1475 results about "Paddy field" patented technology

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 relates to the technical field of agricultural irrigation, in particular to a rice water-saving intelligent irrigation decision-making method and system, and the method comprises the following steps: determining the basic information of a target rice field, and obtaining a rice intelligent water-saving irrigation path by using a random landmark method and an adaptive heuristic optimization algorithm; the water demand of rice in the target rice field is obtained in real time, and then a water demand prediction model is constructed based on CEEMD and LSTM; predicting the water demand of the target rice field by using a water demand prediction model, and obtaining rice water-saving irrigation parameters of each irrigation point on the rice intelligent water-saving irrigation path in combination with rainfall information and a rice growth stage; according to the rice water-saving irrigation parameters, the irrigation system is used for completing irrigation of the target rice field. According to the method, the accuracy and the intelligent degree of rice irrigation decision making can be improved, the complexity of rice irrigation decision making and the dependence on expert experience are reduced, and more optional schemes are provided for rice irrigation control.

The invention relates to the field of agricultural data analysis, in particular to a japonica rice paddy field soil carbon nitrogen ratio dynamic intelligent assessment method, which comprises the following steps: acquiring and preprocessing japonica rice paddy field environment time sequence data to obtain a standardized environment data set; performing nonlinear congestion analysis on the soil moisture content state to obtain a corrected moisture dimension distance component; obtaining a corrected temperature dimension distance component by evaluating the metabolism permissible degree of the soil moisture on the temperature effect; performing double weighted fusion on the moisture and temperature correction distance components to obtain a soil environment state clustering result; through coupling calculation of a soil environment state clustering result and a first-order dynamic decomposition model, a dynamic intelligent evaluation result of the carbon-nitrogen ratio of the japonica rice paddy field soil is obtained, so that the problem that existing clustering cannot accurately distinguish aerobic and anaerobic environment states in a japonica rice paddy field scene and causes deviation of the evaluation result of the carbon-nitrogen ratio of the soil is solved.

The invention relates to the technical field of agricultural fertilization, in particular to a precise fertilization method and system based on rice field nutrient real-time monitoring. The method comprises the following steps: acquiring map data of a rice field; dividing the rice field into a plurality of uniform areas by using a random sampling algorithm and an expansion algorithm; acquiring soil data and nutrient data of the rice field; constructing a rice field region comprehensive evaluation model used for comprehensively evaluating the difference between rice field region nutrients and soil characteristics; classifying the uniform regions according to the difference between nutrients and soil characteristics, and obtaining classified regions; constructing a rice field nutrient demand analysis model; constructing a rice field precise fertilization model; the fertilization amount is determined, and accurate graded fertilization is realized. According to the method, the nutrient and soil characteristics of the rice field are comprehensively evaluated in different regions, the fertilization priority sequence is determined, the nutrient requirements of all the regions are accurately matched to specific fertilizers and fertilization amounts, tight connection of the regions, the nutrients, the fertilizer types and the fertilization amounts is ensured, and precise fertilization management is achieved.

The invention discloses a microbial agent and a method for repairing cadmium-polluted rice field soil by using the microbial agent, relates to the technical field of repairing cadmium pollution of rice field soil, and solves the problem that the cadmium metal enrichment capacity of duckweed is limited due to the fact that the duckweed has certain endurance capacity to cadmium stress. The microbial agent comprises bacillus subtilis ATCC6633 and pseudomonas aeruginosa ATCC27853, and the mass ratio of the bacillus subtilis ATCC6633 to the pseudomonas aeruginosa ATCC27853 is 2: 1, and the method comprises the following steps: step 1, spreading the microbial agent on the surface layer of cadmium-polluted rice field soil, then mixing the microbial agent with the rice field soil, raking the rice field soil, adjusting the water-holding capacity of the rice field soil, applying a base fertilizer, and irrigating the rice field. According to the method, the cadmium tolerance of the duckweed can be promoted, so that the enrichment capacity of the duckweed on cadmium is promoted, the remediation capacity of the duckweed on cadmium-polluted soil is improved, the cadmium content in the soil can be reduced, and the content of metal cadmium in rice is also reduced through a competitive cadmium absorption way.

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 remediation method for the moderately and severely cadmium-polluted rice field aims at strictly controlled cadmium-polluted rice fields, and is divided into two stages of strict control and safe utilization for remediation. In the strict management and control stage, an activating agent is applied to the soil in spring, then microalgae are inoculated, cadmium-enriched plants are planted, the plants are collected after being full of the microalgae, the plants are harvested when the plants grow to the enrichment extreme value, stubbles are reserved for growth, and the steps are repeated till winter; applying an activating agent in winter, planting green manure, and harvesting in spring in the next year; the process is cyclically carried out until the cadmium content of the soil is reduced to a safe utilization level. Entering a safe utilization stage, applying an activating agent in spring, inoculating nitrogen-fixing cyanobacteria, collecting the cyanobacteria after the cyanobacteria overgrow, and repeating the steps until the rice seedlings are transplanted; applying nitrogen-fixing blue-green algae, and planting rice; applying an activating agent after rice harvesting, planting winter green manure, and harvesting in next spring; the process is cyclically carried out until the cadmium content of the soil is reduced to a preferential protection level. The method is low in cost, high in remediation efficiency and good in effect, and a new way is provided for cadmium-polluted rice field remediation.

The invention relates to the technical field of target detection, in particular to a rice disease and insect pest target detection algorithm based on Mamba and YOLOv8, which comprises the following steps: acquiring a rice field RGB image and preprocessing to generate an abnormal image; inputting the abnormal image into a MedVSSM module, extracting global long-distance dependency features through a Mama state space model, extracting local features such as a polypide contour and scab texture in combination with CNN, and fusing and outputting multi-scale features; performing cross-layer semantic enhancement and path aggregation through a C2F-MedVSSM module, and generating an optimized feature map through dynamic weight adjustment; and the YOLOv8 module outputs a target detection frame, a category and confidence, and a final detection result is obtained after non-maximum suppression processing. According to the method, the Mama global modeling capability and the CNN local feature extraction advantages are fused, feature complementation is realized through a dynamic weight mechanism, the detection precision under the scenes of complex illumination, leaf shielding and the like is effectively improved, and the method has a practical application value for rice disease and pest prevention and control.

The invention belongs to the technical field of data processing, and particularly relates to a rice field soil emission reduction potential estimation method and system based on carbon-nitrogen coupling and a medium, and the method comprises the steps: collecting rice field nitrogen application information in a target area through literature statistics, dividing rice fields with similar nitrogen application into the same type, and selecting a plurality of typical areas from each type; carrying out soil sampling at fixed intervals in the typical rice field area, analyzing the nitrogen concentration level of the soil, and obtaining representative samples; performing soil column experimental simulation by using the representative sample, and estimating the average emission of specific gas; and determining significant influence factors, setting different carbon-nitrogen coupling situations to carry out an orthogonal simulation experiment, quantifying factor importance, carrying out weighted accumulation to obtain a comprehensive emission estimation value, and evaluating the carbon-nitrogen coupling-based biochar rice field emission reduction capability. The method can effectively estimate the rice field soil emission reduction potential.

The invention provides a rice yield prediction method and system based on multi-source time sequence remote sensing data, and relates to the field of agricultural informatization and remote sensing technology application, and the method comprises the steps: collecting multi-spectral data and laser radar data, and carrying out the precise geographic positioning of the data through a high-precision satellite positioning system, obtaining multi-source unmanned aerial vehicle remote sensing data; performing camera internal orientation, wave band registration, orthoimage splicing and embedding, radiation correction and remote sensing index calculation on the multispectral data to obtain processed multispectral data; and performing point cloud reconstruction and point cloud filtering on the laser radar data, and calculating the height of the rice plant to obtain processed laser radar data. According to the method, multi-source remote sensing data and spatio-temporal data are fused, and remote sensing characteristics of different phenological periods of rice are dynamically analyzed and filtered, so that the precision and reliability of rice field yield prediction are remarkably improved.

The invention discloses a rice field irrigation control method, and belongs to the field of rice field irrigation, and the method comprises the steps: obtaining soil humidity, water level and meteorological data from a rice field through a sensor network, storing the data in a pre-established distributed database, and obtaining a multi-source data set; according to the data set in the unified format, key features of soil humidity, water level and meteorological data are extracted by adopting a principal component analysis method, and a feature vector set is obtained; if the water consumption of the predicted irrigation scheme exceeds a preset threshold value, the scheme is optimized and adjusted through a linear programming algorithm, and an optimized irrigation scheme is obtained; according to the optimized irrigation scheme, the water consumption in the irrigation process is monitored in real time through flow meter equipment, and accurate measurement data are obtained; and the accurate measurement data is distributed through a quota management module, a regional water resource allocation scheme is generated by adopting a dynamic quota algorithm, and a quota management result is obtained.

The invention discloses a gleying rice field intelligent irrigation and drainage regulation and control method based on reinforcement learning. The method comprises the steps that meteorological data, soil data and field surface water level data are collected; calculating crop state information based on the meteorological data and the soil data by using a crop growth model; inputting the meteorological data, the soil data, the field surface water level data and the crop state information into an offline training strategy model, outputting action suggestions, and performing verification and decision making according to preset regulation and control logic in combination with the meteorological data, the soil data, the field surface water level data, an external data source and agronomic rule base data; a control strategy is generated, irrigation, drainage or oxygenation is executed, and irrigation and drainage regulation and control are completed. Compared with the prior art, the method has the advantages that priority regulation and control logic for ORP / pH value real-time feedback is established, a prospective pre-drainage mechanism for rainfall forecast is fused, water layer constraint in the growth period and conditional filtering rules in the fertilization safety period are fused, and a perception-decision-execution closed-loop control system is formed.

The invention relates to the technical field of soil function improvement, in particular to a rice field soil recarburization and capacity expansion method based on a charcoal-based organic fertilizer. The method comprises the following steps: crushing a biomass raw material, pyrolyzing and cooling to obtain biochar; immersing the biochar in a functional bacteria solution containing actinomycetes and bacillus for activating treatment to obtain modified biochar; mixing the modified biochar and decomposed straw, composting and fermenting to obtain organic fertilizers with different particle sizes; the large-particle-size organic fertilizer is applied to deep soil, the soil is covered with soil after straw is laid on the soil, and the small-particle-size organic fertilizer is applied to surface soil. Through layered application, the large-particle-size biochar repairs the deep soil structure and expands the carbon storage capacity, and meanwhile, the carbon stability is improved through carbon-nitrogen synergistic metabolism driven by functional bacteria, so that the carbon immobilization efficiency is improved, meanwhile, soil recarburization and expansion are achieved, the contradiction between carbon sequestration and structure degradation and nutrient imbalance in a traditional recarburization and expansion technology is solved, and the soil recarburization and expansion efficiency is improved. Synergic improvement of a carbon sink function and agricultural productivity is promoted.

The invention discloses a method and a system for predicting diseases and insect pests of saline-alkaline tolerant rice based on artificial intelligence. The method comprises the steps of collecting rice data, optimizing the rice data, establishing a prediction model for the diseases and insect pests of the saline-alkaline tolerant rice and predicting the diseases and insect pests of the saline-alkaline tolerant rice. The invention belongs to the technical field of agricultural information, and particularly relates to a method and a system for predicting diseases and insect pests of saline-alkali tolerant rice based on artificial intelligence. According to the scheme, rhombic plot segmentation is dynamically adjusted according to the irregular terrain and the saline-alkali accumulation area boundary of the saline-alkali tolerant rice field, and the collaborative weight is calculated based on the saline-alkali stress degree and the disease and insect pest risk level; key region learning is enhanced in combination with a plot total score and a selection probability formula; a plot score, a neighborhood pest and disease density mean value and a neighborhood saline-alkaline gradient are introduced into a spatial branch, so that the capturing capability of spatial correlation is improved; on the basis of the dynamic adjustment tolerance threshold, the influence of the extreme value is limited through the loss upper limit coefficient, a pest and disease prediction error regulation and control function is constructed, and the pest and disease prediction accuracy is improved.

The invention discloses a rice field irrigation and drainage decision-making method and system based on human-in-loop reinforcement learning, and the method is realized based on reinforcement learning, and the method comprises the steps: collecting real-time environment parameters of a target rice field in a current decision-making period; constructing an environment state vector based on the real-time environment parameters; constructing an irrigation drainage decision model of a continuous action space based on a soft actor commentator algorithm, wherein the model outputs an action according to the environment state vector; when it is judged that the output action deviates from a reasonable strategy or has a potential risk, a human-in-loop collaborative intervention mechanism in which a human expert participates is introduced, the output action is corrected, and a human-in-loop reward signal is generated through expert intervention; an output action is executed, environment feedback and a multi-target weighted reward signal containing a human-in-loop reward signal are collected, loop iteration is performed based on a closed-loop control system, and the irrigation and drainage decision model is updated; and obtaining irrigation and drainage decision output of the current decision period according to the updated irrigation and drainage decision model.

The invention provides a method for preparing carbon quantum dot reinforced subcritical artificial humic acid and applying the carbon quantum dot reinforced subcritical artificial humic acid to a rice field, which comprises the following steps: collecting organic wastes, and drying, grinding and sieving the organic wastes to obtain pretreated raw materials; the preparation method comprises the following steps: mixing pretreated raw materials with water, reacting in a hydrothermal carbonization system at 200-280 DEG C for 1-3 hours, modifying through acid-base pretreatment, adjusting a reaction medium or an additive, and naturally cooling to obtain a natural substance subjected to hydrothermal carbonization; carrying out solid-liquid separation on the natural substance subjected to hydrothermal carbonization by using a 100-mesh coarse filter screen to obtain solid-phase hydrothermal carbon and liquid-phase artificial humic acid; carrying out purification treatment on the liquid-phase artificial humic acid for 48 hours by using a dialysis bag to obtain carbon quantum dots and characterizing the carbon quantum dots; the carbon quantum dot reinforced subcritical artificial humic acid is applied to a rice field, and is additionally applied in a fertilization period. Compared with a traditional composting technology, the scheme has the advantages that the carbon conversion rate is increased by 2-3 times, the soil fertility can be improved, and the rice yield is increased.

The invention provides a rice transplanting robot suitable for a small rice field and a control method thereof, and belongs to the technical field of agricultural machinery. Comprising a moving part and a rice transplanting assembly, the moving part comprises a machine body, a plurality of mechanical legs and driving assemblies corresponding to the mechanical legs, the mechanical legs are connected with the machine body through the corresponding driving assemblies, the rice transplanting assembly comprises a rice seedling storage box, a mechanical arm, a mechanical claw, an angle sensor and a finger pressure sensor, and the rice seedling storage box is arranged on the machine body; one end of the mechanical arm is rotationally connected with the machine body, the angle sensor is arranged on the mechanical arm, the mechanical claw is arranged at the other end of the mechanical arm, the finger pressure sensor is arranged on the mechanical claw, and the mechanical arm has multidirectional rotation freedom degrees. And the insertion depth of the seedlings is adjusted according to readings of the angle sensor and the finger pressure sensor. The technical problems that existing rice transplanting equipment is limited in use in small rice fields due to structural defects, and the survival rate of planted seedlings is low can be solved.

The invention discloses a method for extracting plant available cadmium in neutral-alkaline rice field soil, which comprises the following steps: naturally air-drying the soil, grinding and sieving to obtain a soil sample, weighing the soil sample, adding deionized water to enable the water content of the soil to be 35-110% of the maximum water-holding capacity of the soil, balancing at room temperature for 12-48 hours, adding a calcium chloride solution, oscillating and extracting at constant temperature, and filtering to obtain the plant available cadmium in the neutral-alkaline rice field soil. And filtering to obtain an extracting solution, measuring the concentration of cadmium in the extracting solution, and calculating the content of the plant available cadmium in the measured soil sample according to the measured mass of the soil sample and the measured concentration of cadmium in the extracting solution. The method is easy and convenient to operate and low in cost, the change characteristics of the plant effectiveness of cadmium in the neutral-alkaline rice field soil in a field water management mode can be well reflected, and the measured plant effective-state cadmium content in the neutral-alkaline rice field soil and the rice cadmium content have a better linear correlation relationship.

The invention discloses a system for accurately removing abnormal plants in rice breeding based on intelligent image recognition, and relates to the field of agricultural intelligent equipment.The system comprises an image acquisition module, an abnormal plant detection module and an impurity removal control module, the preprocessing module is used for preprocessing rice field plant images and transmitting the preprocessed rice field plant images into the abnormal plant detection module, the abnormal plant detection module is used for analyzing and processing the rice field plant images and identifying to-be-processed abnormal plants, and the impurity removal control module is used for receiving the positions of the to-be-processed abnormal plants and controlling the impurity removal structure to remove impurities.

The invention discloses a rice field aquaculture intelligent management system based on the Internet of Things, and relates to the technical field of intelligent management. During operation of the system, various environmental parameters in a rice field are monitored and collected in real time by deploying an Internet of Things sensor network, and water quality changes and other factors of a farmland environment are monitored; water quality adjusting equipment is controlled to adjust a rice field water body, automatic adjustment is achieved through a feedback control mechanism, water quality changes are responded in real time, a crop growth monitoring sensor and a camera are installed, the growth state of rice is monitored in real time, visual feedback of the growth state is provided through the image recognition technology and the data analysis technology, and the rice field water quality monitoring system is established. The automatic irrigation system is used for controlling the water volume of the paddy field, automatically adjusting the irrigation frequency and the water volume, calculating the comprehensive benefit index TGI of the paddy field by using the data mining and statistical analysis technology, monitoring the environmental parameters of the paddy field and the growth state of crops in real time, identifying abnormal conditions and timely giving out early warning.

The invention provides a rice irrigation online learning forecasting method and system, the method is realized according to a pre-constructed physical mechanism-neural network hybrid model based on physical mechanism model prediction and neural network error correction fusion, and the method comprises the following steps: S1, obtaining real-time environment data of a current decision period of a target rice field; s2, on the basis of the real-time environment data of the current decision period, forecasting a paddy field water layer depth predicted value of the next decision period through a physical mechanism-neural network hybrid model, and generating an irrigation drainage forecast in combination with a crop irrigation drainage mode; s3, real-time environment data of the target rice field in the next decision period after irrigation drainage forecast is executed are obtained, training samples are constructed and put into an experience playback pool, and the physical mechanism-neural network hybrid model executes online learning based on the experience playback pool; and S4, when the next decision cycle starts, returning to S2 until a preset stop condition is met.

The invention relates to a method for killing pomacea canaliculata by compounding tobacco waste and quicklime, which comprises the following steps: S1, collecting the tobacco waste generated in the tobacco planting and processing process, drying in the sun, grinding to obtain tobacco waste powder, and mixing the tobacco waste powder with quicklime powder to obtain compound dry powder; s2, putting the compound dry powder into a rice field to kill pomacea canaliculata before transplanting rice seedlings. The method is simple and easy to implement and remarkable in effect, the labor cost and the material cost are effectively reduced, and meanwhile the purpose of recycling waste is achieved.

The invention discloses a rice field crayfish body surface cleanliness improving method based on accurate aquatic plant management and protection and ecological induction, and relates to the technical field of crossing of aquaculture and ecological agriculture. The lifting method comprises the steps that submerged plants are planted in the rice field in February every year, and the submerged plants are trimmed regularly in the cultivation process so that the top ends of the submerged plants can be kept under the water surface; shrimp seeds are put into the rice field, and feed is fed regularly; in the subsequent breeding process, during regular feeding of feed, feeding of crushed pomacea canaliculata is carried out; and after the young crayfishes grow up and shell, catching the crayfishes to obtain the crayfishes with high body surface cleanliness. According to the method, the growth state of the aquatic plants is precisely controlled to construct a natural clean field domain, the functional bait is fed to increase nutrition, meanwhile, friction cleaning is carried out through the feeding behavior, precise fishing is carried out based on the molting period, and therefore the body surface cleanliness of the crayfishes bred in the rice field is improved.

The invention discloses a rice field insect pest detection method and system based on deep learning, and relates to the technical field of deep learning, and the method comprises the following steps: receiving the soil humidity and environment temperature of a rice field, and determining a temperature and humidity abnormal region of the rice field based on the soil humidity and environment temperature of the rice field; acquiring field image data in the temperature and humidity abnormal area of the rice field; processing the field image data in the temperature and humidity abnormal region of the rice field based on morphological closed operation and morphological gradient operation to obtain processed field image data, inputting the processed field image data into a pre-established YOLOv5s + target detection algorithm model, and outputting to obtain a pest and disease damage identification result; and performing image splicing generation based on the field image data in the temperature and humidity abnormal region of the rice field and the pest and disease identification result to obtain a disaster grading map.

The invention provides a whole-course green rapid improvement method for a severe soda saline-alkali paddy field, and belongs to the technical field of soil improvement. The method is suitable for paddy field improvement of severe soda saline-alkali soil with 0-20 cm surface soil layer alkalization degree of more than 20%, and is characterized in that the whole process is green and rapid. The rapid improvement method for the soda saline-alkali soil paddy field is different from a traditional pure solid improvement mode, a solid-liquid combined whole-growth-period comprehensive improvement technology is adopted, and the production technical problems that solid improvement is low in uniformity and poor in pertinence are solved. The pH value of soda saline-alkali paddy field soil and water is rapidly reduced, salt in the soil is rapidly dissolved, the effect of rapid salt and alkali removal is achieved through field steeping and salt leaching measures, and meanwhile nutrient loss in the salt and alkali removal process is reduced.

The invention relates to the technical field of remediation for synergistic treatment of rice field soil arsenic and greenhouse gas emission reduction, in particular to a zero-valent manganese nitrate synergistic soil remediation agent and application thereof to remediation of rice field arsenic pollution and inhibition of greenhouse effect. The invention provides a technical scheme for synergistically repairing arsenic pollution in the rice field and inhibiting the greenhouse effect based on zero-valent manganese / nitrate, an electron transfer shunt network driven by manganese circulation is constructed, a slow release reaction-dynamic regeneration-electron shunt three-in-one system is constructed, and synergistic interaction of arsenic fixation and greenhouse gas emission reduction is realized. According to the technical scheme, the potential regulation and control contradiction between arsenic fixation aerobic and carbon reduction anaerobic is successfully broken, and an innovative solution is provided for multi-target remediation of rice field soil. According to the self-sustaining circulating system, the specific surface area of the manganese oxide is stabilized at 50m < 2 > / g to 120m < 2 > / g, and the regeneration rate is gt; 95%. Experiments show that compared with a single technology, the synergistic treatment group improves the arsenic fixing efficiency to 100%, and the comprehensive emission reduction intensity of greenhouse gas reaches 76%.

The invention belongs to the technical field of biochar oxygen-releasing materials, and particularly relates to a biochar-based oxygen-releasing material for gleying soil improvement as well as a preparation method and application of the biochar-based oxygen-releasing material. The biochar-based oxygen release material is a CaO2 at phosphorus-rich biochar material obtained by taking modified phosphorus-rich biochar as a carrier and combining with calcium peroxide, and has remarkable beneficial effects in the field of gleying rice field improvement. According to the biochar-based oxygen release material, the mass fraction of calcium peroxide is remarkably increased, so that the oxygen release function of the material is enhanced, and the biochar-based oxygen release material has important significance for improving the ventilation condition of a rice field and promoting the growth of a rice root system. In the preparation process, raw materials such as dimethylbenzene and polyethylene which may cause soil pollution and ecological problems are avoided, and the environment-friendly concept is embodied. Meanwhile, the modified biochar not only serves as a carrier to improve the loading capacity of calcium peroxide, but also has the function of releasing a phosphate fertilizer, and is beneficial to improvement of the soil fertility of the rice field and increase of the rice yield.

The invention relates to the technical field of data processing, in particular to a rice field gate anti-clogging intelligent monitoring control method, which comprises the following steps of: acquiring multi-dimensional monitoring data of a gate at each monitoring moment in real time, and forming a multi-dimensional monitoring data sequence by the multi-dimensional monitoring data of the gate at each monitoring moment in each operation process; one gate operation process corresponds to one multi-dimensional monitoring data sequence; for any gate operation process, obtaining an optimized gate clogging coefficient of any gate operation process according to the multi-dimensional monitoring data sequence corresponding to any gate operation process; the method comprises the steps of obtaining an optimized gate clogging coefficient of each gate operation process, obtaining a gate clogging trend index by using the optimized gate clogging coefficient of each gate operation process, and carrying out gate clogging prevention intelligent monitoring control according to the optimized gate clogging coefficient and the gate clogging trend index of the current gate operation process. And early-stage, quantitative and trend monitoring of the clogging state of the gate is realized.

The invention discloses a rice growth period distinguishing method and an intelligent irrigation control device. Belongs to the technical field of crossing of intelligent agriculture and irrigation and water conservancy engineering and particularly relates to the technical field of rice growth period judgment and intelligent irrigation. The technical problem that an accurate and effective rice growth period judgment method for guiding intelligent rice irrigation is lacked at present is solved. The method specifically comprises the following steps: calculating a rice growth process index through an improved accumulated temperature model; and constructing a multi-factor coupling growth period judgment method, and judging the current rice growth period through the rice growth process index. The system comprises a sensing unit, a judgment and decision-making unit, an electric execution unit and an input and power supply unit, the judgment and decision-making unit receives the environment data of the rice field and the water layer depth data of the rice field, judges the current growth period of the rice according to a rice growth period judgment method, calculates the current water layer deviation and generates a control instruction to the electric execution unit.

The invention discloses a method and system for evaluating the influence of long-term fertilization on rice field soil heavy metals, and the method comprises the steps: obtaining rice field soil data through a sensor, and obtaining a standardized soil physicochemical property data set and a heavy metal content data set; according to the standardized soil physicochemical property data set and the heavy metal content data set, obtaining a feature vector set reflecting correlation between soil physicochemical properties and heavy metal behaviors; aiming at the feature vector set, obtaining a prediction model for representing the influence of the fertilization mode on the heavy metal mobility; if the heavy metal effective state content output by the prediction model exceeds a preset bioavailability threshold value, classifying the soil physicochemical property data set, judging the influence weight of the soil pH value and the organic matter content on the cadmium mobility, and obtaining the classified soil environment risk grade; and according to the classified soil environment risk levels, obtaining comprehensive rice field soil heavy metal pollution evaluation indexes. The pertinence and effectiveness of pollution control are remarkably improved.