46795results about "Cultivating equipments" patented technology

The invention relates to the technical field of agricultural irrigation, in particular to an irrigation decision-making method based on an agricultural system model. The method comprises the following steps: calculating a growth environment index of a target farmland to generate a crop environment growth index; crop physiological status evaluation is carried out according to the crop environment growth index, short-term water demand prediction of the field is carried out, and field crop short-term water demand data is generated; performing irrigation scheme decision processing according to the short-term water demand data of the field crops to obtain optimal irrigation scheme data; irrigation equipment is regulated and controlled by using the optimal irrigation scheme data, and irrigation water quantity influence analysis is carried out to generate irrigation water quantity influence data; and performing adaptive irrigation control optimization according to the irrigation water quantity influence data so as to obtain intelligent irrigation control strategy data. Irrigation equipment can be regulated and controlled according to dynamic requirements of crops, intelligent and accurate irrigation decision control is achieved, excessive or insufficient irrigation is effectively avoided, and the water resource utilization rate is remarkably increased.

The invention relates to the technical field of intelligent agriculture and ecological internet of things, in particular to an ecological irrigation decision dynamic optimization method and related equipment. Comprising the following steps: acquiring multi-source environment data, acquiring a soil profile humidity gradient in real time through a soil humidity sensor array, acquiring future rainfall probability distribution, a temperature change rate and a wind speed predicted value in combination with a weather forecast interface, and synchronously accessing a geographic information system to acquire terrain elevation and crop distribution data. The three technical bottlenecks of model dimension collapse, parameter estimation instability and optimization response lag in a traditional irrigation decision-making system are systematically solved by constructing a space-time coupling analysis framework and a closed-loop optimization mechanism of multi-source heterogeneous data.

The invention discloses an urban greening system based on big data and an optimization method thereof, relates to the technical field of artificial intelligence, and solves the problems of difficulty in heterogeneous data integration, insufficient real-time analysis capability of dynamic data and low management efficiency in an existing urban greening system. Multi-source heterogeneous data are collected through an API interface and Internet of Things communication, and are cleaned, aligned, converted and fused through a distributed real-time data stream processing platform; image recognition and a machine learning algorithm are used for monitoring diseases and insect pests in real time, and a growth prediction model is trained in combination with an LSTM network, so that the quick response capability of the system to environmental changes is enhanced; according to the method, target weights are dynamically adjusted and optimized through the deep Q network, a multi-target optimization model is constructed in combination with mixed integer linear programming, context awareness and a genetic algorithm, intelligent optimization of irrigation, fertilization, pest control and greening layout is achieved, and the management efficiency and the resource utilization rate of an urban greening system are remarkably improved.

The invention relates to the technical field of agricultural intelligent irrigation, and discloses a meteorological data fused water-saving irrigation control method, device and equipment and a medium. According to the method, a historical meteorological data set is constructed through multi-source meteorological data fusion, a dynamic water demand table is generated in combination with a crop water demand characteristic database, and a crop water demand model is established based on soil moisture content data. A grid irrigation unit division and growth period coupling soil moisture content response matrix construction technology is adopted, a cooperative constraint is established between a water demand threshold value and a water saving benefit through a multi-objective optimization learning algorithm, and a personalized irrigation scheme is generated. A soil moisture content dynamic evaluation matrix containing a dynamic time warping operator is designed, and dynamic matching of the soil layered soil moisture content and a standard template is achieved. The contradiction between meteorological response lag and low water resource utilization rate in traditional irrigation is effectively solved, accurate irrigation decision is realized through multi-dimensional data fusion and an intelligent optimization algorithm, and the water-saving benefit and the agricultural water resource utilization efficiency are improved.

Provided is an intelligent irrigation control method for rice fields based on a cloud service platform and a system thereof. Constructing a planting condition data set and a condition coefficient, monitoring the current water supply environment of crops, and constructing a water supply coefficient. If the water supply coefficient is lower than the water supply threshold, based on the current growth data of crops, a plurality of corresponding irrigation strategies are matched from the pre-constructed crop irrigation knowledge graph, and the target strategy is selected from a plurality of irrigation strategies; constructing an irrigation observation coefficient set, and constructing irrigation coefficient. If the irrigation coefficient is lower than the efficiency threshold, use the trained strategy optimization model to optimize the target strategy, and control the irrigation coefficient based on the optimized target strategy to complete the irrigation of crops in the irrigation area.

The invention discloses a water-saving control method for agricultural irrigation, a cloud platform and equipment, and relates to the technical field of irrigation water-saving control, and the method comprises the steps: determining the historical rainfall of the geographic position of a target farmland and the historical soil moisture content parameter of the target farmland; determining farmland water demand rule characteristics of the target farmland based on the historical rainfall and the historical soil moisture content parameters; constructing a farmland water-saving control model according to the farmland water demand rule characteristics; dividing a water demand area of the target farmland based on the crop information; based on the farmland water-saving control model and the soil moisture content parameter of each water demand area, obtaining a predicted irrigation value of each water demand area; acquiring a real-time irrigation value of each water demand area; and based on the predicted irrigation value and the real-time irrigation value, performing real-time regulation and control on a preset intelligent irrigation water valve in each water demand area. The problem that the utilization rate of water resources is low can be effectively solved.

The invention relates to the technical field of intelligent agriculture, and discloses a visual intelligent agricultural planting control system. The system comprises an environment data acquisition module used for acquiring multi-source environment sensing data; the growth feature modeling module is used for extracting crop growth state features through a morphological analysis algorithm; the environment regulation and control decision module is used for generating an environment regulation and control instruction set by utilizing a dynamic threshold matching algorithm; the visual interaction module is used for generating a three-dimensional farmland live-action simulated diagram through multi-dimensional data fusion processing; the strategy execution module is used for driving agricultural facilities to execute actions by adopting a self-adaptive control algorithm; an abnormity early warning module is further arranged, and abnormity early warning signals are generated through correlation analysis. The system realizes comprehensive acquisition and analysis of agricultural planting environment data, precise environment regulation and control, visual and visual display and abnormal early warning, effectively improves the intelligent and precise level of agricultural planting, improves the crop yield and quality, and assists the development of intelligent agriculture.

The invention provides a crop accurate management method and system based on a crop growth model. The method comprises the following steps: collecting environment data of a crop growth period by integrating a remote sensing technology, a field intelligent sensor and an unmanned aerial vehicle, and carrying out space-time alignment to form a comprehensive environment data set; and dynamically adjusting parameters of the crop growth model by using a Bayesian rule, evaluating the influence of environmental uncertainty on crops in combination with Monte Carlo simulation, and providing yield prediction with a confidence interval. Based on the prediction, a particle swarm optimization algorithm is applied to formulate management schemes such as irrigation, fertilization, pest control and harvesting opportunities, and meanwhile, a multi-objective optimization strategy is adopted to balance economic benefits and environmental protection. After the scheme is implemented, the crop growth response is monitored in real time so as to reinforce learning and automatically adjust management measures, and precise agricultural management is realized. According to the technical scheme provided by the embodiment of the invention, the crop management precision and efficiency are improved.

The invention relates to the technical field of automatic irrigation control, in particular to a multi-sensor fusion agricultural precise irrigation system which comprises a leaf area analysis module, an environment evaluation module, a task arrangement module, an execution regulation and control module and a feedback regulation module. According to the method, the crop leaf area index is introduced to be compared with the growth stage target value, the dynamic expression of the group moisture demand is realized, the environmental evapotranspiration influence factor is generated in combination with illumination and temperature disturbance parameters, the timeliness of water demand driving force identification is enhanced, and the task execution sequence is judged by adopting regional water pressure disturbance data; a multi-region irrigation resource allocation process is optimized, a water quantity and pressure difference double-factor adjustment mechanism is utilized, the rationality and response accuracy of time length allocation are improved, water supply state analysis and task target deviation comparison are performed, dynamic supplementary irrigation regulation and control are realized, the closed-loop capability and supplementary irrigation accuracy of irrigation period control are improved, and the irrigation period control efficiency is improved. And the stable allocation capability and the irrigation response efficiency of the irrigation system under the influence of multiple variables are enhanced.

The invention discloses a precise irrigation decision analysis method and system based on crop water demand, and particularly relates to the technical field of crop irrigation. A monitoring area is divided, soil humidity sensors with different depths are installed, moisture state data of a root zone are monitored in real time, moisture gradient change characteristics and moisture retention time deviation characteristics are extracted and analyzed, a moisture fluctuation abnormal index is calculated, the moisture fluctuation abnormal index is compared with a threshold value, and the moisture fluctuation abnormal index is calculated. The method comprises the following steps: dividing underground water fluctuation into high, middle and low underground water fluctuation abnormal levels, and taking corresponding irrigation adjustment measures according to different levels of fluctuation: taking a drainage measure to reduce excessive soil moisture for high-level abnormal fluctuation; crop water demand model parameters are dynamically adjusted through medium-grade fluctuation; a normal irrigation strategy is maintained by low-grade fluctuation, so that negative effects of underground water fluctuation and excessive irrigation on crop root zones and soil structures can be effectively avoided, soil permeability and water storage capacity are maintained, long-term productivity of farmland is guaranteed, and irrigation efficiency and water resource utilization rate are improved.

The invention relates to the technical field of soil monitoring, in particular to a farmland irrigation management and control system based on the Internet of Things, and the system comprises a soil monitoring module, a crop water demand analysis module, an irrigation strategy making module, an irrigation execution control module, a water source management module, a fertilizer distribution module, an irrigation effect evaluation module and an adaptability adjustment module. According to the method, the crop water demand is analyzed by combining the decision tree, the water consumption is accurately calculated, the irrigation strategy optimizes the time and frequency based on the linear programming and simulated annealing method, the scheme adaptation degree is improved, the irrigation link adopts an automatic control and dynamic adjustment mechanism, the environment and crop demand changes are adapted in real time, and the irrigation efficiency is improved. Water source allocation ensures efficient distribution of multiple water sources on the basis of dynamic optimization, the type and quantity of fertilizer feeding are accurately controlled according to crop requirements, the irrigation effect is subjected to self-adaptive optimization of an irrigation plan after crop data evaluation, and accurate, flexible and efficient farmland resource management is integrally achieved.

The invention discloses an ecological garden intelligent management method and system, and belongs to the technical field of landscaping management, and the method specifically comprises the steps: monitoring environmental parameters in real time through multiple sensors, and uploading the environmental parameters to a cloud server; at the cloud end, a dynamic data analysis model is constructed by utilizing historical irrigation data, a plant growth model and real-time monitoring parameters, the water demand of plants is predicted, and an irrigation decision is made; an irrigation threshold value is set according to the decision, and an irrigation program is automatically started and executed through remote control equipment; after irrigation, detailed information is recorded and fed back to the data analysis model, the irrigation effect is evaluated by adopting a data analysis method, and an irrigation decision is finely adjusted accordingly; intelligent and precise irrigation management is realized, and the utilization efficiency of water resources and the growth quality of plants are improved.

The invention relates to the technical field of mine side slope restoration, in particular to a mine side slope ecological restoration method which comprises the following steps: S1, performing field investigation and evaluation, and performing comprehensive surveying and mapping on the landform of a mine side slope by using a high-precision surveying and mapping instrument; the anti-seismic effect and the prevention and control of water and soil loss are prominent, the anti-seismic performance is enhanced through deep rock mass anchoring reinforcement, large-diameter and high-strength anchor cables are adopted, bed rock is deeply stabilized, prestress is applied, the integrity of the rock mass is enhanced, reinforced concrete anti-sliding piles are arranged near potential sliding surfaces, the anti-sliding capacity is improved, and the anti-seismic effect is achieved. When a damping ditch is dug between a slope top and a blasting area, loose gravel is filled to weaken vibration energy, the anti-seismic performance of the side slope is comprehensively improved, and water and soil loss is prevented, a drainage system is constructed to intercept surface water, vegetation is reasonably configured, planting grooves and fish-scale pits are arranged, an ecological restoration composite structure layer is built, and a grass spraying and planting and vine climbing system is combined. And water and soil loss is reduced through multiple measures.

The invention discloses a drip irrigation parameter dynamic regulation and control method combined with multi-point sensor monitoring, and relates to the technical field of irrigation regulation and control, the method comprises the following steps: monitoring a multi-point sensor arranged in a target area to obtain target monitoring data; performing actual demand analysis on the target crop to obtain actual demand data; analyzing the target soil permeability of the target area to obtain a target irrigation period; and performing irrigation processing according to the actual demand data and the target irrigation period to obtain irrigation data. The technical problems that in the prior art, real-time requirements of crops cannot be accurately obtained, irrigation is difficult to implement according to soil characteristics, and the agricultural production efficiency and sustainability are limited are solved. The effects that multiple types of monitoring data are obtained by arranging the multi-point sensors in the target area, precise irrigation is achieved, the crop growth environment is optimized, good physicochemical properties of soil are maintained, and the technical effects of high efficiency and sustainability of agricultural production are improved are achieved.

The invention relates to a restoration method of a wetland ecosystem, and aims to improve the biodiversity of a wetland and restore and enhance the ecological function of the wetland through systematic measures. The method comprises the following steps: firstly, evaluating the current condition of the wetland, and identifying specific or endangered species and alien invasive species; secondly, by introducing or protecting specific animal and plant species, the natural habitat is recovered, and the biodiversity of the wetland is improved; thirdly, the water quality and soil conditions of the wetland are improved by adopting technologies such as hydrological regulation and control and vegetation restoration; fourthly, the connectivity of the interiors of the wetlands and surrounding habitats is enhanced, and species migration and gene exchange are promoted. By means of the measures, a damaged wetland ecosystem can be effectively restored, key functions such as water quality purification, flood regulation and carbon storage of the wetland ecosystem can be restored, meanwhile, endangered species are protected, and the ecological value and landscape aesthetics of the wetland are improved.

The invention relates to the technical field of plant monitoring, in particular to a plant growth monitoring and maintenance system. The system comprises the following modules: a data collection module used for acquiring a plant image and environment data; judging the health condition of the plant based on the plant image; performing plant irrigation and maintenance demand analysis on the health condition according to the environmental data to generate plant irrigation and maintenance demand data; the data processing module is used for performing region segmentation on the plant image through the plant irrigation and maintenance demand data to generate a plant irrigation region; and setting a regional environment anomaly threshold value based on the plant irrigation region, and performing distributed early warning on the plant irrigation region according to the regional environment anomaly threshold value to generate a distributed early warning signal. Through intelligent data collection, environment monitoring, automatic control and precise irrigation optimization, plant health condition evaluation, irrigation demand analysis and efficient water conveying are achieved, and the efficiency and the intelligent level of plant growth and maintenance are improved.

The invention relates to the technical field of intelligent agricultural equipment, and discloses an automatic agaricus bisporus picking method, device and equipment based on artificial intelligence and a medium. The method comprises the steps that RGB images and three-dimensional point cloud data of the agaricus bisporus growth environment are collected in real time through a binocular vision camera and a depth sensor which are installed at the tail end of a picking robot, and environment illumination parameters and cultivation bed coordinate information are obtained; inputting the RGB image into a pre-trained lightweight convolutional neural network, identifying a pileus contour, a stipe position and a maturity level of the agaricus bisporus, calculating a space coordinate, a height and a growth inclination angle of the agaricus bisporus based on the three-dimensional point cloud data, and constructing an agaricus bisporus target database; a mechanical arm picking path is generated based on the agaricus bisporus target database, and path weight parameters are optimized online through a preset reinforcement learning model; according to the maturity grade and morphological characteristics of the agaricus bisporus, the clamping force and the rotating angle of the end effector are dynamically adjusted, and automatic picking of the agaricus bisporus is completed.

The invention relates to the field of saline-alkali soil water-fertilizer regulation, in particular to a saline-alkali soil soybean water-fertilizer-salt regulation dynamic decision-making method and system. A saline-alkali soil soybean water, fertilizer and salt regulation dynamic decision-making system comprises a region division module, a data monitoring module, an irrigation initial decision-making module, a fertilization initial decision-making module, an irrigation decision-making acquisition module and a fertilization decision-making acquisition module. By integrating an advanced sensor technology and an intelligent decision-making model, key parameters are accurately monitored, and the total amount of irrigation and fertilization required in a specific time window in the future is calculated in combination with crop growth stages and historical water and fertilizer records; meanwhile, the irrigation and fertilization frequency can be dynamically adjusted according to soil and environment data monitored in real time, waste of water resources and fertilizer is avoided, the possible soil salinization problem is solved, soil health can be maintained, and crop growth is promoted.

The invention relates to the technical field of environment regulation, in particular to a corn cultivation environment intelligent regulation method based on multi-sensor control, which comprises the following steps: S1, generating a soil moisture three-dimensional distribution diagram in real time through humidity sensor arrays arranged at different depths of a corn root zone; s2, combining infrared temperature data of the canopy and the three-dimensional distribution diagram of the soil moisture, and calculating a moisture motion mismatch degree through transpiration-permeation conduction collaborative analysis; s3, generating a user-defined irrigation mode based on the water movement mismatch degree, and controlling the irrigation duration, the stop interval and the water flow rate. According to the method, coupling modeling is carried out on the plant physiological water demand behavior and the soil moisture supply characteristic, the limitation that a traditional irrigation system only depends on a soil humidity threshold value or meteorological index judgment is broken through, and the biological adaptability and the aging responsiveness of field moisture regulation and control are improved.

The invention relates to the technical field of agricultural intelligent irrigation, and discloses a high-standard farmland intelligent irrigation system based on Internet of Things and data analysis, and the system comprises a soil moisture content sensing module which collects data through a multi-source sensor to construct a three-dimensional soil moisture content distribution model, and generates a soil moisture content characteristic spectrum; the soil moisture content prediction module generates water demand prediction data based on the soil moisture content characteristic spectrum, the meteorological data and the crop growth stage; the irrigation strategy module fuses terrain elevation and pipe network pressure parameters to generate an irrigation control map; the equipment state monitoring module collects water pump current waveform and other data to generate equipment health degree parameters; the pipe network optimization module optimizes pipe network topology and generates an adjusting instruction; the multi-source data fusion module generates fusion evaluation indexes by using an evidence theory, an entropy weight method and the like; and the intelligent execution module generates an execution control instruction accordingly. All the modules cooperate to achieve precise irrigation, and the utilization efficiency of water resources and the intelligent level of farmland management are improved.

The invention discloses a strip-shaped compound planting mode water and fertilizer zoning management method and system, and relates to the technical field of modern agriculture, and the method comprises the following steps: installing a sensor network in a field, collecting data through a soil moisture sensor, an intelligent water meter and an unmanned aerial vehicle multispectral camera, threshold values are set according to the water demand and the fertilizer demand in the crop growth cycle to judge fertigation, crop demands are predicted by applying multiple linear regression and a random forest algorithm, a fertigation plan is optimized, the growth trend is analyzed, the yield potential is evaluated, the influence of the irrigation level is researched, and an irrigation strategy adjustment suggestion is provided. The system collects field data through an integrated sensor network, accurately sets an irrigation and fertilization threshold value, predicts crop demands by using an advanced algorithm, optimizes a water and fertilizer management plan, effectively analyzes a growth trend and evaluates yield potential, provides a scientific basis for irrigation strategy adjustment, and significantly improves modern agricultural production efficiency and resource utilization efficiency.

A method for generating an agricultural cut-point for an agricultural item includes capturing an image of the agricultural item, generating a depth estimation of the agricultural item, segmenting the image of the agricultural item to generate a segmented image that identifies different segments of the agricultural item, detecting an agricultural feature of the agricultural item based on the image of the agricultural item, generating a two-dimensional cut-point based on the segmented image and the agricultural feature, and generating a three-dimensional cut-point based on the two-dimensional cut-point and the depth estimation of the agricultural item.

The invention discloses an intelligent irrigation control method and system based on meteorological information, and relates to the technical field of irrigation control, an area to be irrigated is divided into a plurality of sub-areas, and future rainfall (first rainfall) is predicted through a preset meteorological early warning model; acquiring water absorption data of each sub-region, calculating a water absorption coefficient, and determining absorbable rainfall (second rainfall) in combination with the first rainfall; and judging the water consumption of each sub-region according to the crop growth data and the historical data, and comparing the water consumption with the second rainfall. If the water consumption is greater than the second rainfall, the sub-region needs to be irrigated; according to the method, the soil water content of each sub-region can be finely judged, and whether rainfall meets the water demand or not is evaluated, so that whether irrigation is needed or not is judged, excessive irrigation and water resource waste are avoided, meanwhile, it is ensured that crops obtain enough water, water resource management is optimized, and the crop growth effect and agricultural production efficiency are improved.

The invention relates to a supply and demand multi-source data fusion farmland water-saving irrigation water system design and dynamic water distribution optimization method, which is characterized by comprising the following steps: S1, crop-soil-weather multi-source data coupling modeling, S2, water-saving water system design optimization, S3, dynamic water-saving water distribution strategy, and S4, intelligent terminal control system control. According to the scheme, accurate configuration of water resources is achieved through the artificial intelligence technology, the irrigation water consumption is reduced to the maximum extent, and meanwhile the crop growth requirement is met.

An automated method of classifying irrigation status of fields within a geographical region includes identifying a plurality of field areas delineated by field boundaries within the geographical region. Seasonal normalised difference vegetation index (NDVI) time series data are processed to generate a plurality of aggregate field NDVI feature values for each identified field area. Irrigation status of each identified field area is classified by executing a decision tree classifier that is configured to determine a classification as either ‘irrigated’ or ‘non-irrigated’ based upon the corresponding plurality of aggregate field NDVI feature values.

Systems for plant culture include a chamber featuring one or more walls enclosing a spatial volume internal to the chamber, where the one or more walls include a surface for supporting a plant within the enclosed spatial volume, a gas delivery apparatus with at least one gas source, a nutrient delivery apparatus with a reservoir, a sampling apparatus connected to a port formed in the one or more walls, and a controller configured so that during operation of the system, the controller activates the nutrient delivery apparatus to deliver an aqueous growth medium to the plant, and activates the gas delivery apparatus to deliver into the enclosed spatial volume a mixture of isotopically-substituted gases. Also provided are methods of use of the system for measuring nitrogen in a plant and for identifying microbes capable of providing fixed nitrogen to a plant.

An air-moving device may include an aerodynamic rotor in the form of a bladed disk. The aerodynamic rotor may include a physical geometry that constrains a noise level of the air-moving device during operation. The air-moving device may be a leaf blower.

To provide novel plant environmental stress alleviator and plant growth promoter having an excellent effect of reducing environmental stress such as high temperatures.SOLUTION: Provided is a plant environmental stress alleviator or a plant growth promoter, which contains a plant extract containing acetylcholine or an acetylcholinesterase inhibitor and calcium ions. Also provided is a method for alleviating plant environmental stress or a method for promoting plant growth by applying the plant environmental stress alleviator or the plant growth promoter to a plant.SELECTED DRAWING: None

The invention relates to the field of power system supply and demand interaction, and discloses an intelligent greening irrigation system and a water-saving control method, the system comprises a multi-source data acquisition module, a space-time tensor modeling module, a dynamic decision generation module, an edge calculation acceleration module, a distributed execution control module and a drip irrigation module; the method comprises the following steps: collecting multispectral, meteorological and soil moisture data of a plant canopy in real time to generate an original data set; a fusion data set is obtained through space-time alignment and normalization preprocessing; the water demand is predicted based on an LSTM-Transform model, and an irrigation safety interval is screened; the irrigation uniformity and water pressure distribution are simulated and evaluated through multi-physics field coupling; a double-delay DDPG algorithm is used for optimizing valve opening parameters; the model and the execution error are corrected through Bayesian modeling, and an NDVI feedback optimization strategy is combined. According to the invention, a double-delay depth deterministic strategy gradient algorithm is introduced for dynamic optimization of an irrigation strategy, so that the capability of quickly adapting to environmental changes is achieved.

The invention relates to the field of intelligent agriculture, and discloses an intelligent drip irrigation control method, system and device and a storage medium, and the method comprises the following steps: data collection: obtaining soil humidity data, environmental meteorological data and crop growth state data of a target area through a plurality of sensors, and transmitting the data to a data processing module; the system comprises a data acquisition module used for acquiring soil humidity data, environmental meteorological data and crop growth state data and transmitting the acquired data to a data processing and storage module; the device comprises a machine case shell, and an intelligent control chip, a wireless communication module, an electromagnetic valve and a water flow meter are installed in the machine case shell. By integrating a sensor network, intelligent analysis and a real-time feedback mechanism, the water demand of crops is accurately calculated, irrigation is automatically adjusted, and water resource utilization is optimized; a personalized irrigation plan is made by combining a deep neural network and time sequence analysis, and the water requirements of crops in different growth stages are ensured.