35 results about "Population density" patented technology

The invention discloses a plant disease and insect pest early warning method and system based on plant monitoring, and belongs to the field of plant disease and insect pest early warning, and the method comprises the steps: extracting the contour of a lesion region through an edge detection algorithm, carrying out the smoothing of the contour through the combination of morphological operation, and obtaining a more precise lesion region boundary; according to an image segmentation result and a disease type identification result, a plant health condition evaluation model is established, and the plant damage degree is quantified; environmental data and image data are acquired from a plurality of sensor nodes distributed in a field, and the data are gathered to a regional gateway through wireless transmission; carrying out preprocessing and feature extraction on the converged heterogeneous data in a regional gateway, removing noise data and redundant information, and extracting key features; and carrying out pest detection and counting on the preprocessed image by using a deep learning model, carrying out modeling on a pest number change trend, predicting population density change in a period of time in the future in combination with environmental factors, and generating a detection result.

A method for delineating an ecological corridor based on a maximum similarity model determines ecosystem type according to the determined ecological source, and assigns a habitat suitability index; and according to a maximum similarity matrix between different ecosystems, assigns the habitat suitability index to peripheral pixels of the selected ecological source, to obtain a similar value of the habitat suitability index. The habitat suitability index is corrected according to the surface curvature data and population density data, and resistance surface data is calculated according to the corrected value. Based on the resistance surface data, an ecological corridor is delineated comprehensively to find one or more paths corresponding to a minimum cost distance as ecological corridors. The influence of ecological environment on ecological corridor establishment is considered more comprehensively, so that the ecological corridor is delineated more accurately, and applied into practice to maximum extent.

A method is provided for determining soil properties for an area of land, from soil spectrum data. In an embodiment, the method includes receiving soil spectrum data records from hyperspectral sensors that represent a mean soil spectrum of a specific geo-location of the area of land and removing interference signals from the spectrum data records to create soil spectral bands. The method also includes predicting a plurality of soil property datasets based on a partial least-square regression and the soil spectral bands and selecting specific soil property datasets from the plurality of soil property datasets to represent soil properties of the specific geo-location, wherein the specific soil property datasets include property data and spectral band data for spectral bands used to determine the property data. The specific soil property datasets may then be used to generate a crop prescription of recommended hybrid seeds or population densities for the specific geo-location.

The invention provides a fish school density distribution similarity measurement-based bait casting decision-making method, which comprises the following steps of: acquiring static pictures, dynamic videos and real-time video streams of a water area containing fishes, and preprocessing to obtain an input data set; an Ifeed model is constructed, and the Ifeed model comprises a multivariate input preprocessing and image foreground target extraction module, a fish school feeding behavior analysis and density distribution estimation module, a target key point extraction and full connection distance calculation module and a fish school gathering trend visualization and bait casting decision support module; inputting the input data set into the Ifeed model, and training the Ifeed model through the input data set; static pictures, dynamic videos or real-time video streams of a water area to be fed are collected and input into the trained Ifeed model, and gathering degree visual data and feeding strategies of fish schools in the water area to be fed are obtained. Feed waste can be reduced, water quality pollution is reduced, cost is reduced, efficiency is improved, and the purpose of accurate feeding is achieved.

The invention provides a key species population spatial distribution quantification method and system in a complex ecological system, and the method comprises the steps: carrying out the feature extraction of biological and environmental data collected in a target ecological region, and carrying out the fusion of the extracted multi-modal features, and generating a comprehensive feature vector; based on the target large model, analyzing the comprehensive feature vector, identifying species in the target ecological region, and obtaining species identification information of each species; based on the species identification information and the comprehensive feature vector corresponding to the target ecological region, quantifying a species population space distribution condition, and generating species population quantification information at least comprising a population distribution diagram, a population density diagram and a population dynamic change diagram; and based on the species population quantitative information and the collected original data, generating and displaying a quantitative report. According to the method, the species can be intelligently, efficiently and accurately identified based on the large model, the distribution condition and dynamic change of the species can be visually displayed, a quantitative report is provided, and a powerful basis is provided for ecological protection and management.

The invention discloses a single-point calculation method and system for single-point sampling of microbial population analysis and an inversion method.The method comprises the steps that a sampling device is controlled to sample multiple target intervals, and multiple parallel samples are collected after old water replacement in a pipeline is completed based on preset or instantly-calculated trigger interval time Tc; calculating the variety and population density of single-point microorganisms; and after uncertainty evaluation is carried out on the single points, integrating microorganism species and population density data of all sampling points in the target interval, and generating a microorganism species set and population density of the target interval. Therefore, the model breaks through the boundary effect limitation of the traditional laminar flow theory, so that the triggering interval time Tc is accurately calculated, and sample cross contamination caused by retention of old water particles is thoroughly eliminated. When the parallel sample deviates, the linear characteristic of low-fluctuation data is kept by adopting arithmetic mean; and the high-fluctuation scene is switched to the root-mean-square calculation mode, so that abnormal noise interference is effectively suppressed.

The invention discloses a hyperspectral and morphological feature fused rice planthopper stage and sex identification method, and aims to improve the classification accuracy of the variety, age and sex of rice planthoppers in the initial stage of nymphs. Spectral data of rice planthoppers are obtained through a hyperspectral imaging technology, morphological characteristics are extracted by combining a morphological image processing technology, and the spectral data and morphological characteristic data are combined by adopting a multi-modal data fusion technology. And training and classifying the fused data by using a machine learning algorithm, and accurately identifying the type, age and gender of the rice planthopper. In order to solve the problem of data imbalance, an SMOTE algorithm is adopted to carry out sample oversampling, and class distribution balance is ensured. The severity of insect pests is evaluated in real time through automatic counting and population density generation, and support is provided for prevention and control measures. The screening of characteristic wave bands is expected to provide key technical support for the development of special multispectral cameras. In addition, the model interpretability is improved by adopting SHAP analysis, and the contribution of the features to the classification result is quantified.

The invention provides a fishery resource dynamic monitoring method and system based on underwater detection equipment, relates to the field of dynamic monitoring technology analysis, and realizes fine management of water area monitoring by collecting fish school density data in real time, constructing a fish school density matrix and combining grid unit division and geographical hash coding. The gradient value of the adjacent grid units is calculated based on the density matrix, the gradient matrix is generated, the fish school density abrupt change area can be accurately recognized, the abnormal area is marked, then the sonar sampling frequency of the underwater detection equipment is dynamically adjusted, and the monitoring precision of the abnormal area is improved. Compared with the prior art, the method has the advantages that the monitoring precision, the anomaly detection response capability and the resource utilization efficiency are remarkably optimized, and the adaptability and the prediction capability of a monitoring system are improved particularly in the aspects of fish swarm migration trend prediction and grid division dynamic adjustment.

The invention provides a method for monitoring the population density of rice planthoppers by an unmanned aerial vehicle for the field of agricultural pests. In order to solve the problems that rice planthoppers are seriously damaged and efficient monitoring methods are lacked, the method for monitoring the population density of the rice planthoppers by collecting rice multispectral images through an unmanned aerial vehicle is provided and is characterized in that the unmanned aerial vehicle is used for carrying a multispectral camera to collect rice canopy multispectral images, and the population density of the rice planthoppers is investigated; extracting green, red, red edge and near-infrared reflectivity and texture indexes of the rice canopy; utilizing a continuous projection algorithm to screen reflectivity and texture indexes sensitive to the rice planthopper population density, and utilizing the screened indexes to establish a rice planthopper population density monitoring BP neural network and a random forest model; performing unmanned aerial vehicle multispectral image acquisition on rice in a to-be-detected area, extracting spectral reflectivity and texture indexes, and inputting the spectral reflectivity and the texture indexes into the model to monitor the rice planthopper population density. According to the method, the unmanned aerial vehicle is used for collecting the multispectral image simply and quickly, the monitoring precision is high, and a new method is provided for monitoring large-area rice planthoppers.

The invention relates to the technical field of pest resistance identification, and discloses a rapid identification method for resistance of broad beans to aphids. Insect inoculation and observation are carried out under the manual control condition, the problems of uneven population density and the like in the natural environment are avoided, the identification period is shortened, only several days to several weeks are needed from insect inoculation to obtaining of a reliable identification result, compared with several months in the prior art, the efficiency is greatly improved, and the method is suitable for large-scale popularization and application. Manual control conditions can ensure that environmental conditions of each experiment are consistent, interference of environmental factors on experiment results is reduced, meanwhile, strict experiment operation and a data processing method also improve the accuracy and repeatability of the results, the identification results are more reliable, operation is relatively simple and convenient, complex equipment and a large amount of labor force are not needed, and the method is suitable for large-scale popularization and application. And the identification process can be completed only by carrying out simple insect inoculation and observation recording work in a manually controlled environment.

The invention relates to a corn and peanut intercropping environment monitoring and feedback regulation system in the field of agricultural Internet of Things, and aims to solve the problems of incomplete monitoring, lagging regulation and low resource utilization rate in traditional intercropping planting. The method comprises the following steps: firstly, deploying soil, weather, high-definition cameras and vibration sensors in a distributed manner, and collecting initial data such as soil humidity, pH value, air temperature and humidity, insect population density, corn stalk vibration amplitude and the like; the method comprises the following steps: acquiring numeric data, transmitting the numeric data to a cloud platform through ZigBee and a 4G / 5G network, performing format verification and 3 sigma abnormal value elimination on the numeric data, preprocessing image data, and then identifying insect population density by using a YOLOv5 model to generate preprocessed data; the cloud platform calculates a comprehensive stress index of moisture, nutrition and insect pests in combination with the pre-processed data and precipitation in the future 24 hours, and generates irrigation, fertilization and medication regulation and control decisions; the local control unit drives the equipment to execute, the sensor verifies the effect after regulation and control, secondary regulation and control are carried out if decision triggering judgment is still met, and the method is suitable for corn and peanut intercropping fine planting.

The invention discloses a remote sensing ecological index extraction method and system considering human activity intensity and a medium, and belongs to the technical field of remote sensing ecological environment monitoring and evaluation.The method comprises the steps that S1, for the problems that in high and cold arid area large-scale habitat quality evaluation, multi-source data is difficult to obtain, precision is low, and calculation efficiency is insufficient, a high and cold arid area large-scale habitat quality evaluation is established; providing a remote sensing ecological index improvement method based on a Google Earth engine cloud platform and human activity intensity; s2, introducing a salinity index, and providing an improved remote sensing ecological index extraction method; s3, providing a human activity intensity calculation method in combination with four indexes of night light, building land percentage, population density and landscape development index; and S4, coupling the improved remote sensing ecological index, establishing the remote sensing ecological index considering the human activity intensity, and realizing rapid and refined calculation of the remote sensing ecological index. According to the method, powerful data support is provided for ecological management and scientific decision-making of the cold and cold mountainous area.

The present application discloses a single-point calculation method, system and inversion method for single-point sampling of microbial population analysis, the method comprises: controlling a sampling device to sample multiple target intervals, based on a preset or instantaneous trigger interval time Tc After replacing the old water in the pipeline, multiple parallel samples are collected; the microbial species and population density at each point are calculated. After uncertainty assessment of the individual points, the microbial species and population density data for all sampling points within the target interval are integrated to generate the microbial species set and population density for that target interval. This model overcomes the boundary effect limitations of traditional laminar flow theory, accurately calculating the trigger interval time Tc and completely eliminating sample cross-contamination caused by retained old water particles. When parallel samples deviate, arithmetic averaging is used to maintain linearity for low-fluctuation data; in high-fluctuation scenarios, the calculation mode switches to root mean square (RMS) mode to effectively suppress abnormal noise interference.

The invention relates to a method, a device, equipment and a medium for monitoring spatial and temporal distribution of tiny insects in facility agriculture, and the method comprises the steps: carrying out the interpolation completion of the population density quantized value of the tiny insects corresponding to each time step at each sampling point position in a facility agriculture monitoring region; determining the population density of the tiny insects corresponding to the position of each sampling point in each monitoring grid in each time step; inputting the population density of the tiny insects corresponding to each sampling point position in each monitoring grid at the current time step into a preset discrete space-time dynamic model so as to determine the population density of the tiny insects corresponding to each sampling point position in each monitoring grid at the next time step; and constructing the population density of the tiny insects corresponding to each sampling point position in each monitoring grid in each time step in the future inspection period into a space-time distribution thermodynamic diagram, and displaying the space-time distribution thermodynamic diagram on a graphical user interface. According to the invention, the accuracy and stability of population density prediction are improved.

The invention discloses a high-yield and high-efficiency breeding method for whitebait. The method sequentially comprises the following steps of breeding water area selection, water area trash fish removal, water area pathogen killing, bait organism breeding, fry putting, ecological management and high-efficiency fishing. Wherein the seedlings are put in an alternate-catching and alternate-putting mode, and the seedlings are put in batches; 10%-20% of fertilized eggs are supplemented every year to achieve resource proliferation. According to the whitebait breeding method, fishing in autumn can make whitebait reach the commodity specification of 4-6 cm. Fishing before reproduction is achieved in March to April in spring, resource waste is avoided, and efficient fishing is achieved; the large water surface is more beneficial to natural proliferation, seedlings are put in batches, annual production is achieved, and high yield is achieved. 10%-20% of fertilized eggs are supplemented every year, and the population density is maintained. Silver carps and bighead carps are matched to regulate water quality and improve comprehensive benefits. And the reasonable yield per mu can reach 15-25 kg.

The application provides a forest small and micro water source group community construction method and system based on an ecological chain, relates to the technical field of ecological engineering, and comprises the following steps: selecting a natural catchment area according to forest topographic features, constructing a water area unit with a layered anti-seepage structure, arranging an anti-seepage layer at the bottom to realize water collection, and arranging a water-permeable transition layer at the edge to form a water-land gradient; dividing the water area unit into multiple ecological function areas according to the water depth gradient and spatial position and introducing matching species; dynamically adjusting the species ratio and spatial layout of adjacent function areas based on the functional correlation of material circulation and energy transmission by monitoring the population density and water quality indexes, forming a self-sustaining ecological chain; arranging a microorganism cultivation layer around the periphery, calculating the inoculation proportion of saprophytic microorganisms, calculating the soil organic matter conversion amount according to the population density and decomposition rate, and establishing the dynamic balance of the water supply amount of the water area, the metabolic activity of microorganisms and the physical and chemical properties of forest soil. The water source ecological system constructed by the application has high stability, can effectively improve the water holding capacity of forest soil and reduce the risk of fire.

The application discloses a regional XCO2 prediction method based on multi-source feature uncertainty modeling, and belongs to the technical field of remote sensing environment monitoring and time series prediction. Firstly, the population density feature is introduced on the basis of common features such as vegetation, weather, terrain and land cover, and a multi-source input data sequence of a unified space-time scale is constructed through spatial resampling and time interpolation. Then, a dynamic evidence multi-scale fusion mechanism is constructed, multi-scale evidence representation, credibility coefficient re-calibration and Dempster-Shafer recursive fusion are performed on the multi-source input, and enhanced multi-scale fusion features are generated. Then, the local dynamic information of the current period and the historical same period mode information based on time coding retrieval are extracted through two-way period modeling, and the two-way information is adaptively weighted and fused, and finally the regional XCO2 prediction result of the future period is output. The application improves the reliability of multi-source feature fusion and the prediction ability of complex non-stationary time series.

This specification provides a smart city greening management method, system, storage medium, and device. The method is executed by a management platform and includes: acquiring vegetation data of the monitoring area corresponding to the object platform through a sensor network platform, wherein the vegetation data includes at least one of species information, climate information, soil information, maintenance information, and actual growth parameters; obtaining vegetation anomaly information of the monitoring area based on the vegetation data, wherein the vegetation anomaly information includes the location and amount of vegetation anomalies; obtaining the predicted air quality of the monitoring area based on the vegetation anomaly information, combined with the traffic flow and population density of the monitoring area; and determining the priority of greening treatment in the monitoring area based on the predicted air quality and vegetation anomaly information.

The invention discloses a disease and pest monitoring method based on Internet of Things perception, and aims to solve the problems that under cross-regional migration, a wide-area wind temperature and humidity field and local surface parameters are difficult to physically and consistently fuse, risk hot area delineation lags behind, multi-source alignment is difficult and uncontrollable in uncertainty, and identification and trapping quantification are inconsistent. According to the method, a farmland space-time diagram is constructed, alignment interpolation is carried out, a Fourier neural operator is adopted to predict an arrival probability, a physical constraint operator is refined, quality conservation and nonnegativity are guaranteed through micro-projection, uncertainty is quantified through disturbance reasoning, and a risk hot area and early warning are generated through a space-time diagram model. And open vocabulary detection and segmentation are identified under the guidance of the hot area and are subjected to trapping counting consistency constraint quantification of population density and damaged area, and result reinjection forms linkage output, so that the technical effects of cross-scale physical consistency fusion, accurate delineation of risk hot areas in advance, measurable and available whole-process uncertainty and cross-modal consistency quantification are realized.

The present invention discloses a method, system and medium for extracting a remote sensing ecological index considering human activity intensity, belonging to the technical field of remote sensing ecological environment monitoring and evaluation. The steps include: S1, aiming at the problems of difficult acquisition of multi-source data, low accuracy and insufficient calculation efficiency in large-scale habitat quality assessment in alpine arid areas, an improved method for remote sensing ecological index based on the Google Earth Engine cloud platform and human activity intensity is proposed; S2, a salinity index is introduced, and an improved method for extracting remote sensing ecological index is proposed; S3, combining four indicators of night light, percentage of built-up land, population density and landscape development index, a method for calculating human activity intensity is proposed; S4, coupling the improved remote sensing ecological index, a remote sensing ecological index considering human activity intensity is established to realize rapid and refined calculation of the remote sensing ecological index. The present invention provides strong data support for ecological management and scientific decision-making in alpine mountainous areas.

This invention discloses an effective method for controlling leaf curling in macadamia nuts, comprising the following steps: selecting mineral oil with an effective ingredient content ≥99%, mixing it with water at a volume ratio of 1:100-400, then adding an emulsifier accounting for 0.05%-0.2% of the total weight of the mixture, stirring evenly to prepare a mineral oil emulsion; applying the emulsion using a misting device based on the growth characteristics of macadamia nuts during the shoot emergence period, pre-flowering period, young fruit stage, and mid-fruit development stage, combined with the differences in insect resistance among different varieties; integrating four measures: pruning and shaping control, orchard sanitation, soil regulation, and nutrient enhancement; and regularly monitoring key indicators such as thrips population density, leaf curling rate, and deformed fruit rate using a five-point sampling method, adjusting the control plan based on quantitative data. This invention achieves highly efficient control of thrips, prevents leaf curling, improves macadamia nut yield and quality, and protects the orchard ecological environment by optimizing the mineral oil emulsion formulation, precise application strategies, synergistic agricultural management measures, and dynamic monitoring and adjustment.

The invention belongs to the field of agricultural pest prevention and control, and particularly relates to a grassland spodoptera frugiperda recognition processing method based on big data, and the method specifically comprises the steps: dividing a farmland into a plurality of grids, and obtaining and preprocessing an egg mass image, an environment, a vibration signal and population density data in each grid; grid node features are defined, dynamic edge weights are calculated, the edge connection probability is predicted based on the pre-trained network, the connection state of the graph is updated according to updating rules of the connection probability and the edge connection state, and a dynamic space-time heterogeneous graph is obtained; inputting the space-time dynamic heterogeneous graph into a pre-trained graph convolutional network, and outputting density growth rate and migration probability of spodoptera frugiperda; and performing risk level assessment and early warning based on the density growth rate and the migration growth rate. In addition, the invention also discloses a system and a medium for executing the method. According to the method, efficient prediction of density increase and migration probability of the grassland spodoptera exigua is realized, and the problems of low identification precision and inaccurate prevention and control in prevention and control of the grassland spodoptera exigua are effectively solved.

The invention discloses a land utilization conflict analysis method based on land space planning, and relates to the technical field of land planning, and the method comprises the steps: obtaining planning data of a target region, the planning data comprising a space boundary and a population bearing scale of a residential land, and a space boundary and a pollution emission grade of an industrial land; generating a corresponding population density grade and an initial pollution influence intensity grade based on the population bearing scale and the pollution emission grade; on the basis of geographical and traffic data of the target area, a composite diffusion coefficient is calculated through a diffusion model, and the initial pollution influence intensity grade is corrected, so that the one-sidedness that only space boundaries are seen in traditional analysis is solved, conflict identification of living and industrial land is more accurate, and the method is suitable for popularization and application. And the rectification cost caused by conflicts after planning landing is reduced.

This invention relates to the field of ecological risk assessment technology, and in particular to a method and system for assessing human-wildlife conflict risk in nature reserves. It constructs a list of conflicting species and a standardized distribution dataset by integrating multi-source data. Based on a maximum entropy model, a stacked species distribution model is built to calculate species hazard levels. For three types of conflict—crop damage, livestock predation, and personal injury—arable land, grassland, and population density are matched as exposure indicators, respectively. Based on a "hazard level × exposure level" framework, categorized risk sub-models are constructed, and a comprehensive risk raster map is generated by overlaying these sub-models. Finally, the overall risk assessment value is calculated using nature reserves as units. This invention addresses the problems of existing human-wildlife conflict risk assessment technologies, which fail to effectively integrate multi-species contributions, differentiate conflict types, and adapt to large-scale protected area systems.

This invention relates to the field of ecological restoration, and discloses a method and system for soil health restoration and nutrient cycling regulation of degraded *Allium chinense* populations in karst alpine regions. The method includes the following steps: performing structured initial sampling in the target restoration area to obtain an initial training dataset; constructing a master surrogate model for predicting *Allium chinense* population density and a constrained surrogate model for predicting the feasibility of restoration schemes; optimizing the master surrogate model using a sampling function to select candidate restoration formulas; implementing the candidate restoration formulas in the field and observing the actual population density response, incorporating the new data pairs into the initial training dataset to update the master surrogate model; using the constrained surrogate model combined with a feasibility constraint function to evaluate the economic feasibility of the candidate restoration formulas under given competing plant conditions; integrating the new data to retrain the master surrogate model and / or the constrained surrogate model and updating the recommended restoration formulas. This method provides quantitative support for restoration decisions while reducing the number of field experiment rounds.

The invention provides an intelligent cooperation method and system applied to full-process agricultural management, and relates to the technical field of agricultural intelligent management, a natural year is divided into a plurality of time periods, and the following steps are periodically executed: real-time data are collected, and the real-time data comprise a plurality of real-time detection data; respectively acquiring a standard value range of temperature and a standard value range of humidity based on historical data of the current time period; based on the historical data of the current time period, the standard value range of the temperature and the standard value range of the humidity, performing pest evaluation to obtain a single-period predicted population density; on the basis of historical data of different time periods, pest prediction is carried out based on the mathematical model through real-time detection data, and the multi-period predicted population density is obtained; and performing cooperative calculation based on the single-period predicted population density and the multi-period predicted population density to obtain an insect pest management scheme of the current time period. The method has the advantage that the reliability of pest control under intelligent monitoring can be improved.

The application provides a key species population spatial distribution quantification method and system in a complex ecological system, the method comprising: performing feature extraction on biological and environmental data collected in a target ecological region, fusing the extracted multi-modal features to generate a comprehensive feature vector; analyzing the comprehensive feature vector based on a target large model to identify species in the target ecological region and obtain species identification information of each species; quantifying the spatial distribution of the species population based on the species identification information and the comprehensive feature vector corresponding to the target ecological region to generate species population quantification information including at least a population distribution map, a population density map, and a population dynamic change map; and generating a quantification report and displaying the report based on the species population quantification information and the collected original data. The application can intelligently, efficiently and accurately identify species based on a large model, and can intuitively display the species distribution and dynamic change and provide a quantification report, thereby providing a strong basis for ecological protection and management.

This invention discloses a method for increasing the proportion of migratory individuals in the first generation of salmon roe. The method is characterized by the following steps: First, in January, mature landlocked salmon broodstock are induced to spawn. After the eggs hatch into fry and begin feeding, the fry are placed in a flowing-water rearing pond for two months, with a population density of 200-300 fry / m². 3 The culture water temperature is 6-8℃, dissolved oxygen is 8-10 mg / L, and the fish are fed artificial feed twice a day, with each feeding amount being 2% of the wet weight of the fry. After completing the first stage of cultivation, at the end of March, juvenile fish with a body length of 2-3 cm and a weight of 1±0.2 g are selected and cultured in flowing water for 3-4 months, with a population density of 8000-10000 fish / m³. 3 The water temperature for rearing is 8-10℃, the dissolved oxygen is 8-10 mg / L, and artificial compound feed is given twice a day, with each feeding amount being 0.2% of the wet weight of the fry.

The invention provides a forest small and micro water source community construction method and system based on an ecological chain, and relates to the technical field of ecological engineering, and the method comprises the steps: selecting a natural water collection area according to forest topographic features, constructing a water area unit with a layered anti-seepage structure, arranging an anti-seepage layer at the bottom to achieve water collection, and arranging a permeable transition layer at the edge to form a land and water gradient; dividing the water area unit into a plurality of ecological functional areas according to the water depth gradient and the spatial position, and introducing matched species; by monitoring population density and water quality indexes, on the basis of functional association of material circulation and energy transfer, the species ratio and spatial layout of adjacent functional areas are dynamically adjusted, and a self-sustaining ecological chain is formed; arranging a microbial cultivation layer on the periphery, calculating the inoculation proportion of the saprophytic microorganisms, calculating the soil organic matter conversion amount according to the population density and the decomposition rate, and establishing dynamic balance of the water area water supply amount, the microbial metabolic activity and the forest soil physicochemical property. The constructed water source ecological system is high in stability, the water holding capacity of forest soil can be effectively improved, and the fire risk is reduced.

The invention provides a carbon dioxide leakage risk assessment method and system based on population and vegetation coverage. The method comprises the steps of 1, extracting population density values and vegetation coverage values of grids in a geothermal area to be assessed; step 2, determining each population density grade of the geothermal area to be evaluated based on the population density numerical value; step 3, determining each vegetation coverage grade of the geothermal area to be evaluated based on the vegetation coverage numerical value; step 4, determining a geological activity risk index and a carbon dioxide emission upwelling risk index of the geothermal area according to each population density grade and each vegetation coverage grade of the geothermal area to be evaluated; and 5, according to the geological activity risk index and the carbon dioxide emission upwelling risk index, the carbon dioxide leakage risk grade of the geothermal area is evaluated. According to the technical scheme provided by the invention, the evaluation precision of the carbon dioxide leakage risk is improved.