the make-up carbon pipe and the flow control valve III, the present invention can simultaneously control multiple groups of leaching and absorption tanks to add different carbon powders. Therefore, without stopping the flow of the pulp, while adjusting the carbon density of the corresponding leaching and absorption tank, it will not affect the carbon density of the next group of leaching and absorption tanks. The carbon seal control accuracy is high, and there is no need to stop the flow of the pulp, which greatly improves the gold extraction efficiency.

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.

This application proposes a method and system for constructing a soil carbon sink assessment model, which belongs to the field of model construction. The method includes: using land use type, stratified soil type, topography, climate and meteorology, and NDVI vegetation index as constraints, collecting samples and performing statistics to obtain confidence intervals for stratified soil bulk density, gravel volume fraction, and soil carbon content; performing confidence sorting on soil sample data based on the obtained confidence intervals to obtain high-confidence detection values; using the screened detection values ​​to perform carbon density statistics to obtain stratified carbon density and total carbon storage; using stratified carbon density and total carbon storage as supervision and high-confidence detection values ​​as input to train a soil carbon sink assessment model. By constructing confidence intervals through big data and verifying the credibility of sample data, the method effectively solves the problem of low precision of soil carbon sink assessment using traditional methods and improves the accuracy of soil carbon sink assessment.

The present invention relates to the technical field of mine ecological restoration and carbon reserve estimation, and in particular to a method and system for estimating carbon reserves in ecosystems in coal mining subsidence areas. The method comprises the following steps: S1, dividing the coal mining subsidence area according to land use type, and determining the scope and area of ​​each land use type; S2, dividing the coal mining subsidence area according to spatial state, and forming a land use grid table with a unique code for each land use type; S3, obtaining initial carbon density parameters corresponding to the land use type, calculating the effect of surface subsidence on the carbon density parameters of each sub-area of ​​the coal mining subsidence area, and performing differentiated corrections; S4, calculating the total carbon reserve at different subsidence stages, analyzing the carbon reserve, carbon sequestration, and spatial distribution of each land use type, and comprehensively analyzing the temporal variation characteristics of carbon reserves. The present invention combines land use and subsidence state to analyze carbon density changes, thereby achieving an assessment of the spatiotemporal evolution characteristics of carbon reserves in coal mining subsidence areas.

The application discloses a preparation method of a polyimide film, a high-thermal-conductivity graphite sheet and application of the polyimide film. A diamine, a dianhydride monomer, a large-aromatic-ring monomer and an end-capping agent are added in batches in a solvent, and a polyamide acid resin is obtained through reaction. An inorganic particle dispersion liquid and a chemical flow casting method promoter are prepared respectively. The three are continuously and on-line instantaneously mixed uniformly in a needle mixer, and then a polyimide film is obtained through slushing and imidization. The planar orientation degree and mechanical properties of the film are improved, and the carbon density is improved through the large-aromatic-ring structure and the cross-linking type end-capping agent. The problem of uneven dispersion of the inorganic particles in the resin system due to agglomeration and sedimentation is solved through the continuous on-line instantaneously mixing technology, and the ratio of the resin, the dispersion liquid and the promoter can be on-line flexibly adjusted, which opens up a new way for continuous and stable engineering preparation of the inorganic particle functionalized polyimide film. The graphite sheet prepared from the polyimide film has a thermal conductivity of 2000 W / m*K.

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 utility model relates to a carbon density sampling device of a carbon immersion system, which comprises a vacuum diaphragm pump, a discharge port of the vacuum diaphragm pump is connected with a sampling barrel through a pipeline, a feed port of the vacuum diaphragm pump is communicated with a sampling steel wire hose, and the other end of the sampling steel wire hose is wound on a winding drum of an electric hoisting mechanism and then is slidably connected with a pulley supporting mechanism. One end of the winding drum is connected with an output shaft of a speed reducer, an input shaft of the speed reducer is connected with an output shaft of a motor through a coupler, the motor is electrically connected with a control cabinet, and the electric hoisting mechanism and the pulley supporting mechanism drive the heavy traction sampler to ascend and descend in the carbon immersion tank. According to the device, the lifting of the sampler is controlled through the electric hoisting mechanism, the sampling of slurry at any depth of the carbon immersion tank is realized, the sampling is more flexible, the device can adapt to the sampling of the slurry at different positions, the representativeness of the sample is improved, the carbon density measurement is more accurate, the regular automatic sampling is realized, the operation is simple, the labor intensity is low, and the device is suitable for frequent sampling operation requirements.

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 coal mining subsidence ponding area aquatic vegetation carbon reserve prediction method and system, and belongs to the technical field of resource management. A plurality of common control points are selected from a surface subsidence basin contour line and a remote sensing image; interpolating the common control points, and obtaining the water depth of each common control point by determining the ponding boundary elevation; generating a water depth threshold value of the aquatic vegetation according to the growth boundary of the aquatic vegetation; according to the water depth threshold value of the aquatic vegetation, points with the water depth meeting the water depth threshold value are screened out from the public control points in the aquatic vegetation coverage range, and the ponding area coverage area below the aquatic vegetation growth area is determined; obtaining the organic carbon density of aquatic vegetation in the coal mining subsidence ponding area; according to the aquatic vegetation coverage area and the aquatic vegetation organic carbon density, the carbon reserve of the aquatic vegetation in the coal mining subsidence ponding area is obtained. According to the method, the unsuitable water depth area and the non-effective vegetation coverage area can be eliminated, so that the accurate extraction of the aquatic vegetation coverage area is realized.

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 discloses a low-grain-boundary lithium iron phosphate material prepared by coarsening mesoporous iron source grains and application of the low-grain-boundary lithium iron phosphate material, and relates to the technical field of lithium ion batteries. The lithium iron phosphate positive electrode material comprises a substrate and a carbon coating layer coating the surface of the substrate, the grain boundary density L of the lithium iron phosphate positive electrode material is 10-30, the grain boundary density is equal to Dv50 / D, the carbon density dC is 5-20, and the carbon density is equal to the oil absorption rate / C; the powder flow velocity S is 2s / 50g to 24s / 50g. The grain boundary density L, the carbon density dC and the powder flow velocity S of the lithium iron phosphate positive electrode material are jointly limited, so that the average grain size of the lithium iron phosphate positive electrode material, the coated carbon structure and the contact state among particles are optimized to improve the conductivity of the lithium iron phosphate positive electrode material.

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.