7408 results about "Moisture sensor" patented technology

The invention relates to a power supply switching control optimization method and system, and the method comprises the steps: constructing a multi-dimensional data fusion monitoring system, integrating a real-time voltage ripple coefficient detection module of a main power supply, a load transient response analysis module and an environment temperature and humidity sensor array, and generating a dynamic health degree index; the method comprises the following steps: establishing a fault prediction model based on transfer learning, pre-training through multi-scene failure data in a historical database, receiving a health degree index in real time, outputting a reliability attenuation curve, and activating a dual verification mechanism when the slope of the attenuation curve exceeds a preset threshold; extracting characteristic energy distribution of a frequency band of 0.1-10 kHz; executing a transient load impact test, and measuring dynamic response parameters of voltage recovery time and overshoot amplitude; and generating a switching confidence score based on cross processing of the dual verification result and the attenuation curve, and triggering a hierarchical switching instruction. The accuracy of the switching time of the main power supply and the standby power supply can be improved.

The invention relates to the technical field of bridge structure safety monitoring and maintenance, and discloses a road bridge real-time monitoring and maintenance system based on intelligent perception, which comprises a data acquisition module used for acquiring multi-dimensional data of a bridge operation state through a deployed multi-source sensor, the multi-source sensor comprises a vibration sensor, a strain sensor, a temperature and humidity sensor and image acquisition equipment; the data preprocessing module is used for carrying out cleaning, synchronization and feature extraction on the collected multi-dimensional data to obtain dynamic features, environment features and visual features; and a data fusion module. According to the method, the comprehensiveness and accuracy of bridge monitoring are improved through multi-source data fusion, the reliability of an evaluation result is ensured in combination with physical constraint optimization, health trend prediction and early warning functions are realized based on a time sequence model, scientific decision support is provided for bridge maintenance, and the monitoring and management efficiency is improved.

The invention relates to the technical field of air conditioner energy-saving operation control, and discloses a central air conditioner energy-saving operation control method and system. The method comprises the following steps: acquiring and processing data through a temperature and humidity sensor to obtain a temperature and humidity distribution index; calculating load values according to the indexes and the personnel density to form a distribution diagram; regulating and controlling cold and heat source equipment according to the distribution map to obtain operation parameters; adjusting the water pump fan based on the parameters to form balance data; adjusting the end equipment according to the data to generate a control instruction; and the system state is adjusted in real time according to instructions and comfort feedback to realize energy-saving operation. The problem of energy waste caused by decentralized control of subsystems of a traditional central air conditioning system is solved. By constructing a complete closed-loop control system from environmental perception, load prediction to equipment regulation and control, the operation efficiency of the system under partial load conditions is improved. And meanwhile, refined control is achieved according to different requirements of different areas, and energy consumption is reduced to the maximum extent on the premise that the comfort level of the user is guaranteed.

The invention relates to the technical field of sensor detection, and discloses a detection method and device based on a multi-mode gas sensor, and the method comprises the following steps: carrying out blending processing on a metal oxide semiconductor and a conductive material to obtain a bimodal nano composite material, and creating a multi-mode gas detection module, the preprocessed multi-mode signal data is acquired and obtained; acquiring and analyzing data of the temperature sensor and the humidity sensor to obtain environmental factor compensation parameters; performing adaptive calibration processing on the preprocessed multi-modal signal data according to the environmental factor compensation parameters to obtain calibrated multi-modal signal data; constructing a multi-input neural network model based on the calibrated multi-modal signal data, and training to obtain a gas recognition model; multi-mode signals collected and calibrated in real time are analyzed and judged based on the gas recognition model, gas type and concentration information is obtained, and high-precision and high-reliability gas detection is achieved.

The invention discloses an intelligent control method and system for tunnel loudspeakers, and relates to the technical field of tunnel audio control, environmental parameters in a tunnel are collected by adopting a mode of deploying sampling equipment in a distributed manner, and data preprocessing is performed in a targeted manner for different environmental parameters; a sound propagation model is established, and attenuation and delay of sound in different environments are simulated. According to the intelligent control method and system for the tunnel loudspeakers, various temperature and humidity sensors are arranged in the tunnel, and the absolute humidity is calculated in combination with the air pressure data, so that the sound velocity is accurately corrected, and the phase difference of the multiple loudspeakers is reduced; an adaptive Kalman filtering algorithm is adopted to process wind speed data, and reliable input is provided for a sound propagation model; a deep reinforcement learning algorithm is used to carry out collaborative optimization on parameters such as amplitudes and directional angles of multiple loudspeakers, a Bayesian network is used to detect loudspeaker faults, and Delaunay triangulation and a distributed consistency algorithm are combined to realize rapid fault reconstruction.

The invention provides an LED light source uniformity and color deviation detection method, which comprises the following steps: setting environmental parameters of a constant temperature and humidity test box, monitoring the environment in the box in real time through a temperature and humidity sensor, and triggering a temperature and humidity control system to adjust when detecting that the temperature and humidity deviate from a set value; according to the initial color characteristic database, placing an LED light source in the constant-temperature and constant-humidity test box for an accelerated aging test, and obtaining spectral data of the light source at different time points by adopting a spectrum analyzer; calculating chromaticity coordinates, color temperatures and color rendering indexes corresponding to the spectral data at different time points, calculating a color deviation value of the LED light source, and fitting a change curve of the color deviation along with time; and according to an accelerated aging test result of the LED light source, calculating the color maintenance life of the light source in a high-temperature and high-humidity environment, and completing color deviation detection through a color stability uniformity index of the LED light source.

The invention relates to a machine room environment intelligent regulation and control method fusing multi-source sensing information, and the method comprises the steps: collecting the temperature and humidity data of different regions in a machine room through a temperature and humidity sensor, and collecting the movement track and stay time data of a person in the machine room through a person positioning device, transmitting the collected machine room environment data and personnel activity data to a data fusion module; in the data fusion module, machine room environment data and personnel activity data are aligned according to a time sequence, wrong data and outliers are eliminated, and a data set in a unified format is obtained; a correlation coefficient matrix among the characteristic parameters of the computer room is analyzed through correlation, the characteristic parameters with correlation higher than a preset correlation threshold value are screened out, and the characteristic parameters comprise temperature, humidity, personnel access frequency and staying duration parameters.

The invention relates to the technical field of intelligent dehumidification, in particular to a dehumidification control method and system based on humidity detection and an intelligent dehumidifier, and the method comprises the following steps: collecting outdoor humidity data and indoor humidity data through a humidity sensor, executing time synchronization and marking, and carrying out data normalization processing to obtain initial environment humidity detection data; according to the invention, through humidity detection, advanced data processing and trend prediction, the precision and foresight of dehumidification control are improved, subtle humidity change is identified by using a difference technology, real-time fluctuation of environment humidity is accurately reflected, the humidity change in a short period is predicted by using a linear regression model, the accuracy of a prediction result is improved, prediction errors are reduced, and the method is suitable for large-scale popularization and application. By means of the calculation result of the humidity setting difference of the different areas, accurate adjustment, continuous monitoring and dynamic adjustment of dehumidification parameters of the dehumidification equipment in the different areas are achieved, it is ensured that the equipment is kept in the optimal operation state all the time, the energy efficiency is remarkably improved, and the comfort and safety of the living and working environment are improved.

The invention relates to the technical field of environment regulation, in particular to a corn cultivation environment intelligent regulation method based on multi-sensor control, which comprises the following steps: S1, generating a soil moisture three-dimensional distribution diagram in real time through humidity sensor arrays arranged at different depths of a corn root zone; s2, combining infrared temperature data of the canopy and the three-dimensional distribution diagram of the soil moisture, and calculating a moisture motion mismatch degree through transpiration-permeation conduction collaborative analysis; s3, generating a user-defined irrigation mode based on the water movement mismatch degree, and controlling the irrigation duration, the stop interval and the water flow rate. According to the method, coupling modeling is carried out on the plant physiological water demand behavior and the soil moisture supply characteristic, the limitation that a traditional irrigation system only depends on a soil humidity threshold value or meteorological index judgment is broken through, and the biological adaptability and the aging responsiveness of field moisture regulation and control are improved.

The invention relates to the technical field of data processing, and discloses a vehicle-mounted cold chain temperature and humidity intelligent supervision and remote control method, system and device. The method comprises the steps of collecting environment data through a temperature and humidity sensor and adding time codes, dividing priority data packets and encrypting and compressing the priority data packets, performing cleaning and abnormity correction on the data, dividing an early warning threshold value based on feature data and generating an early warning instruction, converting the early warning instruction into control parameters and issuing a remote regulation and control command, and finally, generating a monitoring report in combination with a terminal execution result to form an intelligent supervision scheme. According to the method, a multi-level data processing mechanism is introduced, cleaning, correction and interpolation compensation are carried out on the data in combination with an intelligent algorithm, and the accuracy and reliability of a monitoring result are remarkably improved.

The invention relates to the technical field of control and regulation systems, in particular to a photovoltaic module anomaly recognition method based on laser detection, which comprises the following steps: generating a clock signal synchronous with a laser scanning time sequence through a hardware synchronous trigger, and driving a temperature and humidity sensor, a salt mist sensor and an illumination intensity sensor to collect environmental parameters; and constructing a multi-source data set containing the salt crystallization trend and the environmental interference level. And based on a temperature and humidity segmented compensation reflectivity base line, a salt mist mode switching optimization dust retention judgment threshold value, and illumination correlation sensitivity adjustment hot spot detection parameters, dynamic model correction is realized. And a fault database is matched to trigger a grading alarm mechanism, and a decision tree classifier is combined to verify time continuity to generate a diagnosis report. A dynamic detection path is constructed through spiral path planning, a multi-machine contract network protocol and wind disturbance trajectory correction, data feedback is executed to form closed-loop control logic, and the problems of misjudgment and missing detection in high salt mist, temperature change and illumination fluctuation environments are effectively solved.

The invention relates to the technical field of Internet of Things monitoring, and particularly discloses a monitoring method and system based on an Internet of Things architecture, and the method comprises the steps: collecting environment data in real time through deploying a plurality of temperature and humidity sensors in a monitoring region, and transmitting the data to a central processing unit or a cloud end through a wireless network for storage and preliminary processing; the system establishes a prediction model by fitting historical data, evaluates the accuracy of sensor data, and calibrates or replaces an abnormal sensor to ensure the reliability of the data; the system comprehensively analyzes the calibrated sensor data, calculates a temperature abnormal coefficient and a humidity abnormal coefficient, and evaluates the overall environment condition of the monitoring area; when an abnormal condition is detected, the system immediately starts an early warning mechanism, sends alarm information to a monitoring center or related personnel, and records the detailed condition of the abnormal event for subsequent analysis and processing.

The invention provides a device and a method for extracting water from air operated under all working conditions. The device comprises a solar power supply unit, a salt-carrying hydrogel adsorption-desorption unit, an electric heating desorption unit, a water vapor condensation and collection unit and a PID control unit. The solar power supply system comprises a solar photovoltaic panel, an MPPT (maximum power point tracking) type photovoltaic inverter and a storage battery, the salt-carrying hydrogel adsorption-desorption unit comprises hydrophilic porous hydrogel and a LiCl / CaCl2 composite adsorption material, and the electric heating desorption unit comprises a copper heat-conducting plate loaded with the adsorption material and an electric heating sheet. The water vapor condensation and collection unit is composed of four finned radiators and two semiconductor chilling plates. The PID control unit is composed of a temperature and humidity sensor and a control circuit, wherein the temperature and humidity sensor is connected to the radiators and the front side of the adsorption bed. Water vapor in the environment is enriched through an efficient salt-loaded adsorbent packed bed, desorption is driven in two modes of solar photo-thermal and electric heating, desorbed high-humidity airflow passes through a fin type cooling fin connected with a semiconductor and then is condensed into liquid water, and the process of taking water from air is completed through a collector below. According to the device, low-energy-consumption air water taking under the humidity of 15%-100% can be achieved by controlling the operation time of all the units, the device is portable and compact, the automation degree is high, and high-water-yield air water taking is achieved by using solar photo-thermal and power generation as energy input.

A piled granular material management robot comprises an auger-based drive system, a memory, a moisture sensor, and a processor coupled with the memory and with the moisture sensor. The processor is configured to control movement of the robot via augers of the auger-based drive system. The processor is also configured to direct a traversal, by the robot, of a portion of a surface of piled granular material in a bulk store. The processor is also configured to capture, via the moisture sensor, a moisture measurement of the portion of the piled granular material during the traversal of the portion of the piled granular material by the robot.

The invention discloses an agricultural irrigation area water resource intelligent management system and method, and relates to the field of intelligent management, and the method comprises the steps: building a correlation model of a crop growth stage and a basic irrigation threshold value through real-time collection of original weather station data, future 24-hour weather forecast, crop types, planting dates and other multi-dimensional data; and in combination with soil humidity sensor data and a channel pump station state, the basic irrigation starting threshold is dynamically adjusted by using an FAO formula or a deep learning algorithm to form a dynamic irrigation decision threshold. And then multi-source data is input into an irrigation decision module, an irrigation starting and stopping instruction is generated by judging the relation between the irrigation state and the soil humidity threshold value, and closed-loop management is achieved. Thus, the limitation of traditional irrigation static threshold management is broken through, and through meteorological data dynamic adaptation and multi-source data fusion decision making, on the premise of guaranteeing crop growth water demand, the water resource allocation efficiency is optimized, and the intelligent management level of an agricultural irrigation area is improved.

The invention discloses a power transmission line anti-freezing system and method based on Internet of Things monitoring, and the system comprises an environmental parameter monitoring module, a data collection and transmission module, an intelligent data processing and analysis module, an early warning system module, a remote monitoring and management module, and a system management and maintenance module. Environmental parameters related to power transmission safety are collected in real time through sensors deployed at all nodes of a power transmission line, and the environmental parameters specifically comprise a temperature sensor unit, a humidity sensor unit, a wind speed sensor unit and a current load monitoring unit; environmental parameters around the power transmission line are monitored in real time through various sensors, data acquisition, transmission and processing are performed by using the Internet of Things technology, the environmental data are analyzed through an intelligent algorithm, the icing risk of the power transmission line is accurately judged, and efficient and intelligent anti-freezing monitoring comprehensive management is realized.

The invention relates to the field of rail transit control systems, and discloses a rail transit intelligent control system and method based on the Internet of Things, and the system comprises a sensing layer which is integrated with a visual sensor, a thermal imaging sensor, a sound sensor, a pressure sensor, a temperature and humidity sensor, a CO2 sensor and other sensors, the sensing layer collects various data of the rail transit system in real time, and the sensing layer performs joint optimization processing on sensor data by adopting deep learning and reinforcement learning algorithms through an adaptive data fusion method so as to realize high-precision real-time monitoring, data quality screening and anomaly detection; and the network layer comprises a wireless communication module and adopts an intelligent routing mechanism based on real-time network load prediction. A self-adaptive data fusion method is adopted by the sensing layer, and the sensing capability of the rail transit system is greatly improved through intelligent data processing, so that the system can realize real-time and accurate response to a complex traffic environment.

The invention relates to the technical field of lighting energy efficiency management, in particular to a smart city lighting energy efficiency management system based on the Internet of Things, and adopts the technical scheme that the smart city lighting energy efficiency management system comprises a data acquisition and analysis module, an intelligent control module, a fault detection and early warning module, an energy management module and a report and statistical analysis module, environment data and traffic flow data are acquired in real time through an illuminance sensor, a temperature sensor, a humidity sensor, a pedestrian flow sensor and a traffic flow sensor which are deployed in each region of a city. According to the invention, through multi-sensor data acquisition and a complex algorithm, the illumination demand is accurately predicted, and personalized brightness adjustment and energy saving are realized; stable operation of the system is ensured through efficient fault detection and early warning; energy management is optimized, and efficient utilization of energy is realized in combination with a genetic algorithm and LSTM prediction; and the report and statistical analysis module provides visual display and decision support, so that efficient utilization of energy and optimization of the lighting effect are realized.

The embodiment of the invention provides an air supply system and an air supply method of an air suspension. The air supply system of the air suspension comprises an air storage tank, a plurality of air springs, an air supply valve body unit, a dryer, a heating element, an electronic control unit and a humidity sensor. The air storage tank is used for storing compressed air; the air spring is used for inputting high-pressure gas to each wheel of the vehicle; the air supply valve body unit comprises a reversing valve; the reversing valve is arranged on an air channel, so that the air supply system is driven to be switched between the air suction mode and the reverse blowing mode through reversing of the reversing valve; the cavity of the dryer is communicated with the air supply valve body unit through an air channel; the heating element is arranged in a cavity of the dryer, the humidity sensor is arranged on the air channel, and the humidity sensor corresponds to the electronic control unit in a signal mode. Therefore, the air supply system of the air suspension has the advantage that the regeneration efficiency of the dryer is improved.

The invention relates to the technical field of cables, and particularly discloses a cable terminal simulation and state evaluation method and system in a humid environment, multiple types of sensor arrays are deployed, distributed temperature and humidity sensors are installed at key parts of a cable terminal, and aiming at the technical problems of one-sided single-dimensional monitoring, simulation model solidification and insufficient static weight adaptation, the cable terminal simulation and state evaluation method and system are provided. Cooperative acquisition of multiple parameters such as temperature and humidity, partial discharge and corrosion current is achieved through deployment of multiple types of sensor arrays, the nonlinear interaction relation between humidity permeation and electric field distortion is dynamically reflected in combination with a multi-physics field coupling simulation model, the one-sidedness of single parameter monitoring is solved, and the risk misjudgment rate is reduced by 40% or above; a dynamic water film thickness model is adopted to update interface parameters of the sealing ring in real time, a simulation result is updated per hour in combination with a finite element-boundary element hybrid algorithm, an electric field distortion simulation error is controlled within 12%, and the simulation precision is improved by more than one time compared with a fixed boundary condition.

The invention discloses a base station air conditioner energy consumption management energy storage method and system, and the method comprises the steps: collecting multi-source data in real time according to the load data of a temperature sensor, a humidity sensor, an air conditioner operation state monitoring device and a communication device in a base station room, and generating a multi-source fusion data set; for the multi-source fusion data set, dynamic refrigeration requirements in a future time window are predicted, and a dynamic optimization model of air conditioner energy consumption is constructed through an energy consumption efficiency curve; according to the prediction result of the dynamic refrigeration demand and the energy consumption optimization model, the operation mode, the wind speed and the temperature set value of the air conditioner are dynamically adjusted, the energy storage scheduling strategy is optimized, and a combined optimization scheme of air conditioner operation and energy storage scheduling is generated; and monitoring the operation state of the air conditioner in real time, dynamically adjusting the joint optimization scheme, and generating a final base station air conditioner energy consumption management scheme. By utilizing the embodiment of the invention, the energy consumption management of the air conditioner has real-time performance and accuracy, and more efficient energy consumption control and energy storage scheduling optimization can be realized.

The invention relates to the technical field of remote sensing surveying and mapping, in particular to a forestry surveying and mapping method based on remote sensing big data, which comprises the following steps: integrating remote sensing images, unmanned aerial vehicle images, temperature sensor data and humidity sensor data to perform space-time alignment processing, filling missing data through time interpolation, and performing abnormal value detection at the same time; and combining the processed data to calculate ecological indexes of the forest area, and outputting a forest health ecological assessment result. According to the method, by integrating remote sensing images, unmanned aerial vehicle images and temperature and humidity sensor data, the accuracy and integrity of data are improved, species identification and ecological function contribution analysis are achieved, key species and contribution of the key species to an ecological system can be accurately identified, and by analyzing the dynamic change trend of forest resources and climate influence, the forest resources can be accurately identified. According to the method, resource configuration can be effectively optimized, the management efficiency and the risk management and control capability are improved, the precision and timeliness of forest resource monitoring, assessment and management are improved, and the sustainable utilization and ecological protection capabilities are enhanced.

The invention provides a refrigerator and an unfreezing method for food materials stored in the refrigerator. The refrigerator comprises a refrigerating chamber, an unfreezing chamber and an evaporation cabin, and an evaporator and a fan are arranged in the evaporation cabin; the controller is connected with the humidity sensor, the temperature sensor, the first air door, the second air door, the evaporator and the fan. The unfreezing chamber is arranged in the refrigerating chamber of the refrigerator, food materials to be unfrozen are unfrozen to the temperature at which the food materials are slightly frozen and easy to cut by utilizing high-humidity gas in the evaporation cabin, and then the temperature in the unfreezing chamber is controlled within the constant range at which the food materials are slightly frozen and easy to cut. By controlling the opening or closing of the first air door and the fan, the effect of conveying the high-humidity gas to the thawing chamber can be realized, so that the problems of long thawing time and non-uniform thawing caused by incapability of accurately capturing the surface temperature of the stored food materials in the thawing process of the stored food materials are solved.

The invention provides a low-power-consumption self-repairing and energy self-supplying mine dust monitoring sensor network system, which comprises a plurality of sensor nodes and a central monitoring platform, and is characterized in that data of all the sensor nodes are transmitted to the central monitoring platform in real time; the sensor network synchronizes environmental parameters, and performs data analysis, processing and visual display on the central monitoring platform; a plurality of sensors are integrated in each sensor node, and the sensors are used for monitoring different environmental parameters in a mine environment; the types of the sensors comprise a dust concentration sensor and a temperature and humidity sensor, and the environmental parameters comprise dust concentration, temperature and humidity; the problems of long-time operation, insufficient energy supply, node failure, unstable data transmission and the like of a dust monitoring system in a complex mine environment are solved. The system integrates a multi-source environment energy collection module, and the technologies of mechanical vibration, airflow kinetic energy, thermoelectric power generation and the like are adopted, so that self-power supply of sensor nodes is realized, and the requirement of frequently replacing batteries or wiring is avoided.

The invention provides a three-dimensional simulation and infection scene decision optimization system and method based on digital twinning, and relates to the technical field of intelligent medical treatment and digital twinning crossing technologies. The three-dimensional simulation and infection scene decision optimization system and method based on digital twinborn comprises the following modules: a data acquisition module, a path optimization module, a risk analysis module, a disinfection scheduling module and a collaborative prevention and control module, by arranging an air microorganism sampler, ultra-wideband positioning equipment and a temperature and humidity sensor, pathogen concentration, personnel trajectory and environmental parameters are collected in real time. Through a multi-source sensor fusion algorithm and a space-time attention mechanism, weight fusion and noise filtering are carried out on pathogen concentration, personnel tracks and environmental parameters in a hospital environment, the problems of information isolation and noise interference in traditional data collection are solved, and the space-time relevance of multi-modal data is remarkably enhanced.

The invention relates to the technical field of air conditioner energy-saving systems, and discloses a central air conditioner energy-saving automatic control system and method.The central air conditioner energy-saving automatic control system comprises a sensor module, a control module, a communication module, an intelligent control algorithm module, a dynamic adjusting module and an energy-saving optimization module; the sensor module comprises a temperature sensor, a humidity sensor, a CO2 sensor, an illumination sensor and an occupation sensor, the temperature sensor is used for monitoring indoor and outdoor temperatures and providing real-time data for decision making of a control system, and an NTC (negative temperature coefficient thermistor), a PTC (positive temperature coefficient thermistor) or a DS18B20 digital temperature sensor is adopted. The environment parameters are monitored in real time, the operation state of the air conditioning equipment is dynamically adjusted, the high-energy-consumption operation mode of a traditional air conditioning system is avoided, cooling / heating according to needs is achieved, energy consumption is effectively reduced, the mode is automatically switched to the energy-saving mode in the state that the number of personnel activities is small or no one exists, and therefore the effect of remarkably reducing the operation load and energy consumption can be achieved.

The invention relates to a method, device and equipment for controlling energy conservation of a clean room with an AI algorithm, and the method comprises the steps: collecting real-time environment parameters and equipment power consumption data through a temperature and humidity sensor, a cleanliness monitor, a dust falling particle catcher and an intelligent electric meter which are disposed in the clean room; associating the real-time environment parameters and the equipment electricity consumption data with production scheduling information to generate a dynamic regulation and control strategy; based on the real-time environment parameters, the equipment electricity consumption data, the dynamic regulation and control strategy and historical energy consumption data, a start-stop strategy of the fan filter unit is calculated through a deep learning model, and the start-stop strategy comprises a three-gear hierarchical regulation and control strategy for an alternating current type FFU. And the voltage and the rotating speed of the fan FFU are dynamically adjusted according to the direct current type FFU, so that intelligent linkage of production requirements and environment control is achieved, and the purposes of reducing cost and increasing efficiency are achieved.

The invention relates to the technical field of power distribution cabinet monitoring, in particular to a power distribution cabinet for realizing environment monitoring based on a sensor network, which realizes omnibearing real-time monitoring of the surrounding environment of the power distribution cabinet by arranging multi-point temperature and humidity sensors on the outer side of a power distribution cabinet body and utilizing a cloud monitoring module to uniformly manage data acquisition and storage. A dynamic weight distribution algorithm is adopted, a comprehensive weight is generated according to the historical abnormal frequency, the position and the environment change rate of the sensor, and a temperature and humidity characteristic matrix is given. The data relevance and weight change are learned through the graph attention network, the global feature vector of the environment state is generated, and the analysis capability of the complex environment data is greatly improved. Meanwhile, in combination with a Gaussian process regression prediction model, the future environment trend is accurately predicted. The alarm score and the alarm level are generated by comparing the deviation interval of the predicted value and the real-time value and combining the parameter change rate, and the real-time alarm information is provided, so that the manual inspection cost is effectively reduced, and the monitoring accuracy is improved.

The invention relates to the technical field of sowing control, in particular to a precise control system and method for cooperative fertilization and sowing, and the method comprises the following steps: monitoring soil humidity distribution data of a target farmland by using a soil humidity sensor, and calculating a soil permeability coefficient based on the soil humidity distribution data in combination with a three-dimensional terrain model and a soil permeability coefficient; constructing a soil humidity gradient model by adopting a finite element analysis method; based on the soil humidity gradient model and in combination with target fertilizer characteristics, establishing a fertilizer diffusion model, simulating the diffusion range and concentration distribution of the fertilizer under the soil humidity gradient condition, and outputting the fertilizer diffusion radius and the concentration gradient distribution of the fertilizer in soil through multivariate regression analysis; and based on the fertilizer diffusion model, calculating the fertilization amount and the fertilization depth in combination with the crop demand parameters. According to the method, the soil humidity gradient model is established, and fertilizer waste and environmental pollution caused by non-uniform fertilization or excessive fertilization amount are avoided.

The invention relates to the technical field of agricultural product distribution, and discloses an agricultural product distribution system based on the Internet of Things, and the system comprises an Internet of Things sensing layer which is provided with various sensors, including but not limited to a temperature sensor, a humidity sensor, an infrared sensor and an RFID tag; the data transmission layer is used for transmitting the data acquired by the sensing layer to a cloud server in real time by utilizing a 4G / 5G and Wi-Fi wireless communication technology; and the cloud computing and big data processing layer adopts a high-performance cloud computing platform and integrates a big data analysis engine. According to the invention, the accuracy and real-time performance of original data are ensured by optimizing the sensor configuration and the data acquisition frequency of the Internet of Things sensing layer and adopting an advanced wireless communication technology, so that the accuracy of agricultural product quality prediction, freshness evaluation and transportation path optimization is remarkably improved; mass data can be processed and analyzed by means of a high-performance platform of cloud computing and a big data processing layer.