934 results about "Carbon sink" patented technology

The invention relates to the technical field of carbon sink prediction, in particular to an ecological restoration area carbon sink dynamic prediction method based on time sequence remote sensing, which comprises the following steps: acquiring a multi-source time sequence data set of a target area, and extracting spatial features and a time sequence fluctuation mode of the multi-source time sequence data set; dividing the ecological restoration region in the target region into a plurality of dynamic aggregation units based on the spatial features and the time sequence fluctuation mode, and combining the dynamic aggregation units with similar features; according to the ecosystem type of each dynamic aggregation unit after combination, constructing a differentiated carbon sink accounting model, extracting multi-scale time sequence features and screening key influence factors; based on the multi-scale time sequence characteristics and the screened key influence factors, constructing a carbon sink dynamic prediction model and outputting a carbon sink dynamic prediction result; and solving a repair decision scheme according to a carbon sink dynamic prediction result by taking maximization of the carbon sink increment as a target. The objective of the invention is to realize carbon sink dynamic prediction of a large-scale ecological restoration area and improve carbon sink prediction precision and calculation efficiency.

The invention relates to the technical field of biomass and carbon sink collaboration, in particular to a tropical rainforest biomass and carbon sink collaborative optimization decision-making system based on deep learning. According to the technical scheme, the system comprises a multi-modal data acquisition system, a space-time coupling preprocessing module, an ecological coupling deep learning model, a carbon sink dynamic evaluation system, a multi-objective decision optimization engine and an intelligent interaction execution platform. Data are comprehensively and safely acquired by means of a multi-modal data acquisition system, the data quality is optimized through a space-time coupling preprocessing module, biomass and carbon sink are accurately analyzed by means of an ecological coupling deep learning model and a carbon sink dynamic evaluation system, a scientific decision is made through a multi-objective decision optimization engine, execution is guaranteed, and the method is suitable for large-scale popularization and application. Convenient interaction and efficient execution are realized by means of the intelligent interaction execution platform, so that the accuracy, scientificity and execution efficiency of tropical rainforest biomass and carbon sink collaborative optimization decision making are remarkably improved, and ecological protection and sustainable development of tropical rainforests are powerfully promoted.

The invention relates to a high and cold wetland dynamic monitoring and health evaluation method and system based on multi-source remote sensing. The method comprises the following steps: step 1, data acquisition and preprocessing; step 2, multi-modal data feature extraction; step 3, performing multi-modal data fusion; step 4, dynamic monitoring; step 5, evaluating wetland health; according to the invention, the calculation method of the SAR polarization decomposition model and the optical wetland index is optimized due to the unique seasonal change and earth surface complexity of the high and cold wetland, a scientific basis is provided for the management of the wetland protection area, and the ecological restoration efficiency is improved; alpine region water resource management and ecological protection policy making are supported; and data support is provided for researching the wetland carbon sink function and the climate change influence.

The invention discloses a CCER forestry carbon sink project development system based on artificial intelligence technology monitoring, and relates to the technical field of carbon sink monitoring and optimization. According to the CCER forestry carbon sink project development system based on artificial intelligence technology monitoring, space-time estimation is carried out on CCER forestry carbon sink project data through the deep learning model, and the dynamic change of the carbon sink amount is accurately predicted. Spatial features are extracted through a convolutional neural network, time sequence data are analyzed in combination with a long-short-term memory network, and a carbon sink prediction model is constructed. And through statistical analysis and a spatial interpolation algorithm, carbon sink quantity change is monitored in real time, an abnormal trend is identified, and early warning is provided. Based on a mixed integer linear programming algorithm, forest land resource allocation and vegetation types are optimized, a carbon sink optimization scheme is generated, and carbon sink benefit maximization is promoted. And through an intelligent carbon sink management scheme, sustainable ecological development and green and economic popularization are assisted.

The invention provides a green land carbon sink amount estimation method for ecological restoration planning, and the method comprises the steps: precisely collecting the vegetation coverage rate, species distribution, soil type and other data of a green land through integrating remote sensing images, unmanned aerial vehicle monitoring, a geographic information system and other technical means; the method comprises a vegetation carbon reserve model, a soil carbon reserve model and an environmental factor correction module, the vegetation carbon reserve is calculated by adopting an Allometric model, the soil carbon reserve is subjected to multi-layer estimation, and the carbon sink amount is corrected by utilizing meteorological conditions and topographic factors, so that the accuracy and applicability of estimation are improved, and the estimation accuracy is improved. According to the method, dynamic estimation of the carbon sink amount is realized through time sequence analysis and in combination with vegetation growth cycles and seasonal changes, and visual result display and decision support functions are provided. The method is widely applicable to various ecological environments such as urban green land, wet land and mountain land, and scientific basis is provided for ecological restoration planning and carbon sink monitoring.

The invention discloses a forest carbon sink function monitoring method based on multi-source data. The method comprises the following steps: collecting data such as vegetation, soil, weather, landform and interference factors; the collected data are preprocessed; the preprocessed data are input into a Biome-BGC model for processing, and ecological system carbon flux parameters are obtained; determining a vegetation carbon sink amount and a soil carbon sink amount based on an ecological system carbon flux parameter, and obtaining a comprehensive carbon sink amount by combining artificial and natural interference carbon emission amounts; determining the forest carbon sink potential through a constructed forest carbon sink potential calculation formula based on the calculated comprehensive carbon sink amount; based on the calculated comprehensive carbon sink amount, time stability, space stability and anti-interference stability are determined, weighted summation is carried out on the time stability, the space stability and the anti-interference stability, and the carbon sink stability is determined; according to the method, the forest carbon sink function is monitored through multi-source data, the monitoring accuracy is improved, and the forest carbon sink potential, the carbon sink stability and the investment value are accurately quantified.

The invention relates to a cotton field carbon sink dynamic evaluation method and system based on multi-source data fusion. The method comprises the following steps: firstly, comprehensively collecting multi-source data of a cotton field area environment, and processing the multi-source data by using a time window alignment technology to generate a multi-source heterogeneous data set; based on the data set, key features are extracted through a data mining means, and a key influence factor weight table is generated; and then, inputting the key influence factor weight table and the multi-source heterogeneous data set into the trained optimization evaluation model together to obtain cotton field carbon sink space-time distribution characteristics. And finally, carrying out correlation analysis on the spatial-temporal distribution characteristics and meteorological continuity data, and generating a curve capable of visually displaying the dynamic change of the cotton field carbon sink along with time. The method overcomes the limitation of a single data source, greatly improves the accuracy and comprehensiveness of cotton field carbon sink evaluation, can accurately reflect the real state of cotton field carbon sink under different time-space conditions, and provides a key basis for agricultural managers to formulate scientific and reasonable cotton field planting management strategies.

The invention discloses a multi-source heterogeneous data fusion method of a sky tower ground well carbon flux observation system, which comprises the following steps of: 1, preprocessing and automatically correcting data to solve the problems of noise, space-time inconsistency, format difference and systematic error in a multi-source heterogeneous data acquisition process; the multi-source heterogeneous data are respectively from satellite remote sensing, unmanned aerial vehicle remote sensing, an eddy covariance flux tower, ground observation and underground observation; step 2, multi-level data fusion: extracting multi-source heterogeneous data from original signals step by step to form unified high-dimensional feature expression, and finally outputting real-time observation data of carbon emission and carbon sink of the coal mining area through a decision model; and step 3, performing comprehensive observation and application output, performing real-time analysis according to the real-time observation data, and outputting a detection result and early warning information. According to the method, an advanced algorithm with adaptive correction and multi-level fusion capability is adopted, and data fusion of five observation modules of the sky tower surface well is realized, so that the key bottleneck in the prior art is broken.

The invention provides a mangrove forest restoration area carbon sink quantity data monitoring system, and relates to the technical field of ecological carbon sink metering, and the system comprises an acquisition module which is used for collecting a carbon exchange original data set of an atmosphere-vegetation-soil interface based on a multi-type sensor network arranged in an intertidal wetland of a mangrove forest restoration area; the data generation module is used for carrying out space-time coordinate matching based on the original data set and generating a multi-dimensional carbon flux fusion data set of geographic position and time joint indexing; and the identification module is used for calculating a local space-time dispersion and a global correlation threshold value based on the fused data set, and identifying and marking abnormal data points. According to the invention, real-time and multi-dimensional monitoring of the carbon sink amount of the mangrove forest restoration area is realized, the monitoring efficiency and the data reliability are improved, and support is provided for assessment of restoration and management strategies.

The invention relates to a building material mining life cycle process carbon emission improvement potential quantification method comprising the following steps: constructing a full life cycle carbon footprint digital model covering mining, transportation, processing and restoration, and integrating a digital twinning and BIM technology dynamic correlation carbon emission factor library; key carbon emission influence factors are identified through Monte Carlo simulation and regression analysis; establishing a multi-objective optimization model, generating an optimal process parameter combination by adopting an intelligent algorithm, and quantifying the emission reduction potential; model parameters are dynamically calibrated in combination with real-time data, and a closed-loop optimization mechanism is formed; space-time distribution visualization and block chain evidence storage technologies are introduced, and accurate monitoring, intelligent decision making and credible tracing of carbon emission are achieved. According to the method, the problems of data lag and single optimization in a traditional method are solved, the accuracy of carbon emission and the feasibility of an emission reduction scheme are remarkably improved, meanwhile, carbon sink offset evaluation is supported, and a whole-process technical support is provided for mine green transformation.

The invention provides a multi-fusion seaweed field ecosystem observation method, system, device and medium, and belongs to the technical field of ecological monitoring, the method comprises the following steps: obtaining chlorophyll a concentration, seaweed canopy spectrum and three-dimensional biomass point cloud; aligning the chlorophyll a concentration of the target sea area with the seaweed canopy spectrum, and fusing the three-dimensional biomass point cloud to generate a three-dimensional biomass model; constructing an in-situ sampling network to monitor water quality parameters, benthic organism video streams and eDNA metagenome sequencing data, calibrating a three-dimensional biomass model, executing anomaly detection through a lightweight LSTM model, and identifying benthic organism species in real time through an improved YOLOv5 model; constructing a graph neural network, outputting a carbon sink prediction value, generating a brown tide early warning signal when the carbon sink prediction value is lower than a dynamic threshold value, optimizing a patrol path of the unmanned aerial vehicle based on reinforcement learning, and improving the sampling frequency of the water quality sensor. According to the invention, multi-fusion monitoring of the seaweed field is realized, the ecological condition is accurately evaluated, and abnormity is warned in advance.

The present invention relates to the technical field of dynamic carbon sink accounting, and specifically relates to a dynamic carbon sink accounting system based on multi-modal AI remote sensing monitoring and blockchain-based evidence storage. The system collects optical remote sensing, radar, photosynthetically active radiation and meteorological data, performs unified spatiotemporal calibration on the data, and then fuses the calibrated data by means of a cross-modal attention mechanism, so as to generate multi-modal feature vectors, and inputs same into a TCN model for carbon stock and trend prediction. A prediction result and metadata are uploaded to a blockchain by means of smart contracts, so as to generate a carbon sink NFT including a geographic fence and a confidence level, thereby realizing trusted evidence storage. A residual mapping function is established in view of on-chain historical data, so as to dynamically optimize the model, and improve the accounting accuracy. The system improves the fusion capability and prediction accuracy, and enhances the credibility and transparency of carbon asset management and transactions.

The invention relates to a mangrove forest vegetation carbon sink monitoring and metering method based on an unmanned aerial vehicle and an AI technology, and the method comprises the steps: constructing a mangrove forest recognition model of a U-Net improved model based on a PyTorch deep learning framework through the unmanned aerial vehicle AI technology, achieving the intelligent recognition and discrimination of various mangrove forest vegetations, and the area and number of regions thereof, and achieving the accuracy of 90% or above; through regular aerial photography monitoring of an unmanned aerial vehicle, in combination with growth parameters measured on site and variable parameters derived from aerial photography data of the unmanned aerial vehicle, an optimal fitting model of mangrove forest height crown breadth, overground biomass and unit leaf area based on aerial survey of the unmanned aerial vehicle is constructed, and the carbon reserve and carbon sequestration amount of mangrove forest region vegetation are accurately calculated. And comprehensively evaluating the mangrove forest carbon sequestration capability in the monitoring area. According to the application, intelligent, digital and comprehensive monitoring and evaluation of the mangrove forest wetland vegetation carbon sequestration capability are realized.

The invention relates to the technical field of carbon sink dynamic accounting, in particular to a carbon sink dynamic accounting system based on multi-mode AI remote sensing monitoring and block chain evidence storage. The system collects optical remote sensing, radar, photosynthetically active radiation and meteorological data, after unified space-time calibration, multi-modal feature vectors are generated through cross-modal attention mechanism fusion, and the multi-modal feature vectors are input into a TCN model to predict carbon reserves and trends. And a prediction result and metadata are chained through an intelligent contract to generate a carbon sink NFT containing a geographic fence and confidence, so that credible evidence storage is realized. And a residual mapping function is established in combination with historical data on the chain, the model is dynamically optimized, and the accounting precision is improved. The system improves the fusion capability and prediction accuracy, and enhances the credibility and transparency of carbon asset management and transaction.

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 intelligent land engineering carbon sink regulation and control system and method based on dynamic management, and belongs to the technical field of land engineering carbon sink regulation and control. The multi-source spatio-temporal data comprises vegetation indexes, soil parameters, meteorological data and carbon dioxide flux data; the data processing and analyzing module is used for performing fusion processing on the multi-source spatio-temporal data, inverting and calculating the carbon sink or carbon source state of the target area in real time based on a built-in carbon sink accounting model, and generating a carbon sink spatio-temporal distribution diagram; the intelligent decision-making module is used for diagnosing a carbon sink capacity limiting factor based on the carbon sink space-time distribution diagram and generating land engineering regulation and control strategies for different sub-regions; and the execution and feedback module is used for issuing the regulation and control strategy to an execution terminal or a management platform. The method improves the carbon sequestration capacity and regulation and control efficiency of land engineering.

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 discloses a forest carbon reserve and carbon sink monitoring and evaluating method based on a multi-source remote sensing technology. The method comprises the following steps: multi-source remote sensing data acquisition: respectively acquiring optical remote sensing data and radar remote sensing data; tree species classification and identification: carrying out tree species classification on the forest in the research area to realize tree species scale space distribution information extraction; for different tree species, biomass model parameters obtained through field investigation are combined, a biomass estimation model based on the tree species is established, model input parameters are biomass model parameters, and vegetation biomass is output; calculating the carbon reserve: calculating the vegetation carbon reserve by utilizing the inverted vegetation biomass and combining the carbon content coefficients of different tree species; estimating the soil carbon reserve, and finally obtaining the total carbon reserve of the forest ecosystem: calculating the carbon reserve variation of the forest ecosystem at different time points based on the multi-source remote sensing data of the long-term sequence. According to the invention, large-range, high-precision and continuous forest carbon reserve and carbon sink monitoring and evaluation considering tree species difference is realized.

The invention provides a coastal salt marsh vegetation and sediment deposition comprehensive carbon sink assessment method and system. The system comprises a vegetation dynamic module, a water power module, a carbon dynamic module and a vegetation dynamic-water power-carbon dynamic coupling module. Output results are divided into vegetation-water and sediment-landform space-time dynamic and ecological system carbon exchange space-time dynamic, and the results are used for landform development and carbon sink dynamic evaluation of the salt marsh-light beach system. On the basis of development of a grid type process model, a vegetation dynamic model, a hydrodynamic model and a carbon dynamic model are coupled together, a biogeochemical coupling model integrating interaction among biological, hydrological and geomorphic processes is constructed, and a biogeochemical process of coastal salt marsh vegetation dynamic and sediment carbon sink is parameterized; and an effective evaluation technology can be provided for community development and carbon sink dynamics of coastal wetland vegetation.

The invention provides a planting agriculture carbon sink metering method and system and computing equipment. The method comprises the following steps: establishing a dynamic farmland ecosystem carbon sink model according to biomass monitoring data, soil carbon content data, meteorological data and crop management measure records of a farmland ecosystem; based on the dynamic farmland ecosystem carbon sink model, analyzing carbon fixation efficiency under different crop types and different farming modes in a farmland ecosystem; and calculating the total carbon sink amount of the farmland ecosystem. According to the technical scheme provided by the invention, the dynamic farmland ecosystem carbon sink model is established, so that the carbon absorption capacity of the farmland ecosystem can be more accurately quantified, and the carbon sink metering precision is improved.

The invention discloses a forest grass wet carbon sink metering method and system, and relates to the field of carbon sink metering, and the method comprises the following steps: 1, collecting and processing multi-source data, 2, fusing the data, aligning the multi-source data, adopting a geographic registration and time synchronization technology, and unifying a spatial resolution and a timestamp, and 3, extracting features. 4, constructing a carbon sink dynamic model; and 5, carrying out carbon sink visualization and decision support. According to the forest and grass wet carbon sink metering method and system, multi-source data collaboration is achieved, optical, radar and laser radar data are fused, the defect that traditional carbon sink metering depends on ground sample plot survey or single remote sensing data is overcome, carbon sink contributions of forests, grass and wet lands are accurately distinguished, the overall spatial resolution is higher, cloud and mist interference is avoided, the single data source error is solved, and the measurement accuracy is improved. Through multi-source data fusion and dynamic model optimization, high-precision measurement and real-time monitoring of forest grass wet carbon sink are realized.

The invention discloses a monitoring system for forest ecological carbon sink calculation, and belongs to the field of ecological environment monitoring and carbon sink evaluation. Comprising a foundation optical remote sensing module, a crown biomass calculation module, a soil carbon component identification module, a vertical carbon flux detection module, a regional microclimate analysis module, a time sequence big data coupling module, a carbon source and carbon sink model fusion module and a result correction feedback module. The method comprises the following steps: collecting vegetation three-dimensional structure data through a multi-frequency laser radar, calculating unit-area canopy carbon storage acceleration in combination with seasonal climate interference parameters, and fusing turbulent carbon exchange and soil carbon change data to construct a regional carbon flux coupling model; the system collects cross-rosa chinensis carbon change data to construct a multi-dimensional feature tensor diagram, dynamically updates a weight and a fitting function, realizes carbon sink dynamic estimation and feedback optimization based on a biology-climate-time three-dimensional cooperation mechanism, and improves forest carbon sink monitoring precision and stability. The beneficial effect is that scientificity and accuracy of ecological environment monitoring and carbon sink evaluation are improved.

The invention relates to the technical field of carbon sequestration capacity monitoring, in particular to a carbon sequestration capacity monitoring and evaluating method for an island ecosystem, which comprises the following steps: S1, multi-source data fusion and habitat partitioning: dividing an island habitat type partitioning map; s2, stratified sampling and carbon reserve actual measurement: measuring the carbon content, and constructing a carbon reserve actual measurement database; s3, constructing a dynamic carbon sink model: carrying out a whole growth cycle environment simulation experiment on sea grass and algae in different habitats, and establishing the dynamic carbon sink model taking habitat types as classification units; s4, carbon sequestration capability quantification and bottleneck diagnosis: calculating carbon sequestration contribution values of various habitat types, identifying low-efficiency carbon sequestration areas through contribution value comparison, and analyzing environmental limitation factors of the low-efficiency carbon sequestration areas; and S5, integrating a sink increasing technology and generating a path: designing a habitat specific sink increasing scheme for the identified low-efficiency carbon sink area. According to the invention, high-precision monitoring, intelligent evaluation and system optimization of the island ecosystem carbon sequestration capability are realized.

Provided is a method for calculating a carbon stock of a mixed forest ecosystem, which method belongs to the technical field of carbon sink estimation of forest ecosystems. The method comprises: acquiring basic geographic data, meteorological data, physiological and ecological parameters, historical operation data and verification data; on the basis of an existing Biome-BGC model, improving a phenological module, adding a thinning operation management module, and optimizing the physiological and ecological parameters, so as to propose an improved Biome-BGC model suitable for simulating a carbon stock of a managed mixed forest ecosystem; taking a mixed pine and oak forest as a research object, and using the improved model to simulate a carbon stock; verifying the improved model; using an extended Fourier amplitude sensitivity test method to analyze the sensitivity of the physiological and ecological parameters; and on the basis of selecting high-sensitivity parameters, using a path analysis method to analyze the influence of the high-sensitivity parameters on the carbon stock. The proposed improved model exhibits a better performance in the aspect of a carbon stock of a mixed forest.

The invention relates to the technical field of remote sensing technology and forest ecological system carbon sink monitoring, and particularly discloses a forest carbon sink dynamic monitoring method based on near-surface multi-source remote sensing data, which comprises the steps of S1, data acquisition, S2, data processing, S3, forest change information monitoring, S4, forest carbon reserve estimation, S5, forest carbon sink dynamic analysis and S6, result verification and uncertainty analysis. By integrating multi-source information such as the multispectral data, the laser radar data and the field survey data, the structure and functional characteristics of the forest ecosystem can be comprehensively and accurately reflected. Compared with a single data source, the comprehensive data acquisition mode has the advantages that the precision and reliability of forest parameter extraction can be remarkably improved, and a solid foundation is provided for subsequent forest carbon reserve estimation and carbon sink dynamic analysis.

The invention discloses a green building microclimate regulation emission reduction and convergence increase method based on park biodiversity, and the method comprises the steps: obtaining biodiversity data and building environment parameters, constructing a three-dimensional ecological space digital twinborn model, and carrying out the multi-physics coupling analysis; loading boundary conditions to generate a comprehensive model, then carrying out microclimate dynamic simulation, training an ecological regulation model to generate initial regulation parameters, constructing a multi-objective optimization model in combination with dynamic regulation simulation information and building design parameters to obtain optimal parameters, and finally adjusting a regulation strategy based on real-time prediction. According to the method, by integrating biodiversity and building environment data and utilizing digital twinning, multi-field coupling simulation and intelligent optimization algorithms, collaborative optimization of microclimate adjustment, carbon emission reduction and carbon sink capacity improvement is achieved, a technical scheme is provided for deep fusion of green buildings and ecological systems, and the method is suitable for popularization and application. The method is suitable for the fields of urban building energy conservation and ecological environment protection.

The invention discloses a collaborative optimization method of new energy facilities in an expressway carbon sink system. The method comprises the following steps: monitoring carbon emission data of a full life cycle of an expressway and carrying out data fusion; carrying out accounting and intelligent analysis processing on the carbon emission data of the whole life cycle of the expressway; according to the invention, based on a'vehicle-road-ring 'carbon emission multi-dimensional integrated monitoring system, intelligent fusion and parameterization analysis of highway and traffic flow carbon emission multi-source data are carried out; a highway full life cycle carbon emission accounting model is established, an associated activity level metering system is constructed, a highway full life cycle carbon emission digital twinborn management system based on big data is constructed, and a highway carbon emission evaluation system is constructed to comprehensively analyze low-carbon high-speed full life cycle carbon emission characteristics. The coupling digital twinning technology realizes comprehensive visualization of carbon emission data, and provides important support for green and low-carbon development of road traffic.

The invention discloses an ecological carbon sink measurement and estimation data fusion platform for the thermal power industry, and belongs to the technical field of data fusion. The method is realized through five steps of a data acquisition module, a carbon sink measurement and calculation module, a prediction model construction module, a data fusion module and a decision support module. According to the invention, through butt joint with a thermal power enterprise energy management system, production data including generating capacity and coal consumption are acquired in real time, and the timeliness of the data is greatly improved. Ecological environment data and meteorological data around a thermal power enterprise are comprehensively collected by using a satellite remote sensing technology and a ground sensor, and the comprehensiveness and accuracy of the data are ensured. According to the method, a dynamic adjustment mechanism is adopted, and the pre-estimation model is continuously optimized according to real-time data feedback, so that the model can adapt to the change of the environment. And time sequence analysis and regression analysis are combined to form an integrated model, so that the accuracy and reliability of a pre-estimation result are greatly 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 relates to a territorial space planning data monitoring evaluation method and system, and the method comprises the steps: obtaining multi-modal data, constructing an atmospheric diffusion model based on the multi-modal data, reversely positioning a pollution source, and obtaining a pollutant responsibility matrix; carrying out space grid fusion calculation through the pollutant responsibility matrix and the carbon sink data to generate a carbon source-carbon sink distribution diagram representing a territorial space carbon balance pattern; based on the regional characteristics of the carbon source-carbon sink distribution diagram, generating a territorial space carbon planning scheme through a low-carbon guiding algorithm; constructing a disaster model based on the multi-modal data, and carrying out space coupling analysis on a disaster risk area and a high-carbon sink potential area output by the disaster model to obtain a land space carbon restoration scheme; a digital twinborn platform is constructed according to multi-modal data, an atmospheric diffusion model and a disaster model, an expected carbon sink increment value of a land space carbon planning scheme is deduced and verified through the digital twinborn platform, and iteration of the implementation scheme is carried out.