580 results about "Carbon sink" patented technology

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 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 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 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 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.

The invention discloses a coastal zone carbon sink data acquisition management system and method, and particularly relates to the technical field of carbon sink evaluation, and the method comprises the following steps: dividing carbon flux parameter partitions based on vegetation types, sediment characteristics and historical carbon storage levels, and establishing initial reference values; plant biomass, soil organic carbon content and non-carbon dioxide greenhouse gas flux are regularly collected according to fixed sampling points, and a carbon sink observation data sequence is formed; when data exception triggers an evaluation process, generating a carbon sink credibility index and an ecological interference response index; inputting the two indexes into a prediction model to output an adjustment vector, generating an adjustment strategy in combination with a weight, and dynamically updating an initial carbon flux reference value of each partition; according to the method, regionalized management is realized by constructing carbon flux parameter partitions, a carbon sink observation data sequence is established, an evaluation index is dynamically generated, intelligent updating of a carbon flux reference value is completed in combination with a prediction model, and the precision, responsiveness and adaptive adjustment capability of carbon sink monitoring are improved.

The invention discloses a wheat and corn crop rotation straw returning carbon footprint evaluation method, and relates to the technical field of carbon emission evaluation, and the method comprises the steps: dividing growth stages according to a dynamic crop rotation time axis, fusing the carbon flux time sequence data of crops in a region with soil microbial activity indexes, and employing an interpretable machine learning method to evaluate the carbon footprint of wheat and corn crop rotation straw returning. Identifying a carbon sink stage and a carbon source stage of the wheat and the corn in different growth stages, and dynamically generating a stage carbon state sequence; based on the stage carbon state sequence, fusing soil basic attribute data and real-time regional climate data, fitting a nonlinear response relationship of straw mineralization rates by adopting an integrated learning method, and dynamically generating a carbon conversion factor sequence; performing space-time coupling on the carbon conversion factor sequence and the staged carbon state sequence, calculating CO2 and N2O emissions and soil organic carbon variation according to time periods and spatial ranges, and generating a carbon dynamic curve through time sequence integration. According to the invention, the timeliness and accuracy of carbon dynamic simulation are improved.

The invention discloses a wetland carbon sink benefit evaluation method and system based on remote sensing data, and relates to the technical field of carbon emission evaluation. A wetland carbon sink benefit evaluation system based on remote sensing data comprises a carbon sink evaluation zoning module and a carbon sink benefit evaluation module. According to the invention, through multi-modal remote sensing data fusion and fine grid division, high-resolution analysis of spatial heterogeneity of a wetland region is realized, different types of carbon sink regions and distribution boundaries thereof can be accurately identified, and scientificity and particles of carbon sink partitioning are improved; a dynamic index characteristic graph layer is constructed through a remote sensing time sequence, a hydrological pulse response factor graph layer is constructed in combination with hydrological data, and clustering division is performed on the hydrological pulse response factor graph layer, so that dynamic identification and pixel level benefit calculation of a dynamic carbon sink area are realized.

The invention discloses a forest carbon sink acquisition method and system for driving an ecological carbon mode through remote sensing observation, and the method and system are corresponding schemes: in the scheme, a set of high-precision and multi-dimensional satellite-ground collaborative observation data system is formed by combining multi-source observation data, and a solid foundation is laid for subsequent carbon sink inversion; a high-resolution prior carbon concentration field of a research area can be generated by using a machine learning method, and a high-precision posterior carbon concentration field can be obtained; on the basis, through inversion analysis and a correction link, high-precision, wide-coverage and low-uncertainty analysis of forest carbon sequestration in a research area is finally realized, and the problems that an existing carbon sequestration estimation method is insufficient in space coverage, high in model uncertainty, limited in multi-source data fusion and the like are solved.

The invention discloses a vegetation carbon sink space optimization method and system based on interpretable machine learning. The method comprises the steps that firstly, multi-source environment data such as administrative division, environment factors and target variables of a target area are acquired, and a space table data set is formed; training an XGBoost regression model based on the data set, predicting the vegetation carbon sink amount and evaluating the performance of the model; quantifying the contribution of each environmental factor to the carbon sink by combining an SHAP interpreter, generating a feature importance and interpretability map, fitting through a LOWESS algorithm to obtain an environmental factor and carbon sink response relation curve, and extracting an optimal threshold interval; further predicting a carbon sink gain according to an optimization threshold value, delimiting optimization partitions, and forming a space optimization scheme; and finally outputting a vegetation carbon sink optimization suggestion report. According to the method, scientific quantification of carbon sink space optimization can be realized, and the interpretability and decision support value of a carbon sink improvement strategy are improved.

The invention discloses a method and system for dynamically measuring and calculating carbon sink in the whole construction process of a wetland protection area, and belongs to the technical field of ecological environment monitoring, and the method comprises the following steps: respectively calculating potential carbon emissions of a building material production and transportation stage, a construction stage and an operation stage after project construction, and obtaining a carbon emission accounting result; a carbon flux model is established, and a carbon flux change data set of the whole project construction process is calculated and obtained according to wetland ecological monitoring data, meteorological data, biological and physiological parameters and construction activity parameters; according to the carbon emission accounting result and the carbon flux change data set, a comprehensive carbon sink measurement and calculation model of the whole construction process is constructed; the actual carbon sink data are compared to correct the measurement and calculation model, and the corrected measurement and calculation model is applied to carbon sink amount calculation in the whole construction process of different types of wetland protection area production and construction projects; comparing a model calculation result with results obtained by other authoritative calculation methods to verify the accuracy and reliability of the model; and the blank in the field of current wetland carbon sink measurement and calculation is filled.

The invention discloses an evaluation method for dynamically monitoring carbon sink of a grassland and wetland ecosystem, which relates to the field of ecological environment monitoring and comprises the following steps: acquiring historical carbon flux observation data of a target area; dividing the target area into a plurality of space grid units, taking each grid unit as a node in the graph structure, and establishing graph edge connection between adjacent nodes; extracting ecological features for each node; calculating the edge weight of each edge in the graph structure according to the ecological characteristic difference between the adjacent nodes; inputting the graph structure containing the node features and the edge weights into a graph neural network model, and training the graph neural network model by using historical carbon flux observation data as labels; and predicting the carbon flux of an unobserved node or a future time period node by using the trained graph neural network model. The method solves the problem that a traditional model is large in estimation error of the carbon flux in the ecological boundary region, and can remarkably improve the carbon exchange prediction precision of the boundary region.

The embodiment of the invention relates to the technical field of artificial intelligence, and provides a coastal salt marsh vegetation carbon sink estimation method and system based on multi-temporal phenological characteristics. The method comprises the following steps: acquiring a remote sensing image of a target coastal salt marsh area in a key phenological period; extracting an initial vegetation contour of a vegetation coverage range in the target area from the remote sensing image by adopting an unsupervised classification method; constructing a phenological decision tree model by taking an NDVI threshold method as a core, and integrating a machine learning enhanced node division mechanism and a phenological period slope analysis method to enhance the phenological decision tree model; inputting the initial vegetation contour into a phenology decision tree model, and performing multi-level classification on vegetation types according to the initial vegetation contour through the phenology decision tree model to distinguish vegetation coverage ranges of various types of vegetation in the target coastal salt marsh area; and vegetation carbon density parameters are obtained, and the estimation of the total carbon sink amount of the target coastal salt marsh area is completed through spatial superposition calculation in combination with a vegetation coverage range, so that accurate classification of vegetation and efficient estimation of carbon sink are realized.

The invention discloses a forest carbon sink dynamic monitoring system based on multi-source data fusion, and relates to the technical field of carbon sink monitoring, and the system comprises a multi-source data collection and fusion module which collects forest-related multi-source data, and generates a standardized fusion data set; the carbon sink reserve estimation module is used for constructing a carbon sink reserve estimation model according to the fused data set, and calculating forest carbon sink reserves based on a model output result; the carbon sink reserve prediction module is used for predicting the forest carbon sink reserve at the next moment by using a time sequence analysis algorithm; the state change monitoring module is used for analyzing a carbon sink dynamic change trend based on forest carbon sink reserve prediction data and historical reserve data; and the early warning and decision support module is used for triggering an early warning signal of a corresponding level based on the abnormal change information. Through fusion of federated learning, ARIMA time sequence analysis, a sliding window and a linear regression technology, efficient and continuous tracking of a carbon sink reserve change trend is realized, and change abnormity can be identified in time.

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 relates to the technical field of wetland ecological restoration. The invention discloses a wetland carbon sink capacity improving method and system based on intelligent regulation and ecological restoration, and the method comprises the steps: laying a multi-parameter sensing network, and monitoring the water level, vegetation growth and soil wetland carbon reserve parameters in real time; obtaining a vegetation NDVI index and surface temperature data, and analyzing the vegetation NDVI index and the surface temperature data to obtain an ecological restoration strategy; according to an ecological restoration strategy, carbon sink dominant plants are combined, and emergent aquatic plants, submerged plants and floating-leaved plants are matched; strengthening the microorganisms, and inoculating a carbon sequestration fungicide; establishing a carbon sink-environmental parameter response model according to the historical intelligent regulation and control data; and inputting the ecological restoration strategy into the carbon sink-environmental parameter response model for analysis, and adjusting ecological restoration data according to an analysis result of the carbon sink-environmental parameter response model. According to the invention, intelligent response to environment change is realized, and the carbon sink efficiency is dynamically optimized.

The invention discloses a forest vegetation carbon sink potential prediction system and method based on a forest growth rule, and belongs to the technical field of carbon sink potential prediction. The invention aims to solve the problem of dynamic response to forest carbon sink potential and long-term environmental climate change. The method comprises the steps of collecting data, establishing model construction, verifying a data set and a data set for model calculation and carbon sink potential evaluation, wherein the data set for model calculation and carbon sink potential evaluation comprises forest basic data and forest environment data; performing data processing; constructing a forest vegetation biomass model for predicting biomass growth of forest vegetation; evaluating the fitting result by adopting a root mean square error RMSE and a determination coefficient R2 to obtain a verified forest vegetation biomass model; and performing forest vegetation biomass growth prediction by using the verified forest vegetation biomass model, and performing forest vegetation carbon sink potential prediction according to an obtained forest vegetation biomass growth prediction result to obtain a forest vegetation carbon sink potential prediction result.

The invention relates to the technical field of intelligent agricultural planting and breeding, and discloses a digital intelligent agricultural planting and breeding combined system which comprises a multi-source sensing module used for collecting environment data, biological sign data and equipment operation data through a planting area sensor, a breeding area sensor and a circular processing equipment sensor; the data verification module is used for dynamically verifying the collected data based on a preset agronomic mechanism model; the hierarchical digital twinning module is used for carrying out collaborative decision making in a three-level architecture of the edge computing node, the local server and the cloud platform and generating an optimization instruction; a virtual-real decision engine module; and a closed loop execution network module. According to the invention, a lightweight twinborn model is deployed through the edge layer to realize millisecond-level equipment control and guarantee timely response of extreme working conditions, the field area layer coordinates planting and breeding material circulation through a dynamic optimization algorithm, and the cloud layer integrates climate data to generate a carbon sink strategy of block chain evidence storage. And the real-time decision-making precision, the resource utilization efficiency and the carbon economic value are improved through three-level cooperation.

The invention discloses an ecological restoration area carbon sink increment real-time prediction method and system based on artificial intelligence, and the method comprises the following steps: multi-source data collection: obtaining a monthly vegetation coverage image through a satellite remote sensing platform, collecting the sensor data of soil temperature and humidity, air CO2 concentration and the like through a laid ground sensor network, and carrying out the real-time prediction of the carbon sink increment of an ecological restoration area; adopting an unmanned aerial vehicle laser radar to obtain vegetation canopy point cloud data according to a preset period, and collecting biomass actual measurement data of a restoration area in a historical preset age limit; preprocessing data, performing radiometric calibration, atmospheric correction and cutting splicing on a monthly vegetation coverage image acquired by a satellite remote sensing platform, and extracting a vegetation coverage and vegetation index time sequence; denoising, ground point separation and single tree canopy segmentation are carried out on vegetation canopy point cloud data acquired by the unmanned aerial vehicle laser radar, and the height and crown breadth of single plant vegetation are calculated. According to the invention, carbon sink increment prediction can be realized more accurately.

The invention discloses a forest carbon sink accurate monitoring system and method based on multi-source data fusion, and relates to the technical field of carbon sink monitoring. In order to solve the problems of low precision and poor timeliness of the traditional monitoring means, the adopted scheme comprises a multi-source data acquisition module used for acquiring satellite remote sensing, unmanned aerial vehicle aerial photography, ground monitoring sensor and meteorological data; the data pre-processing and fusion module is used for pre-processing the collected data and extracting and combining related features through a two-layer fusion method to serve as input data of a carbon sink estimation model; the model construction and optimization module is used for constructing and optimizing a carbon sink estimation model; the result visualization and analysis module is used for displaying a carbon sink estimation result, researching influence factors of forest carbon sinks and predicting a future change trend; and the integration and management module is used for integrating hardware equipment to construct a monitoring platform, developing a monitoring system based on cloud computing and the Internet of Things, and establishing and perfecting a management system. The method is used for monitoring the forest carbon sink.

The invention belongs to the technical field of environmental protection control, and relates to a cost reduction method and device based on carbon asset full life cycle management, and the method comprises the steps: obtaining the carbon emission data converted from the cost input of each stage of an enterprise; the carbon emission data is analyzed; formulating a carbon emission list based on the analysis result of the carbon emission data, the carbon emission thermodynamic diagram and the carbon quota of the enterprise; predicting the carbon emission of the enterprise based on the carbon emission list; establishing an enterprise carbon management personal incentive mechanism based on the result of predicting the enterprise carbon emission; and preposed carbon sink design is carried out, so that the full-life-cycle income of carbon assets is improved. The preposed carbon sink design is emphasized, and carbon sink projects such as afforestation and energy conservation and emission reduction technology application are reasonably planned, so that the carbon emission of enterprises is effectively counteracted, the full-life-cycle income of carbon assets can be improved, and the win-win situation of economic benefits and environmental benefits is realized.

The embodiment of the invention discloses a reservoir carbon sink accounting method based on multi-source remote sensing data, a related device and a medium. The method comprises the following steps: acquiring multi-source remote sensing data of a target reservoir in a target time period; according to the multi-source remote sensing data, performing inversion to obtain a water surface area dynamic diagram, a chlorophyll a concentration space-time distribution diagram, a CDOM space-time distribution diagram and a water surface temperature space-time distribution diagram of the target reservoir in the target time period; the four diagrams are input into a preset carbon sequestration model for carbon sequestration processing, a unit area greenhouse gas flux value space-time distribution diagram corresponding to the target reservoir is obtained, and each pixel in the unit area greenhouse gas flux value space-time distribution diagram carries a corresponding unit area greenhouse gas flux value; and performing space-time integral processing on the space-time distribution diagram of the greenhouse gas flux value per unit area and the dynamic diagram of the water surface area to obtain a target total carbon sink amount of the target reservoir in the target time period. By implementing the method provided by the embodiment of the invention, the accuracy of reservoir carbon sink accounting can be improved.

The invention discloses a typhoon influence distinguishing method and system based on seawater CO2 partial pressure data, and the method comprises the steps: obtaining sea area situation monitoring data in a target sea area typhoon transit period through a multi-source observation platform, and constructing a standardized data set and a climate state background field; a DBSCAN clustering algorithm is adopted to perform water mass division, and the typhoon characteristic water mass distribution is determined in combination with a Manchester-HuitiniU test and correlation analysis. And calculating a seawater carbon dioxide partial pressure abnormal field based on the climate state background field, constructing a typhoon influence index sequential sequence, identifying a core influence area, and generating a typhoon influence trace path. And then seawater carbon dioxide partial pressure grid point reconstruction is carried out, and a typhoon influence space map is constructed. And finally, analyzing space-time change of carbon dioxide flux of air-sea, evaluating carbon sink stability, and generating a typhoon-carbon sink sensitivity zoning map. Seawater carbon dioxide partial pressure is used as a core index, limitation of a traditional method is overcome, and reliable technical support is provided for typhoon marine environment influence assessment.

The invention provides a low-carbon modular artificial wetland system and a carbon sink collaborative purification method thereof, and relates to the technical field of artificial wetlands, the low-carbon modular artificial wetland system comprises a modular box body, and a water inlet layer, a carbon sink layer and a purification layer which are sequentially arranged in the modular box body from top to bottom; the water inlet layer is used for filtering suspended matters through coarse sand; the carbon sequestration layer is used for adsorbing carbon dioxide through biochar and carbon sequestration microorganisms, and meanwhile, the carbon sequestration capacity is enhanced through the plant-microorganism synergistic effect; the purification layer is used for purifying a water body through an iron-based phosphorus removal material and a zeolite denitrification filler. The technical problems of low-carbon material selection, carbon sink potential excavation, renewable energy source coupling and greenhouse gas emission inhibition of an existing constructed wetland system are solved, the technical effects of efficient sewage purification and carbon emission reduction are achieved, and a carbon emission reduction closed loop of the whole life cycle is achieved.

The invention relates to the technical field of digital asset management, in particular to a block chain-based carbon asset full-life-cycle traceability and digital asset management platform. According to the platform, multiple data modeling and intelligent contract mechanisms are fused, and credible management of carbon assets in the whole process from generation, accounting, registration, transaction to auditing is achieved. The method comprises the following steps: collecting operation data of a forestry carbon sink project, constructing a structured Hash fingerprint, generating index root Hash by adopting a Merkle tree, and calculating a carbon sink amount as a basic data source of carbon assets; accounting the carbon sink credibility based on a Bayesian network model and eliminating abnormal data; carrying out anti-fact intervention analysis by utilizing a causal map, and screening abnormal assets with low causal consistency; on-chain matchmaking transaction of the carbon assets and the users is completed through a clustering and greedy matching mechanism; a life cycle auditing module is combined to evaluate responsibility contribution of key behavior variables to ESG indexes, and quantifiable, traceable and verifiable green asset full life cycle management is achieved.

The invention relates to the technical field of inversion systems, in particular to a coastal wetland forest ecosystem biomass and carbon sink rapid inversion system. According to the technical scheme, the system comprises a data acquisition and preprocessing module, an inversion module based on machine learning, a spatial heterogeneity adaptive module, a dynamic monitoring and updating module and a result visualization and output module. According to the method, a data basis is provided for accurate inversion through multi-source data fusion, and meanwhile, an inversion model combining the generative adversarial network and the long-short-term memory network realizes combination of a physical model and deep learning, so that the interpretability of the physical model is reserved, and the model performance is improved by utilizing the strong fitting capability of the deep learning; according to the invention, the adaptability of the system in a complex coastal wetland environment is improved through the spatial heterogeneity adaptive module, and the reliability of an inversion result is improved; according to the invention, real-time dynamic monitoring of biomass and carbon sink is realized through the dynamic monitoring and updating module, and rapid response of ecological system change is supported.

The invention relates to a multi-scale urban garden carbon sink improvement and ecological collaborative regulation and control system, in particular to the field of urban gardens, and aims to construct a computable and evolvable knowledge graph, deeply fuse multi-source data and domain rules and dynamically drive a space optimization decision. Static planning is upgraded into a'prediction-simulation-learning 'closed-loop process; an optimization algorithm uses a knowledge graph to perform efficient optimization, and a multi-target equalization scheme is generated; carrying out high-fidelity and uncertainty-containing long-term dynamic deduction and risk assessment on the scheme by the digital twinborn body; a simulation result feeds back a calibration knowledge rule through meta reinforcement learning, a success mode feedback knowledge base is extracted from the calibration knowledge rule, and the process realizes collaborative self-evolution of planning knowledge, an optimization model and a simulation environment, so that the accuracy, robustness and foresight of decision making are continuously improved, and the decision making efficiency is improved. And finally, multi-scale collaborative optimization of carbon sink, ecological and social services and economic cost is achieved.