306 results about "Environmental factor" patented technology

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 garden landscape plant health assessment method, and belongs to the technical field of resource assessment. Comprising the following steps: step 1, constructing a time sequence database of environmental factors and plant growth states; step 2, according to different plant species and growth stages thereof, dynamically calculating and generating a health reference value adapted to the current environmental condition; step 3, in the optimal evaluation time window, performing preliminary evaluation on the plant health condition, extracting original health indexes, and performing correction in combination with an environment disturbance compensation factor; step 4, performing double comparative analysis on the corrected health assessment result, eliminating false anomalies caused by environmental fluctuation, and calculating an anomaly confirmation credibility index; step 5, grading the plant health conditions, visually displaying the plant health conditions in a thermodynamic diagram form, and marking the environmental interference degree; and step 6, based on an evaluation result, automatically generating a targeted maintenance suggestion, and designing a corresponding maintenance effect verification scheme.

The invention discloses a machine learning-based soil pollution risk assessment method and a machine learning-based soil pollution risk assessment device, and belongs to the technical field of soil pollution risk assessment. The split biological effect and chemical indexes in traditional evaluation are converted into unified risk driving force measurement. A nonlinear interaction entropy calculation mechanism can adaptively capture an antagonism-collaborative balance relationship between microbial communities and pollutants in a soil environment, so that the degradation potential of functional flora on specific pollutants is reflected, and the dynamic regulation effect of environmental factor fluctuation on bioavailability is quantified. According to the cross-scale feature fusion technology, the evaluation model not only can identify the current pollution space distribution, but also can pre-judge the morphological transformation trend of pollutants in biogeochemical circulation, and provides a quantitative basis with ecological interpretation for risk early warning.

The invention discloses a microplastic transport simulation and risk identification method based on multi-factor coupling, which comprises the following steps: by coupling a hydrodynamic model and a microplastic transport model, comprehensively considering multiple environmental factors such as tide, water level, runoff, wind speed, wind direction and the like, constructing a wave flow coupling microplastic migration and diffusion model suitable for complex hydrodynamic conditions of a Pearl River estuary; the model is calibrated and verified through measured data, and the precision of the model is evaluated by using indexes such as a Nash efficiency coefficient and a root-mean-square error; in combination with the simulation result and the spatial distribution characteristics of the typical sensitive area, representative sections and stations are selected, and annual-scale micro-plastic concentration change analysis is carried out; and further introducing an ecological risk index to carry out regional ecological risk grade identification, and determining a micro-plastic high-risk area and influence main control factors. The method has high regional adaptability and expansibility, and scientific support and technical reference can be provided for prevention and control of microplastic pollution of estuary and coastal water.

The invention relates to the cross technical field of artificial intelligence and environment monitoring, and discloses an algal community structure change prediction algorithm and system based on multi-source data fusion, and the algorithm comprises the steps: obtaining water quality, weather and plankton multi-source time sequence data; performing time alignment and missing value interpolation; eliminating and screening key environment factors through recursive features; performing dynamic weighted fusion on the multi-modal features by using a space-time attention mechanism; inputting a three-layer stacked LSTM network to output future algae dominant species abundance prediction; and model parameters are corrected on line based on measured data. The system comprises a multi-source data acquisition module, a preprocessing module, a key factor extraction module, a space-time attention fusion module, a dynamic prediction module and an adaptive correction module. According to the method, the prediction accuracy and stability are remarkably improved, and algal bloom early warning and ecological regulation are effectively supported.

Systems and methods for determining information representing an amount of an indicator of a soil trait based on a machine learning engine, and systems and methods for training the machine learning engine are described. An example method includes obtaining values of a plurality of environmental factors relating to a location, and determining the information representing the amount of the indicator by inputting the values of the plurality of environmental factors into a machine learning engine. The machine learning engine is trained using sample datasets each of which includes values of at least a portion of the plurality of environmental factors of a sample site and information representing an amount of the indicator of the sample site.

The invention relates to the technical field of remote sensing imaging, in particular to a marine organism monitoring method and system based on a remote sensing technology, and the method comprises the following steps: constructing an environment layer based on a remote sensing image and sensing data, analyzing the relation between a spectrum and an environment, monitoring changes, recognizing the spatial distribution of species, extracting a migration path, and superposing environment factors to analyze ecological response. And outputting a remote sensing indication species ecological response interaction layer. According to the method, the remote sensing image and environmental data are sorted and denoised, multi-dimensional factors are effectively integrated, data precision is enhanced, noise interference is eliminated, spectral change and water temperature gradient deviation are combined, abnormal pixels are accurately screened, sea area change trend is insight in advance, key wave bands and hot salt structures are extracted, target organisms are accurately positioned, and identification efficiency is improved. Analyzing a space offset track, restoring a migration path, constructing a dynamic evolution view angle, superposing water temperature and algae changes, analyzing time sequence synchronization characteristics, revealing ecological response turning points, and strengthening early warning and dynamic monitoring capabilities.

The invention relates to the technical field of microbial ecological information processing, and discloses a microbiome functional activity prediction method and system, and the method comprises the steps: collecting dynamic environment factor data in soil in real time through a sensor, and carrying out the preprocessing of the dynamic environment factor data; establishing a partial differential equation model based on the preprocessed data; the partial differential equation model comprises a diffusion term, a reaction term and an environmental factor coupling term; optimizing the differential equation model parameters by using a neural network, and carrying out transfer learning training on the partial differential equation model parameters based on historical data; and predicting the activity grade and function of the microorganisms on the basis of the optimized model for the functional activity of the microorganisms. The adaptability and generalization ability of the model in different soil areas are remarkably improved, and closed-loop processing from function activity numerical value prediction to function classification output is achieved.

The invention provides a seaweed farm ecosystem prediction method, system, device and medium, and belongs to the technical field of seaweed farms, and the method specifically comprises the steps: collecting basic data of a seaweed farm ecosystem, and forming an ecological data set; preprocessing the ecological data set; determining a seaweed field prediction index, using a DMD mode to decompose and extract a time-delay main mode, then using convergence cross mapping to quantify causal intensity, constructing an environmental factor and biological index space-time diagram, and learning a causal edge weight through a graph attention network; predicting index change in combination with the incidence relation, and performing hybrid prediction by adopting a pulse disturbance-recovery coupling model and digital twinborn reinforcement learning; and visually outputting the prediction result. According to the method, a seaweed field ecological system monitoring and predicting system of data, information, knowledge and decision is constructed, the ecological system state characterization capability and risk early warning perspectiveness are improved, and scientific and efficient technical support is provided for marine ecological protection and management.

The invention belongs to the technical field of environment regulation and control, and discloses a livestock breeding environment dynamic prediction and regulation and control method based on machine learning. Comprising the following six key steps: firstly, preprocessing multi-source data, and constructing a standardized feature data set; secondly, performing time sequence feature extraction and environment-biological response correlation analysis to obtain a multi-factor interaction network; then, constructing a dynamic coupling model of the environmental parameters and the production efficiency; a multi-target reinforcement learning regulation and control model is constructed based on the animal husbandry production efficiency prediction space; predictive environmental parameter regulation and risk assessment are carried out in combination with real-time monitoring data; and finally, intelligent environment intervention is executed, and a regulation and control strategy is iteratively optimized through production index feedback. According to the method, the breeding environment is converted from passive response to active prediction, complex correlation understanding between environmental factors and biological indexes is established, and multi-target balance optimization of production efficiency, energy consumption and animal welfare is achieved.

The invention relates to the technical field of marine ecological environment monitoring and data analysis, and particularly discloses a sea area phytoplankton biodiversity index prediction method and system based on multi-model integration and feature engineering. After preprocessing, constructing four groups of nonlinear interaction characteristics of a temperature-salt relationship, oxygen-salt balance, chlorophyll chemical oxygen demand coupling and a nitrogen-phosphorus ratio based on environmental factors, combining station characteristics with basic environment and interaction characteristics, carrying out variance threshold screening, inputting a characteristic set into a multi-model integration framework containing models such as linear regression and gradient lifting, and carrying out multi-model integration; training and tuning according to a time sequence segmentation strategy, selecting model output according to a decision coefficient, using a result if the decision coefficient of the support vector regression model is within a preset range, and otherwise, taking a gradient lifting and extreme gradient lifting tree model to predict a mean value. And the prediction accuracy and the model generalization, stability and reliability are improved.

The invention provides a method for determining a threshold value of functional traits of a river benthonic animal community to nutrition stress, relates to the technical field of benthonic animal community ecology, and comprises the method for determining the threshold value of the functional traits of the river benthonic animal community to the nutrition stress. A method for comprehensively and deeply understanding the relationship between the benthonic animal community and the environmental factors is also provided, and a powerful scientific basis is provided for health management and protection of a river ecosystem; a random forest model is adopted, a plurality of nutrition indexes and environmental factors can be considered at the same time, main driving factors are identified through feature importance scores, and action intensities of different factors are determined; by calculating functional diversity indexes and constructing a site * character abundance matrix, specific functions of the benthonic animals in an ecological system are comprehensively reflected, and traditional species richness and diversity indexes are exceeded.

The invention belongs to but is not limited to the technical field of ecological environmental protection, and discloses a river fish habitat connectivity assessment method, which comprises the following steps: collecting historical site data of target river fish and bioclimatic data influencing river fish distribution to establish a variable set; the method comprises the following steps: screening variables without collinearity by utilizing VIF, constructing a species distribution model of fishes in a target river by utilizing a machine learning algorithm, and screening environmental factors with relatively high contribution degree of the model; according to a machine learning sub-model selected by AUC and TSS, establishing a set model and simulating HS of target river fish habitat suitability distribution; dam information of the target evaluation drainage basin is collected, and a tree connectivity index (DCI) is calculated; stacking the HS and the DCI, calculating a habitat connectivity index (FRCI) of the fish in the target river, and evaluating the habitat connectivity. According to the method, the habitat suitability, the structure connectivity and the function connectivity are considered, the credibility and the preciseness of evaluation of the habitat connectivity are improved, and a scientific basis and a practical tool are provided for protection and management of a river ecosystem.

The invention discloses a soil fertilizer nutrition customization regulation and control method and system. The system comprises a data acquisition module, an environment compensation module, a soil nutrient dynamic prediction module, a target demand management module, a multi-target optimization module, a safety evaluation module, a monitoring self-adaptive learning module and the like, soil nutrition and safety indexes and external environment factors are acquired, data are corrected by using a compensation function, nutrient changes are predicted, a target range is set according to different growth stages of crops, and a monitoring self-adaptive learning module is established. A multi-objective optimization model is established based on organic fertilizer contribution and missing nutrients, an optimal formula is generated by comprehensively considering crop requirements, cost and environmental risks, and the model is dynamically updated through continuous monitoring and feedback closed loop after fertilization. According to the method, the fertilizer utilization rate and the fertilization precision can be remarkably improved, the soil acidification and heavy metal risks are reduced, and the balance of yield, quality and ecological safety is achieved.

The invention discloses a method for monitoring diversity of large benthic invertebrates in a river based on eDNA. The method comprises the following steps: step 1, selecting a river basin to be measured, and querying basic information of the basin; 2, collection and treatment of eDNA samples: designing sampling points and geographic positions, and collection and pretreatment of water samples; 3, measuring physical and chemical indexes; 4, extraction of eDNA, PCR amplification and high-throughput sequencing are carried out; and 5, performing data analysis on the results, wherein the data analysis comprises bioinformatics analysis, alpha diversity analysis, beta diversity analysis and correlation analysis of large benthic invertebrate communities and environmental factors. The diversity of the large benthic invertebrates is more accurately and efficiently investigated and monitored by analyzing DNA fragments existing in the environment.

The invention provides a method and a system for evaluating the biological health condition of cultivated land soil based on exploratory factor analysis, and relates to the technical field of evaluation of the biological health condition of soil. The precise analysis of the complex interaction relationship between the soil microorganism function and the environmental factor is realized; through multi-dimensional index fusion and deep mining of a factor model, key biological function factors are effectively discriminated and quantified, and the scientific evaluation capability of soil ecological functions is improved; in combination with causal path analysis, the scheme can reveal the influence of environmental pressure on a microbial regulation and control mechanism, and intelligent attribution and traceability of abnormal health conditions are realized; based on dynamic fusion and grading judgment of function and environment scores, the scheme supports accurate health condition evaluation and hierarchical intervention, the early warning capability and decision scientificity of soil management are enhanced, and the agricultural sustainable development guarantee level is remarkably improved.

The invention relates to a river ecosystem planktonic bacteria environment threshold identification method, and belongs to the field of environmental ecology and computational biology. The method comprises the following steps: collecting a plurality of environmental factors, fusing and constructing a space-time aligned watershed ecosystem data set, and preprocessing the data set; constructing a prediction model and performing training; environmental factors are collected from a target drainage basin and input into the trained prediction model, and a nonlinear mapping relation between the environmental factors and the microbial community, namely an SHAP value, is obtained through learning; constructing a GAM response curve based on the SHAP values and the environmental factors, fitting a nonlinear response trend of the GAM response curve through a spline function, and identifying an environmental threshold corresponding to each environmental factor; through a Bootstrap resampling method, multiple times of environment threshold recognition are carried out on each environment factor, and a confidence interval is estimated based on environment thresholds obtained through multiple times of recognition, so that the stability and reliability of environment threshold recognition are improved.

The invention discloses a multi-factor coupling ganoderma lucidum environment digital twinborn regulation and control method oriented to high-yield requirements, particularly relates to the technical field of intelligent cultivation of edible mushrooms, and is used for solving the problems of equipment action oscillation and recessive physiological stress caused by physiological response hysteresis of ganoderma lucidum in an existing environment regulation and control system. Carbon dioxide concentration and illumination intensity predicted values are generated in real time by constructing ganoderma lucidum growth digital twins, and an oxidative stress mark is activated by combining hypha respiratory entropy matching degree analysis and an active oxygen accumulation curve; when the matching degree exceeds a threshold value and oxidative stress exists, a metabolic overload signal is generated, and whether the carbon dioxide predicted value is in a physiological hysteresis interval or not is judged; delay compensation and metabolic overload suppression coefficient adjustment are performed on the carbon dioxide predicted value in the delay interval; and finally, inputting the compensated carbon dioxide value and the illumination prediction value into an environment factor coordination rule base, generating a ventilation opening degree and light supplementing power instruction to drive execution equipment, and ensuring the morphological development stability of the sporocarp and the synthesis efficiency of the active component.

The invention discloses a vegetation index time sequence prediction method and device based on deep learning, and the method comprises the following steps: obtaining and processing historical period vegetation indexes and environmental factor data of a preset time period, and obtaining NDVI, EVI and kNDVI data as vegetation indexes, environment factor data including temperature, rainfall, evapotranspiration, wind speed, relative humidity and soil humidity; by determining the intensity and direction of the linear relation between the environmental factors and the vegetation indexes, the environmental factors with high correlation with the vegetation indexes are selected; a CNN-BiLSTM-AM model comprising an input layer, a CNN layer, a BiLSTM layer, an AM layer and an output layer is constructed to predict a vegetation index time sequence, environmental factors with high correlation serve as input data, and NDVI, EVI and kNDVI vegetation indexes serve as output prediction targets. The technical process is simple and clear, and the accuracy of vegetation index time sequence prediction is greatly improved.

The invention relates to a multi-scale ecological system carbon metering method, a metering model establishing method and system, equipment and a medium, and belongs to the technical field of ecological environment monitoring. The establishment method comprises the following steps: collecting continuous data of the carbon flux of the net ecosystem with different scales, and corresponding environmental factors and spatial heterogeneity parameters; performing data preprocessing on the continuous data of the carbon flux of the net ecosystem with different scales; performing feature construction on the continuous data of the carbon flux of the net ecosystem with different scales after data processing, the corresponding environmental factors and the spatial heterogeneity parameters; performing training data set construction on the data after feature construction; and based on the constructed training data set and the integrated learning model, completing the establishment of the multi-scale ecological system carbon measurement model. According to the invention, fusion of continuous data of the carbon flux of the net ecosystem with different scales is realized, and refined monitoring of the carbon of the ecosystem at different spatial positions is realized.

The invention discloses a corn genetic performance prediction method fusing multiple environmental factors. The corn genetic performance prediction method comprises the following steps: acquiring a parent genotype data matrix, a hybrid phenotype data vector and a multi-environmental time sequence climate data tensor; a climate hysteresis effect matrix is constructed, and hysteresis influence degrees of different climate factors on corn growth and development are quantified; constructing an environment time-space relation matrix and an enhanced genome relation matrix considering non-equal contributions of sections; constructing a linear hybrid prediction model fusing the space-time environment information; estimating a variance component through a constraint maximum likelihood method, and solving model parameters based on a Henderson hybrid model equation; and predicting the phenotypic character of the to-be-predicted corn hybrid in the target environment by using the solving parameters. According to the method, the genotype data and the multi-environment time sequence climate data are fully utilized, the genotype effect, the environment space-time effect and the interaction effect of the genotype effect and the environment space-time effect are effectively integrated, and the accuracy of corn hybrid phenotype prediction is remarkably improved.

The invention relates to the technical field of biodiversity monitoring, in particular to a biodiversity monitoring method and system and a storage medium. Biodiversity multi-source data of a target area are collected in real time through a distributed sensor network and a mobile terminal device, the multi-source data comprise species image data, environmental factor time sequence data, voiceprint feature data and geographic space trajectory data, and encryption transmission and format standardization preprocessing are carried out on the data. Through a dynamic weight distribution mechanism driven by an environmental factor influence degree, a self-adaptive mapping relation between the sensor data entropy and the species ecological niche width is constructed, the problem of feature space mismatching of a traditional method in a non-stationary environment is solved, and the ecological interpretation of a species-environment joint feature vector is improved.

The invention discloses a marine sand excavation monitoring system and method which are used for solving the problems that an existing marine sand excavation monitoring system is insufficient in monitoring precision and insufficient in environmental change early warning. Comprising a monitoring acquisition module, an environment monitoring analysis module, a precision monitoring analysis module and an execution module. According to the method, environmental factors and the precision of sand excavation equipment are monitored and analyzed in real time, the influence of sand excavation activities on the environment is effectively evaluated, the environmental quality influence coefficient and the environmental state influence coefficient are calculated, adjustment is conducted in combination with dynamic environmental data, environmental changes are found in time, and the negative influence of sand excavation on the ecological environment is reduced; the precision deviation of the equipment is automatically analyzed through a precision monitoring and correction module, and dynamic correction is performed according to a correlation analysis result, so that the equipment always runs in a high-precision range; the accurate control mode optimizes the sand excavation process, improves the sand excavation efficiency and the resource utilization rate, and meanwhile reduces operation errors and environment disturbance.

The invention discloses a quantitative analysis method for influence of environmental factors on vegetation resistance and restoring force. The influence of the environmental factors on the vegetation resistance and restoring force is quantitatively analyzed by collecting tree ring samples and meteorological data, establishing a composite drought evaluation index and constructing a mixed linear effect model. The method comprises the following steps: firstly, obtaining annual ring width standardized data, calculating monthly vapor pressure deficit and potential evapotranspiration, determining a composite drought year, then calculating vegetation resistance and restoring force indexes, and finally analyzing the influence degree of environmental factors through a mixed linear effect model. According to the method, the evaluation precision is improved through the high-resolution tree ring data, the analysis robustness is improved by adopting the mixed effect model, the key environmental factors can be effectively identified, and a scientific basis is provided for ecological system management.

The invention relates to the technical field of ecological restoration, in particular to a vegetation group ecological restoration design method based on multi-factor weighted scoring, and the method comprises the following steps: investigating and assigning scores; calculating an index; grading and matching; vegetation configuration design; and implementing and verifying. According to the method, key environment factors are investigated, ecological restoration suitability indexes are calculated, a multi-layer matching mechanism is introduced in combination with an actual scene, corresponding grades are matched according to scores, different regions are partitioned, and then a vegetation group ecological restoration scheme is independently designed, so that the ecological restoration target is achieved. According to the layered semicircular vegetation group module, the structural stability is enhanced, the water purification capacity is improved, the habitat continuity and spatial heterogeneity of a land and water transition zone can be built, the service function of an ecological system is remarkably improved, and the mode has the landscape function and the ecological function, is suitable for various types of ecological restoration scenes and has a wide application prospect. The popularization value is realized.

The invention relates to the technical field of biological outbreak prediction, in particular to a marine organism outbreak prediction method and system based on real-time monitoring, and the method comprises the following steps: based on a sea area microsensor array, collecting environment and plankton data, processing interference and noise, normalizing parameters, and combining ecological baseline adjustment; and analyzing the environment and biological response relationship between the nodes, identifying a key area and a change trend, and judging plankton extension characteristics. According to the method, synchronous linkage of environmental factors and plankton response is realized through a joint discrimination mechanism of real-time acquisition, multi-parameter dynamic normalization and fusion of spatial distribution and time sequence data, sea area key change signals are identified by depending on ecological baseline reference, abnormal response is automatically screened in a monitoring network, and an extension situation is tracked; the space-time evolution trend of a risk area is effectively reflected, rapid identification and prospective early warning of potential explosion phenomena are realized through whole-process dynamic evaluation, and a reliable data basis is provided for ecological intervention and scientific management.

The invention belongs to the technical field of resources and environment, and discloses an ecological niche theory and machine learning integrated ecological intelligent zoning method, which comprises the steps of environmental factor and phytoplankton community data measurement, sampling point ecological niche width and overlap calculation, key environmental factor identification and ecological environment zoning. According to the method, data such as hydrochemical element phytoplankton communities, ecological niche overlapping and width can be effectively integrated, and a machine learning algorithm is applied to perform analysis, so that factors which have remarkable influence on an ecological system are identified, and scientific and reasonable ecological zoning is performed on a research area according to the key influence factors. The method not only can accurately reflect regional ecological characteristics, but also can provide important data support for ecological protection, species distribution prediction and water resource management. Meanwhile, the method can provide an effective basis for ecological environment monitoring, ecological restoration and regional environment planning in practical application, and has high application value and wide popularization prospect.

The invention discloses a method for predicting abundance of plankton by using marine ecological observation data, which belongs to the technical field of marine organism prediction, and comprises the following steps: collecting multi-source marine environment data to obtain a data set; processing short-term correlation characteristics among variables from the data set to obtain an environmental factor interaction vector; processing the time dynamic change of the environmental factor interaction vector to obtain a plankton abundance change trend; enhancing the association strength of the environmental factor interaction vector and the plankton abundance change trend to obtain a weighted dynamic feature set; extracting subsets having influence on plankton abundance prediction from the weighted dynamic feature set to obtain a prediction feature group; and constructing a regression model according to the prediction feature group, and processing the newly obtained multi-source marine environment data by adopting the regression model to obtain a plankton abundance prediction value. The method for predicting the abundance of the plankton by using the marine ecological observation data solves the problems that the current prediction result is lagged or not accurate enough, and the timeliness and reliability of a decision are influenced.

The invention discloses a plant growth whole-process total-factor intelligent management and control method and system, and relates to the field of agricultural production, and the method comprises the steps: collecting a plant growth image and cultivation environment data in real time; determining the current growth state of the plant according to the plant growth image and the cultivation environment data; determining a current growth stage according to the current growth state of the plant, and generating an adjustment strategy according to an environment demand corresponding to the current growth stage; and adjusting the environmental factors of plant growth according to the adjustment strategy. According to the method, the environment requirements of the plants in all growth stages can be accurately met, and the growth efficiency of the plants is improved.

The invention discloses a predictive analysis method for spatio-temporal distribution of a species structure, and belongs to the technical field of ecological data analysis. The method comprises the following steps: S1, monitoring species data and related environmental factor data in a specific space-time range; s2, calculating a species uniformity index representing community stability based on the species data; s3, performing quantitative evaluation on the monitored environmental factor data by adopting a preset index evaluation method; s4, establishing a functional quantitative relationship between the species uniformity index and one or more environmental factor state indexes; and S5, simulating and predicting the variation trend of the species uniformity index in the future. By focusing the quantitative correlation between the species uniformity and the environmental factors and constructing the prediction model based on the correlation, the community structure change can be more sensitively captured, and the potential influence of the environmental change on the community stability can be quantitatively predicted; and more accurate and more targeted scientific basis and decision support are provided for prospective biodiversity protection and ecological system management.