20 results about "Population density" patented technology

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 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 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.

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 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 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.

Systems and method for providing marine data are provided herein. The system comprises a display, a processor and a memory including computer program code configured to, when executed, cause the processor to cause presentation of a chart indicating at least a portion of a body of water. The computer program code is further configured to receive sonar data from one or more sonar transducers associated with a watercraft, the sonar data being associated with a corresponding location where it was captured. The computer program code is further configured to determine a population density of fish at a location based on the sonar data and generate a heat map based on the population density. The heat map indicates one or more population densities at corresponding locations on the chart. The computer program code is further configured to cause presentation of the heat map over the chart.

This invention discloses a method for assessing the affected population based on dynamically corrected disaster-reported population data. The method includes the following steps: Step 1, collecting basic data of the study area; Step 2, initializing the population density field; Step 3, constructing a spatial adjacency graph; Step 4, determining the dynamic flood inundation range; Step 5, comparing the reported data to correct population errors. This method, even with limited reported data, dynamically optimizes local population density estimation by combining the spatiotemporal patterns of flood evolution, while ensuring total population conservation, non-negativity, and spatial smoothness. It achieves dynamic updates of population density at the grid scale within administrative regions, thereby obtaining a more accurate and realistic spatial distribution of the population and the number of affected people. This provides a basis for decision support such as population relocation and precise rescue, demonstrating strong practicality.

The invention provides a planting scheme determination method and system based on carbon dioxide absorption. The method comprises the steps that 1, an area range is acquired; 2, the underground geothermal condition is determined; 3, whether terrestrial heat meets the scheme requirement or not is judged; 4, determining the release condition of carbon dioxide; 5, judging whether the carbon dioxide reaches the standard or not, and if yes, determining the density distribution of the carbon dioxide; step 6, determining distribution information of buildings in a specified area; 7, judging whether the building distribution meets the planting requirements or not, and if not, modifying the planting area and quantity; step 8, determining regional population density; step 9, judging whether the population density reaches the standard or not, if yes, entering step 10, and if not, determining a planting scheme; 10, generating a planting scheme and judging whether the planting scheme reaches the standard or not; step 11, determining the planting area and quantity; step 12, sorting the planting schemes; and step 13, determining an optimal planting scheme. According to the technical scheme provided by the invention, the determination precision of the planting scheme is improved.

The invention discloses a disaster-affected population assessment method based on disaster-reported population data dynamic correction. The method comprises the following steps: step 1, collecting basic data of a research area; 2, initializing a population density field; step 3, constructing a spatial adjacency relation graph; 4, determining a dynamic flood inundation range; and 5, comparing the reported data to carry out population error correction. According to the method, local population density estimation can be dynamically optimized in combination with a spatio-temporal mode of flood routing under the condition that only a small amount of reported data exists, total population conservation, non-negativity and spatial smoothness are guaranteed, grid scale population density dynamic updating in an administrative region is achieved, and the population density estimation efficiency is improved. Therefore, accurate and practical population spatial distribution and the number of affected population are obtained, a basis is provided for decision support such as population transfer and accurate rescue, and the practicability is high.

The invention discloses a gene sequencing-based camellia weevils intrusion monitoring and precise medication method and system, and relates to the technical field of agricultural pest control, and the method comprises the following steps: installing an environment sample collection device in a camellia garden, and automatically and regularly collecting environment samples in a period of time; carrying out deep sequencing on the collected environmental sample by utilizing a high-throughput gene sequencing technology, and detecting specific DNA or RNA fragments of the camellia weevils; by means of the sequencing result and the meteorological parameters, a life cycle prediction model of the snout beetles is established, and the life cycle of the snout beetles of the real-time environment samples is predicted; and according to the life cycle and population density of the elephant beetles, an accurate pesticide spraying time table and dosage suggestions are generated. According to the method, gene sequencing is carried out on the environmental samples in the camellia oleifera garden, the population dynamics and the life cycle of the camellia oleifera weevils are determined in combination with a big data analysis technology, and therefore precise prevention and control are achieved, the use amount of pesticide is reduced, and the prevention and control effect is improved.