39 results about "Grassland ecosystem" patented technology

The invention provides a grassland degradation and recovery condition evaluation method integrating an ecological structure and a service index, and the method comprises the steps: constructing a grassland ecological index data set based on multi-source remote sensing data, soil data and meteorological data, and carrying out the inversion of a single-factor monitoring index comprising an ecological system structure index and an ecological system service index; identifying an ecosystem service beam, and performing ecological partitioning on the research area by using the ecosystem service beam; determining the weight of the single-factor monitoring index of each ecological partition, and calculating a grassland health and service comprehensive index according to the weight of the single-factor monitoring index; and evaluating grassland degradation and recovery grades by using the grassland health and service comprehensive indexes. According to the method, the ecological system structure and the service indexes are comprehensively considered, and the comprehensiveness of the indexes is ensured; meanwhile, the difference of grassland ecosystem functions of different regions is fully considered, the index weight is adjusted according to the region characteristics, and the scientificity and reliability of the evaluation result are remarkably improved.

The invention discloses a grassland ecological system monitoring method and system based on remote sensing data, electronic equipment and a storage medium, and the method comprises the steps: collecting an original remote sensing image covering a grassland region through a satellite sensor, and carrying out the preprocessing of the original remote sensing image, and obtaining a multispectral image data set; spectral features in the multispectral image data set are extracted, the reflectivity and vegetation index of each wave band are calculated, and a spectral feature distribution diagram of grassland vegetation is determined; identifying the spectral feature distribution map by using a convolutional neural network to obtain vegetation structure features; extracting and analyzing functional characteristic parameters from the vegetation structure characteristics to obtain a quantitative characterization result; on the basis of the spectral feature distribution diagram, the vegetation structure features and the quantitative characterization result, a temporal and spatial variation trend of the grassland vegetation state is obtained through a time sequence analysis method; and comparing the spatio-temporal change trend with a preset ecological health threshold value through a clustering analysis method, and judging a health state partition of the grassland ecosystem to obtain a health state classification map.

The invention discloses a meadow steppe satellite remote sensing intelligent monitoring system based on sky-ground integration, and relates to the technical field of remote sensing monitoring, and the system comprises an environment perception judgment module which is used for obtaining meteorological data and grassland health state data in real time, analyzing the influence of environment change on the monitoring precision, and sending the monitoring result to a satellite remote sensing module; and a data acquisition mode is intelligently adjusted according to an analysis result, and a monitoring mode suitable for the current environmental condition is preferentially selected. According to the meadow steppe satellite remote sensing intelligent monitoring system based on sky-ground integration, through intelligent fusion of multiple data sources, environment adaptive adjustment, dynamic resource optimization and an abnormal data restoration mechanism, the ecological monitoring precision and efficiency in a meadow steppe complex environment are significantly improved; particularly, the defects of the traditional remote sensing technology on the limitation of resolution and precision are overcome in the aspects of dealing with the monitoring requirements of grassland ecosystem detail analysis, local change monitoring and complex meteorological conditions.

The invention relates to the technical field of grassland ecological restoration, in particular to a degraded grassland soil function improving method based on microbial community regulation and control, which comprises the steps of target plot preparation, improved matrix application, plant optimization configuration and the like. A multi-layer grassland ecosystem is constructed by combining microbial agents and plant functional characters, and the soil nutrient conversion efficiency and the vegetation stability are remarkably improved. The method can effectively solve the problems of insufficient attention of soil microflora and single vegetation in the prior art, is suitable for restoration engineering of degraded grassland in arid and semi-arid regions of Qinghai-Tibet Plateau in China, has wide popularization value, and can remarkably improve soil functions, restore vegetation coverage and biological diversity and assist construction of regional ecological safety barriers.

The invention discloses a grassland ecosystem quality evaluation method combining species diversity and grassland bearing capacity. The grassland ecosystem quality evaluation method comprises the following steps that grassland ecosystem quality is obtained by calculating three first-level indexes including grassland ecosystem elements, grassland ecosystem functions and grassland ecosystem stability through the following formula: EQI = (A * EE + B * EF + C * ES) * 100%; in the formula, EQI is the grassland ecosystem quality index; eE is a grassland ecosystem element index; eF is a grassland ecosystem function index; eS is a grassland ecosystem stability index; a, B and C are first-level weights calculated through the judgment matrix. According to the method, the quality state of the grassland ecosystem can be reflected more accurately and comprehensively, evaluation indexes are standardized, and a reliable basis is provided for grassland sustainable utilization, grass and livestock balance management and ecological protection policy making.

The invention belongs to the field of remote sensing technology application, mainly aims at three typical grassland ecological system types of desert, grassland and meadow in an arid region, and discloses a grassland ecological health four-dimensional quantitative evaluation method based on multi-source remote sensing data. Comprising the following steps: S1, data fusion and long-time-sequence base construction: aiming at three typical grassland ecosystems of desert, grassland and meadow in an arid region, integrating long-time-sequence multi-source remote sensing images and auxiliary environment observation data, and reconstructing a model through a space-time fusion algorithm and missing data; and constructing a long-time-sequence multi-dimensional raster data set which is consistent in spatial resolution and covers non-biological background conditions. According to the system, accurate evaluation of long-time-sequence comprehensive conditions of different typical grassland types in the arid region is realized, the input information dimension and quality of the evaluation model are remarkably improved, and the ecological health problem is innovatively and accurately diagnosed from the deficiency of system bases, functional vitality, structural tissues or stress resistance recovery capability on the spatio-temporal scale.

The invention relates to a thyme biomass prediction method based on multispectral data and machine learning. The thyme biomass prediction method comprises the steps that multispectral image data of a research area is acquired and preprocessed; single plant scale segmentation is carried out, and a target extraction result of the single plant scale is acquired; calculating a vegetation index; and modeling the vegetation index by adopting a machine learning model to obtain a biomass prediction model, training the biomass prediction model, and automatically predicting the thyme biomass to obtain two prediction targets of dry weight and fresh weight. According to the method, the low-altitude multispectral image data of the unmanned aerial vehicle is combined with various machine learning models, a high-precision biomass prediction model is established, the spatial distribution characteristics of large-area thyme biomass are accurately obtained in real time, and a basis is provided for grassland ecological system management and resource evaluation; through prediction of 11 machine learning models, limitation of a single model is overcome, and stability and reliability of a prediction result are enhanced.

The invention relates to the technical field of risk assessment, in particular to a grassland ecosystem comprehensive drought risk assessment system based on multi-source data. The risk feature highlighting characterization value is calculated by determining the feature highlighting factors of the different risk areas of the grassland, so that the feature highlighting sorting sequence of the risk areas of the grassland is rapidly determined, the precision of grassland comprehensive drought risk assessment is improved, and meanwhile, the risk assessment accuracy is improved. According to the grassland actual data uploaded by the user side and the drought risk index information corresponding to the grassland ecological system, the maximum feature highlighting characterization value of each risk area of the grassland can be accurately obtained, and the accuracy of comprehensive drought risk assessment of the grassland ecological area is improved.

This invention discloses a method for assessing the impact of photovoltaic (PV) installations on grassland ecosystems based on multi-source remote sensing data. The method includes the following steps: constructing a multi-source remote sensing dataset of the PV installation area and surrounding grasslands; acquiring time series data of multi-dimensional ecological indicators such as vegetation, water, and energy; establishing multi-ring buffer zones based on the PV boundary, and extracting the variation characteristics of each ecological indicator within and outside the PV area and at different distances; and quantitatively assessing the intensity and spatial decay characteristics of the impact of PV installations on the grassland ecosystem through spatial difference analysis and comprehensive ecological impact indicators. This method enables long-term dynamic monitoring of the ecological effects of PV construction without ground-based observation, and has the advantages of high automation, strong scalability, and wide application range, providing scientific support for the ecological assessment and management of grassland renewable energy development.

The present invention provides a method for quantitatively assessing the resilience of wetland forest and grassland ecosystems under drought stress. The method comprises: data collection, construction of a daily-scale ecosystem service function index (ESS), ecological drought identification, and construction of a recovery resilience curve. The temporal resolution of the index is refined to the daily level, enhancing the capture of short-term droughts on a sub-monthly scale and accurately depicting the dynamic process of ecosystem recovery under drought stress. Drought events are jointly identified using a standardized ecological water deficit, improving the accuracy of identifying drought events that cause significant differences in ecosystem service functions. A quantile regression model is used to quantify the ecosystem resilience of different vegetation types, taking into account the data characteristics of the different recovery time distributions of ecosystems under the same drought intensity. This overcomes the shortcomings of traditional mean regression models, which cannot fully describe the complex characteristics of drought, and provides theoretical support for the sustainable development of ecosystems under extreme climate conditions.

The invention discloses a precise medication method for plateau rat rabbit prevention and control, and relates to the technical field of grassland rat pest prevention and control, and the method comprises the following steps: selecting a plurality of rodenticides in a plateau rat rabbit distribution area, including alpha-chloro-alcohol, curcumenol bait, triptolide, cholecalciferol, bromadiolone bait, difenac barium sulfate and a silicate obstructing agent; the silicate obstructing agent comprises two dosage forms of highland barley and wax pills, each rodenticide is provided with a plurality of dosage gradients, and each dosage is provided with a plurality of repetitions; according to the method, the optimal agent for preventing and treating the plateau rat rabbits, namely the silicate obstructing agent wax pill dosage form and the optimal preventing and treating dosage of 3 granules 21g / hole, are accurately screened out, excessive and blind medication is avoided, the putting gradient is scientifically set according to the rodenticide LD50, and multi-dosage gradient tests are set for different rodenticides; therefore, rodent damage is effectively controlled, the grassland ecological system is protected, the prevention and control cost is reduced, a precise prevention and control scheme is provided, and grassland rodent damage prevention and control technology development is promoted.

The present invention discloses a method for regulating and controlling the grazing of functional livestock species that promotes high-quality development of grasslands, and belongs to the technical field of grassland grazing management. The method of the present invention is based on the spatiotemporal niche differentiation characteristics of different types of livestock for feeding, and regulates grassland functions by timely introducing livestock with low feeding selectivity on the basis of grazing basic livestock species on grasslands. Specifically: first determine the basic livestock species and the functional livestock species introduced, and then start grazing functional livestock species when the basic livestock species' selectivity for dominant plant species on grasslands decreases significantly. The duration of joint grazing is optimally 30 days. At the same time, the grazing intensity of functional livestock species is determined, and finally a joint breeding model of 10 basic livestock breeders and 1 functional livestock breeder is established. This method can improve the function and production efficiency of grassland ecosystems, promote high-quality and sustainable development of grasslands, and is of great significance in the field of grassland grazing management.

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 community level grassland plant competition intensity calculation method, and belongs to the technical field of botany. According to the method, the problem that the existing method is lack of calculating the competitive strength of the plants on the community level is solved, the relative coverage and the relative height of the species in each investigation quadrat are calculated by acquiring the coverage and height data of each plant species in the grassland plant community in the target area, and then the potential growth coverage and the potential growth height are calculated, so that the competitive strength of the plants is calculated. And finally obtaining the plant competitive strength. Therefore, the limitation that a traditional method only pays attention to the competition relation of a single species or a few species is broken through, and the actual influence of competition on species growth can be accurately reflected; by introducing the historical data and the preset threshold value, the community can be more accurately divided into the high-competition area and the low-competition area, and the competition driving factors of different areas are identified, so that a more targeted strategy is provided for grassland management, and the stability and biodiversity of a grassland ecosystem can be effectively improved.

The invention belongs to the technical field of ecological restoration of alpine grassland, and particularly relates to an ecological planting method for shrub grassland in a super plateau semi-arid region. The method comprises the following steps: delimiting a sample plot in a to-be-restored super plateau semi-arid region degraded grassland area, and applying a bio-organic fertilizer and biochar for soil improvement and activation; ditching and soil preparation; after a water-retaining agent absorbs water and is prepared into gel, uniformly applying the gel into the sowing furrows and uniformly mixing the gel with furrow soil; and performing multi-species matching and sowing. According to the ecological planting method, a low-disturbance degraded shrub grassland remediation technology is adopted, a liquid mulching film is directly sprayed on the surface of soil on the basis of soil improvement, the effects of preventing wind, maintaining soil moisture and increasing ground temperature are achieved, the soil moisture condition and the moisture utilization rate can be obviously improved, soil remediation and vegetation planting are both considered, and the ecological planting effect is achieved. The multifunctionality of the ecological system of the shrub grassland is improved.

The utility model discloses a grassland pest killing device based on solar energy, and belongs to the technical field of pest killing, the grassland pest killing device based on solar energy comprises a main body supporting rod, the upper end of the main body supporting rod is fixedly connected with a solar panel, the whole device can be provided with electric power through the solar panel, energy is saved, and the cost is reduced. The ultraviolet insecticidal lamp and the blue insecticidal lamp are used for inducing pests, and the conical mirror surface is matched, so that the illumination range of the ultraviolet insecticidal lamp and the blue insecticidal lamp can be expanded at the same time under the condition that more energy is not consumed, and the pests in a larger range are induced to the water storage base; through mutual cooperation of the water storage base, the annular water guide pipe, the connecting pipe and the atomization spray head, pests induced to the water storage base can be killed in a small range, meanwhile, sprayed chemical agents can be recycled and reused, and the situation that the grassland ecology is damaged due to large-range spraying of the chemical agents is prevented.

The invention relates to the technical field of image processing, in particular to a soil carbon reserve estimation method and model of a grassland ecosystem, and solves the technical problems of low precision and reliability of soil carbon reserve estimation of the grassland ecosystem by an existing estimation method. The method comprises the following steps: acquiring remote sensing spectral image time sequence data of the grassland ecosystem and humidity time sequence data of a plurality of monitoring points of the grassland ecosystem; determining a growth interval of each monitoring point according to the remote sensing spectral image time sequence data and the humidity time sequence data; screening vigorous monitoring points and ideal monitoring points from the plurality of monitoring points based on the growth interval and the humidity time sequence data; the normalized difference vegetation index is determined based on remote sensing spectral image time sequence data; determining the vegetation coverage rate of each monitoring point according to the vigorous monitoring points, the ideal monitoring points and the remote sensing spectrum image time sequence data; and determining the soil carbon reserve of the grassland ecosystem based on the determined vegetation coverage rate of each current monitoring point.

The present invention discloses a remote sensing monitoring method and system for grassland biomass, comprising the following steps: constructing a high-dimensional feature matrix representing the state of the grassland ecosystem by using multi-modal remote sensing data fusion technology; generating spatio-temporal correlation parameters of grassland biomass based on the high-dimensional feature matrix and a dynamic spatio-temporal feature analysis framework; training a non-linear biomass prediction model by combining a deep neural network and a multi-task optimization algorithm; jointly analyzing the results of the prediction model with high-resolution images, and applying region segmentation technology to perform hierarchical processing on heterogeneous grassland areas; performing uncertainty propagation analysis on the partition estimation results and multi-modal data, and outputting optimized grassland biomass estimation results; combining the partition estimation results with cross-spatio-temporal scale data through a collaborative monitoring mechanism of unmanned aerial vehicles and satellite remote sensing to dynamically update the spatio-temporal correlation parameters; generating a spatial distribution map of grassland biomass and an analysis result of dynamic change trends according to the optimized monitoring data.

The application relates to an evaluation method for greenhouse gas emission of a grazing grassland, comprising the following steps: S1: collecting air above the grassland by a static chamber method and measuring the concentration of various greenhouse gases in the collected air and the change rate thereof; S2: calculating the annual greenhouse gas emission of the whole grassland ecosystem; S3: estimating direct or indirect energy consumption in the grassland production process, including direct emission generated by using fossil energy such as diesel, gasoline and coal and indirect emission caused by power consumption such as pumping irrigation, drainage and machine ploughing; S4: calculating the whole-year equivalent of the whole grassland production process; S5: obtaining the whole-year economic output of the whole grassland; S6: calculating the greenhouse gas emission intensity of the grassland according to the whole-year equivalent of the whole grassland and the whole-year economic output of the whole grassland. Since the above technical scheme is adopted, the evaluation method for greenhouse gas emission of the grazing grassland can improve the accuracy of the calculation result, and further improve the accuracy of the obtained greenhouse gas emission intensity.

The invention relates to the technical field of grassland ecosystem monitoring, and discloses an intelligent identification and statistical analysis system for solanum aureum in a grassland ecosystem based on an image identification algorithm and a GIS simulation technology. The system comprises a multi-source data acquisition module, an AI identification module, a GIS statistics and visualization module and a cloud management platform. According to the invention, through an end-to-end collaborative architecture, the system solves the problems of low efficiency, insufficient precision, lack of dynamic analysis and the like of a traditional monitoring means, significantly improves the real-time monitoring capability and treatment efficiency of roselle invasion, and provides precise technical support for grassland ecological protection.

The utility model discloses an ecological model of carbon loss and gain of grazing grassland ecosystem, which couples carbon loss caused by grazing, increase of soil organic carbon pool caused by animal excrement and carbon loss caused by animal trampling plant by establishing grazing carbon balance model and animal trampling carbon loss model, so as to obtain actual carbon loss and gain of grassland ecosystem. In the application, the utility model fully considers the factors of animal grazing, animal excretion and animal trampling in the whole grazing process, perfects the influence of disturbance factors of whole grassland ecosystem on vegetation, reduces the uncertainty of regional grassland carbon cycle simulation and improves the accuracy of results. Therefore, the utility model has high accuracy.

The present application belongs to the technical field of grassland ecosystem management, and particularly relates to a grassland fence device facilitating the passing of wild animals, comprising a first protective net and a second protective net; the first protective net is composed of positioning supports one longitudinally sleeved by support seats and blocking ropes arranged between adjacent two positioning supports one; the second protective net is composed of positioning supports two longitudinally sleeved by support seats and electronic cables arranged between adjacent two positioning supports two; a connecting rod for mounting the blocking ropes and the electronic cables is arranged between the positioning supports one and the positioning supports two; when the monitoring assembly monitors that there is a wild animal within the set range of the first protective net, the lifting assembly is started to drive the connecting rod to move upwards along the positioning supports one and the positioning supports two, so that a passing area is formed below the first protective net and the second protective net. The grassland fence device can effectively promote the free passing of wild animals, while the basic function of the fence is taken into account, and the strong electric shock feeling caused by accidental touch during the passing of the animals can be avoided.

The invention discloses a grassland ecosystem service value evaluation method, and relates to the field of ecological management, the method comprises three steps of biological activity analysis, ecosystem analysis and ecological maintenance management, firstly, in a biological activity analysis link, biological activity data of each grassland area is collected, and through biological activity model analysis, the grassland ecosystem service value is obtained; secondly, in an ecological system analysis link, collecting plant ecological development data of each grassland area, combining a plant ecological model and an animal ecological model to obtain a plant ecological development level and an animal ecological development level, and finally, in an ecological maintenance management link, obtaining a plant ecological development level and an animal ecological development level. And an ecological maintenance and management scheme is formulated by integrating the biological activity value evaluation grade, the plant ecological development grade and the animal ecological development grade, and the method is beneficial to improving the sustainable utilization capability of grassland resources, so that the service value of a grassland ecosystem is improved.

The invention belongs to the technical field of microorganisms, and particularly relates to a peoriae paenibacillus ZM1, a fungicide and application, the peoriae paenibacillus ZM1 is preserved in China General Microbiological Culture Collection Center (CGMCC) on May 26, 2025, and the preservation number is CGMCC No.34680. The invention further discloses a preparation method of the peoriae paenibacillus ZM1, the fungicide and application of the peoriae paenibacillus ZM1 and the fungicide. The invention provides a new way for screening excellent strain resources for research and development of microbial fertilizers, restoring degraded desert steppe soil environment and restoring biological functions of a degraded grassland ecosystem. The strain has the functions of fixing nitrogen, dissolving phosphorus, dissolving potassium, producing indoleacetic acid and producing siderophores, can significantly improve the content of alkali-hydrolyzable nitrogen, rapidly available phosphorus and rapidly available potassium in the desert steppe soil of Inner Mongolia, and can significantly promote plant growth.

The present invention provides a method for dynamically monitoring the balance between grass and livestock, which includes obtaining the theoretical livestock carrying capacity through remote sensing monitoring and obtaining the actual livestock carrying capacity through ground monitoring, and verifying the degree of overloading. The technical solution of the present invention can be accurate to the bearing capacity of the grassland contracted by each herdsman household, accurate to the household, and is relatively fair; when monitoring the balance between grass and livestock, it can eliminate the influence of artificial law enforcement, reflecting fairness and justice; in addition, it helps to monitor the degradation of the grassland and protect the grassland ecosystem.

The present invention discloses a meadow grassland satellite remote sensing intelligent monitoring system based on integrated sky-ground technology. This system, which relates to the field of remote sensing monitoring technology, includes an environmental perception and judgment module for acquiring meteorological data and grassland health data in real time, analyzing the impact of environmental changes on monitoring accuracy, and intelligently adjusting the data acquisition mode based on the analysis results, prioritizing monitoring methods appropriate for current environmental conditions. This meadow grassland satellite remote sensing intelligent monitoring system, based on integrated sky-ground technology, significantly improves the accuracy and efficiency of ecological monitoring in complex meadow grassland environments through intelligent fusion of multiple data sources, adaptive environmental regulation, dynamic resource optimization, and anomaly data repair mechanism. This system, in particular, addresses the needs of detailed analysis of grassland ecosystems, monitoring local changes, and monitoring under complex meteorological conditions, thereby overcoming the resolution and accuracy limitations of traditional remote sensing technology.

This invention relates to the field of image processing technology, and more particularly to a method and system for evaluating grassland ecosystem function based on multi-source data. The method includes the following steps: acquiring remote sensing image data and aerial images of the target area, and performing image geometric registration; extracting vegetation spatial distribution indicators of the target area based on the spectral characteristics of ground features obtained from the geometric registration results; retrieving land surface temperature indicators, and combining the land surface temperature indicator retrieving results with acquired meteorological observation data to construct a land surface energy balance curve; identifying functional trends in each season based on the land surface energy balance curve; dividing functional feature domains according to the spatial clustering results of functional trends in each season, and determining the distribution of functional feature domains in each temporal phase; and evaluating the grassland ecosystem function score based on the distribution of functional feature domains in each temporal phase. Compared with traditional evaluation methods based on single-source data, this invention achieves a more accurate, quantitative, and dynamic evaluation of ecological function.

The present application discloses a kind of grassland ecological system monitoring method, system based on remote sensing data, electronic equipment and storage medium, method includes: using satellite sensor to collect the original remote sensing image covering grassland area, and the original remote sensing image is preprocessed, obtain multispectral image dataset;Spectral features in multispectral image dataset are extracted and each band reflectivity and vegetation index are calculated, to determine the spectral feature distribution map of grassland vegetation;Spectral feature distribution map is identified using convolutional neural network, and vegetation structure feature is obtained;Function characteristic parameters are extracted and analyzed from vegetation structure feature, and quantitative characterization result is obtained;Based on spectral feature distribution map, vegetation structure feature and quantitative characterization result, using time series analysis method obtains the spatio-temporal variation trend of grassland vegetation state;Through clustering analysis method, spatio-temporal variation trend is compared with preset ecological health threshold value, to judge the health state partition of grassland ecological system, and obtain health condition classification map.

The invention relates to the technical field of image processing, in particular to a grassland ecosystem function evaluation method and system based on multi-source data. The method comprises the following steps: acquiring remote sensing image data and an aerial image of a target area, and executing image geometric registration; extracting vegetation spatial distribution indexes of the target area according to the ground feature spectral features of the geometric registration result; inverting a surface temperature index, and combining an inversion result of the surface temperature index with the acquired meteorological observation data to construct a surface energy balance curve; identifying the function trend of each season based on the surface energy balance curve; dividing functional feature domains according to the spatial clustering result of the functional trend of each season, and determining the distribution condition of the functional feature domains of each time phase; and evaluating a grassland ecosystem function score according to the distribution condition of each time phase function feature domain. Compared with a traditional evaluation method based on single-source data, more accurate quantitative and dynamic ecological function evaluation is realized.