1573 results about "Crop growth" patented technology

The invention relates to the technical field of growth state evaluation, and discloses a crop growth state evaluation method and system based on multi-dimensional monitoring. The method comprises the steps of collecting multi-source remote sensing data of a farmland area according to a crop growth period, and performing topographic correction on the multi-source remote sensing data to obtain target vegetation data; based on the multi-source remote sensing data, farmland plot boundaries are extracted, and a farmland space association graph is constructed; inputting the target vegetation data and the farmland space association graph into an elevation perception graph convolutional network for elevation feature analysis, and calculating to obtain a crop abnormal growth index; and generating a growth state evaluation result based on the target vegetation data and the crop abnormal growth index. According to the method, crop growth abnormity caused by regional factors can be accurately identified, so that the accuracy of evaluation results under different terrain and environmental conditions is ensured.

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

The invention provides a crop growth state evaluation method based on the agricultural Internet of Things, and the method is characterized in that the method specifically comprises the following steps: S1, collecting the multi-dimensional data of a crop growth environment in real time, and outputting an original data set; s2, aligning the multi-dimensional data according to geographic coordinates, extracting features, and constructing a multi-modal fusion feature vector; s3, automatically identifying the current growth stage of the crop and the confidence of the current growth stage according to the fused feature vector; s4, calculating a crop health degree score and grading according to the growth stage information and the feature data, and identifying a stress state detection result at the same time; s5, according to a quantitative evaluation result, predicting a growth trend and generating a suggested decision scheme; and S6, the decision is monitored, the decision execution effect is fed back to the system, and the model parameters and the decision rules are continuously optimized. A closed-loop mechanism of evaluation, decision, feedback and optimization is formed, the applicability and evaluation precision of the system are continuously improved, and agricultural production is promoted to be upgraded to precision and intelligence.

The invention relates to the technical field of unmanned aerial vehicle control, in particular to a task allocation and conflict resolution system and method for multi-unmanned aerial vehicle collaborative operation, and provides the following scheme: dividing an initial operation area and generating a response weight by constructing a crop growth state map and a three-dimensional plot model; based on path planning and resource adaptation, a flight route is dynamically generated, and the crop state and the unmanned aerial vehicle state are monitored in real time; when adjustment conditions are met, a multi-dimensional dynamic task evaluation model is constructed, and task migration and conflict decoupling are completed in combination with particle swarm optimization and an autonomous negotiation mechanism. The method is suitable for a precision agriculture scene, the unmanned aerial vehicle path dynamic scheduling in the operation area and the high-priority area precision coverage are realized, and the operation efficiency and the resource cooperation capability are improved.

The invention relates to the technical field of farmland fertilization management, and discloses an intelligent fertilization management method and system based on machine learning. The method comprises the steps that a soil parameter set and an environment parameter set of a target farmland are collected, soil parameters comprise soil humidity, nitrogen phosphorus and potassium content and pH value, and environment parameters comprise illumination intensity, temperature and rainfall; constructing a soil nutrient dynamic change model according to historical data, and predicting a soil nutrient consumption trend in a future preset period; generating an initial fertilization scheme based on the nutrient consumption trend and the crop growth stage characteristics; monitoring the growth state of crops in real time by using a multi-mode sensor, and obtaining a leaf surface color index, a stem height and a fruit development progress to form a growth state data set; and inputting the growth state data set and the initial fertilization scheme into a fertilization decision model, and comparing growth state deviation to adjust the nutrient distribution ratio to generate an optimized fertilization scheme.

The invention provides a greenhouse environment adaptive regulation and control method and system, and relates to the technical field of environment control, and the method comprises the steps: collecting multi-dimensional environment parameters in a greenhouse; based on the environmental parameters, a preset crop growth period database and weather prediction data, taking minimization of a preset cost function as a target, and adopting a model prediction control algorithm to generate an equipment linkage instruction set in a future preset time period, the preset cost function fusing environmental regulation and control deviation and operation cost; issuing the equipment linkage instruction set to each execution equipment, and executing linkage regulation and control; wherein when the equipment linkage instruction set is generated, a conflict resolution mechanism based on a dynamic priority is adopted to determine an execution sequence of a plurality of equipment instructions; the adaptive regulation and control method integrating multi-source data verification, multi-scale prediction, dynamic priority conflict resolution and multi-target cost optimization improves the accuracy, economy and crop suitability of greenhouse environment regulation and control.

The invention discloses a crop disease diffusion prediction method and system based on multi-modal fusion, and the method comprises the following steps: S1, collecting and preprocessing an RGB image sequence and a sensor data sequence of a crop growth environment, and generating an RGB image time sequence difference result and a sensor difference result through time difference processing; s2, mapping the RGB image time sequence difference result and the sensor difference result to a shared time sequence space through a time alignment algorithm, and generating a sensor alignment result and an RGB alignment result; s3, an FD-ViT prediction model is constructed; inputting the sensor alignment result and the RGB alignment result into an FD-ViT prediction model for prediction, and generating a prediction result; and S4, generating a disease diffusion thermodynamic diagram and early warning information according to a prediction result. According to the method, RGB image data and sensor network data are fused, a Transform-based time sequence prediction model is constructed, and early recognition and diffusion trend prediction of crop diseases are realized.

The invention discloses a crop environment quantitative evaluation and decision making system based on growth stage self-adaption, and relates to the technical field of agricultural intelligent decision making. Acquiring an environment original data sequence of field multi-type sensors through a data acquisition module; the dynamic identification module analyzes the sequence by using a preset crop growth stage discrimination model, identifies the current growth stage and outputs a corresponding key environment parameter weight template; the feature fusion module performs weighted fusion on the original data according to the template to generate an environment feature vector with stage adaptability; the state evaluation module converts the vector into a growth state quantitative evaluation value through a growth state evaluation model; and the decision trigger judgment module calls a corresponding preset dynamic threshold interval according to the growth stage for comparison, and generates a decision trigger instruction when the growth state quantitative evaluation value deviates. According to the invention, stage self-adaptive intelligentization of crop growth monitoring and decision making is realized, and the accuracy of environment state evaluation and the timeliness of management decision making are improved.

A crop growth modeling system includes a memory configured to store computer-readable instructions. The instructions cause to the system to use a digital twin component configured to create and manage a digital twin of a farm. The instructions cause to the system to use a data input component configured to receive data related to a defined set of land characteristics and environmental attributes for the farm. The instructions cause to the system to use a processing component configured to integrate the received data with the digital twin and to simulate at least one crop growth scenario based on the integrated data. The instructions cause to the system to use a prediction component configured to determine a predicted crop growth rate for the farm based on the simulations conducted by the processing component.

The invention discloses a regional scale crop nitrogen dynamic regulation and control method and device. The method comprises the following steps of obtaining multi-source data and constructing a space-time database; selecting a crop growth model, and calibrating and verifying the crop growth model based on the space-time database to obtain the crop growth model and generate a localized model parameter set; dividing a target area into space units, inputting soil data, meteorological data and a localized model parameter set of each space unit to a crop growth model, simulating crop growth processes of different nitrogen gradients, extracting optimal biomass and critical nitrogen concentration data point pairs of each space unit, and performing fitting to generate a spatialized pCNDC parameter map layer; inverting an actual biomass and nitrogen concentration spatial distribution map based on remote sensing data, calculating critical nitrogen concentration and nitrogen nutrition index of each spatial unit in combination with a pCNDC parameter map layer, and generating a spatial distribution map; when the nitrogen nutrition index is lower than a preset threshold value, the nitrogen deficiency amount is calculated, and a variable fertilization prescription map is generated.

The invention discloses an intelligent water and fertilizer integrated irrigation system based on an Internet of Things sensor and a large language model, and relates to the technical field of intelligent agriculture. The system obtains soil, weather and plant growth state data in real time through Internet of Things sensors, including a soil sensor, a weather sensor and a multispectral sensor, deployed in a farmland; the method comprises the following steps: based on a large language model architecture, integrating preset agricultural field knowledge base data, performing field adaptive fine tuning on model parameters by adopting a data migration technology, and constructing an intelligent irrigation control model with a dynamic decision-making function; the control model analyzes sensor data and crop growth requirements, generates a water and fertilizer supply strategy, calls an internet-of-things control interface of the water and fertilizer all-in-one machine, and automatically adjusts irrigation water quantity and proportional supply of nutrient elements such as nitrogen, phosphorus and potassium. According to the invention, Internet of Things perception and large model decision are fused, and different environments are adapted through fine adjustment, so that closed-loop precise control is realized, and the water and fertilizer utilization rate and the crop yield are remarkably improved. Actual measurement shows that the system can improve the water and fertilizer utilization rate by 30% or above, and the crop yield is increased by 15-20%.

The invention discloses a crop nitrogen fertilizer management system and method based on multi-agent reinforcement learning, and belongs to the technical field of intelligent agriculture. Comprising the following steps: collecting and preprocessing multi-source data of a target area, calibrating a crop growth-nitrogen cycle model based on the multi-source data, and constructing a dynamic simulation environment; constructing a nitrogen fertilizer application strategy model and a multi-target award function, and performing agent reinforcement learning training by adopting a centralized training-decentralized execution architecture; introducing a large language model, and updating a strategy network and / or a value network of each agent in a training process according to a reward adjustment signal and a decision constraint; when the index fluctuation ratio in the continuous evaluation period is smaller than a preset threshold value, it is judged that the nitrogen fertilizer application strategy model is converged, and an optimal nitrogen fertilizer application strategy model is obtained; and generating a nitrogen fertilizer application scheme based on the optimal nitrogen fertilizer application strategy model in combination with the real-time state data, and generating a natural language interpretation and risk assessment report based on a large language model.

The invention discloses a multi-modal deep neural network model, system and method based on continuous learning. The multi-modal deep neural network model comprises a data acquisition and preprocessing module used for acquiring multi-modal data of a crop growth environment; the feature extraction module is used for extracting key agricultural features; the multi-modal information fusion module is used for effectively fusing the extracted key agricultural features; the knowledge continuous learning module is used for memorizing and storing the crop growth mode to a crop growth mode library and applying a model parameter self-adaptive updating strategy; the intelligent decision-making module is used for performing crop management decision-making based on the fusion features; and the effect evaluation and feedback module is used for evaluating the decision effect. According to the invention, the problem of knowledge forgetting of the existing AI system is solved, and the adaptability and decision accuracy of the intelligent agricultural system are improved.

The invention discloses an agricultural land utilization monitoring management system and method based on remote sensing, and belongs to the technical field of remote sensing image processing. Multi-temporal remote sensing images are acquired, and a land surface energy fluctuation spectrogram is constructed; extracting disturbance characteristics through small-scale grid slices to form a multi-dimensional disturbance characteristic tensor; identifying an abnormal evolution region by using a sparse volume accumulation algorithm, and outputting a preliminary screening identification graph; inputting the region with the continuous evolution characteristic into a time sequence attention mechanism inversion network, estimating a crop growth state trajectory, matching an agricultural planting mode library, and generating a candidate land utilization behavior probability distribution diagram; in combination with regional consistency optimization, a historical planting period and meteorological disturbance data, calculating a purpose change confidence score, and outputting early warning information and a monitoring report; the agricultural land dynamic change identification precision and management capability can be effectively improved.

The invention provides an intelligent agricultural system and an agricultural greenhouse humidity intelligent control method. The method comprises the following steps: firstly, acquiring a humidity sampling data sequence in a specified monitoring time window before a current regulation and control period in an agricultural greenhouse, and calculating a humidity change trend index; comparing the humidity change trend index with a dynamic trend threshold value, distributing a weight for each humidity sampling point, and performing weighted fusion based on the weights to obtain a weighted fusion result; and then generating a humidity regulation and control reference value of the current regulation and control period based on the weighted fusion result, the current real-time humidity and the external environment parameters. And finally, the deviation between the target humidity of the crop growth model and the current real-time humidity and the humidity regulation and control reference value are input into a controller to generate a dynamic regulation and control instruction, and operation parameters of a humidity execution mechanism are regulated according to the dynamic regulation and control instruction. According to the scheme, the robustness of accurate regulation and control of the humidity of the agricultural greenhouse can be improved.

The invention discloses an intelligent agricultural planting management method and system, and belongs to the technical field of intelligent agriculture, and the method comprises the steps of data layer construction, water and fertilizer prediction layer construction, planting optimization layer construction and agricultural planting management. The space-time adaptive water and fertilizer prediction layer construction method combining the crop physiological load and the crop growth stage is adopted, the water demand and nitrogen, phosphorus and potassium nutrient demands of crops in the future stage are accurately predicted, the crop growth state and environment changes are dynamically responded, the water and fertilizer demand difference in different growth stages is considered, and the water and fertilizer prediction efficiency is improved. The accuracy and applicability of prediction are improved through time-space fusion, and accurate fertilization can be realized; a planting optimization layer construction method combining digital twinborn and multi-objective optimization is adopted, a water and fertilizer management strategy is subjected to refined and dynamic optimization under the condition of considering a crop growth stage, a soil nutrient condition and a future weather condition, and an optimal management strategy capable of improving the yield, saving water and fertilizer resources and reducing the nitrogen leaching risk is generated.

The technical scheme of the invention discloses a photovoltaic power generation and greenhouse agriculture integrated system. According to the photovoltaic agricultural integrated technology disclosed by the invention, photovoltaic power generation and greenhouse planting are innovatively combined, and a solar panel with adjustable light transmittance of 30%-90% is mounted at the top of an agricultural facility, so that sunlight is utilized for power generation, and illumination required by crop growth is reserved. The system disclosed by the invention is provided with an intelligent sensor and an AI algorithm, environment data is monitored in real time, the angle, light supplementing intensity, temperature and humidity of the photovoltaic panel are dynamically adjusted, and different crop requirements (such as low illumination of leaf vegetables and medium and strong illumination of fruits and vegetables) are accurately matched. Residual electricity is stored or connected to a grid, photovoltaic waste heat is recycled in winter for heat preservation, sun shading and cooling are conducted in summer, and efficient circulation of energy is achieved.

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 fertilization and irrigation regulation and control, and discloses a fertilization and irrigation two-way regulation and control system under water demand and fertilizer demand coupling modeling, and the system constructs the fertilization and irrigation two-way regulation and control system based on the water demand and fertilizer demand coupling modeling. The multi-source data acquisition module is used for acquiring soil, crop growth period and meteorological data through a layered soil sensor, crop image acquisition equipment and a meteorological acquisition point; the water and fertilizer scheme generation module calculates irrigation amount, fertilization type and dosage based on data, and adjusts intervals in combination with temperature; the operation execution module controls the operation of the irrigation and fertilization equipment and records the state; and the data correction module compares the sensor with the sample data to generate a correction scheme. The system realizes accurate regulation and control through a closed loop of data acquisition, scheme generation, execution and correction, simultaneously covers equipment state monitoring and early warning, and ensures efficient fertilization and irrigation to adapt to crop requirements.

The invention provides a crop growth prediction method and system based on multi-modal data, and relates to the technical field of agricultural information, and the method comprises the steps: 1, collecting real-time environment data and crop physiological data of a heterogeneous multi-source sensor in a greenhouse environment, and constructing a multi-modal original data set; 2, performing preprocessing and space-time alignment on the multi-modal original data set to form a multi-modal data fusion matrix with a unified timestamp; step 3, based on the multi-modal data fusion matrix, selecting a reference data feature set, generating an environmental evolution mode and a physiological evolution mode, and calculating a correlation degree between the two modes to determine a feature response dimension; and respectively selecting dynamic monitoring feature sets inside and outside the feature response dimension, constructing a feature evolution trajectory according to a time sequence evolution relationship, and generating a dynamic compensation coefficient. By integrating environment and crop physiology multi-source data, the association rule of crop growth and environment is predicted, and the agricultural production efficiency is improved.

The invention belongs to the field of crop yield estimation, and discloses a corn yield remote sensing estimation method and system based on multispectral data coupling radiation transmission and a crop growth model, and the method comprises the steps: obtaining and preprocessing a Sentinel-2 multispectral remote sensing image, and obtaining the reflectivity data of a corn planting region; constructing a PROSAIL forward simulation spectrum library, and performing domain correction on the simulation spectrum by using an auto-encoder and a residual error alignment network; establishing a machine learning inversion model based on the corrected simulated spectrum and the actually measured spectrum, and generating regional leaf area index (LAI) distribution; key agronomic parameters are obtained, a localized WOFOST crop growth model is constructed, the LAI state of the model is assimilated by adopting ensemble Kalman filtering at a remote sensing observation moment, and the crop growth process is dynamically corrected; and advancing the model to a mature period, outputting the dry weight of the corn kernels, and realizing remote sensing estimation of the regional corn yield. The method can effectively improve the LAI inversion precision and yield prediction reliability, and is suitable for the fields of agricultural monitoring, grain evaluation, agricultural condition management and the like.

The invention discloses a greenhouse intelligent management system and management method based on crop growth vigor, and belongs to the technical field of intelligent agriculture. The greenhouse intelligent management system comprises a crop growth vigor and environment monitoring system, a control system and matched operation facilities, the crop growth vigor and environment monitoring system can monitor environment information and crop growth vigor conditions of a greenhouse in real time, and meanwhile, the monitoring information is sent to the control system through a communication module; based on a crop growth identification algorithm fusing RGB and hyperspectral images and a corresponding greenhouse intelligent management method, the control system reasonably regulates and controls application parameters of matched operation facilities and dynamically regulates internal environment elements of the greenhouse, so that rapid growth of greenhouse crops is promoted in an intensive mode, and the greenhouse quality is improved. Meanwhile, the labor intensity and the cost are effectively reduced.

The invention relates to the technical field of agricultural engineering and ecological environment protection, in particular to a saline-alkali soil desalination improvement system and method based on brackish water recycling. The system comprises a data collection module which collects soil salinity data, meteorological data and crop growth data of the saline-alkali land in real time; the data processing and analyzing module is used for receiving and preprocessing the soil salinity data, the meteorological data and the crop growth data of the saline-alkali land, constructing a soil salinity prediction model and outputting a soil salinity prediction result; the irrigation strategy decision module is used for generating an optimal irrigation strategy by adopting a reinforcement learning algorithm according to the soil salinity prediction result and the preprocessed crop growth data; and the irrigation control system is used for performing brackish water irrigation operation on the saline-alkali soil according to the optimal irrigation strategy. Precise regulation and control of saline-alkali soil salinity can be achieved, brackish water is adopted as irrigation water, water resources are saved to a certain degree, and therefore the improvement cost of saline-alkali soil is reduced.

The invention relates to an irrigation decision-making method and system based on a crop model and deep reinforcement learning. The method comprises the following steps: creating a virtual environment for simulating crop growth through a decision support system crop growth model after parameter calibration; then constructing a time sequence state matrix, and encoding the time sequence state matrix into a context vector concentrated with historical dynamic information; outputting irrigation actions of each decision-making day through a strategy network of a deep reinforcement learning agent based on a soft strategy-value algorithm; therefore, a decision support system crop model and a deep reinforcement learning technology are deeply fused, an intelligent irrigation decision system with biological rationality is constructed, and the problems of insufficient training data and environment distortion of an existing deep reinforcement learning model in agricultural application are solved.

The invention relates to the technical field of water and fertilizer application and irrigation, in particular to a water and fertilizer application and irrigation dynamic decision-making method based on a crop model. The method comprises the following steps: acquiring a crop growth parameter complete set and a water and fertilizer demand objective function set; performing dimension reduction on the crop growth parameter complete set to obtain a key crop parameter set; constructing a dynamic constraint factor generation rule; constructing a multi-level agent modeling system based on the key crop parameter set and the water and fertilizer demand objective function set to obtain a hierarchical crop agent model; therefore, through system integration of data dimension reduction, multi-level agent modeling, a parallel evolutionary algorithm and dynamic multi-objective optimization, the problems of data processing redundancy, low modeling efficiency, single optimization objective and insufficient constraint adaptability in a traditional water and fertilizer decision method are solved; the agricultural water resource and nutrient cooperative configuration efficiency and the decision precision are improved.

The invention discloses a method for determining the suitable development area of a large-scale drip irrigation farmland in an underground water shallow-buried area, and the method comprises the following steps: coupling an MODFLOW model and an SWNCM-2D model based on programming software, and developing a coupling model suitable for water and salt migration and crop growth simulation of a saturated zone and an unsaturated zone of the large-scale drip irrigation farmland in the underground water shallow-buried area; according to lysimeter underground water burial depth control test data, calibrating and verifying key parameters of the coupling model, performing multi-scenario simulation of different underground water burial depths, and determining an appropriate underground water burial depth under a drip irrigation condition; setting scenes with different drip irrigation farmland area proportions, simulating by adopting a coupling model, and determining a suitable development area of the large-scale drip irrigation farmland in the underground water shallow buried area by taking a suitable underground water buried depth as a control standard; according to the scheme, through multi-model coupling, multi-process simulation and multi-scene analysis, technical support and quantitative basis are provided for development and field management of large-scale drip irrigation farmland.

The invention provides an Internet of Things smart farm monitoring method and system, and relates to the technical field of agricultural Internet of Things, and the method comprises the steps: executing the triggering type sensing operation of crop growth and environment abnormality at a field end side visual node of an Internet of Things smart farm based on a preset triggering condition, and outputting an end side initial sensing data set; performing non-redundant feature screening on an end-side initial sensing data set, extracting a key feature set, executing a staged slow aggregation operation through a low-power-consumption communication network, outputting batch feature data packets according to a link state, and realizing field light-flow transmission; the cloud processing platform receives the batch feature data packets, then calls the agronomy knowledge set for restoration and diagnosis, outputs a farm monitoring diagnosis result, generates a field equipment linkage instruction according to the farm monitoring diagnosis result, sends the field equipment linkage instruction to the execution equipment, and outputs an equipment linkage response result, so that the farm monitoring efficiency and accuracy are improved, and the farm monitoring accuracy is improved. Intelligent management is realized, and the field operation cost is reduced.

The invention relates to the technical field of information management, in particular to a smart agriculture precision big data information management system, which comprises a growth index extraction module, a stage data clustering module, a nutrient fluctuation identification module, an agricultural material strategy generation module and a scheduling information scheduling module. According to the method, the acceleration criterion is constructed based on the continuous period leaf area index change sequence, transition node identification is completed in combination with the photosynthetically active radiation utilization rate and the nitrogen absorption rate change trend, accurate positioning of crop growth stage change time points is achieved, the stability of stage classification in a space region is enhanced, and the accuracy of crop growth stage classification is improved. The agricultural material putting grade is judged and the fertilization frequency and dosage are blended by analyzing a root zone soil nutrient concentration difference value and a rate fluctuation frequency detection mode in combination with moisture content and root activity conditions, and operation period adjustment is guided in a task schedule in cooperation with a meteorological element fluctuation rate. The precision of farmland crop stage identification and the timeliness of nutrient fluctuation response are integrally improved.

The invention belongs to the technical field of irrigation management and control, and provides a multi-source sensing data-driven irrigation area water demand prediction and accurate water distribution method. The method comprises the steps of obtaining multi-source data of a target irrigation area when a preset irrigation condition is met; performing quantitative evaluation on the first soil water content data and the second soil water content data based on the meteorological data to determine respective confidence degrees, and giving dynamic weights based on the confidence degrees, the temporal and spatial variation characteristics and the actual condition of the irrigation area; performing fusion processing on the first soil water content data and the second soil water content data based on the dynamic weight to obtain third soil water content data; on the basis of the third soil water content data and the crop growth stage data of the target irrigation area, the water demand of the stage is predicted; and generating a control instruction of the intelligent valve of the water delivery canal system based on the water demand. The accuracy of water demand prediction can be remarkably improved, the problems of water resource waste and insufficient supply are reduced, and efficient utilization and scientific management of water resources in an irrigation area are achieved.

The invention relates to a special fertilizer suitable for saline-alkali soil and a preparation method thereof. The fertilizer is granular and has a multi-layer structure which is sequentially arranged from inside to outside: a nutrition core layer, a controlled release film layer, a soil improvement fast exchange layer and a microorganism attaching layer. The nutrition core layer contains nitrogen, phosphorus, potassium and medium trace elements, and meets the basic nutrient requirements of crops; the controlled release film layer is self-assembled layer by layer by adopting alginate / chitosan, a humic acid-ferrous complex and a breakable cross-linking point are inserted into the film, and starch is sprayed on the outer layer, so that an ionic strength / alkalinity dual-response controlled release system is formed; the soil improvement quick exchange layer contains microcrystalline gypsum and ferrous sulfate; the microbial epiphytic layer fixes salt-tolerant phosphate solubilizing bacteria, potassium bacteria and exopolysaccharide-producing bacillus by using a charcoal-zeolite carrier, and the bacteria are distributed on the surfaces of the particles in a dotted manner. The fertilizer realizes organic combination of soil improvement, fertilizer supply and microbial regulation and control, can significantly reduce the soil salinity and alkalinity in a saline-alkali soil environment, and improves the fertilizer utilization rate and the crop growth performance.