989 results about "Grassland" patented technology

The invention discloses a forest steppe fire risk assessment method and system, and belongs to the technical field of forest steppe fire prevention. The problem of high false alarm and missing report rate caused by lagging fire risk identification, weak multi-source data fusion and insufficient dynamic response in the prior art is solved. According to the technical scheme, the method comprises the following steps: deploying a ground sensing node array to obtain real-time environment data of a grassland region, fusing high-resolution satellite remote sensing and regional weather forecast data, and constructing a space-time aligned risk assessment data cube; establishing a grassland fire risk factor dynamic coupling model, and dynamically allocating factor weights in combination with the adaptive weight decision tree; generating a comprehensive risk index, carrying out nonlinear mapping to five risk levels, and linking a visual engine to generate a dynamic thermodynamic diagram and push the dynamic thermodynamic diagram to a command terminal; the system supports online updating of the model, and weight parameters are automatically optimized based on a new fire event. According to the invention, high-precision, real-time and spatialized evaluation is realized, the early warning capability and prevention and control decision efficiency are improved, and ecological and economic losses are reduced.

The invention relates to the technical field of strategy analysis, and discloses an alpine grassland rapid restoration strategy analysis method, system and device and a medium. The method comprises the following steps: performing quality evaluation and restoration on an alpine grassland multispectral image to obtain a standard image set; spectral texture features are extracted, degradation classification is carried out, and a feature set and a score table are obtained; evaluating the degradation degree in combination with multi-dimensional indexes, and generating a distribution matrix and an index set; forming a repair scheme library and a parameter list through case analysis and parameter optimization; analyzing a restoration effect in combination with environmental data to obtain an index matrix and an evaluation table; and optimizing the monitoring data to obtain a strategy scheme and an adjustment parameter set. According to the method and the device, the degradation conditions of alpine grassland of different types and different areas are accurately identified, the restoration strategy can be dynamically adjusted according to the real-time monitoring data, and the restoration effect is improved.

The invention provides a natural resource dynamic monitoring method and system based on multi-source data fusion, a medium and equipment. The method comprises the following steps: acquiring satellite remote sensing data, aerial remote sensing data and ground monitoring data; performing data fusion on the satellite remote sensing data and the aerial remote sensing data to obtain fused data; integrating the fusion data with the ground monitoring data to obtain comprehensive monitoring data; performing terrain classification on the comprehensive monitoring data based on deep learning to obtain terrain classification results including cultivated land, construction land, water, forest land and grassland; and optimizing the ground feature classification result based on a space-time logic consistency algorithm to obtain an optimized classification result and ground feature change information. According to the method, through fusion of the multi-source data and classification optimization of the natural resources, in combination with deep learning and a spatial-temporal logic consistency algorithm, dynamic monitoring of the natural resources is realized, the monitoring precision is improved, and reliable data support is provided for natural resource management and environmental protection.

The invention provides an ecological restoration and rapid vegetation planting method suitable for an open grassland mining area, and provides a novel grass seed assembly structure, and according to regional climate characteristics and geographical general situations, in combination with actual planting conditions of a vegetation restoration area, and according to planting principles of local nature, species diversity, ecological complementation and the like. Based on the main biological ecology characteristics of different plant species and the collaborative succession and growth promotion co-fusion development characteristics of the suitable growth environment, multi-system, multi-dimension and multi-species technical assembly is carried out according to local conditions, and a cost-saving, efficient, function-harmonious and landscaping rapid vegetation recovery technical method is created.

The invention discloses an alpine grassland no-tillage restoration suitability evaluation method based on a 3S technology, and particularly relates to the technical field of data processing. Through the combination of multi-temporal satellite images and global positioning system data, the spatial dynamic change of the frozen soil layer of the alpine grassland and the long-term influence of the spatial dynamic change on vegetation growth are accurately obtained, and a frozen soil layer depth and distribution dynamic monitoring model is constructed based on multi-source remote sensing data and historical weather and soil temperature and humidity data. Analyzing the influence of freezing and thawing alternation on plant root growth; establishing a vegetation recovery potential index and a soil moisture supply index by combining the vegetation coverage and the dynamic change of the frozen soil layer, and generating a suitability partition map through a GIS spatial analysis and analytic hierarchy process; through field sampling and long-term vegetation growth monitoring data verification and model optimization, the accuracy of no-tillage restoration suitability evaluation is remarkably improved, and scientific and accurate decision support is provided for ecological restoration of alpine grassland.

The invention relates to an ecological system carbon sink evaluation method, which comprises the steps of generating a continuous climate distribution grid based on a global ecological system type and climate element data, extracting multi-dimensional characteristic parameters of temperature, rainfall and a carbon sink value through an improved weighted K-means clustering algorithm, quantifying climate transition zone boundary fuzziness in combination with a covariance matrix, and evaluating the ecological system carbon sink evaluation result. Dynamically adjusting the weight of the spatial variation coefficient; a simulated annealing algorithm is utilized to optimize correlation between the division scale and the carbon sink response, and a multi-scale regression model is constructed to generate a high-precision carbon sink distribution diagram; further fusing the remote sensing vegetation index and the ground actual measurement data, and eliminating the evaluation error caused by the conflict between the boundary fuzziness and heterogeneity of the transition zone; according to the method, the problems of insufficient climate-ecology interaction dynamic response modeling and low boundary division precision in the prior art can be solved, the reliability of carbon sink evaluation in complex areas such as forest-grassland interlaced areas and coastal wetlands is remarkably improved, and scientific data support is provided for ecological restoration project site selection and carbon trading markets.

The invention relates to the technical field of leaf nitrogen content estimation, in particular to a method for establishing a grassland plant nitrogen content monitoring model based on a remote sensing technology. The method comprises the following steps: controlling an unmanned aerial vehicle platform carrying a narrowband multispectral camera and a linear polarization camera, and collecting a spectral image sequence and a polarization image group at an observation window with a solar zenith angle of 30-40 degrees; extracting a reflectivity value of each wave band in the spectral image sequence, calculating a reflectivity first-order derivative and determining a red edge position offset; extracting horizontal and vertical polarization components in the polarization image group, calculating a polarization degree and constructing a polarization degree difference index; and identifying the center of the fecal spot by searching a pixel with a local maximum polarization degree difference index and red edge position offset blue shift exceeding a preset threshold value. According to the method, excrement spot recognition and remote sensing description of the nitrogen influence range thereof are achieved through double-index complementation of the red edge position offset and the polarization degree difference index.

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

The invention relates to a southern grassland soil carbon reserve dynamic evaluation method and system fused with remote sensing monitoring, and relates to the technical field of soil carbon reserve evaluation, and the method comprises the steps: drawing a target region topographic map based on multi-source satellite remote sensing data, inputting an initial soil carbon reserve inversion model, and outputting an initial soil carbon reserve distribution diagram and a deterministic thermodynamic diagram; extracting an uncertain area according to a certainty degree threshold value, and configuring and labeling supplementary sampling points according to a division rule; acquiring parameter information of the detection robot, generating a digital twinborn model in combination with the topographic map, configuring a supplementary sampling scheme, and sampling supplementary sampling points to obtain supplementary soil carbon reserve data; and returning the supplemented soil carbon reserve data to the initial soil carbon reserve inversion model, generating a corrected soil carbon reserve distribution diagram and a corrected determinacy thermodynamic diagram, judging that the task is completed when determinacy reaches the upper limit of resource consumption, otherwise, continuing to supplement sampling correction. The problems that traditional soil carbon reserve assessment is inaccurate and low in efficiency are solved.

The invention discloses forest steppe fire extinguishing barrier foam and a preparation method thereof.The foam is prepared from 5%-20% of a material A, 0.5%-0.8% of a material B, 1.8%-3% of a coagulant and the balance water through a foam generation system; the material A is prepared from 1 to 10 parts of organic aggregate and 100 to 200 parts of inorganic aggregate; the material B is composed of 2-7 parts of a composite surfactant and 1-6 parts of a cross-linking agent; the coagulant is carbon dioxide gas. The inorganic aggregate is condensed under the action of hydrogen ions generated by dissolution of carbon dioxide to form colloidal particles, the colloidal particles are polymerized and crosslinked with the organic aggregate to form a gel skeleton of a three-dimensional network structure, the foam is converted into gel foam, and a heat-insulating and anti-burning barrier layer is formed on the surface of a combustible. The fire extinguishing agent is high in fire extinguishing efficiency, good in heat insulation effect and environment-friendly, and has wide application prospects in forest and grassland fire disasters.

The invention discloses a bacillus microbial agent and application thereof in artificial improvement of deteriorated grassland, the microbial agent can improve soil and improve plant productivity so as to promote vegetation recovery and deteriorated grassland restoration, especially restoration of deteriorated grassland in alpine regions, and has the characteristics of high efficiency, environmental protection, economy, practicability and the like.

The invention relates to the technical field of ecological remote sensing and image processing, and particularly discloses a method and system for monitoring the ecological restoration state of thinopyrum intermediary grassland in an alpine region. The method comprises the following steps: acquiring a multi-temporal remote sensing image and ground ecological parameters, and constructing a remote sensing-ground combined observation data set; utilizing a semantic segmentation model of a multi-scale attention mechanism to extract a thinopyrum intermedium block mask; vegetation indexes, texture features and ecological factors are extracted from the mask region to construct a multi-dimensional feature sequence; inputting the feature sequence and manual intervention information into a training factor interaction model in a graph neural network and causal modeling architecture, and outputting a repair level and evolution data according to the training factor interaction model; and generating a visual layer in combination with historical data, and updating a remote sensing feature extraction strategy based on model feedback. The alpine grassland ecological restoration state monitoring system realizes intelligent monitoring and dynamic feedback of the alpine grassland ecological restoration state, has strong timeliness, clear causality and adaptive optimization capability, and is suitable for continuous monitoring and management of a complex ecological system.

The invention relates to a grassland ecological degradation recovery effect dynamic evaluation method and device, equipment and a medium, and the method comprises the steps: obtaining interannual remote sensing data for a target region, carrying out the vegetation feature extraction of the interannual remote sensing data based on the sliding operation of a predetermined window on the interannual remote sensing data, and obtaining a reference time sequence; calculating the relative change rate of vegetation in the target area based on the reference time sequence; and according to the relative change rate and a preset threshold value, evaluating the degradation or recovery condition of the vegetation in the target area. According to the method, the optimal spatial analysis scale, namely the window, is determined according to spatial heterogeneity characteristics (semi-variable function range), adaptive conversion from the pixel to the landscape scale can be realized, subjective interference can be eliminated, a reference time sequence reflecting vegetation characteristics can be accurately determined on the basis of the window, and the method has the advantages of being simple in structure and convenient to operate. And the degradation or recovery condition of the target area can be accurately evaluated based on the reference time sequence.

The invention belongs to the technical field of remote sensing application, and provides a grassland biomass inversion method based on a radiation transfer model and machine learning, and the method comprises the following steps: obtaining and processing remote sensing data, DEM data and actually measured biomass data of a target region; performing terrain correction by adopting an SCS + C method; performing global sensitivity analysis on input parameters in the PROSAIL model, and calibrating the input parameters according to the global sensitivity analysis; introducing topographic factors into the model, and correcting the solar zenith angle and the observation zenith angle; generating simulated canopy reflectivity data by using the model, and constructing a data set; constructing a lookup table LUT by combining the simulated canopy reflectivity data and the above-ground biomass data, and performing machine learning modeling according to the canopy spectral data set; and verifying the inversion precision, and outputting a grassland above-ground biomass spatial distribution map. According to the inversion method, terrain factors are introduced on the basis of a traditional model, and the adaptability and estimation precision of the model to a complex terrain area are improved by combining a machine learning method.

The invention relates to the technical field of forest steppe fire emergency command, and discloses a forest steppe fire comprehensive monitoring and emergency command method and system. The method comprises the following steps: acquiring multi-source monitoring data of a satellite, an unmanned aerial vehicle and a ground sensor, and constructing a fire scene multi-dimensional feature vector through cooperative training and feature fusion; inputting the feature vectors into a physical simulation model to generate fire scene spreading speed and temperature gradient data; analyzing a terrain obstacle coefficient based on the spreading speed data and generating a helicopter no-fly zone coordinate and a passing attenuation factor; and solving and outputting a rescue unit deployment instruction through the scheduling matrix, and projecting a holographic image by using an augmented reality terminal to realize dynamic closed-loop feedback. Through deep fusion of a physical mechanism and reinforcement learning, the problem of separation of traditional fire prediction and resource scheduling is solved, intelligent decision-making of fire accurate prediction and emergency response is realized, and the accuracy and timeliness of fire prevention and control are remarkably improved.

The invention belongs to the technical field of genetic breeding, and particularly relates to application of a molecular marker for breast circumference character breeding of grassland short-tailed sheep, the molecular marker is at least one of the following (1)-(3): (1) a nucleotide sequence as shown in SEQ ID NO.1, nucleotide at the 101bp site is A or G; (2) a nucleotide sequence as shown in SEQ ID NO.3, wherein the nucleotide at the 101bp site is C or A; (3) a nucleotide sequence as shown in SEQ ID NO.5, wherein the nucleotide at the 101bp site is T or C; the application refers to any one of the following steps: identifying the chest circumference of the grassland short-tailed sheep; the chest circumference of the offspring of the grassland short-tailed sheep is improved. By detecting the genotype of the molecular marker and selecting a dominant genotype individual as a parent, the average chest circumference of offspring can be effectively improved, and a reliable tool is provided for realizing molecular assisted breeding and precise genetic improvement of the chest circumference character of the grassland short-tailed sheep.

The invention relates to the technical field of biological improvement methods, and discloses a comprehensive biological improvement method for severe saline-alkali grassland. According to the method, S1, early evaluation and planning are conducted, S2, straw returning to the field and soil preliminary conditioning are conducted, S3, shallow ploughing and soil preparation operation is conducted, S4, microbial inoculation is conducted to strengthen soil activity, S5, pioneer plant sowing and planting are conducted, S6, precise irrigation and drainage regulation and control are conducted, and S7, long-term monitoring and later-period management and maintenance are conducted, through straw returning to the field and microbial inoculation, the pH value of soil can be reduced to 8.0-8.5 from the initial 8.5-10.5, and the salt content is reduced to 0.5%-1% from 1%-3%; compared with the prior art, the method has the advantages that the alkalinization degree is obviously reduced, meanwhile, the organic matter content is increased from lower than 1% to 2%-3%, the soil structure is improved, the porosity is increased, the air permeability and the water permeability are enhanced, a good soil environment is created for plant growth, for example, humus generated by straw decomposition can promote soil particle aggregation, a stable aggregate structure is formed, and the water and fertilizer retention capacity of soil is improved.

According to the basic grassland ecological data monitoring and sampling system disclosed by the invention, in the sampling operation, cutting rings can be automatically taken out and stored through rotation of a turntable, rotation of a butt-clamping mechanical claw and lifting of a butt-clamping sliding block, so that an unmanned vehicle can carry a plurality of cutting rings for sampling work, the sampling work efficiency is improved, and the sampling time is shortened. The cutting ring inserting work can be automatically completed through cooperation of the butt-clamping mechanical claw and the pressing head, soil around the cutting ring can be automatically excavated through the hollow drill, the labor intensity of an operator is relieved, the automation level is improved, the butt-clamping mechanical claw can enter an annular groove excavated by the hollow drill, the cutting ring is taken out by clamping the side face of the cutting ring, and the cutting ring inserting efficiency is improved. The cutting ring does not need to be additionally transformed, the universality of the cutting ring is improved, and the sheath is pressed outside the cutting ring before the soil around the cutting ring is dug out, so that the situation that the accuracy of an analysis result is reduced due to soil disturbance in the cutting ring caused by displacement of the cutting ring is avoided.

The invention provides an organic carrier in-root-rhizosphere growth-promoting microbial agent for ecological restoration of degraded grassland as well as a preparation method and application of the microbial agent, and belongs to the technical field of grassland ecological restoration and microbial agents. The organic carrier growth-promoting microbial agent disclosed by the invention is prepared from four kinds of growth-promoting bacteria (PGPE) in roots, four kinds of growth-promoting bacteria (PGPR) in roots and an organic carrier, the PGPE comprises terracotta warrior bacillus K4, bacillus 9-3-1, bacillus deserticola 11-5-4 and halomonas L31, the PGPR comprises serratia rubra 5, serratia rubra 23, cosinia cosinii 11 and enterobacter parabarium 24, and the organic carrier is a carbon-based organic fertilizer, a cattle and sheep manure organic fertilizer or biochar. When the organic carrier growth-promoting microbial agent is applied to the degraded grassland, the biomass and coverage of plant communities can be remarkably improved. The organic carrier growth-promoting microbial agent can be used for ecological restoration of degraded grassland.

The invention discloses an alpine meadow aboveground biomass monitoring method based on PROSAIL-BP, and belongs to the field of ecological remote sensing information processing. In order to overcome the defect that samples in the alpine region are insufficient and have high precision and high reliability, the method comprises the steps that an alpine meadow mask, remote sensing images and field biomass data in a target region are obtained, the remote sensing images are spliced, the wave band reflectivity is normalized, and the grassland region reflectivity is reserved in combination with mask cutting; predefining a PROSAIL model matched with the region features; performing Sobol global sensitivity analysis to obtain a first-order sensitivity index and a total-order sensitivity index of the parameter; screening a key wave band and four high-sensitivity parameters; uniformly sampling to generate a parameter group, simulating a hyperspectrum through PROSAIL, extracting the reflectivity of a key wave band, and constructing a data set by multiplying a leaf area index by a dry matter content as a target variable; and training a three-layer BP neural network, and processing the reflectivity output pixel biomass of the remote sensing key wave band. The method is applied to a remote sensing information processing system and has high precision and high reliability.

The invention relates to the technical field of grass and livestock management, and provides a grass and livestock balance regulation and control method and system based on a light energy utilization model inversion difference value, and the method comprises the steps: obtaining environment detection data, grassland ecological characteristic parameters and historical grazing data of a target grassland; according to the environment detection data, utilizing a pre-constructed light energy utilization model to calculate and obtain inversion biomass distribution of the target grassland; establishing a biomass estimation model according to the historical grazing data and pre-constructed actual measurement quadrat data, and obtaining actual aboveground biomass distribution of the target grassland according to the grassland ecological characteristic parameters and the biomass estimation model; calculating biomass difference distribution data; determining grassland grazing intensity distribution data of the target grassland according to the biomass difference distribution data; and based on the grassland grazing intensity distribution data, dividing the target grassland into a plurality of grazing sub-regions with different bearing capacity grades, and carrying out grass and livestock balance regulation and control. According to the invention, the accuracy of grassland grazing intensity evaluation can be improved, and precise regulation and control of grass and livestock balance can be realized.

The invention discloses a grassland pasturing area settlement identification and classification method and system based on a remote sensing image, and relates to the technical field of remote sensing image processing, and the method comprises the steps: obtaining a current remote sensing image and a historical remote sensing image of a selected region, segmenting the region in each remote sensing image, screening the regions with the vegetation index values lower than a set threshold value, and carrying out the recognition and classification of grassland pasturing area settlement. The method comprises the following steps: marking a changing region in a current remote sensing image, forming a to-be-recognized region set together with all low vegetation index regions screened out from the current remote sensing image, inputting the region set with respective characteristics into a pre-trained classification model, and outputting a type corresponding to each region by the classification model. According to the method, the dynamic change characteristics of grassland pasturing area settlement can be captured, the area characteristic set is input into the pre-trained classification model, and the classification logic is designed in combination with the actual scene characteristics of the grassland pasturing area, so that the model can better adapt to the differences of different terrains, vegetation coverage and settlement distribution.

The invention discloses a data processing method for grassland bearing capacity dynamic evaluation and grazing planning, and belongs to the technical field of grassland ecological management, animal husbandry production informatization and data intelligent decision making. Aiming at the technical problems that a traditional static evaluation method neglects grassland productivity dynamic change and a grazing feedback effect, so that the resource utilization efficiency is low and grassland degradation is easily caused, the method dynamically predicts grassland bearing capacity and optimizes a grazing plan through iterative circulation, recursive calculation is carried out in combination with meteorological data and grazing consumption, and the grassland bearing capacity is evaluated. Collaborative optimization of the bearing capacity threshold value and the grazing plan is achieved, and rolling updating is supported. The method can be used for generating a scientific and dynamic grazing scheme, and the utilization efficiency and ecological sustainability of grassland resources are improved.

The invention relates to the technical field of agriculture and animal husbandry management and control, and discloses an accurate management and control system and method for livestock breeding in alpine grassland, and the system comprises a data collection and monitoring module which is used for collecting meteorological data, grassland data and livestock data in real time; the data processing and analyzing module is used for cleaning, storing and preliminarily analyzing the collected original data; the ecological model and livestock growth model module is used for establishing a grassland productivity model and a livestock growth dynamic model; and the optimal control decision module is used for generating an accurate grazing intensity and feed supply control scheme based on an optimal control theory, and the optimal control decision module comprises an optimal control algorithm unit. According to the invention, through the ecological model and the livestock growth model based on real-time data, in combination with the optimal control decision and feedback mechanism, precise adjustment of grazing intensity and feed supply is realized, and sustainable management of grassland and livestock is ensured.

The invention relates to the technical field of ecological environment monitoring, in particular to a grassland environment resource database construction method, which comprises the following steps of: arranging a multi-modal sensor in a fractal multi-scale grid and synchronously collecting; obtaining a standard frame by adopting quantum random walk compression and space-time registration; extracting a topological bar code through neuromorphic pulse coding and persistent coherent topology analysis, and generating a unified potential space tensor and a missing mask in combination with weighted mutual attention and optimal transmission; constructing an energy function containing carbon and nitrogen conservation and topological deviation, and reconstructing a complete ecological submerged space tensor through Hamiltonian Monte Carlo-diffusion combined sampling; ecological indexes and abnormal events are reasoned in the Shenchang differential graph database, sampling scheduling is driven by prediction errors, closed-loop updating of data, models and sampling is achieved, and therefore the grassland monitoring precision and early warning timeliness are improved.

The invention discloses a forest steppe fire risk assessment method based on variable screening and machine learning, and the method comprises the steps: extracting environment factor variables from preprocessed historical fire remote sensing data, and carrying out the correlation analysis, thereby obtaining a correlation result; performing variable screening on the environmental factor variables based on the correlation result, a cable regression algorithm, a principal component analysis method and a stepwise regression method to obtain screened environmental factor variables; performing data division on the screened environment factor variables to obtain training data and test data, and performing model training and testing on a plurality of to-be-trained fire risk assessment models by using the training data and the test data until a plurality of fire risk assessment models are obtained; and determining an optimal fire risk assessment model based on the plurality of fire risk assessment models and the obtained historical fire records, and then performing inversion on the screened environmental factor variables to obtain a fire risk map of the forest steppe. According to the invention, the real fire risk condition can be accurately reflected.

The invention relates to the technical field of ecological monitoring, and discloses a grassland ecological assessment early warning monitoring system based on artificial intelligence. Collecting vegetation indexes, soil parameters, livestock tracks and topographic data in real time through satellite remote sensing, an unmanned aerial vehicle and Internet of Things equipment; an intelligent algorithm is adopted to process missing values and standardized data, and vegetation phenological characteristics are extracted; the weight is dynamically calculated by fusing the index volatility, the correlation and the ecological coordination rule, and the net influence of the grazing activity on the ecology is accurately quantified based on the contrast area with strict geographical constraints, so that a degradation risk score is generated; high, medium and low risk areas are divided according to scores, and a livestock capacity optimization scheme and targeted measures of a matching policy library are dynamically output. Natural interference and artificial grazing influence are accurately separated, the problem that the risk level is misjudged in a traditional method is solved, and the evaluation adaptability is improved through a dynamic weight model.

The invention provides pseudomonas chlororaphis, a complex microbial inoculant and application of the complex microbial inoculant in restoration of degraded grassland, and relates to the technical field of microorganisms. The pseudomonas chlororaphis provided by the invention is pseudomonas chlororaphis PS08, and the preservation number of the pseudomonas chlororaphis PS08 is CGMCC (China General Microbiological Culture Collection Center) No.33616. The strain has the capacity of dissolving potassium and phosphorus, also has the capacity of producing siderophores, auxin and ACC deaminase, can improve the seed germination rate, promote plant growth, improve plant biomass and promote plant root development, and is further used for repairing degraded grassland. The complex microbial inoculant prepared from the strain is used for repairing degraded grassland, the soil fertility is improved by constructing a soil structure, nutrient and microorganism multi-dimensional microhabitat and enhancing the density and vitality of underground propagules, and on the basis, alfalfa and wild indigenous grass seeds are reseeded, so that synergistic repairing of vegetation and soil is achieved, and the soil fertility is improved. The above-ground biomass and the pasture quality are improved, and the grassland soil fertility is improved.

The invention discloses a grass planting method under a grazing-light complementary grassland slope photovoltaic module, relates to the technical field of photovoltaic power generation and ecological restoration, and aims to solve the problem that in the prior art, normal growth of grass seeds in the area is difficult to guarantee by common spray-seeding density. The method comprises the following steps: S1, pretreating a side slope, cleaning sundries on the side slope below a photovoltaic module mounting area, and digging steps in an area with the gradient being greater than 30 degrees, so that the distance between the step layout and a photovoltaic module array is kept at a safe distance of 0.5-1m; s2, soil improvement: soil shielded by the photovoltaic module is detected, lime is added into the detected acid soil to adjust the PH value to 6.5-7.5, and when 10-15 kg of decomposed cattle and sheep manure is mixed in each square meter, the range of 50 cm of a module foundation embedded part needs to be avoided; s3, grass seed selection and treatment, wherein grass seeds with the proper shade-tolerant adaptation degree are selected. The method has the advantages that the problem that the germination rate of grass seeds is low in a weak light environment is solved, and the slope vegetation coverage rate is effectively guaranteed.

The invention relates to the technical field of soil sampling, and discloses a soil sampling instrument for ecological management of natural grassland, which comprises a vehicle body, a vertical guide frame, a lifting sliding plate, a control motor, a connecting rod, a transmission shaft, a transmission gear, a top plate, an inner lining plate, an inner guide plate, a bottom plate, a placing frame and the like, two vertical guide frames are fixedly connected to the trolley body, guide grooves are formed in the inner side faces of the vertical guide frames, lifting sliding plates are slidably connected into the vertical guide frames, through holes capable of containing the clamping rods are formed in the outer sides of the vertical guide frames, and depth marks are carved at the positions of the through holes outside the vertical guide frames. According to the device, pit digging and sampling are integrated, during pit digging, soil dug out by the spiral drill rod is conveyed upwards through the soil discharging spiral blade, so that a large sampling frame composed of the top plate, the inner lining plate, the placing frame, the inner guide plate and the bottom plate enters a pit dug out by the spiral drill rod, and then continuous sampling is conducted on the pit wall through the sampling mechanism; the function of integrating pit digging and sampling is realized.