594 results about "Environmental adaptation" patented technology

The invention provides an intelligent factory production scheduling method and system based on digital twinning, and the method comprises the steps: firstly obtaining a real-time monitoring data set, including equipment operation states, material flow tracks and environment sensing data, of a physical workshop; generating a three-dimensional digital twinborn model containing an equipment dynamic operation topology, a material real-time distribution thermodynamic diagram and an environment state simulation layer, then performing operation state correlation analysis on the three-dimensional digital twinborn model, and generating a feature set of equipment operation efficiency, material flow bottleneck, environment interference and the like; generating a dynamic scheduling parameter set containing equipment load balancing, material priority distribution and environment adaptation compensation parameters, and finally calling a preset scheduling strategy optimization algorithm to perform multi-objective optimization processing on the dynamic scheduling parameter set to generate an equipment scheduling, material distribution and environment regulation and control instruction set. And synchronizing to a physical workshop to execute real-time scheduling operation, thereby realizing intelligent and efficient production scheduling.

The present invention relates to a pose recognition method and system combining environmental adaptation with estimation and classification, and a medium, the method comprising: on the basis of an application scenario, obtaining a human pose estimation model; inputting image data to be tested into the human pose estimation model to output human pose keypoints; performing data enhancement and normalization on the human pose keypoints; inputting the human pose keypoints that have undergone data enhancement and normalization and high-order pose features into a pose classification model to obtain a pose recognition result, the high-order pose features comprising bone vectors, the mean of bone vector moduli, relative lengths of bones, and an included angle between the line connecting each human pose keypoint and a trunk midpoint and the horizontal direction. Thus, according to the present invention, pose recognition is divided into two processes of pose estimation and pose classification, which facilitates algorithm updating and iteration being performed when there is better estimation and classification performance, and the structure is flexible.

The invention relates to the technical field of slope protection, in particular to a slope protection intelligent detection system based on deep learning. According to the technical scheme, the system comprises a multi-source heterogeneous data sensing module, a data fusion and feature extraction module, a slope state intelligent diagnosis and early warning module, an edge-cloud collaborative computing architecture and a system optimization module. Registration and feature complementation of multi-source heterogeneous data are realized through a multi-modal detection network, an overfitting phenomenon is effectively inhibited through a physical information neural network architecture, risk quantitative evaluation is realized through construction of a dynamic risk evaluation model, early warning response time is shortened in cooperation with a four-level early warning strategy, the false alarm rate is reduced, and the early warning efficiency is improved. Besides, the detection precision of the system in an extreme scene is improved through a physical constraint adversarial training method, so that the environmental adaptability of the system is improved, continuous updating and evolution of the model are realized through an online incremental learning module, and the problem of performance degradation of a traditional system caused by change of geological conditions is solved.

The invention discloses a multi-dimensional anti-bird intelligent recognition method and system based on thermal imaging, and relates to the technical field of intelligent monitoring and ecological protection. Multi-modal data is collected through thermal imaging, visible light, millimeter wave radar and a voiceprint sensor, after PTP protocol synchronization and Kalman filtering preprocessing, 3-5-second tracks of birds are predicted by using an LSTM network, and the anti-bird intelligent recognition method and system based on the thermal imaging are obtained. And the cross-modal features are fused through a Transform architecture, so that 95% of classification accuracy is realized. A bird repelling strategy is generated in real time through edge calculation, and the model is updated through cloud federal learning. The system integrates an oil-electric hybrid unmanned aerial vehicle and a ground device, supports dynamic path planning based on a thermodynamic diagram and differentiated repelling of directional sound waves, laser stroboflash and the like, has the night recognition accuracy rate of 92% and the bird repelling response time of 0.8 second, and is suitable for scenes of electric power, airports and the like. Through multi-dimensional perception, dynamic modeling and eco-friendly expelling, the problems of poor environmental adaptability, single strategy and the like of a traditional scheme are solved, and the anti-bird efficiency and the ecological safety are improved.

The invention discloses a humanoid robot somatosensory control system and method based on deep learning. The method comprises the steps that physiological data and environment information of a user are collected through a wearable device and an environment sensor, and full-modal synchronous collection is achieved in combination with audio and visual data. The system carries out noise reduction processing on the collected sound, extracts a voice instruction and recognizes non-language sound features to judge the physiological state of the user; and analyzing the posture and gesture intention of the user through a human body detection and posture recognition algorithm. And multi-modal data is further fused, the intention of a user is accurately judged in combination with scene context and a security verification loop, a multi-dimensional state space and an atomic action library are constructed, and a composite reward function is designed to realize intelligent decision and execution of robot actions. The system comprises a multi-modal data acquisition module, a user intention understanding module, a dynamic action planning module, a safety monitoring and exception handling module and a continuous learning and collaborative optimization module, and can effectively improve the intention recognition precision, environmental adaptability and task execution efficiency of the robot in a complex home scene. And intelligent and personalized family service support is provided for the elderly.

The invention relates to the technical field of ecological environment monitoring, and particularly discloses a system and a method for monitoring distribution of plant communities along an expressway. An unmanned aerial vehicle carries a hyperspectral imager and a multi-parameter sensor to synchronously obtain vegetation spectral data and environmental parameters of soil salinity, water content, pH value, temperature and humidity; constructing a multi-dimensional data set with space-time matching; an improved end member extraction algorithm is combined with a soil salinity threshold triggering mechanism to realize self-adaptive updating of a dynamic end member library; an environmental constraint adversarial generative network is introduced, virtual end members conforming to ecological characteristics are generated and verified, and the coverage capability of an end member library is improved; performing physiological and ecological dual verification on the end members based on the knowledge graph to form an optimized end member set; and an environmental adaptability weight is introduced in the unmixing process, confidence evaluation and fuzzy logic analysis are combined, a graded credibility vegetation map is output, and accurate identification and spatial positioning of invasive plants and local vegetation are realized.

The invention relates to a multi-sensor fusion positioning method and device based on environmental characteristics. The method comprises the following steps: respectively acquiring RTK data, laser radar data and IMU data; converting the RTK data into RTK positioning data, converting the laser radar data into laser radar positioning data, and obtaining IMU positioning data according to IMU output; monitoring the quality state of the output signal of each sensor in the current environment, and calculating to obtain a corresponding quality score; constructing and forming an input matrix according to the quality score of each sensor, mapping the input matrix to a high-dimensional feature space through an embedded layer and adding a position code, inputting the input matrix into a Transform model for time sequence modeling and extracting environment dynamic features, and finally outputting a real-time fusion weight of each sensor; and performing weighted fusion by using the real-time fusion weight to obtain final pose output. The method has the advantages of being simple in implementation method, high in positioning precision and stability, high in environmental adaptability and robustness and the like.

The invention discloses an environment adaptability self-optimization method and system of an intelligent sensing robot, and relates to the technical field of robot control, and the method comprises the steps: obtaining a historical environment data set; acquiring a historical performance data set of the target robot; obtaining an optimal matching relationship; constructing a feature data set; constructing an environment-performance prediction model based on the feature data set; generating predicted performance data of the target robot; and optimizing and adjusting a strategy according to the predicted performance data. The technical problems that in the prior art, an intelligent sensing robot cannot dynamically and accurately predict self performance according to different complex and changeable environments, so that the environment adaptability of the intelligent sensing robot is insufficient, and diversified scene requirements are difficult to meet are solved, the performance of the intelligent sensing robot in the different environments is predicted, and the intelligent sensing robot is more intelligent. And optimization and adjustment can be carried out as required based on a prediction result, so that the method can better adapt to the environment, and the performance is improved.

The invention relates to the technical field of dynamic correction and intelligent compensation of measured data of a current sensor, and discloses a self-adaptive data compensation method for the current sensor, which comprises the following steps of: acquiring an original output signal and environmental parameters in real time, identifying multi-dimensional error sources such as temperature drift, nonlinear error and electromagnetic coupling error, establishing an influence factor model, and calculating the dynamic correction and intelligent compensation of the measured data of the current sensor. Dynamic compensation is realized by fusing directivity judgment of each error source and a weighting mechanism, and online learning and model parameter updating are carried out by introducing a long-short-term memory network structure, so that the problems of poor environmental adaptability, limited compensation precision and lagging model updating in the prior art are effectively solved.

The invention discloses bacillus velezensis capable of tolerating various environmental pollution and promoting growth and application of the bacillus velezensis. A plant endophyte bacillus velezensis L381 strain is identified in wild rice, and the strain is high in environmental adaptability, can grow in the environment with the temperature of 15-40 DEG C and the pH value of 5-8, and can tolerate the NaCl concentration of 4%; the rice growth promoting agent has a good growth promoting function, can secrete auxin IAA, solubilize phosphorus, dissolve potassium and secrete siderophores, and can effectively promote rice growth. Meanwhile, the strain has stress resistance to environmental stress such as heavy metals and antibiotics, and tetracycline can be degraded; the strain has a wide antibacterial spectrum, and can be used as a biocontrol bacterium for preventing and treating various plant diseases. The plant endophyte L381 can adapt to variable temperature conditions and variable soil environments in the natural environment, has very strong practical application potential, not only improves the functions of bacillus velezensis, but also widens the application range of the strain.

The invention discloses a quantum encryption handheld laser gas monitoring method and system, and relates to the technical field of handheld laser gas monitoring. The quantum encryption handheld laser gas monitoring method and system comprise the following steps: S1, collecting environmental parameter information, storing environmental monitoring data, and establishing an environmental monitoring database; s2, performing laser spectrum monitoring on the target gas, and performing data quality evaluation based on a laser spectrum monitoring result; s3, executing quantum key analysis on the laser spectrum monitoring data qualified in data quality evaluation; and S4, performing environmental adaptability analysis by integrating the evaluation results of the environmental monitoring data and the laser spectrum monitoring data and the quantum key analysis result. The problems that an existing handheld gas monitoring device is unstable in operation under the complex environment conditions of high temperature, low temperature, high humidity, dust interference, air pressure fluctuation and the like, and the measurement precision is difficult to guarantee are solved.

The invention relates to the technical field of detection, and provides a non-contact thickness measurement detection method and detection equipment, a set of multi-dimensional, high-precision and self-adaptive thickness measurement system is constructed by fusing two non-contact detection technologies of laser and ultrasonic, a complete closed loop is formed from signal acquisition and processing to result fusion compensation, and the thickness measurement accuracy is improved. The method effectively breaks through the thickness measurement limitation of a single technology, adapts to complex and variable industrial measurement scenes, realizes accurate and stable measurement of the thickness of a measured object, gives consideration to dynamic compensation and environment adaptation, provides reliable technical support for the fields of high-end manufacturing, precision detection and the like, assists in improving the product quality control level, and has a wide application prospect. And the non-contact thickness measurement technology is promoted to develop to intelligence and high precision.

The invention is suitable for the field of tracking rotary tables, and provides a follow-up identification adjustment control method and system for a tracking rotary table, and the method comprises the steps: collecting the peripheral data of the tracking rotary table in a multi-modal manner; acquiring and fusing the acquired surrounding data of the tracking turntable; establishing a three-dimensional space error model; generating a prediction control strategy based on the three-dimensional space error model; and generating an intelligent feedback optimization result based on a prediction control strategy, and the method has the beneficial effects that by means of deep fusion and accurate analysis of multi-modal data, the tracking turntable is endowed with excellent environmental adaptability and target tracking capability. Complex environment interference such as atmospheric refraction, illumination change and shielding can be effectively handled, the tracking precision is improved, online self-adaptive adjustment can be achieved through an intelligent feedback optimization mechanism and a control strategy, the stability, reliability and intelligence of the system are remarkably enhanced, and an advanced and efficient technical solution is provided for target tracking application in multiple fields.

The invention provides a karst geological environment monitoring method and system, and relates to the technical field of geological environment monitoring. According to the method, a sensor array is deployed at a preset monitoring node of a target karst area, and multi-source data including geological, environmental and chemical sensing data are collected. The monitoring nodes are equipped with edge computing devices, and real-time noise reduction and feature extraction are carried out on data to form a sensing feature set. And uploading the feature set to a cloud, and carrying out dynamic risk assessment to predict the surface collapse probability. And establishing an adaptive early warning mechanism according to the probability, and generating a comprehensive monitoring report. Through technical innovation and multidisciplinary fusion, the defects of an existing karst monitoring system in the aspects of real-time performance, comprehensiveness, early warning precision and environmental adaptability are overcome, technical indexes are remarkably improved, and efficient and reliable technical support is provided for prevention and treatment of geological disasters in the karst area.

The invention relates to the technical field of indoor environment monitoring, and discloses an indoor temperature and humidity acquisition system based on a distributed architecture, which comprises a distributed sensor node cluster, a data aggregation gateway and a central processing unit. The distributed sensor node cluster is deployed in different indoor areas to collect data, the data aggregation gateway preprocesses the data, and the central processing unit divides the areas according to spatial and temporal distribution characteristics of the data, constructs various data collection strategies, calculates node energy consumption estimation, data redundancy and transmission delay, and screens an optimal strategy to control node sampling and communication. The system is further provided with a fault-tolerant module, an edge calculation unit and the like, and can detect faults, predict trends, adjust strategy weights and the like. The system is comprehensive and accurate in data acquisition, good in data processing and transmission performance, intelligent in node management and high in environmental adaptability, has expansibility and collaboration, and can be widely applied to temperature and humidity monitoring of various indoor places.

The invention discloses a warehouse environment adjusting method, system and device based on the industrial Internet of Things and a medium, and relates to the technical field of the industrial Internet of Things, and the method comprises the steps: obtaining image data in a warehouse, and dividing materials stacked in the warehouse into at least one stacking unit according to the image data; determining an environmental adaptation factor of the at least one stacking unit according to the stacking parameters and the material parameters; acquiring a surface environment parameter of the at least one stacking unit, and determining an internal environment parameter of the at least one stacking unit according to the environment adaptation factor and the surface environment parameter; according to the internal environment parameters, the environment compatibility of the at least one stacking unit is determined, whether the environment in the warehouse needs to be adjusted or not is determined according to the environment compatibility, and if yes, an environment adjusting instruction is generated according to the internal environment parameters. The method and the device have the effect of improving the intelligent level of warehouse management.

The invention relates to the technical field of intelligent agriculture, in particular to an intelligent disease and pest monitoring and prevention system which comprises an environment self-adaption module, a spectrum collection module, a dynamic threshold value analysis module, a prevention decision module, an execution terminal and a data storage module. A polarization filter is adopted to suppress reflection of light on the blade surface to obtain a spectrum fusion image; the dynamic threshold value analysis module is used for establishing a dynamic proportion threshold value of the insect spot area and the medicinal material biomass by adopting a U-Net network in combination with a growth cycle recognition algorithm so as to obtain graded insect pest evaluation parameters; and the prevention and control decision module is used for combining the graded insect pest evaluation parameters with the environmental parameter set and generating a prevention and control scheme by adopting a pesticide application decision tree model based on northern climate characteristics. According to the invention, through dynamic environment adaptive adjustment and northern climate optimization pesticide application decision, efficient and accurate prevention and control of diseases and insect pests in a complex agricultural environment are realized.

The invention discloses an agricultural seed screening method and system based on vision, and relates to the technical field of agriculture, and the method comprises the steps: obtaining the three-dimensional image data of seeds through a 3D imaging technology; extracting morphology, surface features and internal features of the seeds through an image processing algorithm; screening according to the characteristics of the seeds; the multiple seeds are subjected to parallel screening through a multi-channel synchronous screening system; based on the environmental adaptability model, screening seeds in combination with specific environmental conditions; and a dynamic tracking and correction technology is adopted in the screening process to ensure the screening precision. According to the method, the three-dimensional shape, the surface characteristics and the internal structure information of the seeds are accurately obtained, so that the quality of the seeds can be comprehensively evaluated, high-quality seeds meeting the standard are screened out, and meanwhile secondary seeds not meeting the requirement are removed. According to the multi-channel synchronous screening system, the processing capacity of each screening channel is optimized through a dynamic load balancing algorithm, and the screening efficiency is remarkably improved.

The invention relates to the technical field of laying hen breeding, and discloses an automatic acquisition and analysis system for laying hen breeding data. The information acquisition unit is used for collecting physiological parameters, behavior data, environmental parameters and health indexes of laying hens in real time; the disease monitoring unit is used for analyzing the collected data and identifying physiological abnormality and behavior change before the disease occurs; the antibody level monitoring unit is used for evaluating the immune response capability of the laying hens to specific pathogens; the environment adaptability evaluation unit is used for evaluating the adaptability of the laying hens to the extreme environment according to the environment parameter change and the physiological response of the laying hens; the breeding decision support unit is used for integrating all data and analyzing the production performance, disease resistance and environmental adaptability of laying hens; an optimal feeding scheme is recommended, medicine use is reduced, and resource allocation is optimized.

The invention discloses a geological disaster real-time monitoring method and system based on a ground sensing network, and belongs to the technical field of geophysical exploration, and the method comprises the steps: obtaining an acceleration parameter and a moisture content parameter of each sensor node; according to the change rate of the acceleration parameter and the moisture content parameter, identifying an environment sudden change event, generating an environment trigger signal to activate an emergency sampling mode of a target node and an adjacent node, and constructing a dynamic monitoring cluster; requesting a plurality of nodes in the dynamic monitoring cluster to synchronously measure similar parameters, and comparing measurement results to generate a compressed alarm packet; adjusting weight factors of different monitoring parameters by using the compressed alarm packet and pre-acquired real-time environment parameters, and generating an environment calibration risk index; and when the environment calibration risk index exceeds a preset risk threshold value, outputting a geological disaster early warning instruction. According to the method, multi-parameter cooperative triggering, dynamic cluster response, weight adaptive evaluation and compression transmission technologies are adopted, and the monitoring precision, timeliness and environmental adaptability can be comprehensively improved.

The invention relates to the technical field of intelligent manufacturing, and particularly provides an intelligent ultra-high performance concrete preparation system and a control system thereof.The preparation system comprises an environment adaptability adjusting subsystem used for automatically adjusting preparation operation parameters of equipment according to external environment temperature and humidity changes of an ultra-high performance concrete mixture; the dynamic optimization proportioning subsystem is used for sensing real-time states of viscosity, fluidity and the like of the ultra-high performance concrete mixture in the preparation process through a built-in sensor array; adjusting a water-cement ratio and an aggregate proportion according to real-time states of preparation operation parameters, viscosity, fluidity and the like; and the operation energy efficiency management subsystem is used for adjusting the working mode of the equipment according to the preparation operation parameters. The control system comprises an equipment layout assembly, an equipment communication assembly and a program execution assembly. While the quality of the ultra-high performance concrete is ensured, the preparation efficiency and the environmental adaptability are improved, the energy consumption is reduced, and green and intelligent production of the ultra-high performance concrete is realized.

The invention relates to the technical field of relieving cabin control, and discloses a mobile emotion relieving cabin control system and method.The system integrates an environment monitoring module, an intelligent strategy construction module and a cloud control execution module, environment parameters in a cabin and user physiological parameters are monitored in real time, and a multi-target optimized environment regulation and control strategy model is constructed. Through a reinforcement learning algorithm, the system can dynamically adjust environment settings such as light, music, temperature, humidity and the like so as to achieve an optimal soothing effect. And meanwhile, the cloud control execution module realizes remote monitoring and intelligent regulation and control, so that the convenience and practicability of the system are further improved. The intelligent emotion relieving cabin overcomes the defects of a traditional emotion relieving cabin in the aspects of intelligence, individuation and environmental adaptability, a novel and effective technical solution is provided for emotion management in modern fast-paced life, and the intelligent emotion relieving cabin has wide application prospects and market value.

The invention discloses an intelligent production and quality monitoring system and method for premixed concrete, and particularly relates to the technical field of concrete production quality monitoring, and the method comprises the following steps: constructing an initial control parameter set; environment data is collected and acts on the initial control parameter set to generate an applicable control parameter set; performing mixing production based on the applicable control parameter set, collecting process behavior data, and inputting the process behavior data into a machine learning model to predict compressive strength and setting time; comparing the prediction result with a performance target, if the tolerance is exceeded, determining that the quality is low and correcting control parameters, and if the tolerance is not exceeded, determining that the quality is high and allowing discharging; according to the method, the initial control parameter set is constructed by using the historical weighing deviation and the strength actual measurement result, the applicable control parameter set is dynamically generated in combination with the real-time environment data, the performance result is predicted through the model and compared with the preset target, and a quality judgment and parameter correction closed loop is formed. And the parameter rationality, the environmental adaptability and the quality control intelligent level of concrete production are improved.

The invention is suitable for the technical field of indoor robot positioning, and provides a multi-sensor fusion indoor robot positioning method, which comprises the following steps: data acquisition: providing environment data and self state information for an indoor robot; preprocessing sensor data, and providing input for data processing and analysis; constructing a sensor data fusion model, and integrating data of a plurality of sensors; and robot position information is updated and output in real time. The invention also provides a multi-sensor fusion indoor robot positioning system which is used for realizing the multi-sensor fusion indoor robot positioning method. According to the invention, by integrating information collected by different sensors, the positioning accuracy and stability of the indoor robot are significantly improved; the method has the characteristics of high positioning precision, strong anti-interference capability, good environmental adaptability and the like, and is suitable for multiple scenes such as indoor robot navigation systems and intelligent residences.

The invention relates to a dexterous two-hand cooperative control method based on multi-agent reinforcement learning, and the method comprises the steps: initializing a neural network and an environment of dexterous two hands, and collecting the data during environment interaction; based on data during environment interaction, the joints perform sequential decision actions according to a greedy strategy; evaluating the decision action through a Q-value distribution network, and calculating a reward return at the same time; on the basis of reward return, entropy regularization is introduced, a strategy gradient optimization method is used, and a joint strategy is updated; and based on a preset condition, ending updating of the joint strategy, and outputting an optimal cooperative control strategy. The method is suitable for complex scenes such as grabbing, rotating and assembling, and has high cooperation efficiency and environmental adaptability.

The invention discloses a dynamic sea condition ship-machine cooperative attitude control system and method for sea grass transplantation, and belongs to the crossing field of marine ecological restoration and intelligent ship control. The system comprises a mother ship attitude control layer, a communication management layer, an underwater operation end and a sensor network layer, the mother ship attitude control layer, the communication management layer, the underwater operation end and sensor network layer and the mother ship attitude control layer are sequentially connected through lines; the method integrates inertial navigation, visual measurement, event-triggered communication and adaptive filtering technologies, realizes high-precision attitude synchronization and stable operation of the ship and the underwater operation device in sea conditions of four levels and below, and can be widely applied to marine ecological engineering scenes such as seaweed bed restoration, coral transplantation and submerged vegetation reconstruction. And the operation efficiency and the environmental adaptability are effectively improved.

The invention discloses a deep geothermal fluid cause and migration path tracing method, and relates to the technical field of earth scientific exploration, and the method specifically comprises the following steps: A1, collecting a geothermal fluid sample, A2, fusing high-precision three-dimensional seismic data, logging data and drilling data, constructing a multi-scale fracture network model, quantifying fracture connectivity and permeability anisotropy, and calculating the fracture connectivity and permeability anisotropy according to the fracture connectivity and permeability anisotropy. A3, developing a long short-term memory (LSTM) network model, inputting temperature, pressure and chemical field data monitored in real time, dynamically predicting a fluid migration path, and introducing a generative adversarial network (GAN) optimization model, and A4, deploying a distributed optical fiber sensor network. According to the sensing method for the aviation high-speed moving target, through the core-shell structure tracer agent, high fluorescence efficiency and geological compatibility are considered, a complete innovation chain is formed in the aspects of material design, detection method and environmental adaptability of the nano tracer agent, and the precision and sustainability of tracing of a migration path of deep geothermal fluid are remarkably improved.

The invention relates to the technical field of sports artificial intelligence and data management, in particular to a physical exercise AI standing long jump evaluation data management system which comprises a dynamic environment calibration module, a multi-mode identity recognition module, a three-dimensional posture analysis module and the like. The dynamic environment calibration module uses a multispectral sensor array and a deep learning algorithm to establish a dynamic coordinate system without fixing a marker; the multi-mode identity recognition module fuses gait and face features to realize non-contact identity authentication; the three-dimensional attitude analysis module performs high-precision modeling and analysis on the long jump action through a multi-view camera and an advanced algorithm; the intelligent violation determination engine combines rules and an AI model, automatically identifies violation behaviors and can dynamically adjust determination criteria. The method gets rid of the limitation of a traditional evaluation mode, realizes high-precision and intelligent evaluation, effectively solves the problems of poor environmental adaptability, inaccurate identity authentication, unguaranteed data security and the like, and is scientific and reliable in evaluation result and safe and efficient in data management.

The invention discloses a method for predicting plant photosynthetic ability by using plant leaf traits, and relates to the technical field of agricultural science, and the method comprises the following steps: obtaining image data of a target plant leaf in a natural state through a high-resolution imaging device, and synchronously recording environment parameter information during collection; extracting multi-dimensional feature parameters of the leaf based on the image data, wherein the multi-dimensional feature parameters comprise a color index, a texture feature, an area, a thickness estimation value and an edge contour form; and carrying out stress feature identification on the character data by utilizing an association rule of the environmental parameters and the leaf characters. According to the invention, by introducing a stress identification and data correction mechanism, the accuracy of photosynthetic capability prediction is improved; the method has good environmental adaptability and crop universality, and is suitable for various agricultural scenes; and a prediction feedback closed-loop system is constructed, continuous optimization and intelligent adjustment of the model are realized, and the stability, reliability and practical application value of the system are remarkably enhanced.

The invention relates to the technical field of artificial intelligence, in particular to an information interaction method and system for an intelligent robot with a body, and the method comprises the steps: achieving the precise time alignment of multi-source sensor data through a hardware clock synchronization protocol, carrying out the cross-modal feature fusion through a self-adaptive weight distribution mechanism, and constructing a unified environment state expression; analyzing the user instruction in combination with the language model and the dynamic knowledge graph, and generating an execution intention containing environmental adaptation parameters; and generating an action strategy considering a task target and environmental adaptability based on a multi-target optimization algorithm, and converting the action strategy into a control instruction through a hierarchical execution mechanism. The problems that in the prior art, multi-source sensing data synchronization precision is insufficient, and heterogeneous data fusion efficiency is low are effectively solved, and the interaction accuracy, the response speed and the self-adaptive capacity of the robot in the complex dynamic environment are remarkably improved.