5138 results about "Irrigation" patented technology

The invention discloses an intelligent crop growth prediction and optimization method based on multi-source data fusion, and belongs to the technical field of crop analysis, and the method specifically comprises the steps: obtaining space remote sensing data, ground sensor data and meteorological data, and carrying out the space-time alignment processing to generate fusion data representation; inputting the fused data representation into a prediction model, and outputting a crop future growth state sequence and a yield prediction result by coupling a crop growth mechanism and a dynamic environment response relationship; based on a yield prediction result, simulating long-term effects of different management strategies in a virtual growth environment, and screening out an irrigation scheme and a fertilization scheme with the optimal target function; transmitting the irrigation scheme and the fertilization scheme to a farmland execution terminal; collecting crop state feedback data and environmental parameter feedback data, and updating internal parameters of the prediction model based on the feedback data; according to the invention, crop growth management is converted from passive response to active regulation and control, and a whole-process intelligent solution is provided for precision agriculture.

The invention provides an intelligent management method for natural resource engineering data, and relates to the technical field of data processing, and the method comprises the steps: inputting a time-space coupling model into real-time weather forecast data, simulating water distribution schemes under different rainfall scenes, automatically recognizing a river reach position of which the ecological flow is lower than a design threshold value, and carrying out the calculation of the river reach position. In combination with a historical water replenishing strategy case library, generating a multi-objective optimization scheduling scheme including a water replenishing time sequence, a water replenishing path and a water replenishing amount; three monitoring points are arranged at a downstream section of an ecological protection area, a flood control section and a canal head of an irrigation area in a drainage basin, and ecological flow, flood control section water level and canal water level data are monitored in real time respectively; and dynamically correcting the water supplementing time sequence, the water supplementing path and the water supplementing amount in the multi-objective optimization scheduling scheme to obtain a corrected scheduling scheme. According to the method, refinement and toughness of drainage basin water resource management can be improved.

The invention relates to the technical field of agricultural information management, and particularly discloses an agricultural information management system and method based on a big data platform, and the method comprises the steps: firstly deploying a multi-source data collection module at an edge calculation node, and obtaining and standardizing the soil moisture content, meteorological environment and equipment operation data in real time; secondly, constructing a local dynamic irrigation strategy model, and realizing multi-objective optimization through a reinforcement learning algorithm; establishing a federated learning framework at the cloud, dynamically distributing node weights by adopting an attention mechanism, and realizing model aggregation of privacy protection in combination with secure multi-party computing; an optimal irrigation instruction is generated through a multi-source data fusion engine, and a three-level response exception handling mechanism is established; and finally, a closed-loop feedback system containing short-term incremental learning and long-term architecture optimization is formed. The corresponding management system comprises six functional modules, namely a data acquisition module, a local modeling module, a federated learning module, a real-time decision-making module, an abnormal monitoring module and a closed-loop optimization module.

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 provides an intelligent agricultural environment monitoring system based on the Internet of Things, and relates to the technical field of data processing. The feature extraction and analysis module is used for performing segmented statistical analysis on the soil humidity original data according to the soil humidity original data in combination with historical soil humidity data, historical environment temperature data and preset near-period soil humidity feedback data, and automatically extracting a humidity change rate and an abnormal trend index data set under multiple time windows; the self-learning judgment module is used for dynamically adjusting the current judgment threshold value based on a self-learning algorithm, judging the soil humidity original data in combination with the humidity change rate and the abnormal trend index, and generating a judgment signal for judging whether local irrigation response is triggered or not; the response control module is used for automatically generating an irrigation equipment starting instruction; the irrigation execution module is used for executing irrigation operation according to the irrigation equipment starting instruction; according to the invention, the accuracy of agricultural environment monitoring is improved.

The invention discloses an intelligent service system and method for garden management. The intelligent service system comprises an Internet of Things sensing module, an edge computing module, a cloud service module and an application service module. And comprehensive intelligentization and refinement of garden management are realized. Through file recording, multi-module collaborative operation and deep analysis and intelligent decision of big data, the efficiency and quality of garden maintenance are improved, and the tourist experience and the garden safety management level are also significantly enhanced. The system can monitor the health state of plants in real time, accurately predict and prevent diseases and pests, optimize the irrigation strategy and guarantee the optimal growth conditions of the plants. Meanwhile, through the digital twinborn platform, a manager can deduce different maintenance schemes in a virtual environment, scientifically evaluate a long-term effect and make an optimal decision.

The invention relates to the technical field of resource management, in particular to a seawall ecological protection-oriented vegetation water demand prediction method and system, and the method comprises the steps: collecting the environment monitoring data of a seawall region; establishing a salinity stress factor model based on the soil data, calculating a comprehensive salinity stress coefficient of the monitoring point, and performing multi-factor dynamic correction on the vegetation water demand to obtain a vegetation basic water demand; establishing a multi-dimensional time sequence model of a tidal cycle and a soil moisture supplement relationship considering geologic structure differences, and obtaining resource scheduling weights under different tidal stages and environmental conditions; according to the vegetation data, the underground data and the environmental data of the different seawall regions, based on a multi-region collaborative water resource allocation network, obtaining a deployment optimization scheme based on regional characteristic parameters; and generating a vegetation water demand prediction result and a graded resource scheduling scheme by integrating the vegetation basic water demand, the resource scheduling weight and the allocation optimization scheme, and performing irrigation.

The invention discloses a farmland environment multi-parameter intelligent monitoring system and method based on Internet of Things, particularly relates to the technical field of agricultural Internet of Things monitoring, and is used for solving the problem of stress resistance evaluation distortion caused by neglecting crop physiological recovery hysteresis in the prior art. Collecting soil water content data in real time through an Internet of Things sensor, and extracting a soil water content change rate when a preset threshold value is reached; secondly, constructing a space-time gradient matrix based on different soil layer water deficit rates, and generating a stress cumulative intensity index through principal component analysis and information entropy fusion; then analyzing a coupling relationship between the soil moisture change rate and the stress accumulation effect in the historical stress event on physiological recovery delay to generate a recovery delay coefficient; the physiological state lag degree is evaluated by combining the stomatal conductance change rate ratio of the stress period and the recovery period and the recovery delay coefficient; and finally, dynamically prolonging the irrigation execution time according to the lag degree until the irrigation execution time is lower than a set tolerance value, thereby remarkably improving the reliability of crop stress resistance test data.

The invention relates to the technical field of image processing, in particular to an irrigation prescription map inversion method based on unmanned aerial vehicle spectrum data. The method comprises the following steps: carrying out unmanned aerial vehicle data acquisition on a farmland area to obtain a multispectral image, DEM data and meteorological interpolation data; performing micro-landform parameter calculation according to the multispectral image, DEM data and unmanned aerial vehicle GPS data, and constructing a terrain-spectrum coupling model to obtain a terrain relief degree map and the terrain-spectrum coupling model; performing vegetation multi-dimensional state analysis by using a terrain-spectrum coupling model to obtain a vegetation index map, a canopy temperature map and a water stress preliminary judgment map; performing net radiation calculation by using the meteorological interpolation data to obtain a net radiation diagram; according to the net radiation diagram, the vegetation index diagram and the topographic relief degree diagram, soil heat flux estimation is conducted, and a soil heat flux diagram is obtained. According to the method, collaborative inversion is carried out by eliminating the terrain effect, and the precision and reliability of irrigation prescription map inversion are improved.

The invention discloses an artificial intelligence rice water and fertilizer real-time monitoring method and system, and relates to the technical field of agricultural intelligent decision making, and the method comprises the steps: inputting a farmland feature data set into a soil thermodynamic diagram generation model, carrying out the high-resolution reconstruction of the farmland feature data set through a GAN adversarial network, and generating a whole-field high-precision soil thermodynamic diagram; based on the whole-field high-precision soil thermodynamic diagram, a collision relation between the fertilization amount and historical farming data is detected according to an FCL causal algorithm, a preliminary causal diagram is generated, and a causal diagram structure of the fertilization amount and the historical farming data is constructed by adopting a multiple linear regression method; based on a causal diagram structure, the multi-order causal effect of the fertilization amount, the soil parameters and the historical yield is analyzed through a dynamic allocation algorithm, and a water and fertilizer regulation and control strategy is formulated in combination with a multi-objective optimization algorithm. According to the method, the soil thermodynamic diagram generation model is constructed, so that the fuzzy problem of the edge of the field and the salinization area is solved, a high-fidelity soil space state substrate is provided for water and fertilizer regulation and control, and invalid irrigation is reduced.

The invention relates to the technical field of agricultural information fusion, in particular to an agricultural multi-source information fusion method and system and a storage medium. The method comprises the following steps: collecting and fusing multi-source agricultural data, generating a three-dimensional reconstruction agricultural model, then analyzing a cross-layer coupling structure of the model, obtaining a ground-air segmentation agricultural model, deriving a regional supply-demand relationship, analyzing and compensating nutrient demands based on growth demand parameters, mapping a fertilization prescription, generating a fertilization prescription map, and finally obtaining a fertilization result. And performing supply development simulation by utilizing the fertilization prescription map to obtain a simulated supply response model, determining irrigation opportunity according to the simulated supply response model, generating an irrigation prescription map, interactively integrating the fertilization prescription map and the irrigation prescription map, and generating agricultural fusion feedback information. According to the invention, a more accurate and more reliable agriculture-oriented multi-source information fusion method is realized.

The invention discloses a planting optimization method and system based on crop yield estimation in a saline-alkali land area, and relates to the field of crop yield prediction and planting optimization, and the method comprises the steps: data collection and fusion processing; performing dynamic prediction on soil water and salt migration, and realizing water and salt space-time distribution prediction by adopting a space-time cubic modeling self-adaptive model combined with physical correction; crop salt tolerance modeling: establishing a multi-interval salt tolerance model based on physiological response fitting and threshold segmentation, and introducing covariable correction; carrying out migration salt tolerance coupling modeling, introducing a salinity memory factor and a stage sensitivity regulation factor, and dynamically simulating growth response of crops under salinity stress; and crop yield estimation and planting optimization: generating a regional yield prediction and sowing configuration scheme based on coupling model output, and forming an optimization strategy in combination with measures such as irrigation and fertilization. According to the method, model-driven integrated optimization from yield estimation to planting decision of crops in the saline-alkali region is realized, and the method has high precision and wide applicability.

The invention relates to the technical field of data twinning, in particular to a saline-alkali soil water-salt motion simulation method based on big data analysis. The method comprises the following steps: collecting saline-alkali soil multi-source monitoring data; based on the saline-alkali soil multi-source monitoring data, salt soil layer correlation feature analysis and water-salt driving factor coupling feature analysis are carried out, and salt soil layer correlation feature data and water-salt driving factor coupling feature data are obtained; simulation relation model design of saline-alkali soil water-salt movement is carried out through the saline-alkali soil level correlation feature data and the water-salt driving factor coupling feature data, and a saline-alkali soil water-salt movement simulation model is generated; based on the saline-alkali soil water-salt motion simulation model, designing saline-alkali soil farmland irrigation simulation parameters; and carrying out water-salt motion simulation prediction and feedback of the saline-alkali soil farmland irrigation on the saline-alkali soil farmland irrigation simulation parameters. According to the saline-alkali soil water-salt motion simulation analysis method, multi-driving-factor fusion and accurate spatial heterogeneity description are achieved.

The invention discloses a rice field irrigation drainage strategy dynamic optimization method and system. The method comprises the steps of collecting environmental parameters of a target rice field in a current decision period; determining a to-be-optimized target of the target rice field, and constructing a multi-target optimization model in combination with the environmental parameters; performing dynamic planning solution on the multi-objective optimization model by adopting an intelligent algorithm to obtain a real-time optimized irrigation drainage strategy of the current decision period; implementing a real-time optimized irrigation drainage strategy of the current decision period; monitoring whether the final period of the rice growth period is reached when the current decision-making period is ended or not: otherwise, acquiring actual environment parameters when the decision-making period is ended, correcting the state vector at the moment, updating the initial state vector of the next decision-making period of the target rice field through the transfer function, and repeating the solving and implementation process of the irrigation and drainage strategy optimized in real time; if yes, the circulation is ended, and the optimal irrigation drainage strategy in the whole growth period of the target rice field is obtained.

The invention relates to an intelligent agricultural management system based on a planting-harvesting-distribution full link, and belongs to the field of agriculture, and the system comprises an online seed selection module, an order tracking module, an environment monitoring and early warning module, a pest and disease identification module, a market prediction module, a logistics scheduling module, a product estimation module and a growth state evaluation module. In the planting link, intelligent monitoring and abnormal early warning are carried out on the crop growth environment through the intelligent agent, accurate planting schemes for different crops can be formulated for farmers, and irrigation and fertilization schemes suitable for different regions can be formulated; in the production process, the intelligent agent can intelligently identify bad fruits and give out a treatment scheme, accurately identify diseases and pests and provide an effective prevention and treatment means; in the marketing link, the market trend can be predicted, the production scheme is reasonably arranged, and the production and marketing balance of agricultural products is ensured.

The invention relates to the technical field of saline-alkali soil improvement, in particular to a saline-alkali soil treatment and improvement method which comprises the following steps: S1, early-stage investigation and planning; s2, physical improvement; s3, chemical improvement; s4, biological improvement; s5, irrigation improvement; s6, later monitoring and maintenance; according to the saline-alkali soil treatment and improvement method, a traditional single improvement mode is broken through, physical, chemical, biological and engineering technologies are fused, a systematic improvement scheme is formed, and physical improvement effectively improves the soil structure and accelerates salt discharge through land leveling, deep ploughing, upturned soil sunning and concealed pipe laying for drainage; a soil conditioner and an acidic material are precisely applied for chemical improvement, and the soil acidity and alkalinity and ion composition are adjusted; in biological improvement, salt is absorbed by virtue of salt-tolerant plants and microbial agents, and soil microbial communities are improved; a fresh water resource or salt water desalination technology is reasonably utilized for irrigation improvement, and salt leaching and water resource recycling are achieved; through the synergistic effect of multiple means, the salt content of soil can be more efficiently reduced, and the soil structure is improved.

This application provides a wind-resistant, stable power generation agricultural-solar hybrid system, including pillars, beams, photovoltaic supports, and photovoltaic panels erected in farmland. The photovoltaic panels are equipped with a buffer mechanism and an overload release device. The buffer mechanism effectively absorbs kinetic energy and reduces the swaying amplitude and speed of the photovoltaic panels through its elasticity, and automatically resets the photovoltaic panels after strong winds. The overload release device protects the photovoltaic panels from damage caused by strong winds. This application overcomes the technical defects of existing static wind-resistant systems, reduces system wind resistance, can withstand super typhoons, provide stable power generation and irrigation, and promote agricultural production.

The invention discloses a remote monitoring and management rice seedling raising irrigation plant protection system, computer equipment and a medium, and relates to the technical field of rice seedling raising irrigation, the remote monitoring and management rice seedling raising irrigation plant protection system comprises a sensing acquisition module, an edge processing module, a data fusion module, a prediction modeling module, an execution control module, a user interaction module and an energy safety component; the sensing acquisition module is used for acquiring soil humidity, temperature, pH value, air temperature and humidity, illumination intensity, wind speed and direction, rainfall and crop image information through multiple types of sensors deployed in a rice seedling raising field, and outputting original environment data; the edge processing module is connected with the sensing acquisition module and is used for preprocessing the original environment data, generating preprocessed data and uploading the preprocessed data to a cloud server through an NB-IoT protocol; and the data fusion module is deployed in the cloud server and is used for receiving the preprocessed data from the edge processing module.

The invention relates to a crop irrigation demand prediction method based on an intelligent integrated prefabricated pump station, and belongs to the technical field of artificial intelligence. The method comprises the following steps: collecting a crop image in a prefabricated pump station irrigation scene, obtaining auxiliary data, calculating a daily accumulated reference evapotranspiration value, and obtaining a time-synchronized multi-modal data set; labeling and dividing the multi-modal data set; constructing an irrigation demand prediction model, wherein the irrigation demand prediction model comprises a spectral reflectivity enhancement module, a double-flow separation convolution module, a cross gating fusion module, a region sensitive pyramid module and a coupling prediction head module; inputting data in the divided training set into the model, and training the model through weighted total loss to obtain a trained model; inputting a to-be-detected image into the trained model to obtain a soil water content and transpiration prediction result; and performing irrigation decision based on the soil water content and transpiration prediction result. The accuracy of irrigation prediction can be improved.

The invention discloses an accurate analysis method based on smart agriculture multi-modal data, and relates to the technical field of smart agriculture, comprising the following steps: collecting and preprocessing soil humidity and meteorological data, extracting features to construct an environment sequence table, establishing a prediction and anomaly detection model, and outputting a soil humidity prediction value and an environment anomaly probability. And constructing a decision model to generate a regulation index, dynamically adjusting an irrigation strategy, visualizing farmland data distribution and trend in combination with a thermodynamic diagram and a broken line diagram, synchronously recording an operation log, and realizing a multi-modal data driven precise agricultural decision closed loop. Through multi-modal data fusion and a dynamic decision-making mechanism, the problems of traditional agricultural data island and disaster response lag are effectively solved, the regulation and control index is optimized, the water resource utilization rate is improved, the invalid irrigation risk is reduced, the farmland state is fed back in real time, precise partition management is assisted, the disaster response efficiency is improved, and abnormal losses are reduced. And agricultural intelligent upgrading is promoted.

The invention discloses an intelligent fertilization and irrigation regulation and control device and method based on deep learning. The method comprises the following steps: S1, constructing an agricultural environment original data set; s2, preprocessing the original data set to generate an agricultural data set with consistent time and space; s3, constructing a multi-layer neuromorphic calculation model which comprises a photosynthetic layer, a root system layer and a transpiration layer; s4, performing correlation analysis based on the photosynthesis dynamic curve, the root system water and fertilizer absorption curve and the transpiration-moisture supply curve, and constructing a photosynthesis-root system-transpiration synergistic curve; s5, on the basis of the photosynthesis-root system-transpiration synergistic curve, calculating a promoting factor of photosynthesis on root system absorption and a regulating factor of root system absorption on transpiration intensity, and constructing a self-adaptive regulation and control function; and S6, carrying out time sequence analysis on the self-adaptive regulation and control function, and formulating a final fertilization and irrigation regulation and control scheme. According to the method, a multi-layer neuromorphic calculation method is adopted, the photosynthesis-root system-transpiration cooperative relation is accurately modeled, and intelligent fertilization and irrigation regulation and control are achieved.

The invention discloses an agricultural environment monitoring method and system based on digital agriculture. The method comprises the steps that S1, original environment data signals are collected through a heterogeneous sensor network deployed in a farmland area; s2, performing space-time fusion processing on the original environment data signal to generate a standardized environment state signal; s3, inputting the standardized environment state signal into a pre-trained multi-mode diagnosis model to generate an environment-crop coupling diagnosis signal; s4, according to the environment-crop coupling diagnosis signal, an agricultural operation decision signal is generated, and the agricultural operation decision signal comprises quantitative parameters of the irrigation amount, the fertilization formula or the disaster prevention and control instruction; and S5, converting the agricultural operation decision signal into an equipment control instruction signal, and driving an execution mechanism to complete precise agricultural operation. According to the agricultural environment monitoring method and system based on digital agriculture, the problems of data splitting, extensive decision making and execution lagging in agricultural environment monitoring can be solved.

The invention discloses irrigation area patrol equipment and method based on an augmented reality technology, and relates to the technical field of agricultural hydraulic engineering and informationization, and the equipment comprises a video collection module, a video and audio interaction module, a communication module, a central processor, a temperature and humidity sensor, a positioning module, and an AI intelligent support module. Through the function of comparing the data with theoretical data, the requirement of patrol on the professionality of patrol personnel is reduced; the invention further provides a working system of AR field perception-AI intelligent support-expert cooperative support-auxiliary operation guidance, basic information is collected and sent based on the head-mounted AR patrol equipment and transmitted to the AI intelligent support module, a theoretical value under normal operation is given through a digital twinborn irrigation area AI calculation function in the AI intelligent support module and compared with the collected information, and the theoretical value is calculated. And in addition, the method can be combined with an expert online study and judgment function, so that the discovery of real-time inspection problems and field study and judgment can be realized.

The invention belongs to the field of crop growth monitoring, and particularly relates to a crop irrigation decision-making method and system based on crop growth period water demand characteristics, and the method comprises the steps: firstly obtaining a remote sensing satellite image of a to-be-decided crop soil region, and the segmented growth period and transition time period attributes of each crop; secondly, monitoring the soil water content of a crop soil area to be decided by using a temperature vegetation drought index according to a remote sensing satellite image; and finally, combining the soil water content with the related attributes of the crop growth period, and generating a variable irrigation prescription map by means of a preset variable precise irrigation decision model, so as to realize the irrigation decision of the crop soil area to be decided. By comprehensively utilizing the remote sensing data and the crop growth period information, the irrigation scheme can be reasonably planned according to the water demand characteristics of different crops in different growth stages, the water resource utilization efficiency is effectively improved, and the crop growth is guaranteed.

The invention relates to the technical field of organic fertilizers and planting of watermelons and muskmelons, and provides a special fertilizer for milk irrigated watermelons and muskmelons and a double-stubble planting method for sweetness enhancement of milk irrigated watermelons and muskmelons. The special fertilizer for the milk irrigated watermelons and muskmelons comprises black goat manure, sophora alopecuroide, sesame seed meal, bran, organic fresh milk, brown sugar and zymophyte. The black goat manure is pulverized into powder by a pulverizer, the sophora alopecuroide is pulverizedinto 1 cm to 2 cm, and the ratio of the black goat manure, the sophora alopecuroide, the sesame seed meal, the brans, the organic fresh milk, the brown sugar and the zymophyte is 2700-3200: 80: 50: 150: 5: 1. The double-stubble planting method for sweetness enhancement of the milk irrigated watermelons and muskmelons comprises the following steps: selection of a nursery seedling plate and soil, cleaning and germination accelerating of seeds, soil preparation, transplanting, irrigation, topdressing, shaping and forking of watermelon and muskmelon vines and fruit bearing of the watermelons and the muskmelons. The problems that in an existing watermelon and muskmelon planting process, soil nutrients are damaged due to the fact that chemical fertilizers are used for a long time, and the sweetness of melons is reduced are solved.

The method may comprise receiving historical data (e.g., mineralogy data, irrigation data, raffinate data, heat data, lift height data, geographic data on ore placement and / or blower data); training a predictive model using the historical data to create a trained predictive model; adding future assumption data to the trained predictive model; running the forecast engine for a plurality of parameters to obtain forecast data for a mining production target; comparing the forecast data for the mining production target to the actual data for the mining production target; determining deviations between the forecast data and the actual data, based on the comparing; and changing each of the plurality of parameters from the forecast data to the actual data to determine a contribution to the deviations for each of the plurality of parameters.

The invention relates to an intelligent breeding method for crayfish offspring seeds in a winter fallow field, and belongs to the technical field of aquaculture. In order to solve the problems of dissolved oxygen management lag, high ammonia nitrogen accumulation risk and overlarge energy consumption caused by low utilization rate of winter fallow fields and dependence of water quality regulation and control on artificial experience in the prior art, the method comprises the following steps: deploying a water quality dynamic monitoring system based on the Internet of Things to collect data of water temperature, dissolved oxygen, pH value, ammonia nitrogen and nitrite in real time; the power and the water level of the oxygenation equipment are dynamically adjusted after analysis of the edge calculation module; graded regulation and control are achieved by combining a three-level response mechanism, when the dissolved oxygen content is lower than 6 mg / L or the pH value is abnormal, the power of the nanometer oxygenation device is started to 50%, and when the dissolved oxygen content continuously deteriorates, the irrigation system is linked to inject the water body with the temperature being adjusted in advance. According to the method, through intelligent multi-parameter cooperative control and ecological bait design, the resource utilization rate of the winter fallow field is increased, the survival rate of the young crayfish is increased to 85% or above, and the method is suitable for large-scale and green efficient breeding of the young crayfish in a rice-shrimp rotation mode.

The invention relates to an irrigation area water regimen monitoring method, device and system using a sensor technology, and belongs to the technical field of irrigation area water regimen adjusting systems, and the method comprises the following steps: S1, receiving model parameter update uploaded by each irrigation sub-area under a federated learning framework, and using the model parameter update to iteratively update a central side irrigation area digital twinborn model; s2, acquiring an actual measurement value of the sensor and manual intervention data from a system log; s3, calculating a global average distortion index and a manual intervention rate; the global average distortion index is used for representing a deviation degree between the digital twin model and a physical world reflected by a sensor actual measurement value, and the manual intervention rate is used for representing a decision conflict degree based on manual intervention data statistics; s4, on the basis of the preset weight, the global average distortion index and the manual intervention rate are combined, and the man-machine system trust entropy is worked out. According to the method, a benchmark which does not depend on the state of the sensor and has definite physical significance is provided for the measured value of the sensor.

The invention relates to the field of intelligent irrigation, and discloses an intelligent irrigation system and method for a crop root system, and the system comprises a root zone sensing module which is used for collecting soil environment parameters through multi-mode soil sensor arrays disposed at soil layers of different depths, generating a sensing matrix, and transmitting the sensing matrix to a root zone sensing module; the sensing matrix is used for representing water potential, pH value, nutrient concentration and temperature information in each soil layer; and the root system density analysis module is connected with the root zone sensing module, and is used for constructing a root system density weight matrix based on root system space distribution information obtained by underground computed tomography, and multiplying the sensing matrix by the root system density weight matrix element by element to generate a root zone state vector. Accurate generation of regulation and control signals is achieved, the micro-fluidic execution module can adjust the irrigation flow and the fertilization proportion in real time according to the actual requirements of the root zone, and therefore it is guaranteed that the rhizosphere environment is always close to the optimal physiological interval.

The invention provides a photovoltaic power generation and air water taking irrigation system, comprising: a photovoltaic power generation module for generating electric energy by using light energy; the gel adsorption module is connected with the photovoltaic power generation module and is used for adsorbing water vapor in air by using gel and driving the gel to desorb and release the water vapor by using waste heat generated by the photovoltaic power generation module; the condensation water collection module is connected with the gel adsorption module and is used for condensing the water vapor to obtain condensed water; the crop irrigation module is connected with the condensation water collection module and is used for irrigating crops by utilizing the collected condensate water; the soil detection module is connected with the crop irrigation module and used for controlling irrigation according to the detected crop root system soil humidity, collaborative production of water, electricity and crops can be achieved under the condition that no external input exists in the island reef environment, and the problems that in the island reef environment, fresh water is in shortage, electricity is insufficient, and self-sufficiency of agricultural production is difficult are solved.