50 results about "Water stress" patented technology

The invention discloses an irrigation index prediction method based on meteorological data and field moisture capacity, and relates to the technical field of intelligent agriculture, and the method comprises the steps: collecting real-time meteorological data, real-time soil volumetric water content data, soil matrix potential data and future precipitation forecast data, and carrying out quality control and time alignment processing to obtain a multi-source data set; the method comprises the following steps: calculating evapotranspiration of a reference crop according to real-time meteorological data, obtaining actual water demand of the crop in combination with a crop coefficient, calculating a moisture stress coefficient according to real-time soil volumetric water content data and a field moisture capacity static parameter value, and obtaining a basic irrigation demand index in combination with future precipitation forecast data. According to the method, the evapotranspiration amount is separated by adopting the Shuttleworth-Wallace model, the irrigation index integrating the crop evapotranspiration amount, the water deficit amount and the effective precipitation amount is generated on the basis of multi-period rolling water balance calculation and risk decision rules, and online calibration of soil parameters and optimization of irrigation decision are achieved.

The invention relates to the technical field of intelligent agricultural intelligent irrigation control, and discloses an intelligent irrigation analysis control system based on a crop growth period. The system comprises a crop image sequence, soil humidity profile data and a regional meteorological sequence. And the data analysis module is used for performing multi-stage growth state interpretation on the image sequence, generating a crop structure state profile in each growth period, performing vertical moisture exchange identification on the soil humidity data, and generating a root zone moisture dynamic map. And the habitat mapping and stress evaluation module synchronously executes habitat adaptability mapping and moisture stress risk mapping. And the irrigation decision module corrects the habitat mapping result according to the stress evaluation result and generates an irrigation decision profile. And the control instruction generation module generates a partition valve control instruction in combination with the decision profile and the alternate irrigation rule. According to the invention, deep fusion analysis of the crop growth state, the root zone moisture and the meteorological environment is realized, and the irrigation decision accuracy and the water resource utilization efficiency are improved.

The invention relates to an intelligent fruit tree planting monitoring method and system, and belongs to the technical field of intelligent agriculture. According to the scheme, a multi-source sensor network is deployed to collect soil, crop and environment data, a salinity-moisture collaborative stress decision-making model is constructed, and parameters such as soil salinity, canopy temperature and weather forecast are deeply fused; dynamically generating a non-fixed precise irrigation instruction; meanwhile, a low-visibility robust vision algorithm is integrated, and it is ensured that fruits and diseases and pests can still be reliably recognized in severe weather; by establishing a collaborative scheduling mechanism, efficient linkage of water and fertilizer management, plant protection operation and farming operation is realized. According to the method, the technical span from single threshold judgment to multi-factor intelligent decision is realized, the utilization efficiency of water resources and fertilizers is effectively improved, the production cost is remarkably reduced, and the mode of fruit tree planting is promoted to be converted from traditional experience management to data-driven intelligent management.

The invention provides a garden plant irrigation control method and system, and relates to the technical field of garden plant irrigation control, and the method comprises the steps: periodically collecting a canopy image of a target plant in a garden scene through a visual perception device, carrying out the preprocessing and feature extraction of the image, and obtaining the plant visual features of the target plant; then, according to the visual features and water shortage judgment standards preset for different plant types, whether the target plant is in an acute water stress state or not is judged. Once it is confirmed that the plant is in an acute water stress state, the system immediately triggers emergency irrigation of the area where the target plant is located, and the problem that in the prior art, an automatic irrigation system excessively depends on environmental parameter measurement and weather prediction when facing sudden extreme weather, and the irrigation efficiency is high is effectively solved. The acute water stress of the plant cannot be found and responded in time.

The invention relates to the technical field of digital rural agricultural treatment, in particular to a digital rural agricultural treatment method and system based on artificial intelligence. Obtaining a water demand characteristic parameter of the type of a crop currently planted in the Badzhou field from a crop database; superposing the spatial distribution data of the soil moisture content and the spatial distribution map layer to generate a Badzhou field moisture state map layer; the accurate water supplementing amount of the Badzhou field is obtained through water demand analysis of the Badzhou field with the water stress; an irrigation path is planned according to the position proximity relation and gradient information of all the Buddha fields in the Buddha field moisture state graph layer, and differential irrigation schemes are generated by combining the precise water supplementing amount corresponding to each Buddha field; sending the differential irrigation scheme to irrigation execution equipment, and supplementing water to each palm field; the problem that precise irrigation is difficult due to scattered distribution and large environmental difference of the palm field in the mountainous area is effectively solved.

The invention relates to the technical field of agricultural irrigation control, in particular to an intelligent cotton irrigation control method and system based on stem flow data, and the method comprises the following steps: collecting data, calculating a stem flow rate, constructing a monitoring data set, extracting morning data, calculating a morning optical flow conversion coefficient, deducing a noon theoretical potential stem flow rate, and calculating a noon optical flow conversion coefficient. Comparing the theoretical value with the actual value to calculate the instantaneous deficit depth, calculating the deficit width based on the duration continuously lower than the theoretical value, screening the maximum depression depth, multiplying the maximum depression depth by the deficit width to calculate the pore closing resistance index, and constructing an opening instruction when the index is greater than a threshold value. According to the method, the potential rate is deduced through the morning conversion relation, the deficit depth and width are quantified, the pore closing resistance index is calculated to evaluate water stress, the instruction is triggered based on the physiological response, water demand diagnosis is achieved, timely water supplementing is ensured, the irrigation precision is improved, and the photosynthetic efficiency is guaranteed.

The invention relates to the technical field of intelligent irrigation, and discloses an intelligent irrigation system for a fruit tree planting greenhouse. The system comprises a multi-dimensional data acquisition module, a data fusion processing module, an irrigation decision module and an irrigation control execution module which are connected in sequence. The system obtains a root soil image, a leaf surface temperature distribution map and stem micro-deformation data, and performs multi-dimensional fusion calculation to generate a spatial water potential distribution map capable of representing the overall moisture condition of crops. And calculating specific regional water demand and preferential irrigation sites based on the map, and further driving an execution terminal to realize precise irrigation. According to the method, the physiological water demand signal of the crop can be responded, the spatial difference of water stress can be identified, the limitation that the traditional technology depends on single soil information is overcome, the crossing from environment monitoring to crop body sensing is realized, and the irrigation accuracy and the water resource utilization efficiency are improved.

The invention relates to the technical field of intelligent agriculture, and discloses a water and fertilizer integrated irrigation control method for wheat planting, and the method comprises the following steps: deploying a composite physiological sensor (comprising a high-sensitivity piezoelectric acoustic probe and an ion selective microelectrode array) to collect acoustic and ion signals reflecting moisture and nutrient states, extracting feature vectors, and calculating a water and fertilizer integrated water and fertilizer integrated water and fertilizer integrated water and fertilizer integrated water and fertilizer integrated water and fertilizer integrated water and fertilizer integrated water and fertilizer integrated water and fertilizer integrated water; the method comprises the following steps: calculating physiological rhythm synchronism to determine a dynamic decision weight so as to eliminate abnormal data, weighting and fusing feature vectors of normal sentinel plants, constructing partitioned comprehensive physiological features, decoding the features into a specific physiological state by utilizing a classification model, and finally, according to the state, applying a coupling control logic of water stress priority, so as to obtain a comprehensive physiological state of the sentinel plants. And generating and executing a water and fertilizer control instruction. By directly monitoring the physiological status of crops and introducing a self-checking mechanism, accurate and self-adaptive closed-loop water and fertilizer management is realized.

This invention discloses a smart irrigation management system based on soil and crop moisture status identification, belonging to the field of crop moisture status monitoring technology. The system includes: acquiring leaf electrophysiological signal sequences and multi-depth water potential parameters of the rhizosphere microdomain of the target crop; determining the onset time of water stress and its corresponding first characteristic frequency based on the leaf electrophysiological signal sequences; determining the active water-uptake layer and its corresponding second characteristic water potential based on the multi-depth water potential parameters; performing a coupled comparison based on the first characteristic frequency and the second characteristic water potential, and determining whether irrigation triggering conditions are met based on the comparison results. If the irrigation triggering conditions are met, a differentiated irrigation quota is determined for different irrigation areas. This invention determines irrigation triggering conditions by comparing the consistency and degree of deviation between the first and second deviation directions, avoiding premature or late irrigation caused by misjudgment based on a single indicator.

The invention discloses a tea garden early warning management system based on big data analysis, which relates to the technical field of tea garden early warning management and comprises a data acquisition module, a feature set generation module and a distributed analysis module. According to the method, a distributed collaborative learning framework is constructed, each tea garden locally trains a model and only uploads encryption parameters to the cloud, and a universal knowledge base is formed through fusion, so that a single tea garden can use experience of multiple regions for reference, and the early warning capability for novel diseases and insect pests and complex water stress is remarkably improved; the system realizes strategic transformation from passive response to active prevention and control by combining a self-evolution risk assessment rule and a near-end strategy optimization algorithm based on multi-source data acquisition and a spatio-temporal joint perception model, can generate future risk prediction, provides decision support for preventive management, forms a closed-loop optimization mechanism through strategy execution feedback, and improves the risk assessment efficiency. The early warning precision and decision effectiveness are continuously improved, and the intelligent level and the anti-risk capability of tea garden management are comprehensively enhanced.

This invention discloses a method for inverting equivalent water thickness in plant leaves across species. This method selects equivalent water thickness with more explicit physical meaning as a representative indicator of plant leaf water content. Based on traditional vegetation indices, it combines continuous removal of preprocessing parameters to construct absorption feature parameters, thereby reducing noise in hyperspectral data while deeply mining its inherent spectral characteristics. It innovatively introduces one-heat coding technology to explicitly process species information and employs a Bayesian optimized machine learning model, effectively improving the accuracy, stability, and computational efficiency of cross-species water inversion. This invention can accurately invert the water status of crops across species, providing a new method for rapidly acquiring crop water status based on hyperspectral reflectance characteristics. It can be widely applied in intercropping and relay cropping patterns and large-scale survey and analysis scenarios, greatly improving the accuracy and speed of water stress diagnosis.

The present application provides a sand ecological-hydrological process critical threshold identification system and application thereof, comprising a multi-parameter sample collection device, a soil physicochemical index determination module, a vegetation water stress response monitoring module, a critical threshold calculation unit and a steady state transition early warning module; by determining soil aggregate stability index data and soil water characteristic curve data, monitoring vegetation transpiration conductance data and leaf water potential data, using multi-level lag response analysis and dynamic response surface construction method, calculating critical water content threshold value and steady state transition critical point data, accurate identification and dynamic early warning of sand ecological system steady state transition are realized.

The present application relates to the field of ecology, water resources management and remote sensing technology, and provides an integrated estimation method and device for ecological water demand and water supplement. The method comprises: performing deviation correction or fusion processing on precipitation, evapotranspiration and soil moisture content data respectively; determining a reference evapotranspiration based on the obtained consistent driving field and constructing a baseline vegetation correction coefficient, performing residual correction on the correction coefficient under the condition of no water stress, and determining the ecological water demand according to the corrected vegetation correction coefficient and the reference evapotranspiration; inputting the consistent driving field, the ecological water demand and the remote sensing vegetation observation information into multiple discrimination gates, performing logical operation on the discrimination results, and dividing each pixel into a multi-level water supplement priority label; determining the total loss term based on the consistent driving field, the ecological water demand and the water supplement priority label, and determining the ecological water supplement. The problem that the ecological water supplement accounting in the prior art is difficult to meet the water conservation constraint and is prone to systematic bias is solved.

The invention provides bacillus zhangzhouensis and application thereof, and belongs to the technical field of biological agriculture, the bacillus zhangzhouensis is preserved in the China General Microbiological Culture Collection Center (CGMCC), the preservation number is CGMCC No.34248, the preservation number is CGMCC No.34248, and the preservation number is CGMCC No.34248. The bacterial strain and the biological agent containing the bacterial strain have a good effect of improving plant stress resistance (water stress resistance), can maintain basic physiological activities of crop plants under the condition of soil water shortage or waterlogging, can further promote growth of the crop plants, and achieve yield improvement and crop quality improvement. And the bacillus zhangzhou is collected from the natural environment, is safe, environment-friendly and pollution-free, and has a wide application prospect.

The application relates to a wheat root growth model established by coupling root system, rhizosphere and soil by using water carbon flux and a method thereof, and is based on the response of wheat root parameters, root density and root activity to water, and establishes a wheat root growth model by coupling root system, rhizosphere and soil by using water carbon flux, so as to form a crop root growth model system with applicability. The wheat root growth model is further coupled with a rhizosphere model on the basis of CA and L-System, a root system-rhizosphere-soil model with water carbon flux as an internal link is constructed, and the wheat root growth model is fully applied and developed. Model results show that the root mean square error (RMSE) of the test value and the model value of the root length distribution under water stress treatment and sufficient water treatment is 0.0261 and 0.0396 respectively, the model accuracy R 2 is 0.9179 and 0.8758 respectively, the simulation accuracy is relatively close, and the accuracy is very high.

The invention provides a sand ecological-hydrological process critical threshold recognition system and application thereof. The sand ecological-hydrological process critical threshold recognition system comprises a multi-parameter sample collection device, a soil physicochemical index measurement module, a vegetation water stress response monitoring module, a critical threshold calculation unit and a steady-state transition early warning module. The method comprises the following steps: measuring soil aggregate stability index data and soil moisture characteristic curve data, monitoring vegetation transpiration conductance data and leaf water potential data, and calculating a critical moisture content threshold value and steady-state transition critical point data by adopting a multi-level lag response analysis and dynamic response curved surface construction method; and accurate identification and dynamic early warning of steady-state transition of the sand ecosystem are realized.

A data gathering module receives georeferenced data from a UAV over a target area. A processing module receives the captured georeferenced sensor data and analyzes the data to create detailed landscape condition maps using techniques such as machine learning and vegetation indices (NDVI, NDRE). In some embodiments the module automatically identifies and segments different landscape features such as turf, plants, weeds and hardscapes. The data processing module pinpoints specific problem areas or polygonal zones that require remediation for issues such as water stress, nutrient deficiency, weed infestation or pest damage and generates a specific remediation recommendation. An action module generates a set of targeted commands to accomplish the remediation recommendation, and a verification module compares the pre- and post-mediated areas to generate a verification report.

The invention relates to a plant spacing setting method, system and device of a rice transplanter and a medium, and the method comprises the steps: obtaining meteorological prediction data, a water demand interval of a growth cycle of a to-be-planted crop in a preset time period, soil humidity, terrain elevation data and historical environment data of each sampling region, and a historical crop remote sensing index of the crop under the historical environment data; calculating an environmental influence coefficient of each sampling area; determining a moisture convergence path network of the target planting area; determining a soil humidity change sequence of each sampling area in a preset time period according to the meteorological prediction data and the moisture convergence path network; performing numerical value superposition on the soil humidity change values in the soil humidity change sequence and the corresponding soil humidity to obtain a soil humidity sequence; and calculating a water stress risk value of the growth cycle, and determining the planting distance of each sampling area. The rationality of the planting density is improved by setting the plant spacing of the rice transplanter.

The application provides a kind of intelligent pruning and water management synergic control method and system of fruit tree cultivation, belong to cultivation management technical field, this method includes: based on the thermal infrared image sequence of the leaf surface of different directions of fruit tree canopy, the transpiration rate change of each direction leaf is extracted, and the initial time of local water stress of corresponding direction leaf is identified;When the time difference of local water stress initial time between any two direction leaves reaches the preset stress threshold, a thinning pruning scheme is constructed;After completing thinning pruning according to the thinning pruning scheme, the global recovery slope in the preset period after pruning and the global attenuation slope in the stressed period before pruning are calculated from the transpiration rate change curve of the whole canopy of fruit tree to obtain the water compensation priority coefficient;Based on water compensation priority coefficient, irrigation control is executed.The technical scheme provided by the application can solve the problem of pruning and irrigation synergy failure caused by the deviation of canopy hydraulic zoning and whole plant average diagnosis.

The invention discloses a method for regulating and controlling leaf color change of Chinese tallow through water stress. The method comprises the following steps: A, selecting Chinese tallow container seedlings which grow robustly and are free from plant diseases and insect pests; b, performing water stress treatment 20-50 days before the natural color changing period of the Chinese tallow in the container; c, water stress treatment comprises drought treatment in which watering is stopped, semi-submerging treatment in which the container is placed in a water storage device and the water level of the water storage device does not exceed half of the height of the container, and full submerging treatment in which the upper edge of the container is located below the water level of the water storage device; and D, compared with the sapium sebiferum subjected to normal moisture management in the container, the appreciation time of the sapium sebiferum can be advanced by 16-20 days through the drought treatment, the appreciation time of the sapium sebiferum can be advanced by 10-15 days through the half-flooding treatment, and the appreciation time of the sapium sebiferum can be advanced by 21-30 days through the full-flooding treatment. The invention provides three gradient schemes of drought, half flooding and full flooding, the treatment intensity can be flexibly selected according to different landscape requirements and time nodes, and the effect is stable and controllable.

The invention relates to the technical field of agricultural monitoring, and particularly discloses a real-time monitoring system for the transpiration amount of forage grass crops. The system comprises a multi-source data sensing module, a stress response correction module, a dynamic self-adaptive calibration module and a transpiration fusion calculation module, and identifies a water stress state and generates a stress correction factor by collecting crop physiology, microclimate and soil data in real time; the initial transpiration estimated value is dynamically calibrated in combination with a meteorological sudden change compensation mechanism, and finally a high-precision real-time transpiration monitoring value is calculated and output through neural network fusion. According to the system, the adaptability to crop moisture conditions and environmental changes is improved, and reliable data support is provided for precise irrigation.

The present application provides a kind of regulation method for inducing phalaenopsis flower bud differentiation by day and night temperature difference, belong to plant culture technical field, the present application is by collecting each strain phalaenopsis genome single nucleotide polymorphism marker and historical phenotype data constructs temperature threshold prediction model, formulates differential day and night temperature control scheme and embeds optimal cooling curve planning module;During induction, use stem flow sensor and leaf microclimate probe to collect stem flow rate and leaf water potential in real time;Multi-channel physiological signal is input into day and night rhythm perception temperature control network, and the flower probability prediction value and target control temperature parameter are output in real time by artificial intelligence;With the flower rate and refrigeration energy consumption as double target, the energy-saving flowering game optimization model is constructed, the pareto frontier is solved on the agent surface, and the result is written back to the temperature control scheme;The technical problem that temperature control decision cannot simultaneously perceive plant rhythm physiological state and real-time water stress state, leading to the disconnection of temperature control strategy and actual physiological demand of plant is solved.

The application belongs to the technical field of crop irrigation control, and specifically discloses a multi-modal water-saving sprinkling irrigation system based on the Internet of Things, which comprises a data acquisition module, a stress analysis module connected with the data acquisition module, a sprinkling irrigation control module connected with the stress analysis module, and a fault processing module connected with the sprinkling irrigation control module. The physiological dynamics of crops are monitored in a multi-modal manner through the cooperation of an embedded stem micro-variation sensor and an infrared thermal imager, so that the occurrence of water stress can be accurately identified at an early stage. When water stress is identified, a capillary pulse sprinkling irrigation mode is automatically started, so that water supply is realized in a demand-oriented, accurate, small-flow and high-frequency manner. Not only is the timeliness and physiological adaptability of the irrigation response significantly improved, but also the root zone suitable water potential can be effectively maintained, water efficient absorption and utilization are promoted, water resource waste is reduced, and normal physiological metabolism of crops is ensured.

The invention discloses a multi-source data fusion diagnosis method for a grassland water stress state, and relates to the technical field of grassland ecological hydrology monitoring. Sequentially performing system deployment, macroscopic wide-area scanning to generate a preliminary diagnostic graph, near-earth targeted observation to generate a verification data set and mechanism model data assimilation, and finally outputting a water stress level distribution graph. The problems that in the prior art, observation means are isolated, diagnosis results stay in representation, mechanism interpretation is insufficient, and system flexibility is poor are solved. According to the method, accurate and deep diagnosis of the vegetation water stress state is realized through multi-level cooperation and mechanism fusion, and the diagnosis accuracy and efficiency can be continuously improved through a self-learning optimization mechanism.

A rice transplanter spacing setting method, system, device and medium, wherein the method comprises: obtaining meteorological prediction data, water requirement interval of a growth cycle of a crop to be planted in a preset time period, soil moisture of each sampling area, topographic elevation data, historical environmental data and historical crop remote sensing index of the crop under the historical environmental data; calculating an environmental influence coefficient of each sampling area; determining a water collection path network of a target planting area; determining a soil moisture change sequence of each sampling area in the preset time period according to the meteorological prediction data and the water collection path network; superimposing the soil moisture change value in the soil moisture change sequence and the corresponding soil moisture to obtain a soil moisture sequence; calculating a water stress risk value of the growth cycle, and determining a planting spacing of each sampling area. The application improves the rationality of planting density by setting the spacing of the rice transplanter.

This invention relates to the fields of molecular biology and genetic breeding, and particularly to the dCAPS marker associated with the length of adventitious roots in wheat under waterlogging stress and its application. This dCAPS molecular marker is based on the 234,677,747th base of wheat chromosome 6B; the SNP base difference is either C or T. This molecular marker is accurate and efficient to detect, convenient and stable to amplify, and can be used for marker-assisted selection to improve the efficiency and accuracy of identifying the waterlogging stress resistance of wheat plants.

The invention provides a farmland evapotranspiration prediction method based on multi-source remote sensing time sequence data, and the method comprises the steps: improving an LSTM time sequence prediction model through the high-resolution remote sensing data (temperature, humidity, wind speed and water vapor content) in a key time period of a plain region sample city in China, inputting the sample data into the improved model, and carrying out the training, and meteorological variable rolling prediction in the next 24 hours is realized. The potential evapotranspiration (ET0) is calculated by using a Penman-Monteith formula, and then the potential evapotranspiration (ET0) is calculated; the accuracy of model prediction is also verified through experiments, the comparison between experimental results and true values shows that the wind speed prediction error is 0.5 m / s, the temperature prediction error is 0.65 DEG C, the relative humidity error is 2.97%, the water vapor content error is 3.98 kg / m < 2 >, and the ET0 calculation error is less than 1% by improving the model, and the model has the advantages of stability in prediction in a short-time data scene, and the prediction accuracy is greatly improved. Due to the uniqueness of the structure and the short-term property of data, the method can replace traditional agricultural parameters to evaluate the water stress risk, and meanwhile, technical support is provided for agricultural irrigation decision making.

The application belongs to the technical field of sugarcane cultivation, and particularly relates to a device for drought stress resistance treatment of sugarcane, which comprises a support frame and a plurality of cultivation mechanisms arranged at intervals on the support frame, the cultivation mechanism comprises an outer barrel body and an inner barrel body, the upper and lower ends of the inner barrel body are both provided with openings, the inner barrel body is installed on a partition plate, a temperature control area is formed between the outer barrel body and the inner barrel body, a plurality of planting barrels that are adjacent to each other are arranged in the inner barrel body, and a water control assembly is arranged at the upper end of the planting barrel. The outer barrel body and the inner barrel body are arranged, and the inner barrel body is provided with a plurality of planting barrels that are adjacent to each other, so that the single-bud barrel of sugarcane seeds can be planted into a single planting barrel, the root system of the sugarcane seeds is effectively prevented from spreading and winding in disorder, and the data detection of the subsequent root system is affected. The water control assembly at the upper end of the planting barrel can effectively control the precision of water supplement and water control, so that the water stress is consistent, and the precision of the drought resistance evaluation is affected.

The application discloses a kind of intelligent irrigation methods and systems of traditional Chinese medicinal material seedlings based on reinforcement learning, comprising: obtaining the state perception parameter of current seedling cultivation environment, including soil moisture stress degree, seedling growth stage index and canopy wilting representation value;Based on state perception parameter and cumulative water stress degree, obtain the effective component accumulation estimate value of current seedling;State perception parameter and effective component accumulation estimate value are input into pre-trained irrigation decision model, and the irrigation action decision including irrigation duration and irrigation intensity is obtained;After executing irrigation, obtain updated state perception parameter and updated cumulative water stress degree, and calculate updated effective component accumulation value;Reward value is calculated based on the change of state before and after updating, and the irrigation decision model is updated online using reward value.The present application solves the technical problem that the prior art cannot dynamically balance seedling survival guarantee and effective component improvement.

The invention discloses a method for selectively extracting plant xylem embolism conduit juice, and belongs to the technical field of plant physiology research. In order to solve the problem that pure embolism catheter juice cannot be distinguished and obtained in the prior art, a plant stem section in a specific water stress state is obtained, the morphological lower end of the plant stem section is connected with a hydrophobic extracting solution, the upper end of the plant stem section is connected with a collecting system applying 0.01-0.1 MPa negative pressure, the extracting solution is driven by the negative pressure to selectively enter and flow through an embolism catheter, and the juice in the embolism catheter can be obtained. Meanwhile, due to the fact that the applied pressure is far smaller than the pinhole membrane fixing force borne by a water column in the functional catheter, the juice in the functional catheter can be reserved. Specific extraction of embolism catheter juice is realized for the first time, and a reliable method is provided for directly analyzing physicochemical properties of the juice and researching an osmotic regulation mechanism of xylem embolism repair.