5543results about "Simultaneous control of multiple variables" patented technology

The invention relates to an intelligent reflux valve adjusting method based on a self-adaptive control algorithm, and aims to improve the accuracy and response efficiency of fluid adjustment. The method comprises the steps that fluid flow, pressure, temperature and flow direction data collected by sensors installed at an inlet and an outlet of the reflux valve are obtained, and the initial target valve opening degree under the current fluid working condition is calculated through a working condition opening degree prediction model in combination with structural parameters of the reflux valve; based on the relation among the flow, the pressure and the valve opening, the initial target valve opening is adjusted through a fuzzy rule base, so that the final target valve opening is obtained, and therefore the dynamic change of the complex fluid working condition is adapted; according to the final target valve opening degree, an execution mechanism is controlled to adjust the operation state of the reflux valve, and precise adjustment is achieved; working condition prediction and fuzzy control are combined, the adjusting precision and stability of the reflux valve are improved, and the method is particularly suitable for fluid conveying systems with large flow and pressure fluctuation and has wide application prospects in the industries of energy, chemical engineering, environmental protection and the like.

An Internet-of-Things-based intelligent safety monitoring method and system for power plant operation. The method comprises: deploying sensors and collecting monitoring data in real time; transmitting the collected data to a central server, performing data processing, integrating the monitoring data, calculating a comprehensive environmental index, and preliminarily assessing an environmental monitoring condition; and comprehensively determining a device fault condition, remotely monitoring device status, starting a device emergency response mechanism, and performing intelligent safety monitoring of power plant operation. Power plant devices and environment parameters are monitored in real time, so that device anomalies and potential safety hazards are promptly captured to achieve rapid response. The stability of the power plant is improved, and a video monitoring system is integrated in combination with image recognition technology to monitor anomalous events in the power plant area. The level of safety management is enhanced, and decisions can be quickly made in emergency situations, improving flexibility and efficiency and enhancing management effectiveness and the accuracy and stability of the monitoring system.

The invention discloses a livestock breeding house environment detection method and system, and relates to the technical field of livestock environment intelligent perception and regulation, and the method comprises the steps: collecting original thermal image data, original audio signals and original gas data, and generating a space-time alignment multi-modal data set; performing space-time gridding processing on the multi-modal data set, extracting temperature gradient, voiceprint MFCC characteristics and gas concentration space distribution, and constructing a temperature-voiceprint-gas three-dimensional incidence matrix; based on the temperature-voiceprint-gas three-dimensional incidence matrix, dynamically compensating the body surface temperature of the animal in combination with the temperature and humidity index, inputting the gas concentration as a weight factor into the LSTM time sequence prediction model, and outputting a stress risk level and a pollution risk level; and according to the stress risk level and the pollution risk level, generating and executing a corresponding environment regulation and control strategy. According to the invention, high-precision monitoring and dynamic regulation and control of the animal house environment state can be realized, the animal health guarantee level is improved, and the breeding risk and the operation and maintenance cost are reduced.

The invention relates to the technical field of thermal power plant parameter regulation and control, in particular to an intelligent temperature and pressure regulation method and system for a thermal power plant, and the method comprises the following steps: collecting temperature, pressure and flow data based on heat transfer and flow channels among a boiler, a steam turbine, a condenser and heat exchanger equipment in the thermal power plant; constructing a network structure representing interaction between devices, evaluating heat exchange characteristics of nodes, and constructing heat flow path structure matrix information; according to the method, the complex interaction among the boiler, the steam turbine, the condenser and the heat exchanger equipment is accurately quantified by constructing the heat flow path network structure among the equipment and evaluating the heat exchange characteristics of the nodes. Thermal inertia feedback matrix information is introduced, and transient changes of temperature and pressure are monitored in real time, so that delay and inertia effects in heat transfer and fluid flowing between equipment can be more accurately captured, and the response speed and precision to different loads and working conditions are improved.

The invention provides an intelligent incubation bin anti-interference control method and system based on error self-learning, and relates to the technical field of intelligent control, and the method comprises the steps: respectively calculating a temperature deviation value and a humidity deviation value according to an optimization parameter sequence of a temperature prediction error and an optimization parameter sequence of a humidity prediction error; performing normalized weighted summation on the temperature deviation value and the humidity deviation value, and performing dynamic scaling through a Gaussian kernel function to obtain a correction factor; dynamically adjusting the activation function slope of a neural network hidden layer according to the correction factor and a BP neural network, reconstructing the weight of the fuzzy rule base based on the numerical distribution characteristics of the correction factor, and generating the corrected weight of the fuzzy rule base; and based on the corrected fuzzy rule base weight, constructing a three-dimensional parameter adjustment curved surface, and dynamically adjusting proportion, integral and differential parameters of a PID controller through a curved surface gradient search algorithm to generate a control signal. According to the invention, the control accuracy is improved.

The invention relates to the technical field of power distribution cabinet temperature control, in particular to an intelligent temperature and humidity control system for a power distribution cabinet. Comprising an environmental parameter acquisition module, a data analysis processing module, a temperature and humidity regulation and control module, an energy consumption recovery optimization module and a monitoring alarm module. The environmental parameter acquisition module monitors environmental data such as temperature, humidity and voltage through a sensor, and the data analysis processing module analyzes a trend based on a time convolutional neural network. The temperature and humidity regulation and control module achieves optimal control through an optimization algorithm, and the energy consumption recovery optimization module recovers and optimizes energy through a genetic algorithm. And the monitoring alarm module adopts a Bayesian network to be combined with a self-adaptive sliding window mechanism to monitor environmental fluctuation in real time, and gives an alarm when the environmental fluctuation is abnormal. Efficient operation, safety and stability of the power distribution cabinet in a complex environment are ensured, the safety and reliability of the power distribution cabinet are improved, the operation and maintenance cost can be effectively reduced, and energy conservation and environmental protection are achieved.

The invention relates to the technical field of intelligent agriculture, and discloses a visual intelligent agricultural planting control system. The system comprises an environment data acquisition module used for acquiring multi-source environment sensing data; the growth feature modeling module is used for extracting crop growth state features through a morphological analysis algorithm; the environment regulation and control decision module is used for generating an environment regulation and control instruction set by utilizing a dynamic threshold matching algorithm; the visual interaction module is used for generating a three-dimensional farmland live-action simulated diagram through multi-dimensional data fusion processing; the strategy execution module is used for driving agricultural facilities to execute actions by adopting a self-adaptive control algorithm; an abnormity early warning module is further arranged, and abnormity early warning signals are generated through correlation analysis. The system realizes comprehensive acquisition and analysis of agricultural planting environment data, precise environment regulation and control, visual and visual display and abnormal early warning, effectively improves the intelligent and precise level of agricultural planting, improves the crop yield and quality, and assists the development of intelligent agriculture.

The invention relates to the technical field of environment monitoring, and provides a collaborative control method and device for a measurable, displayable, adjustable and controllable environment system, and the method comprises the steps: collecting an original data set of a monitoring region, carrying out the preprocessing, and constructing a multi-dimensional environment data knowledge graph containing time-space correlation attributes; through a neural network learning environment parameter and health risk space-time correlation characteristic, outputting a healthy human settlement environment comprehensive evaluation result based on a fuzzy comprehensive evaluation matrix of adaptive threshold adjustment; inputting the comprehensive evaluation result of the healthy human settlement environment into a Kriging interpolation algorithm, generating an indoor four-dimensional space-time dynamic thermodynamic diagram in combination with environment parameter space-time distribution characteristics, generating a health risk prediction thermal layer based on risk weight superposition, and forming a visual composite atlas; outputting a regulation and control strategy by taking energy consumption, equipment cost and a health risk value as optimization targets; and controlling the household cooperative equipment. According to the invention, predictive control is carried out on the environmental equipment, and the accuracy and effectiveness of environmental regulation and control are improved.

The invention relates to the technical field of agricultural internet of things and environment intelligent control, in particular to a greenhouse environment adaptive regulation and control system based on artificial intelligence, which comprises an environment acquisition module used for acquiring multi-dimensional environment data in real time through a distributed multi-source sensor; the central controller is used for generating an optimized regulation and control strategy; the regulation and control execution module is used for driving execution equipment to carry out regulation; the central controller comprises a multi-source data fusion unit, an AI decision-making unit and a dynamic optimization engine which are respectively responsible for data filtering and fusion, generating an initial regulation and control strategy based on a space-time joint AI model, and reconstructing and optimizing the initial regulation and control strategy through a multi-target optimization algorithm. According to the method, the response real-time performance is improved through multi-source sensing and data fusion, predictive regulation and control and multi-parameter cooperation are achieved through the AI model, balance of energy consumption, growth and carbon emission is achieved in combination with multi-target optimization, and long-term self-adaption and strategy iteration of the system are supported.

The invention discloses an electrical cabinet condensation defense method and system based on environmental parameter monitoring. The method comprises the following steps: acquiring temperature and humidity information of multiple points in an electrical cabinet and outside the cabinet in real time; calculating a dew point temperature and a condensation risk index in the cabinet; predicting the lowest temperature and humidity change rate in the cabinet in the next time period in the control period; based on the minimum temperature and humidity change rate prediction value in the cabinet and the current condensation risk index, whether the electrical cabinet meets the condensation risk trend criterion is judged, if yes, the electrical cabinet enters a condensation risk suppression linkage control mode, and if not, the electrical cabinet enters a condensation risk defense self-adaptive mode; and in the condensation risk suppression linkage control process, when the current condensation risk index of the electrical cabinet is lower than a condensation risk index threshold value, entering a condensation risk defense self-adaptive mode, and otherwise, entering a next control period. Data acquisition is comprehensive and accurate, the dew point temperature can be accurately calculated, the control strategy is intelligent and flexible, equipment can operate according to needs, condensation is effectively prevented, and the energy-saving effect is remarkable.

The invention relates to the technical field of cell culture, in particular to an artificial intelligence-based cell culture synchronous control system, which comprises a data acquisition module, a data processing module, an intelligent prediction module, a control strategy generation module, an execution mechanism control module, a feedback regulation module and a user interaction module, the data acquisition module acquires environmental parameters in a culture environment in real time, the data processing module preprocesses the acquired environmental parameters, the intelligent prediction module predicts the change trend of the culture environment in future time by using a machine learning algorithm, and the control strategy generation module generates a cell culture control strategy. The execution mechanism control module converts the control strategy into an execution instruction, the feedback adjustment module monitors the effect of the execution instruction and adjusts the control strategy according to feedback information, and the user interaction module provides a user interaction interface. According to the invention, through comprehensive real-time monitoring, intelligent prediction, optimal control and real-time feedback adjustment, the precision, efficiency and safety of cell culture are improved.

The invention provides a multi-region independent temperature control intelligent frequency conversion refrigeration and preservation method and system, and relates to the technical field of refrigeration and preservation. A region division module, a temperature control module, an environment monitoring module, a data analysis module, an intelligent frequency conversion control module and a user interface module work cooperatively; fine temperature control management of different areas in the refrigeration equipment is realized, the system adopts three-dimensional laser scanning and multispectral image acquisition technologies to automatically divide temperature areas, a quantum-level precision thermistor array and an intelligent fuzzy PID controller ensure accurate temperature control, and meanwhile, humidity and air flow are monitored to optimize a storage environment, so that the temperature control efficiency is improved. The data analysis module is combined with deep learning and an association rule mining algorithm to provide decision support for frequency conversion control and realize energy consumption optimization, the user interface module provides visual operation experience, and the whole scheme improves the intelligence, accuracy and energy efficiency of refrigeration and preservation, and is suitable for various cold chain scenes.

The invention discloses a method for dynamically adjusting a tea fermentation environment by utilizing fuzzy logic control. The method comprises the following steps: S1, collecting and obtaining environment parameter data of each layer in real time; s2, fuzzy processing is carried out on the temperature, humidity and oxygen concentration in each time period through a fuzzy logic controller, and fuzzy control output of each time period is generated; s3, combining a hybrid neural network architecture and a meta-learning technology to construct an environment parameter change model; s4, optimizing the fuzzy rule base by using the environment parameter change model; s5, converting the fuzzy control output of each time period into a control instruction; s6, optimizing the fermentation environment through a cross-layer parameter equalization strategy to obtain the optimal state of the environmental parameters in the fermentation process; and S7, displaying environmental parameters of each spatial level and time stage in real time, and supporting switching between a manual mode and an automatic mode. The method combines fuzzy logic control and a deep learning technology, realizes accurate dynamic adjustment of the tea fermentation environment, and has the advantages of high adaptability and the like.

The invention relates to the technical field of tobacco logistics and intelligent control, and discloses an intelligent temperature and humidity regulation and control system and method for tobacco transportation, and the system comprises a multi-dimensional environment sensing module, a self-adaptive strategy generation module and a closed-loop efficiency verification module. The method comprises the following steps: capturing three-dimensional space parameters in a transportation carrier in real time through distributed sensing nodes, outputting a structured environment situation matrix, and providing a space-time reference for a regulation and control decision; after receiving the environmental perception data, executing dynamic strategy optimization, establishing a transportation stage-spatial position two-dimensional regulation and control strategy library, processing sensitivity differences of tobacco leaves in different regions by adopting an asymmetric weighting algorithm, and generating a regulation and control scheme containing a gradient regulation instruction; real-time evaluation and strategy iteration of the regulation and control effect are achieved, microenvironment response detection is deployed, tobacco physical characteristic change data are collected, and a triangular verification model is constructed. The method effectively controls moisture content distribution, inhibits browning and mildew risks, and maximally retains tobacco leaf quality characteristics.

The invention discloses a self-adaptive grouting control plugging method based on mining-induced fracture real-time monitoring, and relates to the technical field of mine safety engineering and hydrogeology. Through cooperative work of the grouting mechanism, the data monitoring system, the data collecting and processing module and the self-adaptive control module, automation and intellectualization of grouting protection can be achieved, and through introduction of the fracture roughness coefficient and the time-dependent viscosity, rough fracture flow resistance and grout rheology and time-varying characteristics are quantified, and the grout diffusion radius calculation error is reduced. The data weight is dynamically adjusted through a micro-seismic travel time residual error and sound wave velocity field joint inversion algorithm in combination with a weighted robust LM algorithm, and accurate fracture positioning is achieved. And a PID gain compensation and saturation function mechanism is adopted, so that self-adaptive safe and efficient grouting is realized. The fracture dynamic state is monitored in real time through the multi-source data fusion technology, grouting parameters are dynamically optimized in combination with an intelligent algorithm, complex geological interference is effectively restrained, slurry waste is reduced, and efficient and accurate plugging of mining-induced fractures is achieved.

The invention provides a multi-construction-method collaborative dust fall intelligent control method based on deep learning. The method comprises the following steps: collecting mine field data of a mine field in real time; wherein the mine field data comprises mine field environment data, equipment operation data and topographic data; constructing a digital mine field model based on the mine field data; wherein the digital mine field model comprises a terrain model, an equipment model and a dust generation and diffusion model; based on the mine field data and the digital mine field model, constructing a reinforcement learning agent; based on the reinforcement learning agent, performing dust fall control on the mine field; the dust fall control is monitored on the basis of the pre-created human-computer interaction interface, intelligent and efficient control over the mine field dust fall process is achieved, the dust fall effect and the resource utilization efficiency are improved, and meanwhile the flexibility and reliability of the system are improved through human-computer cooperative control.

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 vehicle-mounted temperature control, and discloses a method and system for intelligently controlling the temperature and humidity of a vehicle-mounted cold chain and a storage medium. The method comprises the following steps: acquiring and preprocessing temperature and humidity data through a sensor; performing state evaluation based on a three-way decision model; constructing a temperature and humidity trend analysis model to predict a change trend; constructing a control decision system by adopting multi-agent reinforcement learning; executing a multi-mode control strategy; and adaptively updating the control system according to the execution effect. According to the application, the future temperature and humidity change trend is predicted according to the historical data and the real-time state, collaborative optimization control of temperature and humidity is realized, self-learning and continuous optimization capabilities are provided, and the accuracy, stability and energy utilization efficiency of cold chain temperature and humidity control are effectively improved.

The invention provides a high-altitude energy storage monitoring system. The system comprises an environmental data acquisition module, an intelligent temperature control analysis module, an intelligent energy scheduling module, a health monitoring module, a self-adaptive adjustment module and a system optimization module. The invention provides an intelligent and environment-adaptive energy storage system monitoring and maintenance strategy, the temperature control strategy, the energy distribution and the maintenance plan can be dynamically adjusted according to the real-time change of the high-altitude environment, the efficient operation of the energy storage system is realized, and the service life of equipment is prolonged. A sensor technology is used for collecting multi-dimensional environment data, and an intelligent algorithm is combined to monitor the operation state of the equipment in real time, predict the health state of the equipment, optimize energy scheduling and formulate a self-adaptive maintenance strategy, so that the fault risk and the maintenance cost are reduced, and the stable operation of the system in a complex environment is ensured.

The invention provides an intelligent agricultural environment monitoring system based on the Internet of Things, and relates to the technical field of data processing. The feature extraction and analysis module is used for performing segmented statistical analysis on the soil humidity original data according to the soil humidity original data in combination with historical soil humidity data, historical environment temperature data and preset near-period soil humidity feedback data, and automatically extracting a humidity change rate and an abnormal trend index data set under multiple time windows; the self-learning judgment module is used for dynamically adjusting the current judgment threshold value based on a self-learning algorithm, judging the soil humidity original data in combination with the humidity change rate and the abnormal trend index, and generating a judgment signal for judging whether local irrigation response is triggered or not; the response control module is used for automatically generating an irrigation equipment starting instruction; the irrigation execution module is used for executing irrigation operation according to the irrigation equipment starting instruction; according to the invention, the accuracy of agricultural environment monitoring is improved.

The invention relates to the technical field of process control, in particular to an intelligent environment monitoring and adjusting system for lip fish culture, which comprises an environment parameter monitoring module, a multivariable linkage calculation module, a self-adaptive adjusting module, a fuzzy logic control module and an abnormity emergency response module. According to the method, the trend value is formed through time-phased grouping analysis and relevance calculation of the water quality parameters, dynamic prediction of the breeding environment change is achieved, the cooperative adjustment capacity among the dissolved oxygen, the water temperature and the ammonia nitrogen concentration is optimized through analysis of the multi-parameter linkage relation, the accuracy of environment control is improved, and the method is suitable for large-scale popularization and application. The cooperation efficiency of oxygen pump frequency and temperature control equipment response is improved through real-time matching calculation of equipment operation parameters, the rapid adaptive capacity to water quality changes is enhanced, abnormal fluctuation monitoring is combined with equipment linkage adjustment, rapid response during water quality sudden change is ensured, and the adverse effect of environment fluctuation on fish growth is reduced.

The invention belongs to the technical field of fermentation, and discloses a cloud computing-based fermentation tank fermentation process management and control service system and a cloud computing-based fermentation tank fermentation process management and control service method. Comprising the steps that an equipment end collects fermentation parameters in real time in the fermentation process and sends the fermentation parameters to a cloud computing platform; the fermentation parameters comprise fermentation characteristic parameters and fermentation control parameters; the cloud computing platform analyzes the fermentation characteristic parameters to obtain optimal fermentation control parameters; the cloud computing platform identifies deviation parameters in the fermentation control parameters collected in real time; the cloud computing platform judges and generates an early warning instruction or an adjusting instruction according to the deviation parameter; if the adjusting instruction is generated, the cloud computing platform calculates the adjusting quantity corresponding to each deviation parameter and sends the adjusting quantity to the corresponding equipment end; intelligent monitoring and control in the fermentation process are achieved through the cloud computing platform, the fermentation quality and yield can be effectively controlled, the fermentation efficiency is improved, and therefore the automation level and stability of the fermentation process are improved.

The invention, which belongs to the technical field of intelligent breeding equipment, discloses an underwater bionic robotic fish school cooperative breeding environment dynamic monitoring and regulation system comprising a bionic robotic fish body core module, a group cooperative control core module and an intelligent regulation core module. The bionic robotic fish module simulates a real fish swimming mode and carries various water quality sensors to autonomously cruise, so that interference to cultured fishes is reduced; the group cooperation module utilizes a cluster intelligent algorithm and an underwater acoustic communication technology to realize coordinated movement of multiple robotic fishes and full coverage of a monitoring area; the intelligent adjusting module is based on an abnormal detection algorithm, and integrates a miniature oxygenation device, a pH adjusting device and the like to achieve precise regulation and control of the local environment. By the adoption of the system, dynamic sensing and active regulation and control of the culture environment are achieved, a bionic monitoring regulation and control solution is provided for modern aquaculture through intelligent cooperation of the robot fish school and the management system, and the system has the important value of improving the culture efficiency and improving the growth environment.

The invention relates to a multi-scene self-adaptive intelligent bottom argon blowing control method and system. The method comprises the steps that after a steel ladle reaches a working position, the air permeability is determined, a ladle setting signal is received, argon blowing is started, an infrared thermal imager is started to monitor a picture to judge the liquid steel level, and the liquid level area of the whole steel ladle is completely filled; pLC (Programmable Logic Controller) of all equipment in the whole refining process is intelligently linked, the bottom blowing stirring power is determined, the bottom blowing gas pressure and the bottom blowing gas flow are calculated, and relevant parameters such as the exposed area and the exposed edge slag layer height of molten steel in a continuous video stream are tracked and calculated in real time according to the metallurgical purpose of different stages, so that the bottom blowing control is further optimized; and a flow data instruction is issued to a bottom blowing PLC to control the pressure and flow of bottom blowing gas. According to the requirements of field production, video images of molten steel in the argon blowing state are automatically and continuously received in a zero-delay mode, the argon flow and pressure are automatically distributed through intelligent bottom blowing control, it is guaranteed that each furnace achieves the good bottom blowing stirring effect, field production is guided, and the production labor intensity is reduced.

The invention relates to the technical field of process adjustment, in particular to an automatic adjustment method and system for the drying process of traditional Chinese medicinal materials, and the method comprises the following steps: monitoring the moisture evaporation condition of the traditional Chinese medicinal materials in the drying process in real time through a weight sensor, calculating the moisture evaporation speed of the traditional Chinese medicinal materials; and analyzing the relationship between the moisture evaporation speed and the environmental condition, dynamically adjusting the temperature and humidity condition to match the moisture evaporation speed requirement, and generating dry environment basic data. According to the method, the moisture evaporation rate and temperature and humidity conditions are monitored in real time, the drying environment is dynamically adjusted, uniform removal of moisture is ensured, heat and humidity distribution analysis is combined, the temperature and humidity in the drying chamber can be accurately adjusted, uneven local drying is avoided, the moisture evaporation rate and temperature and humidity changes in the drying process are dynamically adjusted and optimized, and the drying efficiency is improved. And the risk of excessive drying or moisture residue is avoided, control parameters are adjusted according to the difference between water evaporation and the target drying process, and the drying effect stability is improved.

The invention relates to the field of intelligent agriculture, and discloses an intelligent drip irrigation control method, system and device and a storage medium, and the method comprises the following steps: data collection: obtaining soil humidity data, environmental meteorological data and crop growth state data of a target area through a plurality of sensors, and transmitting the data to a data processing module; the system comprises a data acquisition module used for acquiring soil humidity data, environmental meteorological data and crop growth state data and transmitting the acquired data to a data processing and storage module; the device comprises a machine case shell, and an intelligent control chip, a wireless communication module, an electromagnetic valve and a water flow meter are installed in the machine case shell. By integrating a sensor network, intelligent analysis and a real-time feedback mechanism, the water demand of crops is accurately calculated, irrigation is automatically adjusted, and water resource utilization is optimized; a personalized irrigation plan is made by combining a deep neural network and time sequence analysis, and the water requirements of crops in different growth stages are ensured.

The invention relates to the technical field of environment intelligent regulation and control, in particular to a vegetable greenhouse environment intelligent regulation and control method and system. The method comprises the following steps: acquiring all-weather illumination data and internal environment data of the vegetable greenhouse through a sensor; performing air heat flow exchange path identification based on the internal environment data of the vegetable greenhouse to obtain internal air heat flow exchange path data; according to the all-weather illumination data of the vegetable greenhouse, carrying out heat flux period temperature and pressure deposition simulation on the internal air heat flow exchange path data, and carrying out gas exchange barrier effect analysis to obtain gas exchange barrier effect data; performing regional environment staged regulation and control on the gas exchange barrier effect data to obtain regional environment staged regulation and control data; and carrying out environment regulation and control model construction on the regional environment stage regulation and control data to obtain an environment regulation and control logic model. According to the invention, the intelligent environment regulation and control technology is optimized, so that the intelligent environment regulation and control technology is more accurate.

The invention discloses a temperature and humidity control method for a marine energy storage container, and relates to the technical field of container temperature and humidity control, and the method comprises the following steps: monitoring the temperature and humidity in the container in real time, and obtaining the real-time environment parameters of the environment in the container through a sensor; through real-time monitoring, an intelligent algorithm and multi-level control, the system dynamically adjusts the temperature and humidity, and the accuracy and the response speed are improved. Different from traditional static setting, the system is combined with sensor data and a prediction model to cope with environment and load changes in advance, temperature and humidity are prevented from exceeding the standard, and equipment faults are reduced. The energy waste is reduced, the energy consumption distribution is optimized, the overall energy efficiency is improved, the cost is reduced, and the safe and stable operation of the equipment is ensured by a regionalized control and global optimization strategy.

The invention belongs to the technical field of intelligent control, and particularly relates to an intelligent regulation and control method for an operating environment in an SF6 ring main unit. The method comprises the following steps: firstly, acquiring temperature, gas pressure and humidity data of a plurality of sampling points in the ring main unit, and constructing a time sequence environment data set through time synchronization and missing interpolation processing; analyzing the temperature data, extracting fluctuation characteristics, determining an environment disturbance factor in combination with an electric arc trigger intensity index, and obtaining a cabinet body temperature control safety factor in combination with a dynamically adjusted temperature rise threshold and a gas insulation state; then constructing an environment equivalent thermal inertia model and a hot-pressure covariant model based on historical data, and calculating a thermal stability index of the cabinet body; and finally, whether adjustment is triggered or not is judged according to the thermal stability index and the temperature control safety factor, if yes, a temperature abnormal area is positioned through a convolutional neural network and a clustering method, the temperature control response level is calculated, a temperature control strategy library is called to be matched with adjustment parameters, and accurate control is achieved.