66 results about "Ecological process" patented technology

The invention discloses an inland river basin intelligent ecological scheduling rehearsal method fusing a scheduling process and an ecological process. The inland river basin intelligent ecological scheduling rehearsal method comprises the steps of multi-source data acquisition and digital twinborn construction; carrying out reservoir intelligent scheduling reinforcement learning modeling; ecological process lag response modeling is carried out; spatial diffusion modeling of ecological influence; performing cross attention guided ecological response interpolation; and carrying out rehearsal and visual display on the ecological scheduling scheme. According to the method, a hydrological-ecological response modeling mechanism is introduced, and a time-space response relationship between scheduling behaviors such as water level and water volume and ecological indexes such as vegetation indexes and habitat indexes is combined, so that lagging response characteristics of an ecological process to the scheduling behaviors can be quantitatively described, and the defect that a traditional scheduling model is insufficient in ecological expression capability is overcome. A reinforcement learning algorithm is utilized to fuse multi-source data for state perception and strategy iteration, and the scheduling strategy can be dynamically adjusted according to the current hydrological situation and ecological feedback result of the watershed. Compared with static rule type scheduling, the regulation and control efficiency and ecological adaptability are remarkably improved.

The invention discloses an ecological shoreline diagnosis method and system based on hydrological-biological communication, and is applied to the technical field of water environment ecological restoration and shoreline health assessment. Comprising the following steps: dividing intertidal zones of a shoreline research area, and generating uniformly distributed sampling points in each tidal zone; extracting a plurality of indexes related to hydrological and biological connectivity, and constructing a comprehensive hydrological-biological connectivity state index through principal component analysis; dam feature data of a shoreline area are obtained, correlation analysis and collinearity screening are carried out, and key shoreline features are screened through a random forest algorithm; constructing a mechanism connectivity index based on a circuit theory; constructing and training a Bayesian network prediction model; and performing connectivity state diagnosis, attribution analysis and restoration scheme effect simulation on the target shore section by using a Bayesian network prediction model. By coupling hydrological and biological connectivity, static element evaluation is changed into dynamic ecological process diagnosis, and dominant factors are accurately positioned.

The invention provides a wetland ecological safety early warning method and system based on remote sensing analysis, and the method comprises the steps: firstly obtaining a multi-dimensional remote sensing data set containing multi-temporal imaging, spectral radiation and microwave scattering data in a target wetland region, and carrying out the interpretation to obtain an ecological process signal set; establishing a mapping relation model of the ecological process signal and the wetland ecological safety state, and obtaining a current ecological safety state description; performing cross-process correlation analysis on the ecological process signal to form an ecological process abnormal conduction chain; constructing an early warning trigger node system and generating an early warning rule; and acquiring real-time multi-dimensional remote sensing data, interpreting the real-time multi-dimensional remote sensing data, matching the interpreted real-time multi-dimensional remote sensing data with an early warning rule, and generating wetland ecological safety early warning information containing an abnormal ecological process link, an abnormal conduction path and an early warning response node. The wetland ecological safety risk can be comprehensively and accurately pre-warned, and the stability of the wetland ecological system is guaranteed.

The invention provides an ecological system service flow simulation and collaborative tradeoff relation quantification method based on a graph neural network, and relates to the technical field of ecological system service simulation and quantification. According to the method, the Huaihe River basin serves as a research area, firstly, a landscape is abstracted into a graph structure, ecological patches serve as nodes and are endowed with multi-dimensional features, ecological process flow paths serve as edges, and flow attributes are quantified; then, constructing a graph neural network model, and learning inherent rules of generation, flow and consumption of an ecological system service through training; and finally, realizing ecological system service, particularly dynamic simulation of supply service and adjustment service, and quantifying the collaborative tradeoff relation by adopting a multi-index fusion method. According to the method, the nonlinear correlation of the ecological process can be accurately captured, the flow simulation and relation quantification precision is improved, scientific support is provided for ecological planning and management of the Huaihe River basin, and compared with an InVEST model, the simulation error is reduced by 20%-30%, and the collaborative tradeoff recognition accuracy is improved by 15% or above.

The invention discloses a forest ecosystem monitoring system and method based on space-time big data, and relates to the technical field of data processing systems. The system comprises a multi-source heterogeneous data acquisition and preprocessing module, an ecological process mechanism embedded spatio-temporal feature extraction module, a self-adaptive dynamic state characterization and evolution module, an abnormal event early perception and causal traceability module and a multi-dimensional ecological function comprehensive evaluation and early warning module. The method comprises the following steps: collecting and processing multi-source heterogeneous spatio-temporal data to generate a multi-source spatio-temporal data set; extracting a multi-dimensional spatio-temporal feature vector; constructing a dynamic forest state characterization model, and generating a forest state index map; executing abnormal event early perception and causal traceability analysis; and evaluating the health state of the forest ecosystem and generating early warning information. According to the method, the characterization capability of the complex ecological process is improved, more accurate and coherent dynamic description of the ecological system state is realized, and a reliable basis is provided for ecological management and decision making.

The invention provides a multi-level agricultural ecological barrier construction and evaluation method and system, and relates to the technical field of ecological barrier construction. The method comprises the following steps: firstly, constructing a multi-objective optimization model and an agricultural ecological unit layering system, and solving based on a random generation and evolution algorithm to obtain a barrier configuration scheme considering ecological process adjustment, ecological service supply and cost control; and further constructing a structure, process and service multi-level evaluation index system of the field layer, the operation subject layer and the drainage basin or region layer, forming a structure sub-index, a process sub-index and a service sub-index through standardization and weighting calculation, and fusing the structure sub-index, the process sub-index and the service sub-index into a multi-level agricultural ecological barrier comprehensive index. Key configuration parameters are identified through sensitivity analysis, parameter boundaries are adjusted, a search strategy is optimized, iteration updating is conducted on the multi-target optimization model, and an agro-ecological barrier configuration scheme with the maximum comprehensive benefit and the reasonable spatial pattern and a quantitative evaluation result are obtained.

The invention belongs to ecological environment engineering, and particularly relates to a river and lake wetland zoning and classifying ecological water demand fine accounting method, which comprises the following steps of S1, performing zoning and classifying on a river and lake wetland of a research area; s2, establishing a space-time coupling hydrodynamic model based on a partition classification relationship; s3, constructing a multi-factor ecological response model, and calculating an ecological suitability index of each partition; s4, establishing a multi-objective optimization model, and performing optimization solution on the ecological water demand of the partition by combining ecological benefits, water balance and process smoothness objectives; and S5, performing robust correction on an optimization result by adopting an uncertainty analysis method to obtain a final ecological water demand distribution result. By establishing a river and lake wetland partition classification spatial database based on digital elevation, river network distribution, land utilization and ecological attribute information, fine description and dynamic association of different ecological functional areas are realized, and the effects of ecological process differentiation characterization and remarkable improvement of water demand accounting precision are obtained.

The invention discloses a river ecology small sample enhancement method based on a generative diffusion model, and the method comprises the steps: obtaining observation data in an ecological environment field, and carrying out the data preprocessing; performing time variation diffusion modeling by adopting a generative diffusion model architecture; constructing an ecological constraint function, and solving an optimal solution curve; carrying out data sampling, and outputting ecological time sequence data; and performing data simulation verification and prediction analysis. By introducing a time variation diffusion process and an ecological process modeling constraint mechanism, high-precision interpolation and long-period trend deduction of ecological variables are realized while the model generation capability is maintained.

The present application relates to the technical field of ecological environment, in particular to a water ecological system evaluation system and method based on a water habitat coupling mechanism model. The technical problem is to integrate key ecological mechanisms while maintaining high computational efficiency. The system includes: an input layer that receives measured data related to the water environment of the target water habitat; a configuration layer that selects several indicators from the geographic indicators, hydrological indicators, hydrodynamic indicators and meteorological indicators input by the input layer, and configures multiple different environmental factors; key ecological processes are integrated through selective mechanism coupling, and interspecific competition factors are configured for algae and biological interaction factors are configured for fish; the simulation layer uses a differential evolution algorithm to automatically calibrate and optimize the parameters configured by the configuration layer; obtain the spatial and temporal changes of aquatic plants, algae and fish; the output layer outputs the spatial distribution results of the three species of aquatic plants, algae and fish, and provides suggestions for scenario analysis results and environmental economic balance decision-making.

The application provides a kind of microbial population evolution monitoring method and device based on magnetic field intensity regulation, related to microbial monitoring field, through multi-source sensor real-time collection microbial community, methane production, magnetic field parameter and environmental working condition data, construct supervision data set, establish dynamics model containing magnetic field influence function, and adopt supervision learning to carry out parameter online identification and rolling prediction, finally through optimization algorithm real-time adjustment magnetic field parameter, realize the closed-loop optimization control of microbial permeability process.The application adopts the above-mentioned microbial population evolution monitoring method and device based on magnetic field intensity regulation, has the advantages of strong real-time, high interpretability, good engineering implementability, etc., is suitable for coal seam microbial permeability, coalbed gas production optimization and mine ecological process evaluation and other application scenarios.

The application discloses a natural ecosystem authenticity determination method and device, relates to the technical field of ecological evaluation, and comprises the following steps: acquiring land use data of a research area, and calculating an ecological structure authenticity index; identifying a natural reference area based on ecological environment zoning and the ecological structure authenticity index; acquiring ecosystem service data, and calculating an ecological process authenticity index and an ecological function authenticity index based on the natural reference area; acquiring species distribution data, and calculating a biological and indicator species authenticity index; acquiring human activity data, and calculating a human disturbance intensity index; and finally, normalizing and weightedly fusing the indexes to obtain a natural ecosystem authenticity comprehensive index. The application solves the problems of single dimension, neglect of regional natural benchmark differences and lack of a multi-factor comprehensive framework of the existing evaluation method, and significantly improves the scientific nature of natural reserve management.

The application relates to the technical field of marine ecological monitoring, in particular to a marine ecological dynamic prediction method and system based on multi-modal remote sensing and a space-time graph. The method comprises the following steps: performing multi-modal remote sensing feature depth fusion according to obtained remote sensing image data, wherein the multi-modal remote sensing feature depth fusion comprises multi-modal feature map extraction and space embedding, local cross-modal feature interaction and alignment, and global context aggregation and node embedding generation; constructing a space-time ecological process reasoning network STEP-Net based on coupled space diffusion and time inertia, wherein the space-time ecological process reasoning network STEP-Net comprises multi-order space diffusion field modeling and ecological state transition unit construction; and utilizing deep space-time representation generated by the STEP-Net to perform future-oriented and uncertain trajectory prediction and cause and effect attribution based on deep Taylor decomposition. The application constructs a brand-new technical framework integrating fine fusion, mechanism reasoning and interpretable prediction around the precise prediction and scientific early warning demand of marine ecological system dynamic evolution.

The invention discloses a space-time pattern prediction method based on forest carbon sink climate change. The method comprises the following steps: collecting vegetation data, soil data, meteorological data, topographic data and special interference factor data of a target forest area; preprocessing the collected multi-source data, and performing space-time matching on the preprocessed data; quantifying a climate index of the target forest region; taking a Biome-BGC ecological process model as a core, and integrating a graph neural network to construct a carbon sink-climate index-terrain coupling model; setting various climatic change scenes, and predicting a carbon sink spatial and temporal distribution pattern under different scenes based on a carbon sink-climatic index-terrain coupling model. The space-time pattern prediction method based on the forest carbon sink climate change is adopted, and the prediction precision is improved.

The invention relates to the technical field of remote sensing monitoring and ecological evaluation, in particular to a karst rocky desertification evolution identification method fusing an ecological process. The method comprises the following steps: acquiring a suitable detection height of a detection area according to a proportion condition of pure pixels in an image acquired by remote sensing at different heights and an overall pixel purity index; the method comprises the following steps: determining a rocky desertification comprehensive evolution index based on remote sensing data, analyzing a rocky desertification change degree, establishing a prediction model, and judging the influence degree of different driving factors on the current rocky desertification evolution process according to the difference between a prediction result and an actual result and the influence caused by the change of the different driving factors. And finally, generating a stony desertification evolution diagram and a stony desertification risk zoning diagram. According to the method, stony desertification static monitoring in the past is converted into dynamic evolution analysis, and deep understanding of stony desertification evolution trend from representation to mechanism is realized.

The application provides a bionic colon micro-ecological monitoring and adjusting system and method based on an electronic nose, relates to the field of in-vitro intestinal fermentation simulation and micro-ecological process control, and comprises the following steps: a bionic colon culture module provides an anaerobic continuous flow fermentation environment for intestinal flora and generates headspace gas reflecting the metabolic state of the flora; a gas sensing module outputs a multi-dimensional response signal after pre-treating the headspace gas; a processing module performs compensation processing and feature extraction on the multi-dimensional response signal, compares the feature representation with a steady-state odor fingerprint reference model, obtains the deviation degree and drift direction of the current micro-ecological state, and generates a differential regulation instruction; and a regulation execution module adjusts the substrate supply amount, liquid dilution rate and pH of the bionic colon culture module respectively according to the differential regulation instruction. The application realizes long-term online monitoring and self-adaptive adjustment of the micro-ecological state in the continuous flow bionic colon culture process.

The invention discloses an unmanned aerial vehicle multispectral monitoring method for grassland fragmentation dynamic analysis, and relates to the technical field of grassland ecosystem monitoring, and the unmanned aerial vehicle multispectral monitoring method for grassland fragmentation dynamic analysis comprises the steps: collecting standardized space-time sequence data through an unmanned aerial vehicle multispectral system; processing the data to generate a land cover distribution map and a three-dimensional model; quantifying landscape pattern and soil erosion indexes in a two-dimensional space and a three-dimensional space based on the model; the quantitative indexes are coupled with the ecological process model, and influences on species exchange, soil conservation and carbon storage functions are evaluated; and finally, trend analysis and visual expression are carried out. According to the method, the technical problems of low efficiency, insufficient satellite remote sensing resolution, difficulty in three-dimensional monitoring and incapability of quantitatively associating pattern change with ecological function influence of a traditional method are solved, so that efficient, accurate and three-dimensional quantification of grassland fragmentation dynamics is realized, and the ecological consequence of the grassland fragmentation dynamics is scientifically evaluated.

The invention discloses a multi-parameter comprehensive evaluation method for ecological health of underground water, relates to the technical field of water resource protection and ecological environment evaluation, and aims at collecting multi-source data such as water quality, hydrology, ecology, humanistic society and the like through a sensor network and comprehensively covering key elements of an ecological system. Constructing a model framework coupled with an ecological-hydrological process, fusing an ecological process model and a groundwater dynamic model, and simulating interaction between the ecological process model and the groundwater dynamic model; a pollution source-water body-organism complex relation is mined by applying an underground water ecological cognition map and a map neural network, an inference rule is optimized by combining a dynamic inference engine of autonomous learning, and high accuracy and strong adaptability of an evaluation model are realized. Finally, according to the reasoning result, management decision suggestions such as pollution prevention and control, water resource scheduling and ecological restoration are output in a graded mode. A scientific, reliable and operable technical scheme is provided for underground water resource protection and environment management, and the method has important application value.

The application provides a forestry carbon sink project full life cycle measurement method and system based on digital twinning, relates to forestry carbon sink measurement, and comprises the following steps: acquiring multi-source heterogeneous basic data such as forest three-dimensional skeleton topology, carbon dioxide flux time sequence, mycorrhizal network distribution and meteorological data, coupling forest digital gene and ecological process tensor to generate a digital gene tensor field; then obtaining a double-path carbon flux field of dominant conduit transmission and implicit mycorrhizal transfer through carbon transmission inversion driven by a conduit bundle network; constructing a carbon sink phase change field based on a stability boundary constraint by analyzing a carbon sink phase change critical point; obtaining a carbon re-allocation map regulated by a biological network through a cross-scale carbon re-allocation path deduction; and finally outputting an optimized and adjusted carbon sink evaluation value through full life cycle carbon sink entropy optimization measurement processing. The application realizes the transformation from static estimation to dynamic simulation of carbon sink measurement, and significantly improves the accuracy and reliability of full life cycle evaluation of a carbon sink project.

The invention provides a fine simulation method and system for total primary productivity of tree species in the technical field of ecological remote sensing and carbon cycle simulation. The method comprises the following steps: S1, acquiring leaf hyperspectral reflectivity data, photosynthetic physiological parameter data and canopy hyperspectral remote sensing image data of different tree species in a target area; s2, extracting characteristic wave bands based on the maximum carboxylation rate in the hyperspectral reflectivity data of the leaves and the photosynthetic physiological parameter data, constructing a characteristic wave band combination, and constructing a Vcmax hyperspectral inversion model based on the characteristic wave band combination; step S3, tree species classification is carried out by using the canopy hyperspectral remote sensing image data, and a dynamic Vcmax distribution diagram is generated in combination with the Vcmax hyperspectral inversion model; and S4, replacing static Vcmax parameters of the ecological process model based on the dynamic Vcmax distribution diagram, and inputting driving data into the ecological process model to simulate a GPP space-time pattern. The method has the advantages that the accuracy and practicability of tree species total primary productivity simulation are greatly improved.

The invention relates to the technical field of machine learning, in particular to an urban green land NDVI prediction method and system based on machine learning, and the method comprises the steps: obtaining the multi-dimensional state data of a target green land; based on the multi-dimensional state data, constructing a vegetation ecological process prediction network corresponding to the target green land; the vegetation ecological process prediction network at least comprises a photosynthesis prediction sub-network, a respiration consumption prediction sub-network and a stress suppression prediction sub-network; and performing NDVI prediction on the target green land based on the vegetation ecological process prediction network to obtain an NDVI prediction value, thereby realizing NDVI prediction based on the ecological process, and improving reliability and interpretability of NDVI prediction.

The invention discloses a Bayesian inference-based high-temperature stress vegetation stomata adjustment modeling system and method, and relates to the technical field of ecological process simulation. A data acquisition module is used for acquiring and preprocessing meteorological driving data; the stomatal conductance calculation module is used for calculating basic stomatal conductance based on a Jarvis stomatal conductance empirical model; the high-temperature stress correction module is used for correcting Rubisco enzyme related parameters based on a temperature power function and constructing a high-temperature stress stomatal conductance correction function through a Logistic function; the correction parameter inversion module is used for inverting optimal parameters in combination with the actually measured carbon water flux data, the Bayesian theorem and the Monte Carlo-Markov chain algorithm; and the carbon-water flux simulation module is used for substituting the optimal parameters into a high-temperature stress stomatal conductance correction function to perform high-temperature stress correction on the basic stomatal conductance, and after the corrected stomatal conductance is obtained, the corrected stomatal conductance is coupled to the ecological process model to simulate the carbon-water flux process. The simulation precision of the model at extreme high temperature can be improved.

This invention discloses a comprehensive monitoring method and system for the ecological health of garden wetlands based on hyperspectral imaging, belonging to the field of ecological environment remote sensing monitoring technology. It acquires hyperspectral image data and corresponding climate data of the target garden wetland at multiple temporal phases, extracts ecological characterization features of vegetation, water bodies, and soil, and accumulates and converts the climate data into a climate-driven sequence. Based on the climate-driven sequence, it determines the boundary points of ecological response stages, constructs an ecological process scale, and maps multi-temporal observation data from calendar time to the ecological process scale. Then, it performs non-uniform rearrangement and collaborative correction on the initial ecological process sequence to obtain a stable ecological response feature sequence. Based on this, it extracts change trajectories, identifies abnormal change segments, and generates ecological health status evaluation results and early warning information. This invention can reduce misjudgments caused by phenological shifts.

The present application provides a kind of wetland ecological security early warning method and system based on remote sensing analysis, first, obtain the multi-dimensional remote sensing data set of target wetland area including multi-temporal imaging, spectral radiation and microwave scattering data, and obtain the ecological process signal set by interpretation;Establish the mapping relationship model of ecological process signal and wetland ecological security state, obtain the current ecological security state description;Cross-process correlation analysis is carried out on the ecological process signal, and the abnormal conduction chain of ecological process is formed;Construct early warning trigger node system and generate early warning rules;Real-time multi-dimensional remote sensing data is obtained, and after interpretation, it is matched with early warning rules, and wetland ecological security early warning information including abnormal ecological process link, abnormal conduction path and early warning response node is generated.The present application can accurately and comprehensively early warn wetland ecological security risk, and protect the stability of wetland ecosystem.

The invention discloses a gradient moisture regulation-based salinization wetland vegetation restoration method, which comprises the following steps of: firstly, dividing a degraded wetland into three stages of water subareas, namely a low water subarea, a middle water subarea and a high water subarea, according to a moisture gradient, and formulating a vegetation restoration scheme according to the moisture subareas; a water supplementing measure is made based on the quantitative relation between the soil water content and the biomass on the vegetation land, and the soil is improved through salt-nutrient coordinated regulation and control. According to the method, hydrological engineering and ecological process regulation and control are creatively fused, a differentiated water replenishing mode is broken through, soil moisture increasing and salt restraining synergy is achieved, a replicable recovery normal form is provided for the semi-arid wetland, and a quantifiable natural solution is also provided for coping with land desertification and climate change.

The invention discloses a functional microbial agent for near-natural recovery of moderately-degraded high-cold grassland as well as a preparation method and application of the functional microbial agent. The composite bacterial agent is prepared by mixing bacterial culture solutions of four bacterial strains, namely bacillus pumilus E-B-2-B05 (CGMCC No. 21387), paenibacillus silage P-B-2-B03 (CGMCC No. 21386), bacillus subtilis P-B-4-SCB23 (CGMCC No. 27084) and bacillus velezensis K-B-5-SCB13 (CGMCC No. 27088) according to the volume ratio of 3: 2.5: 2: 2.5. The invention further discloses a preparation method of the composite bacterial agent. Through the synergistic effect of multiple strains, the complex microbial inoculant can effectively promote plant growth, enhance rhizosphere microbial activity and improve the ecological process of soil, can effectively improve grassland biomass, optimize a plant community structure, enhance competitive advantages of high-quality pasture, and promote moderately-degraded alpine grassland vegetation to recover to a near-natural state. The microbial agent is simple in preparation method, convenient and flexible to apply and environment-friendly, and has good ecological and economic benefits.

The invention discloses a territorial spatial pattern optimization method and system based on ecological process driving, and relates to the technical field of territorial spatial pattern optimization and ecological process analysis, and the method comprises the steps: carrying out the rasterization of a research region, extracting the factors of terrain, land utilization, vegetation, hydrology and the like, forming an attribute vector, and generating an ecological source region and control boundary expression; constructing a comprehensive ecological resistance surface, and outputting resistance accumulation, path frequency and flux fields; mapping and aggregating the process fields into ecological process indexes according to planning units; constructing a planning unit attribute table and recording purposes and costs; establishing a purpose selection variable and setting a unique constraint; and iteratively solving to obtain final use configuration, and writing the final use configuration into the planning unit and the grid unit to generate territorial space pattern data. According to the method, the ecological process is taken as a core drive, and through fine-scale process simulation and planning unit optimization coupling, ecological connectivity improvement, use conflict reduction and more reasonable layout of a spatial pattern are realized.

The present application relates to the field of ecological hydrology, and particularly relates to a kind of urban green space ecosystem evapotranspiration spatiotemporal variation characteristic analysis method, according to different vegetation, different community and different regional scale, adopt field survey, positioning observation, mobile observation and model simulation method, carry out the ecological hydrological process analysis of urban green space ecosystem atmosphere-vegetation-soil.The present application reveals the dominant factor that influences the evapotranspiration and ecological process of green water ecological system in the region by analyzing the spatiotemporal variation characteristic of evapotranspiration and ecological process of green space ecosystem in different spatiotemporal scale in city, clarifies the response mechanism of evapotranspiration and ecological process of urban green space ecosystem by coupling each factor of evapotranspiration and ecological process of urban green space ecosystem, so as to provide data reference for urban green space management planning and sponge city construction.

The application discloses a kind of plant evaluation methods for water area surrounding green land: S1, the dust-retention capacity parameter of candidate plant and the rain nutrient enrichment parameter of penetration are acquired;S2, plants are divided into two categories of high / low dust-retention capacity, while calculating nutrient flux enrichment multiple, and divided into two categories of high / low leaching risk according to threshold value;S3, construct two-dimensional evaluation matrix, and plants are divided into corresponding risk quadrant.The method includes two ecological processes of dust-retention and leaching into a unified quantitative framework, for the first time reveals the function coordination and trade-off relationship between air purification and water environment protection, without relying on fixed threshold value, through clustering analysis and environmental benchmark linkage to realize the dynamic division of risk quadrant, provides reproducible, migratory scientific evaluation tool for water area surrounding green land plant screening.

The application discloses a high-temperature stress vegetation stomatal regulation modeling system and method based on Bayesian inference, and relates to the technical field of ecological process simulation, wherein: a data acquisition module is used for collecting and preprocessing meteorological driving data; a stomatal conductance calculation module is used for calculating basic stomatal conductance based on a Jarvis stomatal conductance empirical model; a high-temperature stress correction module is used for correcting Rubisco enzyme related parameters based on a temperature power function, and constructing a high-temperature stress stomatal conductance correction function through a Logistic function; a correction parameter inversion module is used for inversing optimal parameters in combination with measured carbon flux data, Bayesian theorem and a Monte Carlo-Markov chain algorithm; and a carbon flux simulation module is used for substituting the optimal parameters into the high-temperature stress stomatal conductance correction function to correct the basic stomatal conductance under high-temperature stress, obtaining corrected stomatal conductance, and then coupling to an ecological process model to simulate a carbon flux process. The application can improve the simulation accuracy of the model under extreme high temperature.

The invention discloses an underground water ecological threshold judgment method based on soil salinization prevention and control. The method comprises the following steps: acquiring data; respectively determining underground water ecological thresholds by adopting a field measurement method, a dynamic monitoring data deduction method, an ecological process analysis method and a remote sensing interpretation model method; and analyzing the ecological threshold values obtained by each method, determining recommended values of the ecological threshold values, and generating a spatial distribution diagram of the underground water level buried depth ecological threshold value and the underground water TDS ecological threshold value for preventing soil salinization. Based on field measurement, dynamic monitoring data deduction, ecological process analysis and a remote sensing interpretation model, the underground water ecological threshold is scientifically judged from different dimensions, cross validation and comprehensive analysis are carried out on results obtained by multiple methods, the recommendation threshold is finally determined, the limitation of traditional single index or static evaluation is overcome, and the method is suitable for popularization and application. And the comprehensiveness, accuracy and reliability of underground water ecological threshold judgment are ensured.