288 results about "Environmental agent" patented technology

The invention discloses a multi-modal error dynamic compensation method and system for an intelligent electric meter, and the method comprises the steps: constructing an environment perception unit, and obtaining an environment factor parameter; performing weighted calculation on the environmental factor parameters to generate an environmental metabolic index; constructing an error prediction model based on the hormone release promoting factor action mechanism in combination with the environmental metabolic index so as to obtain a prediction error offset; original signals are collected in real time, multi-modal error detection is carried out, and error vectors are generated; acquiring a compensation trigger threshold according to the error vector; and comparing the predicted error offset with a compensation trigger threshold, and triggering a multi-stage compensation mechanism. According to the invention, the metering parameters of the electric meter can be dynamically adjusted on line in real time under complex working conditions, so that the stability and accuracy of the electric meter are improved.

The invention provides a dynamic health monitoring system integrating artificial intelligence and wearable equipment and a method thereof, and relates to the field of health monitoring, and the method comprises the steps: collecting physiological parameters and environmental data of a user, eliminating time migration of multi-source data through a timestamp alignment technology, extracting dynamic coupling features of the physiological parameters and the environmental data, and obtaining a dynamic health monitoring result; and generating the health state characterization guided by the environment. The method comprises the following steps: synchronously acquiring user voice input information, and extracting and analyzing acoustic features to generate emotional state change features after performing Mel spectrum conversion; then, cross-modal semantic association between emotions and health states is established through a modal soft constraint mechanism, and personalized guidance suggestions are generated and displayed, so that the dynamic perception ability of health state assessment and the scene adaptability of suggestion generation are improved; therefore, the problems of environmental factor splitting, subjective and objective data isolation and insufficient dynamic nonlinear relation modeling in a traditional method are solved.

The invention relates to the technical field of agricultural data management, in particular to a grain data management method and system based on artificial intelligence, and the method comprises the following steps: based on a grain transportation container and a storage environment, extracting monitoring data of temperature and humidity monitoring equipment, a gas component detector and an illumination sensor, summarizing environment parameters, screening abnormal environment data, and storing the abnormal environment data; and obtaining an environment state record. According to the invention, through accurate analysis of temperature and humidity, gas components and illumination data, perception and alarm capabilities of sudden changes of a storage environment are enhanced, comprehensiveness of quality change monitoring is improved, mining capability of relevance of environmental factors and microbial pollution is enhanced, optimal storage conditions are matched, quality loss risks are reduced, pollution factors are accurately identified, and the method is suitable for popularization and application. Through combination of air quality data, pollution hidden danger screening and risk pre-judgment capabilities are improved, abnormal environment data are deeply analyzed, quality deterioration alarm is optimized, abnormal environment identification precision is improved, and a grain quality guarantee system is enhanced.

The invention provides an ecological restoration area sea grass bed health state monitoring method, and relates to the technical field of sea grass bed ecological restoration, and the method comprises the steps: obtaining a sea grass bed ecological region, carrying out the region division, and determining a plurality of sea grass bed monitoring regions; according to the image sensor, combined with the image feature detection channel, sea grass state feature analysis is carried out, and a plurality of sea grass state feature analysis results are generated; performing illumination detection feature analysis according to the illumination sensor in combination with the illumination feature analysis channel, and generating a plurality of illumination detection feature analysis results; and carrying out abnormity management on the corresponding sea grass bed monitoring area, carrying out abnormity linkage analysis and carrying out abnormity management. The technical problems that the prior art is limited by resolution and environmental factors, the microscopic state change of the sea grass bed cannot be accurately captured, the health state of the sea grass bed is difficult to comprehensively analyze by integrating multiple monitoring dimensions, and the accuracy and comprehensiveness of state monitoring of the sea grass bed are insufficient are solved.

The invention discloses a chemical gas phase risk detection system and method, and belongs to the technical field of gas detection. In order to solve the problems that the identification precision is not high, the risk grade division is not clear and the diffusion condition of chemicals cannot be accurately predicted, the chemical characteristics are comprehensively considered, the risk factor of each sampling point position is calculated, and the risk division can be accurately performed on each sampling point position according to the formulated risk grade division standard and threshold value, so that the risk classification accuracy is improved. A high-risk area can be quickly identified, the diffusion range and the influence degree of chemicals under different risk levels are simulated and predicted by combining environmental factors such as wind speed, wind direction, temperature and humidity in a sampling environment, a scientific basis is provided for formulating countermeasures, potential hazards of chemical accidents to personnel and the environment can be reduced, and the method is suitable for popularization and application. The initial early warning threshold value is dynamically adjusted according to the detection result, the risk assessment and the actual site situation, it is ensured that the early warning system can adapt to the complex and changeable chemical environment, and the early warning accuracy is improved.

The invention discloses a site pollution abatement scheme generation method and device, and the method comprises the steps: collecting the pollution data of a target region in multiple dimensions, and the pollution data at least comprise pollutant characteristic data, site characteristic data and environmental factor data; performing data processing on the pollution data of each dimension, and fusing the pollution data of each dimension after data processing to obtain fused multi-modal data; inputting the fused multi-modal data into a pre-constructed scheme generation model to obtain a pollution abatement scheme output by the scheme generation model; wherein the scheme generation model is constructed by utilizing the historical site pollution data subjected to data processing and a historical treatment scheme corresponding to the historical site pollution data. Through model construction and calling, the pollution control scheme for selection and reference is generated, and the technical problem that generation of the site pollution control scheme completely depends on artificial experience and site investigation is solved.

The invention relates to an environment smell analysis method based on an intelligent terminal and related equipment, and the method comprises the following steps: carrying out the real-time collection of environment smell through a sensor network, obtaining an environment smell data flow, carrying out the preprocessing of the environment smell data flow, and obtaining the optimized environment smell data; performing feature extraction and decomposition on the optimized environmental odor data to obtain an environmental odor multi-dimensional vector; performing classification prediction on the environmental odor multi-dimensional vector to obtain an environmental odor prediction type; performing correlation analysis of environmental factors on the environmental odor prediction type to obtain an environmental odor-environmental factor thermodynamic distribution diagram; transmitting and analyzing the environmental odor based on the environmental odor-environmental factor thermodynamic distribution diagram through an intelligent terminal to obtain an environmental odor diffusion path and an environmental odor diffusion range, and solving the problem that a traditional method is lack of dynamic prediction capability on the odor diffusion path and range; the actual condition of odor pollution cannot be comprehensively reflected.

The invention belongs to the technical field of medicines, and discloses a near-infrared cyanine probe for detecting penicillin G acylase as well as a preparation method and application of the near-infrared cyanine probe. According to the research, a near-infrared fluorescent probe Cy-NEO-PA responding to penicillin G acylase (PGA) is developed, so that the influence of environmental factors on the formation of an acinetobacter baumannii biological membrane is observed. Research results show that glucose inhibits the generation of PGA to enhance the formation of a biological membrane, while phenylacetic acid (PAA) stimulates the generation of PGA and inhibits the formation of the biological membrane. The observation results highlight the excellent capability of Cy-NEO-PA in accurately measuring PGA kinetics, and reveal the key effect of PGA in biological membrane development. In addition, the Cy-NEO-PA has good biocompatibility, strong active oxygen generation, efficient photo-thermal conversion and bacterial targeting ability, so that the Cy-NEO-PA becomes a promising drug for resisting bacterial infection and promoting wound healing through photo-thermal / photodynamic therapy.

The invention discloses a soil pollution diffusion prediction method based on differential evolution, and relates to the technical field of soil pollution control, and the method comprises the steps of collecting soil sensing data in real time, and uploading the soil sensing data to a data center. Real-time sensing data are compared and calibrated, sensing data errors caused by environmental factors are effectively reduced, data input into a model are more accurate and reliable, meanwhile, model parameters are optimized and solved by combining differential evolution and a particle swarm optimization algorithm, the differential evolution is used for global optimization to find a proper parameter combination, and the accuracy of the model parameters is improved. Particle swarm optimization performs local fine adjustment based on a differential evolution result, and the two are alternately performed, so that the accuracy of soil pollution diffusion prediction is greatly improved, the diffusion trend of pollutants in soil can be more accurately grasped, and a more reliable basis is provided for ecological environment management and human health protection.

The invention relates to a plant growth environment evaluation method based on root exudates data identification, and belongs to the technical field of plant growth evaluation.The method includes the steps that chemical component data of root exudates of each plant planting area is evaluated and analyzed, and the plant planting areas in the abnormal state are obtained; therefore, environment characteristic data in the target area and chemical component data of root exudates in the plant planting area in the abnormal state are obtained, knowledge graph analysis is combined to obtain disease types possibly generated by plant root growth, and finally early warning is carried out according to the disease types possibly generated by plant root growth. And related protection strategies are generated based on the types of diseases possibly generated by plant root growth. According to the method, bacterial colony analysis is carried out on the root analyte in combination with environmental factors, so that the bacterial colony type of the plant planting area is predicted, the type of germs is determined, prevention and control are carried out in advance, and economic losses are reduced.

The invention discloses lysozyme, antibacterial peptide derived from the lysozyme and application of the lysozyme in preparation of antibacterial drugs. According to the invention, 1, 640 protein sequences recorded in a ruminant rumen caterpillars genome database are systematically predicted based on a lysozyme prediction model, and finally, the lysozyme with the amino acid sequence as shown in SEQ ID No.1 is relatively strong in antibacterial activity and stable in antibacterial activity, and is slightly influenced by environmental factors. According to the invention, an antibacterial peptide region of the lysozyme is analyzed and determined, three peptide fragments with clear antibacterial activity are identified, and the amino acid sequences of the peptide fragments are respectively shown as SEQ ID No.2, SEQ ID No.3 or SEQ ID No.4. The antibacterial peptide provided by the invention has relatively strong membrane destroying ability and broad-spectrum antibacterial activity, and compared with integral protein, the antibacterial peptide is simpler in structure and more flexible in synthesis and modification, and is easier to break through a bacterial drug-resistant mechanism.

The invention belongs to the technical field of novel pesticides, and particularly provides a bactericide suspending agent and a preparation method thereof.The effective component of the bactericide suspending agent is prothioconazole, and on the basis of the effective component of the bactericide suspending agent, microencapsulated oxidized vegetable oil accounting for 5-15% of the weight of the whole bactericide is added to serve as a synergistic auxiliary agent. The prothioconazole bactericide has the advantages that the adhesion capacity of the prothioconazole bactericide on the surfaces of crops can be obviously improved, the adverse effects caused by environmental factors such as rain wash are reduced, and the absorption of the bactericide is facilitated, so that the synergistic effect is achieved.

The invention relates to the technical field of pesticide residue detection, provides a fruit juice processing pesticide residue and residual quantity detection method and system, and solves the problems that a test result is greatly influenced by environmental factors, the operation is complicated, the sensitivity is low and the like. The method comprises the following steps: arranging a mass spectrum imaging device to collect a raw material image, and screening to obtain an area image with pesticide residues; extracting mass spectrum characteristics of the image, and calculating to obtain pesticide residues; the detection result is judged, if pesticide residues exist, position information and an area image are displayed on visual equipment, and a qualified rate report of the pesticide residues is output; if the pesticide residue does not exist, the detection is continued. According to the invention, the monitoring comprehensiveness is improved, the visualization and integration of data are realized, the pesticide residue is quantitatively evaluated, the pesticide residue problem can be quickly responded and processed, and the product safety is guaranteed.

The invention relates to the technical field of smart breeding and big data analysis, and discloses a smart breeding monitoring method and system based on big data analysis. The method comprises the following steps: periodically acquiring original data of a farm sensing network, and constructing a multi-dimensional state image through time-space synchronization and normalization processing; inputting the image into a pre-trained behavior-environment decoupling analysis network, and stripping an independent behavior feature tensor and an independent environment background field tensor in parallel; and based on the two, calculating a coupling degree index through a cross impact evaluation matrix and generating a comprehensive anomaly scoring graph. According to the graph, a strategy generation engine dynamically synthesizes a regulation and control instruction set containing directional airflow guide, refined feeding and temperature and humidity coordinated regulation. According to the method, deep separation and correlation analysis of animal behaviors and breeding environment factors are realized, an abnormal root can be accurately traced, and early warning and systematic precise regulation and control are realized.

The invention relates to the technical field of psychological state assessment, and particularly discloses a psychological state assessment method and system based on environmental perception, and a readable storage medium, and the method comprises the following steps: collecting an environment scene and an assessed object behavior image through an RGB camera, and extracting an object category, a spatial layout and a dynamic event through a semantic segmentation model; acquiring an environment voiceprint through a microphone, and recognizing a voice category by using a voice recognition model; synchronously acquiring facial micro-expression feature points, voice rhythm features and head, neck and shoulder vibration frequencies of the evaluated object; and generating a semantic vector from the object / scene label through a semantic embedding model. According to the psychological state assessment method based on environmental perception, through three core innovations of quantitative modeling of environmental semantics, causal reasoning and a dynamic attention mechanism and personalized adaptive calibration, the neglect limitation of traditional psychological assessment on environmental factors is broken through, and the accuracy, robustness and practicability of psychological state assessment are remarkably improved.

The invention discloses a line fault risk space-time distribution prediction method under bias-heterogeneous environmental factors, and the method comprises the following steps: S1, obtaining a set of historical line fault records, carrying out the normalization processing of the historical line fault records and corresponding feature factors, and obtaining a set of the historical line fault records; constructing an input mapping space between historical line fault records and corresponding fault features to obtain an evaluation database A; s2, for a data bias-heterogeneous environment, parallel learning of discrete features and continuous features in fault features is completed, and common factors and rare factors are obtained; and S3, establishing a fuzzy conditional correlation pattern identification model FCCPI, respectively extracting a high-risk HR factor and a rare high-risk RHR factor in the common factors and the rare factors through the fuzzy support degree and the conditional fuzzy support degree, normalizing the fuzzy support degree and the conditional fuzzy support degree, integrating the fuzzy support degree and the conditional fuzzy support degree to obtain a support degree score, and quantifying the risk level through the support degree score.

The invention belongs to the technical field of data processing, and relates to a disinfectant verification method based on Jerlin codes. The invention aims to solve the problem of biological potential safety hazards that the identity and the state are separated in the anti-counterfeiting verification of the existing disinfectant, and the titer of the liquid medicine falls off the threshold value due to environmental factors cannot be identified. The method comprises the following steps: in response to a code scanning operation, analyzing a Jerlin code to obtain a factory fingerprint, and splicing cloud logistics and terminal storage data to form a full-link environment monitoring sequence; calculating an environment pressure acceleration coefficient of each time slice by using a nonlinear kinetic model, and evaluating the damage degree of the environment to chemical properties; calculating accumulated chemical loss and residual titer based on the coefficient; and comparing the residual titer with a threshold value, and if the residual titer reaches the standard, generating a dynamic hash token containing titer information for cloud verification. According to the invention, deep combination of physical efficacy and digital verification is realized, and the use safety and reliability of the disinfectant are greatly improved under the condition that an expensive sensor is not needed.

The invention relates to the technical field of medicine management, in particular to an intelligent medicine expiration date management method and system, and the method comprises the steps: collecting environment parameters of medicine storage in real time; constructing a characteristic database of drug stability, and constructing a degradation rate model of the corresponding drug based on the characteristic database; based on the environmental parameters and a degradation rate model, calculating an accumulated value of the current storage condition to the drug quality, and generating a quality risk level; and generating a medicine optimization management instruction based on the quality risk level and the remaining time of the medicine fixed effective period. By means of the method, the problem that the medicine effect fails in advance due to the fact that dynamic environment factors are neglected in a traditional method is effectively solved, accurate early warning of quality deterioration is achieved through dynamic monitoring and degradation modeling, supervision blind areas are eliminated, and the risk of medical resource waste is reduced.

The invention relates to a bridge disease two-stage analysis and risk assessment method and system, belongs to the technical field of bridge disease analysis, and solves the problems that an existing bridge disease analysis and risk assessment method is low in bridge disease prediction precision and incomplete in risk assessment. The method comprises the following steps: acquiring data of a plurality of bridges, including geometric characteristics, material characteristics, environmental factors and disease data; preprocessing the data, and carrying out data balance processing on the preprocessed data; a two-stage analysis method is adopted to carry out feature optimization on bridge diseases, and a multi-label classification model is constructed to predict joint disease probability distribution; and based on the combined disease prediction probability, carrying out risk clustering analysis on the bridge by adopting a fuzzy C-means clustering method, dividing bridge risks into different categories, and outputting a risk clustering result. According to the method, the accuracy of bridge disease prediction and the reliability of risk assessment are improved, the adaptability is higher, and good application prospects and economic benefits are shown.

The utility model belongs to the technical field of experimental research, and particularly relates to an experimental device for researching a subsurface zone pollutant migration rule, which comprises a water injection component, a column body, a filling medium and a detection component, the column body is of a cylindrical cavity structure, the upper end and the lower end of the column body are each provided with a water injection opening, and the two water injection openings are each communicated with one output end of the water injection assembly; the filling medium is arranged in the column body and is used for simulating a soil layer structure from surface water to underground water; the detection assembly comprises a processor, an environment monitoring module and a pollutant collection module; the environment monitoring module is used for monitoring different depth environment indexes in the filling medium; and the pollutant collection module is used for collecting pollutants at different depths in the filling medium at regular time and transmitting the pollutants to the processor for analysis. The device can avoid the interference of external environmental factors, and provides reliable reference data for accurately analyzing the migration rule of the subsurface zone pollutants in the vertical direction.

The invention relates to the field of rice disease and insect pest questioning and answering, in particular to a rice disease and insect pest questioning and answering method based on circular retrieval. The scheme comprises the following steps: collecting data; constructing a rice disease and insect pest knowledge base; encoding a rewritten problem into vector representation by using an embedded model; executing cosine similarity matching in the knowledge base; whether the retrieved context can answer the question of the user or not is judged, if yes, the answer to the question is output through a large language model, if not, the large language model is used as a generation model, a targeted reverse question is generated in combination with prompt words, and the user is guided to provide more rice disease information, environmental factors and symptom description in a reverse question mode; and then integrating and optimizing to obtain a new question, executing cosine similarity matching in the knowledge base through the obtained question, and realizing efficient and accurate answering through the circular retrieval. The method is suitable for rice disease and insect pest questioning and answering.

The invention discloses a sensitivity data acquisition method and system based on environmental factors, and the method comprises the steps: obtaining a data set in a preset time period, the data set comprising meteorological data, environmental protection data, pollen concentration data and doctor seeing data; performing model training by using the data set to construct a prediction regression model; calculating each index coefficient and intercept based on the prediction regression model, wherein each index coefficient at least comprises a relative humidity coefficient, an air pressure coefficient, a temperature coefficient, a pollutant concentration coefficient, a pollen concentration coefficient, a precipitation coefficient and a wind speed coefficient; obtaining current environment data and future predicted environment data, wherein the current environment data and the future predicted environment data are in one-to-one correspondence with the index coefficients; and calculating the sensitivity data based on the current environmental data, the future predicted environmental data, the index coefficients and the intercept, so that the sensitivity data can be graded, and protection early warning can be provided for users in time according to the grading condition.

The invention belongs to the technical field of crop disease and pest control, and discloses a greenhouse tomato disease and pest diffusion distribution diagram prediction method, which comprises the following steps: S1, collecting current growth data of tomatoes in a greenhouse in real time; s2, according to the current growth data, constructing a diffusion velocity model of diseases and insect pests, and predicting a disease radius # imgabs1 # after the time # imgabs0 # of the diseases and insect pests through the diffusion velocity model; and S3, predicting according to the current growth data acquired in the step S1 and the disease radius # imgabs2 # predicted in the step S2 to obtain a disease and pest diffusion distribution diagram. The current pest and disease damage data of the tomatoes in the greenhouse are collected in real time, environmental factors (temperature, humidity and carbon dioxide concentration) and interaction among different pest and disease damage are comprehensively considered, then the pest and disease damage diffusion distribution diagram is predicted and generated, and the generated result is higher in credibility and more accurate. A user can treat diseased plants according to the diffusion distribution diagram and prevent and treat infected plants, so that accurate prevention and treatment of plant diseases and insect pests by the user are facilitated.

The invention discloses antistatic medical gauze and a preparation method thereof, and relates to the technical field of medical gauze. The antistatic medical gauze comprises the following raw materials in parts by mass: 60-80 parts of cotton fibers, 1-5 parts of an antistatic agent, 5-15 parts of a water-soluble polymer and 0.5-1 part of an antibacterial agent. Compared with the traditional technology that the antistatic effect is transient and is easily influenced by environmental factors, the invention realizes long-acting and stable charge neutralization capability by introducing a specific micromolecule antistatic agent (such as a compound shown in the formula 1) and combining a moisture absorption conductive mechanism, an ionization effect and surface energy regulation. The trend shows that the static dissipation is faster and the effect is lasting, the problems of potential safety hazards and dust adsorption caused by charge accumulation are effectively avoided, and the defect that the effect of an antistatic agent in the prior art is not lasting is overcome.

The invention relates to an NAPL contaminated site comprehensive evaluation method based on environmental medium driving. The NAPL contaminated site comprehensive evaluation method comprises the following steps: collecting a contaminated site soil sample and an underground water sample and measuring physicochemical property parameters of the soil sample and the underground water sample; simulating the migration process of the NAPL in the underground environment by using a microfluidic experimental device to obtain a migration form change image in the migration process of the NAPL; for a migration form change image in the NAPL migration process, a multi-scale image processing algorithm is adopted to carry out feature parameter extraction; inputting the measured physicochemical characteristic parameters into a model constructed based on multi-factor coupling so as to compare an error between a model prediction result and an experimental measurement value; and in combination with GIS analysis, the pollution condition is determined according to a model prediction result, the risk level is judged, and a repair suggestion is given. According to the method, multiple key environmental factors are comprehensively considered, accurate data are obtained through a microfluidic experiment, a reliable migration model is constructed, and the NAPL pollution condition is accurately evaluated.

The invention discloses a garden disease and pest identification method and system based on computer image processing, and belongs to the technical field of disease and pest monitoring, and the method specifically comprises the steps: collecting a visible light image, an infrared image and environment parameters of vegetation; performing space-time registration on the visible light image and the infrared image and generating a three-dimensional temperature field model; the three-dimensional space is divided into grid units, and the temperature statistical characteristics and the vertical direction temperature gradient of each unit are extracted; obtaining an expected temperature gradient direction through a preset standard three-dimensional radiation field reference model; and when the actual gradient direction is consistent with the expected gradient direction, calculating a gradient direction deviation value of each grid unit, calibrating the grid unit with the deviation exceeding a threshold value as an abnormal region, and extracting a corresponding visible light image to carry out pest recognition. According to the invention, early-stage accurate identification of diseases and insect pests is realized, systematic diseases and local insect pests are effectively distinguished, interference of environmental factors is overcome, and the reliability of insect pest monitoring is improved.

The invention discloses a sea-land interaction zone underground water pollution source tracing method based on multi-pollutant fingerprints, and the method comprises the steps: constructing a fingerprint feature library of eight key pollutants including cadmium, arsenic, nitrate nitrogen, ammonium nitrogen, phosphate, total petroleum hydrocarbon, sulfamethoxazole, pH value and the like; statistical characteristic models of four pollution sources of industrial emission, agricultural non-point sources, oil exploitation and aquaculture are established, Z-Score standardization and a similarity calculation method based on a standard normal distribution probability density function are adopted, comprehensive similarity evaluation is performed in combination with a risk weight distribution mechanism, a similarity threshold value of 0.7 is set to perform pollution source identification and sorting, and the pollution sources are identified and sorted according to the similarity threshold value. And correction is performed by considering environmental factors such as tide, season and geology of a sea-land interaction zone. The method can quickly and accurately recognize the groundwater pollution source of the sea-land interaction zone, the traceability accuracy rate reaches 80% or above, and scientific and technical support is provided for prevention and control of groundwater pollution of the sea-land interaction zone and formulation of an environmental protection policy.

The invention provides a deep learning-based dangerous rock mass collapse landslide monitoring and early warning method and system, and relates to the technical field of geological disaster monitoring and early warning, and the method comprises the steps: obtaining rock mass monitoring data; according to the rock mass monitoring data, carrying out dangerous rock mass collapse landslide risk preliminary identification; when it is preliminarily recognized that the dangerous rock body does not have the collapse landslide risk, rock body monitoring data are obtained again; when the collapse landslide risk of the dangerous rock body is preliminarily identified, acquiring environment monitoring data; according to the rock mass monitoring data and the environment monitoring data, through a deep learning algorithm, carrying out dangerous rock mass collapse landslide risk fine identification; when it is finely recognized that the dangerous rock body does not have the collapse landslide risk, rock body monitoring data are obtained again; and when the collapse landslide risk of the dangerous rock body is finely identified, an early warning signal is sent out. According to the invention, environmental factors are considered, and timely and accurate early warning information can be provided in the face of complex and changeable natural environments.

The invention discloses a cibotium barometz sporophyte transformation method and system based on multi-factor coupling regulation, and relates to the technical field of biology. The method comprises the following steps: synchronously acquiring environment data and sporophyte image data; preprocessing the environment data, performing feature extraction on the image data, and splicing the processed data into a comprehensive feature vector; quantifying the weight, correlation degree and interaction level among the environmental factors through an entropy method, grey correlation analysis and a coupling coordination degree model; constructing an improved CNN-LSTM neural network, extracting features through a double-branch structure, fusing a coupling analysis result to carry out cross-modal fusion, and training to obtain a prediction model; when the predicted conversion rate is lower than a threshold value, environment parameters are automatically adjusted until conversion is completed; according to the method, the problems of environmental factor data isolation and regulation lag in the prior art are solved, accurate prediction and intelligent regulation of the sporophyte transformation process are realized, and the transformation success rate and the production efficiency are remarkably improved.

The invention relates to the technical field of animal behavioral experiments, and discloses an animal behavioral experiment multi-scene simulation system. According to the invention, through cooperative control of multiple environment modules and seamless switching of a dynamic scene, continuous switching of natural scenes such as illumination, sound, smell, terrain and the like is accurately simulated; on the basis of real-time biological signal detection and intelligent feedback, environment adjustment or scene switching is automatically triggered according to animal behaviors / physiological states, and closed-loop self-adaptive adjustment is formed; in combination with an intelligent decision and an AI scene generation engine, self-definition and iterative optimization of a complex script are supported; the parameter coupling controller simulates the interaction effect between environmental factors, the fault-tolerant mechanism and the effect evaluator guarantee the data reliability, the closed-loop verification loop efficiently establishes a causal model of environmental parameters and behavior response, and a high-authenticity, self-adaptive and reproducible experimental platform is provided for animal behavior research.