17 results about "Forestry" patented technology

The invention discloses an urban block green land planning method and device based on thermal environment big data, and the method comprises the steps: obtaining the thermal environment data, vegetation coverage data and a vegetation database of a target block; constructing a thermal environment map of the target block based on the thermal environment data, and obtaining a thermal environment feature vector of each sub-block according to the thermal environment map; acquiring a microclimate influence value of each vegetation type; performing correlation analysis on the thermal environment feature vector and the microclimate influence value of each sub-block to obtain the microclimate adjustment contribution degree of each vegetation type to each sub-block; and according to the microclimate adjustment contribution degree, determining a target vegetation type to be planted in each sub-block and a planting proportion of each target vegetation type, and obtaining a vegetation configuration scheme of the target block. The microclimate adjusting capability of the urban green land can be improved.

The application provides a drought propagation analysis method and system considering multi-dimensional characteristics of drought events. The method comprises the following steps: calculating a drought duration propagation index based on a meteorological drought total duration and an agricultural drought total duration; calculating a drought intensity propagation index based on meteorological drought intensities of previous meteorological drought events and agricultural drought intensities of previous agricultural drought events; calculating a drought intensity trend propagation index based on meteorological drought intensity trends of previous meteorological drought events and agricultural drought intensity trends of previous agricultural drought events; calculating a drought fluctuation propagation index based on meteorological drought index fluctuation rates of previous meteorological drought events and agricultural drought index fluctuation rates of previous agricultural drought events; calculating a drought probability propagation index based on a meteorological drought occurrence probability and an agricultural drought occurrence probability of a target region within a given time period; and constructing a comprehensive drought propagation index by using a CRITIC objective weighting method. The application can accurately describe the propagation process between meteorological drought and agricultural drought.

The invention provides a southern irrigation area fruit tree irrigation method based on space-air-ground integrated monitoring. The southern irrigation area fruit tree irrigation method comprises the steps that space-air-ground facility equipment is used for collecting data of the surface temperature, humidity, fruit tree transpiration intensity and soil moisture content of an irrigation area; constructing a time sequence fusion data set for the space-air-ground multi-source data, and generating a high-precision soil moisture content distribution map; day-by-day weather and fruit tree growth data are obtained, and subarea division is carried out on the fruit tree park according to soil texture and fruit tree plant distribution; constructing a fruit tree water demand model, constructing an LSTM rainfall forecasting model, and constructing an irrigation amount forecasting model based on a water amount balance principle; a decision-making system of feedforward prediction and feedback decision-making is constructed, and an irrigation decision-making instruction is issued to a water taking valve attached to the high-level water tank for irrigation by means of an internet-of-things platform; analyzing the water stress condition of fruit tree canopies according to an unmanned aerial vehicle thermal infrared imaging technology, and optimizing a fruit tree water demand model; and establishing an irrigation expert knowledge base, and iteratively updating an irrigation strategy.

A panoramic system can receive content (e.g., live video) recorded in a landscape orientation and use a portrait dynamic crop window to display a portrait area of the received content. The panoramic system can further display the content in a landscape dynamic crop window that has a moveable user interface control that can pan the portrait crop window as the content plays in one or both of the dynamic crop windows.

1. Name of the Design Product: Ornament (Colored Sand Christmas Tree). 2. Use of the Design Product: For handicraft ornaments. 3. Design Key Points of the Design Product: Lies in the combination of shape and color. 4. Picture or Photo that Best Illustrates the Design Key Points: Perspective view. 5. The claimed design includes color. 6. The top view and bottom view are uncommon views, so the top view and bottom view are omitted.

The invention provides a planting industry structure adjustment method and device based on climatic year and risk assessment, and a storage medium. The method comprises the following steps: estimating estimated meteorological data, estimated crop yield data and estimated cultivated land data corresponding to each grid region in a target year; carrying out grain production key climate factor identification to obtain a meteorological yield and a meteorological factor influence coefficient, and carrying out climate year type division to obtain a climate year type division result; determining the risk degree of the annual specified crops; determining the suitability grade index of the specified crop in each grid area; determining a suitable grade area of the specified crop; an adjustment result of the planting structure in the target year is determined, the constraint condition comprises a suitable grade area, and the adjustment result comprises an optimal planting area of the specified crop in the specified region; therefore, the accuracy of structure adjustment in the planting industry is improved.

An object of the system according to the embodiment is to enable even a person who does not have knowledge about agriculture to identify characteristics of land, diseases of plants, and pests, and to realize stable production of agricultural crops in response to climate change.SOLUTION: A system includes a climate data analysis part, an image analysis part, a land characteristic analysis part, and a harvest prediction part. The climate data analyzer analyzes the climate data. The image analysis part specifies the disease and the harmful insect of the plant based on the weather data analyzed by the weather data analysis part. The land characteristic analysis unit analyzes the characteristics of the land based on the disease or pest of the plant specified by the image analysis unit. The harvest prediction unit predicts a harvest period and a harvest amount of the crop based on the property of the land analyzed by the land property analysis unit.SELECTED DRAWING: Figure 1

The invention discloses an arbor, shrub and grass mixed pixel vegetation index time series data decomposition method. The method comprises the following steps: acquiring coarse resolution mixed pixel vegetation index time series data, high resolution vegetation index time series data and arbor, shrub and grass distribution data of a target vegetation community; counting the green degrees of trees, shrubs and grass and the fluctuation range of the difference between the trees, shrubs and grass based on the high-resolution data to form a decomposition time constraint condition; embedding a time constraint condition in the linear mixed model, and resolving a vegetation index reference time sequence of the arbor, shrub and grass end members in the target area; and allocating a residual error obtained by weighted summation of the mixed pixel vegetation index and the arbor, shrub and grass reference value according to the area proportion and pixel-oriented and time sequence adjustment weight to arbor, shrub and grass. According to the method, the vegetation index time series data of the mixed pixels can be decomposed into the sub-pixel-level arbor-shrub-grass vegetation index time series data, the decomposition result is reliable, the community mobility is good, and effective technical support can be provided for sub-pixel-level vegetation refined monitoring.

The application provides a point cloud map-based automatic precise pesticide application method and device for a dense orchard, which comprises the following steps: setting a plant protection robot in a target orchard, and constructing an offline point cloud map corresponding to the target orchard; performing semantic segmentation on the offline point cloud map to obtain a pose matching database and a tree crown area database; performing block segmentation on the offline point cloud map based on a fruit tree planting row matrix and a fruit tree planting plant matrix in the target orchard to obtain an orchard topological model; and the plant protection robot travels along a preset route in the target orchard and applies pesticides. According to the scheme, the dynamic objects and the static objects are accurately separated through semantic segmentation, the influence of "tunnel effect" is eliminated by using matching based on topological constraints, the point cloud matching positioning is obtained, and the travel route of the plant protection robot is ensured, so that the tree crown layer is applied with pesticides.

The invention relates to a cultivated land water holding capacity assessment method, in particular to a cultivated land water holding capacity assessment method based on soil moisture memorability. The invention aims to solve the problem that the existing method for evaluating the water holding capacity of the cultivated land is difficult to expand to long-time-sequence large-range monitoring. The method comprises the following steps: 1, collecting daily soil moisture time sequence data and terrain classification data of a single pixel of cultivated land; 2, identifying a cultivated land area according to the terrain classification data, and visually interpreting and judging whether the cultivated land is a paddy field or a dry field according to remote sensing images in recent five years; 3, extracting daily soil moisture time sequence data of a single pixel of the SMAP, and determining descending trend data according to a descending trend determination rule; 4, exponential function fitting is carried out on the trend of more than four data pairs in the descending trend; and 5, the water holding capacity of the cultivated land is calculated. According to the invention, a new view angle and method are provided for the evaluation of the water holding capacity of the cultivated land, and technical support is provided for the long-time-sequence large-range monitoring of the water holding capacity of the cultivated land. The invention belongs to the field of cultivated land water holding capacity evaluation.

A blockchain system is disclosed for tracking and exchanging carbon credits between providers and acquirers. The blockchain system can be a closed-loop gateway that exchanges tracked tokens from an original carbon credit provider through subsequent acquirers. Additionally, future projected carbon credits can be generated and / or verified.

The invention relates to the field of crop planting, and particularly discloses an intelligent cultivation system and method for crop irrigation, and the method comprises the steps: firstly, carrying out the inversion of a spatial distribution map capable of finely representing the water stress state of crops through remote sensing data, and carrying out the initialization of a distributed soil water balance model based on the spatial distribution map; therefore, a high-fidelity initial soil humidity field is generated. On the basis, the initial field is used as a starting point, a weather forecast data driving model is used for carrying out future soil humidity dynamic deduction, meanwhile, sparse ground sensor real-time data is used as a correction anchor point, and model prediction is continuously corrected through a data assimilation technology; and the problems of limitation of a single data source on a space-time scale and accumulation of model errors along with time are overcome. Finally, based on the accurate prediction of the future soil humidity, a dynamic variable irrigation prescription map is generated, the conversion from passive response to active prediction is realized, and the water demand requirements of crops at different spatial positions and time points are met.

The invention discloses a full-coverage low-power-consumption path planning method for fruit-bearing forests in hills and mountainous regions. The method comprises the steps that an energy consumption model is determined according to mechanical parameters of operation machines and tools; constructing a map information model of a unit grid type according to the fruit-bearing forest elevation map information and the fruit-bearing forest planting information; according to the map information model, carrying out classification processing and regional decomposition on the map in sequence; according to a region decomposition result, determining a potential entry point and a taboo entry of each subunit, and calculating and screening an optimal region traversal sequence and an optimal entry point through a genetic algorithm; according to the region traversal sequence and the starting point and the ending point of the traversal path in each subunit, all the subunits are connected, full-coverage path planning of the whole fruit-bearing forest operation region is completed, related energy consumption is calculated, low-power-consumption coverage of the whole operation region is achieved, and behaviors of generating extra energy consumption are reduced.

The invention relates to the technical field of vegetation coverage measurement, in particular to a vegetation coverage measurement system and method based on unmanned aerial vehicle remote sensing. According to the method, the measurement image of the road surface is identified through the first coverage measurement model, the area with plant fallen leaves is obtained, the road surface fallen leaf coverage is obtained through calculation, and the road surface fallen leaf sweeping index is obtained by combining multiple influence factors; the vegetation coverage of the road is recognized through a second coverage measurement model, and the vegetation coverage area in the green belt and the vegetation coverage area outside the green belt are obtained by means of image comparison of the contour of the green belt; and starting from a proportional relation among the road vegetation coverage, the green belt vegetation coverage and the road surface vegetation coverage, calculating to obtain a growth state index of green belt vegetation. According to the method, the vegetation coverage of each region of the road is identified, so that the road greening vegetation is effectively supervised, and managers are assisted to make decision analysis.

The invention discloses a runoff sudden drought change attribution method. The method comprises the following steps: step 1, collecting hydro meteorology and various geospatial data of a research area; 2, dividing the runoff length sequence into a natural period and an influence period; 3, selecting n daily scale hydrological models, and calibrating each model parameter based on natural period data; 4, calibrating each model parameter based on the influence period data; 5, simulating runoff sequences under different scenes and gathering multi-model results; step 6, identifying runoff sudden drought events in different scenes, and extracting runoff sudden drought features; 7, analyzing the influence of climate change, human activity and the synergistic effect of the climate change and the human activity on runoff sudden drought change; the invention provides a runoff sudden drought change attribution method, which makes up the deficiency of runoff sudden drought analysis in existing research, quantifies climate change, human activity and contribution of synergistic effect of the climate change and the human activity, has strong operability, and is suitable for analyzing the evolution of runoff sudden drought in various regions.

The new and distinct variety of olive tree variety ‘I 79’ is provided. The variety can be distinguished by its outstanding features of spreading growth habit, high yield, and high oil content.

The invention discloses a pesticide seed treatment agent performance determination method based on Internet of Things, and particularly relates to the technical field of pesticide seed treatment agent performance determination, the performance determination method comprises the following steps: S1, system establishment and data acquisition; s2, data transmission and preprocessing; s3, performance index calculation: calculating the germination rate through an image recognition algorithm and sensor data, evaluating the growth potential of seedlings by adopting a target detection algorithm based on deep learning in combination with a comprehensive scoring method, and comprehensively judging and calculating the pest control effect through image recognition and sensor data; s4, data analysis and result output; according to the performance determination method of the pesticide seed treating agent based on the Internet of Things, real-time acquisition, transmission and processing of data in the performance determination process of the pesticide seed treating agent are realized through the Internet of Things technology, and the determination efficiency is improved; a scientific basis is provided for performance evaluation of the pesticide seed treating agent, the pesticide seed treating agent with excellent performance can be screened out, and agricultural production is promoted.