228 results about "Carbon cycle" patented technology

The invention discloses an intelligent measurement and calculation method and system for forest carbon reserves, and relates to the technical field of carbon reserve measurement and calculation, and the system obtains a forest electronic distribution diagram through a GIS technology and surveying and mapping data, collects various carbon reserve related data, and then carries out the correction and denoising of the collected data through a FLAASH algorithm and other preprocessing technologies; the AI sampling optimization model extracts forest features based on a convolutional neural network CNN, and dynamically adjusts a sampling area; secondary acquisition is carried out, data preprocessing is carried out, the total carbon reserve of the forest is estimated by calculating the carbon reserve of each grid, and the forest succession stage, the carbon sink change and the ecological carbon cycle are further analyzed; and finally, in combination with the dynamic influence coefficient of the carbon reserves and a preset threshold value Q, judging the change trend of the carbon reserves, triggering early warning and giving a coping strategy.

The invention discloses an operation balance optimization method for a waste incineration thermal power plant. Efficient and environment-friendly waste treatment and green energy recovery are achieved through regulation and control of core parameters. Comprising an intelligent control system, a carbon cyclic utilization technology and a flue gas purification technology, an optimization model taking comprehensive net income maximization and carbon emission intensity minimization as targets is constructed, a garbage heat value, incinerator temperature, steam parameters, power generation efficiency, station service power consumption rate and the like are selected as core parameters, an operation balance optimization target function is constructed, and the comprehensive net income maximization and the carbon emission intensity minimization are realized. And through an intelligent control system, key parameters such as the incinerator temperature, the excess air coefficient and the flue gas treatment temperature are monitored and adjusted in real time, it is ensured that garbage is fully combusted in the incinerator, harmful gas emission is reduced, and the power generation efficiency is improved. Compared with the prior art, efficient operation of the waste incineration thermal power plant and maximum utilization of green energy are achieved, and remarkable economic and environmental benefits are achieved.

The invention discloses a multi-modal data fused karst forest soil carbon cycle feature extraction method. The method comprises the following steps: acquiring soil physical and chemical protection component carbon data, bioactive component carbon data, isotopic tracing data and the like of different succession stages of a karst forest to establish multi-source heterogeneous data; integrating the obtained multi-source heterogeneous data into a'physical-chemical-biological-isotope 'four-dimensional feature space through feature tensor decomposition; constructing a carbon saturation capacity prediction hybrid optimization model, and outputting lt; the maximum carbon sequestration capacity and component carbon proportion of 53 [mu] m microaggregates; the method comprises the following steps: performing mineralization culture on soil in different succession stages of the karst forest, analyzing a dynamic proportion of exogenous carbon to original soil carbon and an input / output path of soil carbon in combination with a 13C / 15N double-standard-sample time-varying calibration curve, and calculating a carbon stability disturbance threshold. According to the method, collaborative analysis of multi-source data is realized, and the temporal-spatial resolution and the model precision of carbon cycle feature extraction are remarkably improved.

The invention discloses a multi-model coupled regional terrestrial ecosystem carbon reserve estimation method, which is suitable for the field of carbon cycle and carbon reserve estimation. The method comprises the following steps: firstly, collecting remote sensing data, and then estimating overground, underground and soil annual carbon density mean values of a regional terrestrial ecosystem by adopting an ARIMA model; the method comprises the following steps: clustering regions with different carbon densities by adopting a CatBoost model, dividing the carbon densities of a regional ecological system into low, medium and high levels, and avoiding the influence of spatial heterogeneity among different density levels on subsequent model training; an RNN model is constructed to evaluate the carbon density conditions of different levels of unit grids, and interannual dynamic carbon density parameters of different types of ecosystems are obtained based on multi-source data; and finally, calculating the carbon reserves of the unit grids, analyzing the annual average change trend and the spatial distribution condition of the carbon reserves, and realizing accurate evaluation of the regional terrestrial ecosystem carbon sink. The method is simple in step and good in using effect, and the change condition of the carbon sink capacity in the corresponding time can be obtained according to the sampling time interval of the input data.

The invention discloses an intelligent monitoring and evaluating method and system for terrestrial carbon disturbance in a permafrost region, and belongs to the field of carbon cycle research in the permafrost region. The method comprises the following steps: collecting 13 background element data in a permafrost region; calculating to obtain a permafrost region carbon disturbance evaluation index; carrying out credibility evaluation on the permafrost region carbon disturbance evaluation indexes; monitoring regional background elements and river particle organic carbon output characteristics and changes of the permafrost region, and calculating a change rate of a carbon disturbance evaluation index according to monitoring data; using a numerical model to identify a carbon disturbance high-risk area and time period of the permafrost region, and identifying key disturbance factors and influences; and analyzing the change trend of the carbon disturbance evaluation index, and establishing a prediction model by using a machine learning method. The method has the advantages of comprehensiveness, dynamics, effectiveness and support for carbon disturbance attribution and predictive analysis.

The invention discloses a rhizosphere deposited carbon flux real-time monitoring method adopting a sensor array, and relates to the technical field of soil carbon cycle monitoring, and the method comprises the steps: determining a layered sampling depth and a horizontal concentric circle partition range based on the three-dimensional distribution characteristics of a target plant root system, and mapping the layered sampling depth and the horizontal concentric circle partition range to a preset three-dimensional grid coordinate system; gas sensors and environmental parameter sensors are deployed according to root system partitions, and a multi-dimensional sensor array is constructed. Through the three-dimensional grid coordinate system and the root system density gradient model, accurate spatial mapping of rhizosphere carbon flux is realized, high matching of sensor layout and root system biomass distribution is ensured by utilizing division of vertical layering and horizontal concentric circle partition, and the gas sensor and the environmental parameter sensor are cooperatively deployed, so that the reliability of the system is improved. The device can capture instantaneous changes such as root exudate pulse and microbial respiration, generates a continuous and consistent carbon flux data set, and provides a high-resolution dynamic observation basis for researching plant-soil-microbial interaction.

The invention discloses a regional GPP assimilation inversion system fusing foundation-multi-source satellite chlorophyll fluorescence, and belongs to the technical field of terrestrial ecosystem carbon cycle monitoring. The signal unmixing and simulation generation module is used for unmixing the total fluorescence of the mixed pixels into pure chlorophyll fluorescence (SIF) and driving an ecological model to generate SIF and GPP simulation values based on environmental stress data; the state component decoupling module is used for decoupling the total GPP analog value into a light response fast component, a light response slow component and a light response memory component; the state evolution prediction module is used for predicting dynamic evolution of the slow component and the memory component; and the self-adaptive assimilation inversion module is used for performing inversion on the region GPP by taking the slow component and the memory component as state variables, taking the SIF observation value as observation data and combining a prediction result of the state evolution prediction module. According to the method, systematic deviation caused by model simplification or data singleness in a traditional method is overcome.

The invention relates to the technical field of forestry investigation, and discloses a forestry investigation system for carbon sink forest management, which comprises a data acquisition module, a data processing module, a data integration module, a model construction and analysis module, a decision and early warning module, an output module and an implementation monitoring module. Through multi-dimensional index investigation, information can be obtained from multiple levels of an ecological system, the carbon cycle process and ecological functions of the carbon sink forest are comprehensively known, more comprehensive data support is provided for scientific management, in addition, more variables influencing carbon sink are introduced, so that the model better conforms to the actual situation, the accuracy and generalization ability of the model are enhanced, and the method is suitable for popularization and application. According to the method, carbon reserve changes of the carbon sink forest under different conditions can be better predicted, finally, effect prediction of multiple management schemes is provided for managers through scene simulation and model analysis, scientific and reasonable management decision making is assisted, and meanwhile, an early warning mechanism can find potential risks in time and guarantee stable development of the carbon sink forest.

The invention relates to the technical field of forest wetland carbon sink monitoring and evaluation, and particularly discloses a forest wetland carbon sink intelligent monitoring and evaluation system and method. According to the method, a vegetation carbon sequestration potential evaluation result is obtained by analyzing vegetation structure feature data of forest, water and soil composite ecology of the forest wetland, a water-soil-carbon cooperative evaluation result is obtained by analyzing specific hydrological-soil carbon cycle key elements of the forest wetland, and environmental disturbance response factors are obtained by combining various environmental disturbance factors. Through multi-source data fusion and intelligent modeling, a carbon sink quantitative evaluation value is output, and abnormal feedback is triggered; the problems that in the prior art, adaptability to a forest wetland compound ecological system is insufficient, evaluation refinement is insufficient, and dynamic response is lacked are solved. According to the method, accurate and dynamic monitoring and evaluation of forest wetland carbon sink are realized, carbon sink short plates can be accurately positioned, optimization support is provided, and technical support is provided for protection, restoration and optimization management of forest wetland carbon sink resources.

The invention provides a method for determining the influence of a straw returning mode on tobacco-rice rotation soil carbon emission, and belongs to the technical field of soil carbon emission. The method comprises the steps that test plots are divided, and different straw returning treatment groups are arranged; installing a static gas tank measuring system and regularly collecting a gas sample; measuring the emission of CO2 and N2O and the concentration of CH4 by using a gas chromatographic method, and calculating the carbon emission flux of the soil; analyzing a straw decomposition process by applying a soil microorganism kinetic equation; analyzing the dynamic change of a soil carbon library based on a carbon recurrent neural network model; constructing a carbon flow network topological structure by using a minimum spanning tree algorithm and identifying key nodes; according to the method, comprehensive influences of environmental factors, crop growth parameters and microbial community structures on carbon emission are integrated through a multi-head cross attention mechanism, accurate quantification and prediction of straw returning carbon emission are achieved, and a scientific basis is provided for optimizing a farmland carbon management strategy and promoting conversion from a farmland carbon source to a carbon sink.

The invention relates to a numerical mode simulation method for CO2 flux of high-resolution rice, wheat and corn, which comprises the following steps: calculating a vegetation index and a land surface water index, and carrying out inversion to obtain key vegetation parameters of a land ecosystem; the meteorological data and the topographic data are processed, meteorological field initial and boundary condition files are generated, and CO2 chemical initial and boundary condition files are generated; integrating the gridded artificial source CO2 emission data file; applying the optimized photosynthetic and respiratory action parameters to a photosynthetic and respiratory action calculation formula of the terrestrial ecosystem, and constructing a CO2 flux simulation system of the crop system; outputting the CO2 flux of the rice, the wheat and the corn in the simulation area. The method has the advantages that CO2 flux seasonal change characteristics in the key phenological period in the growth process of rice, wheat and corn are simulated, carbon cycle characteristics in the growth process of crops can be better reflected, and more reliable data support is provided for ecological meteorological research.

The invention discloses a carbon sink calculation method based on a space-time fusion technology and flux data, and the method comprises the steps: carrying out the space-time fusion synthesis of a remote sensing image, calculating a leaf area index and chlorophyll concentration data with high space-time resolution, and improving the time and space resolution of carbon sink data; calculating daily net primary productivity data by using a light energy utilization rate model based on the leaf area index data and the meteorological data; correcting parameters of the light energy utilization rate model based on the daily net primary productivity data and the daily flux data; migrating the corrected parameters of the light energy utilization rate model to a flux-free site area based on the ratio of the chlorophyll concentrations to obtain the migrated parameters of the light energy utilization rate model; the migrated parameters of the light energy utilization rate model are transmitted back to the light energy utilization rate model, the net primary productivity is calculated, and the carbon sink data are calculated based on the net primary productivity, so that the carbon sink calculation method is higher in applicability, and reliable technical support is provided for carbon cycle research of an ecological system.

The invention provides a carbon reduction effect evaluation method and system for a healthy agricultural product production process. According to the method, by tracking carbon migration paths of chemical fertilizer application, organic fertilizer degradation and soil respiration and combining matching analysis of chemical reaction chain data and an agricultural carbon emission database, a carbon retention area and methane generation and distribution characteristics in the production process are accurately identified. Modeling is carried out on the concentration of an intermediate product by applying a dynamic compensation engine, a soil pore optimization scheme and an organic fertilizer gradient feeding strategy are generated, and conversion of degradation residues to a stable carbon form is accelerated by regulating and controlling a micro-aerobic environment. Finally, a carbon cycle equilibrium map is constructed, greenhouse gas emission reduction monitoring and effect quantitative evaluation in the whole production period are achieved, and the soil carbon sequestration capacity is synchronously improved. According to the technical scheme provided by the invention, the agricultural production carbon cycle efficiency can be optimized.

The invention belongs to the technical field of methane leakage monitoring, and discloses a method and a system for estimating methane leakage flux of an ocean station movable cold spring. The method comprises the following steps: acquiring physical states of bubbles at different depths of a station cold spring area and physicochemical properties of methane gas in the different depths of the station cold spring area, and obtaining a station cold spring plume three-dimensional static molar concentration model; calculating the molar flux of methane in a bubble state and the rising speed of bubbles to obtain the eruption rate of the seabed cold spring nozzle; and calculating the annual cold spring methane leakage flux of the station by combining the station cold spring plume three-dimensional static molar concentration model, the eruption rate and the bubble rising speed with the periodic leakage characteristics of the cold spring plume. The methane flux of the station cold spring leakage is 6.44 * 10 < 5 > mol / yr, which is greater than the previous estimation result. Previously, understanding of contribution of methane leakage of a cold spring system to climate change and carbon cycle is underestimated.

The present invention relates to the field of earth observation technology, and provides a method for synchronous observation and real-time monitoring of water, heat, carbon and radiation fluxes of unmanned aerial vehicles. The method is applied to a system for synchronous observation and real-time monitoring of water, heat, carbon and radiation fluxes of unmanned aerial vehicles, and the system includes an airborne observation system and a ground real-time monitoring system. The ground real-time monitoring system is used to store preset user parameters, receive and process in real time a variety of observation data collected by the airborne observation system, and display water, heat, carbon and radiation flux observation results in real time. The method includes: obtaining corresponding observation data based on preset user parameters, and calculating water vapor flux, heat flux, carbon flux, four components of surface radiation and photosynthetically active radiation flux based on the observation data. The method can realize the synchronous observation of water vapor flux, heat flux, carbon flux and four components of surface radiation and regional values ​​of photosynthetically active radiation flux of ecosystems, and provide basic observation conditions for the study of the coupling process of water cycle, carbon cycle and energy cycle of ecosystems.

The invention discloses a lake and reservoir future carbon income and expenditure scenario prediction accounting method based on climate change influence, and relates to the technical field of ecological environment monitoring and carbon cycle research, and the method comprises the steps: S1, collecting climate data, social economic data and lake and reservoir ecological data of a target lake and reservoir and a surrounding region, and carrying out the cleaning and missing value supplementation of the data; according to the method, the social economy-climate coupling model is constructed, social economy factors such as population growth and economic development and climate change factors are cooperatively incorporated into lake and reservoir carbon income and expenditure prediction, comprehensive simulation of lake and reservoir carbon income and expenditure multi-driving factors is realized, the fitting degree of a prediction result and an actual situation is improved, and the prediction efficiency is improved. By setting multiple groups of social economy-climate scene combinations, lake and reservoir carbon income and expenditure prediction results under different development paths are output, a carbon income and expenditure accounting sub-model based on a process mechanism is embedded, and model parameters are verified and optimized in combination with historical data, so that the accuracy and reliability of lake and reservoir carbon income and expenditure prediction accounting are further improved.

The invention discloses an ecological carbon sink monitoring method based on data processing, and relates to the technical field of ecological carbon sink monitoring, and the method comprises the following steps: S1, obtaining the current physiological parameters of each plant in vegetation of a to-be-monitored region, and determining the vegetation influence degree of the to-be-monitored region; s2, collecting the carbon emission of the to-be-monitored area in the historical time period; and S3, determining the final carbon emission at the next moment according to the vegetation influence degree of the to-be-monitored area and the carbon emission at the historical time period. According to the method, the complexity and the collaboration of the carbon circulation of the ecological system are comprehensively reflected, and the complex mechanism of the carbon circulation of the ecological system is precisely restored, so that the monitoring result can better reflect the real ecological nature of carbon sink.

The invention discloses a method for detecting soil active organic carbon based on fluorescence spectrometry, and belongs to the technical field of soil detection. The method comprises the following steps: sequentially drying, sieving and grinding fresh soil to obtain a soil sample, and sequentially performing active organic carbon extraction, purification, decoloration and pH regulation on the soil sample to obtain a soil liquid to be detected. The method comprises the following steps: adding fluorescein into a soil liquid to be detected to form a soil internal standard detection liquid, obtaining a soil fluorescence spectrum through three-dimensional fluorescence spectrum scanning, carrying out background deduction and correction coefficient correction, and calculating the active organic carbon concentration of the soil by using tryptophan, tyrosine and SMP fluorescence concentration standard curves. According to the method, an internal standard method is combined with a fluorescence spectrum technology, and through multi-step sample pretreatment and spectrum correction, the detection accuracy and the anti-interference capability are improved, the active organic carbon concentration of the soil can be rapidly and accurately determined, and an effective means is provided for soil carbon cycle research and quality evaluation.

The invention discloses a soil organic carbon dynamic monitoring system based on model fusion, which belongs to the technical field of soil monitoring and ecological environment information, and comprises the following steps: S1, obtaining multi-source data related to soil organic carbon monitoring, and preprocessing to obtain a standardized data set; s2, generating mechanism characteristics with mechanism property and space-time relevance through the carbon cycle process model; s3, constructing an artificial intelligence prediction model, and enabling the artificial intelligence prediction model to learn a mapping relation between the soil organic carbon and multi-source input so as to realize rapid inversion of the soil organic carbon; s4, fusing the carbon cycle process model and the artificial intelligence prediction model to obtain a fusion model; s5, realizing long-term continuous monitoring through dynamic inversion, operation state monitoring and abnormity self-repairing, and sliding window increment updating; and S6, generating a standardized result for supporting multi-field long-term application and system continuous optimization. By adopting the system, high-precision and high-efficiency multi-scale dynamic monitoring of soil organic carbon is realized.

The invention discloses a multi-source remote sensing reflectivity fusion method based on orthogonal matching pursuit and physical constraint, and the method comprises the steps: taking a multi-source remote sensing image as a data basis, and constructing a multiband sparse dictionary through time sequence alignment and overlapping sampling; then, an orthogonal matching pursuit algorithm introducing physical constraints is utilized to gradually select an image atom which is most matched with a target date in a sparse reconstruction process, and physical consistency of a reconstruction result in the aspects of hydrological conditions, phenological characteristics, spectral reflectivity and the like is ensured; and finally, through residual error return correction, day-by-day seamless 30-meter six-waveband reflectivity reconstruction is realized. According to the method, the stability and the physical interpretability of a reconstruction result are remarkably improved while efficient calculation is kept, continuous, multi-band and high-resolution time sequence data generation can be achieved in complex ecological environments such as lake wetlands, and high-precision remote sensing data support is provided for hydrological process simulation, ecological environment evaluation and carbon cycle research.

The invention relates to the technical field of earth observation, and provides an unmanned aerial vehicle hydrothermal carbon and radiation flux synchronous observation and real-time monitoring method which is applied to an unmanned aerial vehicle hydrothermal carbon and radiation flux synchronous observation and real-time monitoring system which comprises an airborne observation system and a ground real-time monitoring system. The ground real-time monitoring system is used for storing preset user parameters, receiving and processing various observation data acquired by the airborne observation system in real time, and displaying hydrothermal carbon and radiation flux observation results in real time; the method comprises the steps of obtaining corresponding observation data based on preset user parameters, and calculating water vapor flux, heat flux, carbon flux, earth surface radiation four-component and photosynthetically active radiation flux based on the observation data. Synchronous observation of four components of water vapor flux, heat flux, carbon flux and earth surface radiation and photosynthetically active radiation flux regional values of an ecological system can be achieved, and basic observation conditions are provided for research of the coupling process of water circulation, carbon circulation and energy circulation of the ecological system.

The invention discloses a satellite-borne sunlight-induced chlorophyll fluorescence spectrum detection imaging system, and belongs to the technical field of spectral imaging. The system includes a telescope optical system, a field-of-view separator assembly, a hyperspectral spectrometer system, and a hyperspectral spectrometer system. A vegetation chlorophyll signal from the earth surface is condensed by the telescope optical system and then is separated into a first view field object signal and a second view field object signal by the view field separator assembly, and chlorophyll fluorescence information and vegetation photochemical index information are synchronously acquired by the hyper-spectral spectrograph system and the hyperspectral spectrograph system respectively. According to the invention, synchronous high-precision detection of chlorophyll fluorescence and vegetation physiological parameters is realized, dynamic spectrum sampling is supported to match fluorescence peak valley characteristics, and an advanced remote sensing technical means is provided for global vegetation photosynthesis and carbon cycle research.

The invention relates to the technical field of environmental science, discloses a wetland carbon cycle model construction and optimization method and system, and aims to solve the problems of complex wetland carbon cycle process, insufficient model precision generalization, difficulty in coordination of multi-carbon component management decisions and the like. The method comprises the following steps: acquiring preprocessed multi-source wetland data, and constructing multi-scale spatial-temporal characteristic representation; synergistically simulating the dynamic state of the carbon components of the wetland through a mechanism fusion network; and based on a simulation result, a management strategy is generated through multi-objective optimization, scene prediction is performed, and a response is evaluated. The system comprises a data acquisition and preprocessing module, a multi-scale spatial-temporal feature coding module, a mechanism fusion network module, a multi-target optimization decision module and a scene prediction visualization module. According to the technical scheme, a high-precision and high-generalization model can be constructed, deep learning and a physical mechanism are fused, multi-target optimization decision and scene prediction capabilities are provided, multi-carbon components are effectively simulated and optimized, and the wetland carbon management precision and efficiency are improved.

The invention relates to the technical field of land management and carbon circulation, and discloses a soil organic carbon estimation method based on a random forest and a lightweight gradient elevator. Soil organic carbon estimation is used as an entry point of carbon balance treatment and climate regulation, multiple machine learning model technologies are integrated, influence factors of soil data are mined and analyzed, and a soil organic carbon estimation model set is constructed and evaluated and analyzed, so that the soil organic carbon is estimated. A set of space-time adaptability model evaluation technical method integrating multi-source data and incorporating climatic factors is innovatively developed. According to the method provided by the invention, the generalization ability and the stability of the machine learning evaluation model are optimized, so that the precision and the efficiency of soil organic carbon estimation are improved, and a more accurate and efficient application method is provided for soil organic carbon estimation under different regions and environment conditions.

The invention discloses a soil-plant carbon element tracking method and system based on isotope labeling, particularly relates to the technical field of soil carbon cycle in-situ detection, and is used for solving the technical problem that a carbon migration path in a rice rhizosphere heterogeneous micro region cannot be distinguished in the prior art. The method comprises the following steps: arranging a spatial coding microprobe array on a rice root system and a soil interface of a rice field, synchronously applying a C13 labeled compound, and continuously obtaining a grid node oxidation-reduction potential and a carbon isotope abundance value; dividing the boundary of a root surface oxidation layer and an anaerobic layer based on day and night oscillation phase difference mutation; detecting peak displacement migration from carbonyl bonds to carbon-carbon bonds in a boundary transition region by adopting an in-situ Raman spectrum, and correcting a space boundary; partitioning and clustering carbon isotope abundance values according to the corrected boundary, and identifying hot spot boundaries of a root tip secretion active region, a microorganism enrichment region and a mineral binding region; calculating the carbon isotope flux gradient in each region and generating a spatial distribution map; in-situ analysis of a rhizosphere carbon migration path is realized, and rice field carbon cycle research precision is improved.

The invention provides a soil resource sustainable utilization evaluation and prediction system, relates to the technical field of sustainable utilization evaluation and prediction of soil resources, and aims to accurately capture dynamic characteristics of soil carbon circulation by monitoring soil respiration rate, carbon content and other environmental data in real time and combining a multilevel carbon distribution-respiration rate mapping model. The system integrates MLP and LSTM technologies, and dynamically updates a multilevel carbon distribution-respiration rate mapping weight to reflect a nonlinear mapping relationship between soil carbon storage and respiration rate. By constructing a soil resource optimization map and a multi-factor comprehensive evaluation index, the system can visually display potential influences of different soil management measures, and provides scientific decision support for soil resource management.

The invention provides a power generation system and method based on carbonation calcination cycle coupling thermochemical energy storage. The power generation system comprises a carbonate calcination reactor, a COturbine power generation unit, a carbonation reactor, a thermochemical energy storage unit, a heat exchange network and a control system. According to the invention, industrial waste heat or renewable energy electric power is utilized to drive carbonate calcination to generate high-temperature COs to push the turbine to generate power; meanwhile, a calcined product CaO / MgO enters a carbonation reactor to absorb COO in smoke or the atmosphere, carbon sequestration circulation is completed, high-temperature heat needed by calcination is supplied by high-grade heat energy stored by a thermochemical energy storage unit in the charging stage, and the heat energy comes from surplus electric power or system power generation tail gas waste heat; the thermochemical energy storage unit releases heat to drive calcination in the discharging stage, and power grid peak regulation and energy time shifting are achieved. The problems of intermittency and carbon emission of traditional thermal power / wind and light power generation are solved, and integration of efficient, low-carbon and schedulable power output and carbon capture is achieved.

An environmental impact evaluation index calculation system that determines, as an index, an environmental impact of a company on a social environment, the environmental impact evaluation index calculation system determining a CO2 emission storage amount from total CO2 emissions emitted from within a company facility, a total soil carbon amount and a carbon absorption capacity within the company facility estimated using a soil carbon cycle model, and a carbon absorption capacity in a downstream region within the company facility estimated using a river simulator, and setting the CO2 emission storage amount as a first environmental impact index.

The invention discloses a method for analyzing sensitivity of terrestrial ecosystem carbon cycle to drought, and relates to the field of carbon neutralizer.The method comprises the steps that terrestrial ecological data are collected and preprocessed, a standardized soil humidity index is calculated, and a drought event is recognized through a three-dimensional clustering method; drought characteristics are extracted, the correlation degree of the standardized land ecological data and the drought characteristics is calculated by using a Pearson's correlation method, and importance degree sorting is performed on the standardized land ecological data in combination with an interpretable machine learning method to obtain main climate driving factors; analyzing the influence degree of the drought characteristics on the change trend of the main climate driving factors by using a multi-source linear regression method; and analyzing the influence degree of the main climate-driven factors on the carbon cycle during the drought period by using the partial correlation coefficient between the main climate-driven factors and the carbon flux. The invention discloses the change rule of the land carbon sink driven by different climates.

The invention discloses an NEP estimation method based on feature selection and machine learning, and belongs to the technical field of ecological environment monitoring and carbon cycle research, and the method comprises the steps: obtaining remote sensing image data and environment factors of a target region, and carrying out the preprocessing of the obtained remote sensing image data, and obtaining remote sensing factors; based on the remote sensing factor and the environmental factor, obtaining a net ecosystem productivity NEP feature influencing multi-source data fusion of the target area, performing screening, obtaining a preset number of key factors, and performing training through an extreme gradient lifting regression algorithm; analyzing the training result of the extreme gradient lifting regression algorithm by adopting a Shapril addition interpretation method; the extreme gradient lifting regression algorithm is verified through the evaluation indexes, and target area NEP estimation is completed; according to the method, high-precision estimation and driving mechanism analysis of the NEP of the complex mountain ecosystem are realized by fusing multi-source data and a machine learning technology, and clear mechanism explanation is provided for ecological research.