386 results about "Growth data" 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 discloses a sheep flock accurate nutrition supply method based on an intelligent feeding algorithm, and the method comprises the following steps: collecting the body type, physiology, movement and environment data of sheep through a multi-modal sensing device, and constructing individual three-dimensional representation based on a neural radiation field model, historical growth data and a real-time health state are analyzed by utilizing an individual nutritional requirement calculation model, an optimal feed distribution strategy is determined by combining an intelligent sheep flock optimization algorithm, a feeding scheme is dynamically adjusted by adopting deep reinforcement learning, and through a cloud-side collaborative calculation architecture, the calculation efficiency is improved and a feeding decision is optimized. A non-intrusive health monitoring technology is combined, a group health evaluation model is constructed, the feed ratio is adjusted in real time, accurate matching of individual and group nutritional requirements is achieved, and through prediction of the individual and group nutritional requirements, self-adaptive adjustment is conducted on feeding parameters, and a dynamic nutrition supply strategy is optimized. The system effectively improves the feeding precision, reduces the feed waste, and optimizes the health management of the sheep flock.

The invention relates to the technical field of self-adaptive control, in particular to a tobacco field variable fertilization control system and method, and the system comprises a sensing data collection module which collects soil humidity data through a soil humidity sensor, monitors the growth data of crops at the same time, and carries out the synthesis to obtain real-time soil and growth data; and performing time sequence analysis on the real-time soil and growth data, identifying a data trend and an abnormal point, and generating a trend analysis result. According to the method, the soil humidity and the crop growth data are collected, and the time sequence analysis is combined, so that the trend and the abnormality are quickly identified, and the prediction accuracy is improved. The convolutional neural network is used for carrying out recognition and probability evaluation on diseases and insect pests, so that disease and insect pest management is more accurate, and response is quicker. Through the technologies, the system can dynamically adjust irrigation parameters based on real-time data, optimize water distribution, and ensure that an irrigation strategy is perfectly matched with crop requirements and potential risks.

The invention discloses a storage battery capacity attenuation trend prediction method, and belongs to the technical field of storage battery prediction. By collecting voltage, current and temperature data of each monomer in real time and combining historical capacity attenuation and internal resistance growth data, the method identifies a voltage and capacity difference value, evaluates cyclic stress non-uniform distribution, and determines a current sharing proportion and a load unbalance degree. Identifying an abnormal mode of new battery overload and aged battery deep discharge, constructing a mixing abnormal working condition identification mode, if the unbalance degree exceeds the standard, adaptively adjusting the charging and discharging time and the current switching frequency, establishing a load balance control framework, predicting the capacity attenuation rate and the residual cycle index of each monomer, and determining the capacity matching degree and the life matching degree; finally, a comprehensive residual life estimation value and a credibility interval are generated through fusion; the performance balance of the mixed battery pack is remarkably improved, the overall service life is prolonged, and the method is suitable for real-time monitoring of a battery management system.

The invention discloses a farmland precise irrigation decision-making method based on fusion of a physical process and machine learning, and the method comprises the following steps: S1, data collection: collecting meteorological data, soil data, fertilization and irrigation data and cotton growth data in a cotton growth period, S2, data preprocessing: carrying out the cleaning, standardization and normalization processing of an original data set, and carrying out the calculation of the data set; the method comprises the following steps: S1, preprocessing a crop mechanism model, S3, data enhancement: constructing and calibrating the crop mechanism model by using preprocessed data, S4, selecting key feature variables, and dividing the obtained data set into a training set and a test set by using an original data set and an enhanced data set, S5, model training and optimization, and S6, model verification and evaluation. According to the method, by coupling the crop growth mechanism model and the machine learning algorithm, not only can the growth conditions of crops under different moisture conditions be simulated and predicted, but also the influence of soil salinity and alkalinity on crop growth can be evaluated, so that comprehensive decision support is provided for irrigation management.

The invention discloses an AI-assisted carbon nanotube growth control method and system, and relates to the technical field of control systems. Comprising the steps that a three-dimensional AI virtual platform builds a carbon nano tube model by collecting carbon nano tube growth data and environment data in unit time, the growth data comprise the growth rate, the direction and the diameter size, the environment data relates to gas flow and electric field intensity, the carbon nano tube model is mapped into a three-dimensional coordinate system, and the three-dimensional coordinate system is constructed; wherein the X axis, the Y axis and the Z axis respectively represent the length, the width and the height of the carbon nanotube, and recording initial coordinates. By using the three-dimensional AI virtual platform and the real-time data acquisition technology, the environment change in the growth process of the carbon nanotubes can be accurately captured, and the environment change can be quickly coped with during actual control by combining the growth data and dynamically adjusting the environment parameters in the growth process; and unstable growth or deviation caused by incapability of timely adjustment in a traditional method is avoided.

The invention provides a corridor closed harvesting automatic identification method and system, and relates to the technical field of harvesting, and the method comprises the steps: obtaining environmental parameters in a corridor closed region, calculating the growth condition of plants in a corridor, and obtaining the growth data of the plants; meanwhile, an image of a plant in the corridor is obtained in real time and segmented, a plant area image is obtained, the maturity of the plant is recognized, and plant maturity data is obtained; according to the plant growth data and the plant maturity data, whether the plants in the corridor meet the harvesting condition or not is judged, if the plants meet the harvesting condition, a harvesting execution mechanism is controlled to conduct harvesting operation on the plants in the corridor, harvesting amount data is obtained, and the commodity rate and the inventory are calculated based on the harvesting amount data; environmental parameters in the plant growth model are adjusted, according to the adjusted environmental parameters, the step of calculating the commodity rate and the inventory is repeatedly executed until the commodity rate reaches a preset commodity rate threshold value and the inventory is lower than a preset inventory threshold value, and corridor closed harvesting automatic identification is completed.

The invention relates to the technical field of citrus fruit cracking prevention and control, and provides a citrus fruit cracking prevention and control method based on intelligent monitoring and regulation, and the method comprises the following steps: collecting soil humidity data, fruit growth data and meteorological condition data of monitored citrus; identifying soil humidity data through a soil humidity analysis model to obtain a soil humidity evaluation state; identifying fruit growth data through a fruit growth analysis model to obtain a fruit growth evaluation result; identifying meteorological condition data through a meteorological condition risk assessment model to obtain a meteorological condition influence degree; identifying a soil humidity evaluation state, a fruit growth evaluation result and a meteorological condition influence degree through a comprehensive risk judgment model to obtain a citrus fruit cracking risk degree of the monitored citrus; an irrigation system and a fertilization device of the monitored citrus are controlled based on the citrus fruit cracking risk degree, and the irrigation amount, the fertilization amount and ventilation equipment are adjusted; when it is monitored that the citrus fruit cracking risk degree exceeds a preset threshold value, early warning is sent to planters to take prevention and control measures. According to the method, the accuracy of citrus fruit cracking risk assessment is realized, so that the management efficiency of citrus planting and the yield and quality of fruits are improved.

According to the chicken coop temperature prediction algorithm based on the TCN-Transform, the temperature, the humidity and the ventilation quantity in the chicken coop are automatically adjusted in a mode of combining real-time monitoring and a prediction model, so that the growth environment of broiler chickens is optimized; a sensor is used for collecting internal and external environment data of a henhouse, and in combination with a prediction model based on TCN and Transformer, a future temperature change trend is estimated; a PID controller is adopted, the ventilation requirement is calculated in real time according to the target temperature, the current temperature, the predicted temperature and the outdoor temperature, the starting number and starting time of fans are output so that it can be ensured that the temperature of the henhouse can be kept within the target range all the time, PID control parameters are dynamically adjusted through the growth data of chickens, and the ventilation strategy is optimized so that the requirements of different growth stages can be met. By using the chicken coop temperature prediction algorithm based on TCN-Transformer, the prediction precision and the reaction speed can be improved, dynamic adjustment is performed according to the actual chicken coop environment, and the production benefits of a farm are improved.

The invention discloses a tree growth prediction method and system based on an Elman dynamic recurrent neural network, and relates to the technical field of tree growth prediction, and the method comprises the steps: carrying out the collection of tree growth data, and carrying out the preprocessing of the collected data; performing feature extraction on the preprocessed data; constructing an Elman dynamic recurrent neural network model, and training the model; the trained model is evaluated and optimized, and the analysis result is accurate. According to the method, the dynamic change of time sequence data can be captured, high-quality input data and deep feature mining can be provided, the robustness and adaptability of the model are improved, accurate prediction of tree growth is realized, and finally, power transmission line managers are helped to take measures in time, and safe and stable operation of the power transmission line is ensured.

The invention discloses a dark tea extract intelligent production control system, and relates to the technical field of plant extraction, the system comprises a data acquisition and monitoring module, an intelligent control module, an analysis optimization module and a visualization module; the data acquisition and monitoring module acquires environmental data and eurotium cristatum growth data in real time; the intelligent control module automatically adjusts the temperature, the humidity and the oxygen concentration in the fermentation process based on the environmental data and the eurotium cristatum growth data; the analysis and optimization module predicts and adjusts production conditions in real time by using a neural network and an optimization model; the visualization module provides real-time monitoring and remote operation functions through a graphical user interface, so that an operator can monitor the state of the system in real time. According to the method, the growth environment of eurotium cristatum can be accurately controlled, the fermentation and extraction processes are optimized, the quality consistency and production efficiency of dark green tea are improved, and the problems of quality fluctuation and inaccurate control in a traditional production method are solved.

The invention provides a product whole-process supervision method and system based on a block chain, belongs to the field of computers, and solves the problem that the block chain cannot verify the authenticity of input data in a commodity traceability system. The method comprises the following steps: acquiring initial state information after agricultural and sideline products are produced, wherein the initial state information comprises growth data, initial appearance data and initial weight data; obtaining full-process environmental condition parameters of storage, transportation and sales and stage state information of the agricultural and sideline products at each process transfer node, wherein the stage state information comprises stage appearance data and stage weight data; and on the basis of the initial state information or the stage state information and each process environment condition parameter, predicting ideal state information of the agricultural and sideline products at the next process transfer node, so that the agricultural and sideline products can be obtained under the condition that the matching degree of the ideal state information and the recorded stage state information is lower than a preset matching degree. And generating a process environment condition parameter authenticity alarm and recording the process environment condition parameter authenticity alarm to the block chain system.

The invention relates to the technical field of digital video devices, in particular to a digital video device control method and device. The method comprises the following steps: acquiring digital monitoring data, performing real-time scene acquisition and dynamic region optimization according to the data, and generating optimized dynamic region data; video frames are read based on the dynamic region optimization data, and abnormal behavior analysis and behavior intention evaluation are carried out; calculating the complexity of monitoring behaviors by combining video frame data and behavior intention data, and evaluating resource consumption increase; the processing bottleneck is evaluated according to the resource consumption growth data, and the processing capability attenuation analysis is carried out, so that the monitoring device is adjusted, and the system performance is optimized; according to the method, the resource allocation and processing capability of the video device is dynamically adjusted through real-time analysis and processing of monitoring data. According to the invention, the digital video device is adjusted, so that the prediction identification of the digital video device is more accurate and efficient.

The invention relates to the technical field of biological breeding, in particular to a biological breeding management method and system based on big data. The method comprises the following steps: acquiring original germplasm data; multi-source heterogeneous data integration is carried out on the original germplasm data, the data integration comprises genotype sequencing integration and historical phenotype integration, and integrated germplasm data is generated and stored in a database in a distributed mode; obtaining original growth data, performing data preprocessing, generating standardized germplasm resource comprehensive data including a crop image feature set and an environment response parameter set, and synchronizing the data to the database; and performing whole genome selection prediction on the integrated germplasm data based on the standardized germplasm resource comprehensive data to generate a genotype-phenotype prediction model. By integrating germplasm resources, field management, breeding management, phenotype management, genotype management and whole genome selection, the accuracy and adjustment flexibility of biological breeding management are improved.

The invention relates to the technical field of ecological environment protection, in particular to a mangrove forest carbon sink monitoring and metering method based on an unmanned aerial vehicle, a radar and an AI technology, and the method comprises the steps: 1, obtaining the laser radar data of a mangrove forest through a laser radar carried by the unmanned aerial vehicle; step 2, acquiring elevation data in a mangrove forest vegetation layer area, and obtaining point cloud data after topographic error correction; 3, obtaining a multispectral image of the mangrove forest, and obtaining a multispectral data matrix; 4, identifying forest growth data features, constructing a mangrove forest carbon sink prediction model, and predicting the mangrove forest carbon sink amount; step 5, marking the image region with the NDVI value higher than a preset NDVI threshold value as a blade over-dense region; and according to the area of the overdense leaf region and the multispectral data matrix, calculating a light depression factor by using a photosynthetic depression factor formula, determining the carbon sink deviation of the overdense leaf region by using a regional carbon sink deviation formula, and obtaining a real carbon sink value of the mangrove forest according to a carbon sink calculated value obtained by prediction.

The invention relates to the technical field of resource management, in particular to a wheat-corn annual light and temperature resource comprehensive management system, which comprises an environment monitoring module for collecting soil humidity and meteorological condition data, combining growth data of wheat and corn, and calculating a growth stage demand index to obtain growth demand data. According to the invention, the real-time optimization of resource allocation is realized by continuously collecting data such as soil humidity and meteorological conditions. For an adjustment strategy, irrigation and illumination plans are dynamically adjusted according to real-time environmental changes, and deviation is rapidly identified and corrected in a feedback mechanism, so that resource application is more accurate. The improvement of performance evaluation makes it possible to measure resource consumption and crop growth efficiency through numerical indexes, thereby providing data-driven decision support for agricultural production, ensuring maximum efficiency and economy of resource utilization, and improving crop yield and quality.

The invention discloses a vegetable greenhouse environment management method and system, a storage medium and a program product, and the method comprises the steps: obtaining historical environment parameters of a vegetable greenhouse, vegetable growth data and the growth state of vegetables in the vegetable greenhouse at the current moment; establishing a multistable growth model according to the historical environmental parameters and the vegetable growth data, wherein the multistable growth model comprises a plurality of stable growth points; positioning a growth state in the multistable growth model, and determining a dominant stable growth point closest to the growth state; establishing an attractor potential field, setting an advantage stable growth point as an attractor center, and calculating a potential energy gradient from a growth state to the attractor center; based on the potential energy gradient, a progressive regulation and control path of vegetable greenhouse environment parameters is constructed, and a staged environment regulation and control instruction is generated; and the staged environment regulation and control instruction is sent to the environment control equipment, so that the environment control equipment carries out environment regulation according to the staged environment regulation and control instruction and the progressive regulation and control path, and the timeliness and the accuracy of farming operation and environment regulation are improved.

The invention discloses a nutrition recipe intelligent generation system and method, and belongs to the technical field of nutrition information processing and intelligent recommendation. The system is suitable for individual nutrition management of individuals of all ages, and particularly, optimization design is carried out for infant stages of 0-72 months. The system intelligently generates a personalized nutrition recipe based on individual basic information, growth indexes, nutrition standards, available food materials, user preferences and feedback and other multi-dimensional data, and supports nutrition intake evaluation and dynamic optimization. The system comprises core modules of data input, nutritional requirement calculation, recipe generation, nutrition analysis and interactive display and the like, and expands and supports functions of sensitization screening, regional and seasonal adaptation, work and rest linkage, family sharing and the like. Through fusion of individual growth data and environmental conditions, the system realizes scientific and high-adaptability nutrition recommendation, improves feeding convenience and intervention accuracy, and has a wide application prospect.

The invention discloses an intelligent growth monitoring and analysis method and system for mariculture, and the method comprises the following steps: firstly, collecting breeding environment data in real time through a first sensor group; and acquiring growth data of the breeding object through the second sensor group. Then, the cultivation environment data and the growth data are transmitted to a cloud platform by using a data acquisition system, and the data are preprocessed by the cloud platform to generate a standardized data model; based on the standardized data, a deep learning algorithm is used for analyzing the growth trend of the breeding object and predicting the growth change in a period of time in the future. Through combining the breeding environment and the growth data, the cloud platform performs risk assessment, generates early warning information in real time, and identifies potential risk factors. According to the early warning information, the cloud platform automatically adjusts breeding environment parameters and management measures, and healthy growth of breeding objects is ensured. According to the method, intelligent decision support is provided for breeding management, and the breeding efficiency and the growth quality of bred objects are improved.

The invention relates to the technical field of automatic feed distribution, in particular to an automatic feed production regulation and control method and system.The method comprises the following steps that healthy and growth rate data are collected and input into a data stream, the number of dynamic Bayesian network nodes and a connection mode are adjusted, real-time data are analyzed, and the learning rate and the transition probability are adjusted; and a prediction error matching feedback mechanism is established, key indexes are extracted, a distribution instruction is generated through a fuzzy logic controller, the feed ratio is adjusted to balance cost nutrition and environmental influence, and an optimal configuration result is obtained. According to the method, health and growth data are comprehensively utilized, feed feeding is more personalized and accurate, a feedback mechanism is analyzed and optimized in real time, it is ensured that the feed ratio responds to specific nutritional requirements of animals in real time, accordingly, resource waste is reduced, healthy growth of the animals is enhanced, feeding instructions are finely regulated and controlled through fuzzy logic, and the feeding efficiency is improved. The method adapts to different stages of animal growth, and a particle swarm optimization algorithm is adopted to balance the cost, nutrition and environmental influence.

The invention discloses an infrastructure digital twinborn dynamic registration method, system and device based on a deep neural network, a medium and a product, and relates to the field of digital twinborn registration, and the method comprises the steps: building a digital twinborn model based on the project design of an infrastructure project; predicting growth data of the digital twin model; based on the predicted growth data, using the trained deep neural network to predict a compensation error; and compensating the digital twin model based on the compensation error to realize dynamic registration of the digital twin model. According to the method, the error between the digital twinborn model and the physical entity can be reduced, and the fitting precision of the digital twinborn model and the actual construction state is improved.

The invention relates to a smart agriculture supervision method and system based on the Internet of Things, and relates to the field of smart agriculture, and the method comprises the steps: obtaining real-time data generated in the growth process of crops in an agricultural greenhouse, taking the real-time data as growth data, and uploading the growth data to a database in a cloud platform, bidirectional communication with a client in a cloud platform is realized through a subscription table in a subscription or release form, growth data in the subscription table is acquired to establish a decision model, the predicted growth condition of the current festival environment of crops is judged according to the decision model, and target plants in different areas in the agricultural greenhouse are determined; and obtaining the real growth condition of each target plant, determining the health degree of the target plant based on the real growth condition, and if an abnormal target plant exists, adjusting the operation parameters of each device related to the area where the abnormal target plant is located based on the predicted growth condition and the real growth condition of the target plant. The application has the effect of improving the operation accuracy of each device in the greenhouse.

The invention relates to the technical field of data transmission, in particular to a sheep breeding growth monitoring data transmission method and system, and the method comprises the steps: collecting growth data monitored in a sheep breeding growth process; according to the figure data and the feed intake data, obtaining a trend fluctuation difference factor and a corrected trend fluctuation abnormal factor of each kind of figure data every day; obtaining the trend characteristics of each kind of figure data every day according to the change condition of the figure data; according to the trend characteristics of each type of figure data every day and the correction trend fluctuation abnormal factor of each type of figure data every day, obtaining an adjustment coefficient of a compression window corresponding to each type of figure data every day; adjusting the compression window according to the adjustment coefficient of the compression window corresponding to each type of figure data every day; and according to the adjusted compression window, compressing the figure data through a revolving door compression algorithm, and transmitting the compressed figure data. The compression rate and the compression efficiency of the figure data can be improved.

The invention discloses a shellfish carbon sequestration speed quantitative estimation method based on a neural network model, and belongs to the technical field of environmental science and ecology, and the method comprises the steps: obtaining water quality parameter data, building a water quality monitoring data set based on the water quality parameter data, and carrying out the preprocessing; performing data alignment on the shellfish growth data and the preprocessed water quality monitoring data set to obtain a time sequence data set; training a long-short term memory neural network model based on the time sequence data set, obtaining a predicted value of the pH value of the water body in a preset time period through the trained model and monitoring data obtained in real time, and calculating the release rate of carbonate ions, a carbon sequestration speed estimated value is obtained by combining the net absorption amount of carbon dioxide in the shellfish growth process in unit time; and calculating the total carbon sequestration amount of the shellfish in the whole water body based on the carbon sequestration speed estimated value. According to the method, accurate estimation of the carbon sequestration amount of the shellfish culture water body is realized through a method of combining high-precision sensing monitoring and a deep learning model.

The invention discloses a feed feeding control system and method based on data analysis, and relates to the technical field of feed feeding, and the method comprises the steps: obtaining feed feeding data and corresponding livestock growth data according to a historical feed feeding cycle; analyzing the feed feeding data, and extracting a matching sequence of historical feed feeding data; combining the corresponding livestock growth data to establish an associated data group; constructing a feed ratio prediction model based on a support vector regression method; performing feed preparation and feeding according to the real-time matching sequence; analyzing an adaptive value of the real-time matching sequence of the current feed feeding period according to the real-time monitoring data set, and judging the continuous condition of the current feed feeding period according to the adaptive value; and constructing an update cycle matching model based on the update cycle matching set, and analyzing the total number of days of the subsequent feed feeding cycle according to the update cycle matching model. The rationality of feed feeding is improved, and intelligent and reasonable feeding is realized.

The invention belongs to the technical field of monitoring of a hot spring algae culture process, and discloses an intelligent monitoring and analyzing method for growth data of hot spring algae culture. According to the invention, by identifying the abnormal growth state and dynamically adjusting the spectrum composition and intensity of the multiband illumination sensor array when the abnormal growth state is identified, spectrum-growth state closed-loop control is realized, the accuracy and effectiveness of illumination control are improved, proper illumination is provided for the hot spring algae in time, and the quality of the hot spring algae is improved. The photosynthesis and normal growth are guaranteed, the culture efficiency and quality are effectively improved, and the loss caused by abnormal growth is reduced. According to the method, the growth cycle is identified based on the relative growth rate and the cell division rate, so that the growth change can be more sensitively captured, the exact growth stage of the hot spring algae can be accurately grasped, a key support is provided for subsequent accurate regulation and control of culture conditions, and the scientificity and effectiveness of culture are greatly improved.

The invention relates to the technical field of concrete curing, in particular to an intelligent control method and system for a precast concrete component curing kiln, and the method comprises the steps: obtaining historical and real-time environment parameters of the curing kiln, generating monitoring data with an identifier in combination with a component type and a current process stage, and matching a corresponding preset temperature and humidity regulation and control model. And optimizing the model by using a historical temperature and humidity response sample and component strength increase data to generate a personalized target temperature and humidity regulation and control model. And analyzing real-time parameters based on the model, outputting a control instruction containing a target temperature rise rate, a humidity threshold value and heat preservation time, and converting the control instruction into a kiln body adjustment control signal according to a dynamically adjusted execution rule. And finally, sending to an execution terminal to realize cooperative regulation and control of heating, humidification, ventilation and heat preservation systems. According to the method, multi-source data and process characteristics are fused, the adaptive learning ability is achieved, the maintenance environment can be accurately regulated and controlled, and intelligent, refined and efficient operation of the maintenance process is achieved.

The invention relates to the technical field of automobile batteries, in particular to a battery cell matching system with improved cycle life, comprising: a battery cell parameter acquisition module for acquiring initial capacity, internal resistance value and voltage curve of a to-be-matched battery cell for a new energy automobile; the dynamic attenuation modeling module is used for establishing a dynamic attenuation model according to capacity attenuation and internal resistance growth data in the battery cell recycling process; the matching optimization module is used for combining the output of the dynamic attenuation model with the current matching strategy and adjusting the battery cell group in the matching scheme to obtain a battery pack; the high-temperature correction module is used for dynamically adjusting a matching correction value of a high-temperature cycle attenuation coefficient according to the attenuation performance of the battery cells in the battery pack under high-temperature cycle; and the control module is used for determining a screening threshold value of the static parameters of the battery cells according to the maximum difference quantity of the initial capacities of the plurality of battery cells in the same battery pack, or determining a dynamic characteristic weight coefficient according to the internal resistance difference of the plurality of battery cells in the battery pack. According to the invention, the stability of battery cell matching is improved.

The invention discloses an artificial intelligence-based analysis method and system, and relates to the technical field of artificial intelligence, and the artificial intelligence-based analysis method comprises the following steps: S1, receiving an analysis request, analyzing the analysis request, obtaining a plurality of pieces of correction push data, and sending the correction push data; s2, receiving correction selection data sent according to the multiple pieces of correction push data, taking recommended acquisition time in the correction selection data as verification acquisition time, and taking an allowable difference value in the correction selection data as a correction verification value; s3, acquiring actual growth data when verification acquisition time arrives, analyzing the actual growth data by using the to-be-analyzed abnormal data and the correction verification value to obtain a correction analysis result, and if the correction analysis result is failure, acquiring a new analysis request, and executing S1; and if the correction analysis result is successful, ending. The correction success rate of the abnormal plant growth condition is improved.

The invention discloses a corn drought resistance identification device and method, and belongs to the technical field of agricultural scientific research. According to the method, the problems of inaccurate environment control, long identification period, poor repeatability and the like of an existing method are solved, the drought resistance index of the corn is calculated through the random forest model, the drought resistance grade of the corn is divided, and the drought resistance index threshold value is set; the drought resistance indexes of the corn in different growth periods are analyzed, the drought resistance index threshold value is dynamically adjusted according to the corn growth data monitored in real time, and the drought resistance of the corn in different growth periods can be reflected more accurately; feature importance scores are calculated through a random forest model, feature variables are sorted, and it can be determined that certain features make the greatest contribution to the drought resistance index; based on feature importance analysis, suggestions on corn drought resistance research and breeding are provided, researchers can be helped to optimize breeding strategies, errors caused by subjective judgment are reduced, and the breeding efficiency of drought-resistant varieties is improved.