4194results 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 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 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 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 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 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, 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 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 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.

The invention belongs to the technical field of glue filling control, and particularly relates to a glue filling amount control method and system. According to the method, the glue filling deviation can be controlled within an extremely small range through multi-time iteration control of a stage theoretical value and real-time feedback, the filling consistency and the bonding strength are remarkably improved, initial compensation is conducted in combination with various working condition parameters such as temperature, pressure and materials, the pressure and the diffusion dynamic state are monitored in real time, and the working efficiency is improved. The system can automatically adapt to environmental fluctuation and glue performance change, accurately control the filling amount, respond to overflow and viscosity abnormity in time, reduce glue waste, avoid product reworking caused by excess or insufficient glue, combine closed-loop feedback with maintenance strategies, automatically identify and process abnormal states, improve the automation degree and operation stability of a production line, and improve the production efficiency. Manual intervention is reduced, rapid configuration and expansion can be carried out according to different workpiece shapes, materials and production working conditions, and the device is suitable for various glue filling scenes.

The invention discloses an integrated control system for potato breeder's seed production, and relates to the technical field of potato breeding, the integrated control system for potato breeder's seed production comprises an environment monitoring module, a crop growth monitoring module, a fertilization demand module, a water and fertilizer supply control module, an equipment state monitoring module and a feedback correction module; the environment monitoring module collects environment parameters of a crop growth environment through a deployed sensor, and the environment parameters comprise temperature, soil humidity and illumination intensity. Through water and fertilizer supply and real-time monitoring, it is ensured that crops can obtain sufficient water and nutrients in all growth stages, irrigation and fertilization strategies are dynamically adjusted according to environmental conditions and plant growth states, the problem of poor growth caused by excessive or insufficient fertilization is avoided, the overall yield of the crops is increased through accurate regulation, and the yield of the crops is increased. The quality of the potatoes is improved, so that the goals of high quality and high yield are achieved.

The invention discloses a mass concrete intelligent temperature control system, and relates to the technical field of concrete temperature control, the system comprises a sensor monitoring module, a data collector, a data transmission module, a central control module, an execution mechanism and a data recording and remote monitoring module; by integrating the sensor monitoring module, the data collector, the data transmission module and the central control module, real-time monitoring and accurate analysis of key parameters in the concrete and in the construction environment are achieved, and a temperature and humidity prediction unit in the central control module can predict the temperature and humidity of the concrete by means of a finite element analysis method and a CFMH dynamic prediction formula. According to the method, the temperature and humidity trends of the concrete in different time and areas can be accurately predicted, the concrete quality problem caused by temperature and humidity fluctuation is greatly reduced, the quality stability of concrete construction is improved, a scientific and reliable decision basis is provided for constructors, the construction process is optimized, and the construction cost is reduced.

The invention relates to the technical field of communication machine room energy conservation, in particular to a temperature energy-saving control method and system for a communication machine room, and the method comprises the steps: obtaining multi-region temperature and humidity and equipment load data in the machine room, inputting a pre-training model to obtain a cabinet heat dissipation risk level, judging an environment state through combination with an incidence matrix, entering an operation mode if a requirement is satisfied, and entering a control mode if a requirement is satisfied. If not, a local temperature control adjusting mechanism is triggered; the method comprises the following steps: calculating a refrigeration efficiency ratio according to the air volume of ventilation equipment, air conditioner power consumption, real-time electricity price and the like, generating a candidate scheme set by using a dynamic weight multi-target optimization algorithm in combination with factors such as equipment life, and selecting a target scheme through virtual verification; and according to air conditioner waste heat and cabinet heat dissipation and storage heat, a heat energy distribution strategy is dynamically generated by using a deep Q network reinforcement learning algorithm. Therefore, the problems that in the prior art, energy is wasted, cost is high, heat dissipation requirements of all areas are difficult to accurately adapt, the service life and stability of equipment are affected, and fault risks are increased are solved.

The invention relates to the technical field of phytotron control systems, in particular to a seed seedling phytotron control system and a control method thereof, which integrate multi-dimensional environment perception, intelligent analysis and decision, precise environment regulation and control, a central controller and a data storage and analysis platform. A seedling raising environment is monitored in real time through temperature and humidity, illumination, CO2 concentration, soil moisture and an image sensor, an intelligent analysis decision module preprocesses data, establishes a growth model, generates a control strategy and optimizes a target by using an advanced algorithm, and an accurate environment regulation and control module executes the strategy and regulates temperature, humidity, illumination and the like. The central controller coordinates operation of the modules and realizes man-machine interaction, and the data storage and analysis platform is responsible for storing data and supporting cloud analysis so as to realize efficient seedling culture management and significantly improve the seed emergence rate from 75% of a traditional method to 92%, and greatly reduce seed waste.

The invention provides an anti-condensation temperature and humidity regulation and control system and method for remote deep learning prediction aided decision making, and the method is characterized in that a field controller is connected with a temperature and humidity collection device, obtains the environment temperature and humidity, calculates the dew point temperature, obtains the surface temperature of a protected part, and starts and stops a heating and / or dehumidification device by comparing the difference between the two values with a preset threshold value; the temperature and humidity acquisition device acquires field environment temperature and humidity data for the field controller to calculate the dew point temperature, and the temperature and humidity acquisition device is also used for acquiring the surface temperature of the protected part; the heating device is used for raising environment temperature; the dehumidification device is used for reducing the environment humidity; the communication module is used for data transmission between the field controller and the remote prediction terminal, sending field data and receiving prediction information; the remote prediction terminal obtains field data through the communication module; an anti-condensation system structure combining local autonomous control and remote prediction assistance is established, and redundancy fault-tolerant capability is achieved; even if the remote prediction function fails, the field control can still operate independently and reliably.

The invention provides a mobile henhouse ventilation regulation and control method and system based on an environment model, and relates to the technical field of intelligent breeding, and the method comprises the steps: obtaining original temperature data, original humidity data, a three-dimensional space wind speed component, an opening geometric distribution data set, and original latitude and longitude coordinate data and time data; calculating a dynamic environment weight coefficient matrix according to the original latitude and longitude coordinate sequence and the time data; generating a self-adaptive heat exchange flow field according to the dynamic environment weight coefficient matrix, the original temperature data and the original humidity data; according to the self-adaptive heat exchange flow field and the trepanning geometric distribution data set, a ventilation compensation amount demand curve is obtained through calculation; generating a ventilation intensity adjusting instruction set according to the ventilation compensation amount demand curve; and according to the ventilation intensity adjusting instruction set, outputting an optimal ventilation control parameter. According to the method, ventilation response self-adaptive accurate matching, airflow dead zone self-suppression and energy consumption-effect dynamic balance under the moving sudden change working condition are achieved.

The invention discloses a straw feed fermentation treatment control system and method, particularly relates to the technical field of production control, and is used for solving the problem of insufficient identification and regulation of an imbalance phenomenon in a straw fermentation pile caused by non-uniform gas diffusion in the existing fermentation process. The method comprises the following steps: acquiring environmental parameters of a fermentation heap, performing three-dimensional gas concentration field reconstruction processing, identifying a layered air mass region in combination with a fuzzy clustering algorithm, measuring heap pressure distribution and deformation characteristics, analyzing a heap body structure abnormal region, and marking a risk analysis region; through a three-dimensional tensor model and a fractal analysis model, the time dependency and the gas diffusion equilibrium degree of the gas exchange and metabolism process are evaluated respectively, and finally fermentation pile structure parameters and ventilation device operation parameters are dynamically adjusted based on a closed-loop control system; through accurate region identification and a dynamic regulation and control mechanism, the uniformity of the fermentation process and the quality consistency of the straw feed are remarkably improved.

The invention relates to the technical field of electrical cabinet monitoring, in particular to an electrical cabinet active anti-condensation method and system based on a condensation mechanism mathematical model. According to the invention, a mathematical model based on a condensation formation mechanism is constructed, and environmental meteorological parameters, station room environmental parameters, electrical cabinet internal microenvironment parameters and cable trench state parameters are monitored through a four-stage monitoring system; and fusing the multi-source heterogeneous data, inputting a condensation mechanism mathematical model to calculate a condensation risk index, and adopting a hierarchical response mechanism to cooperatively control an execution mechanism according to the obtained condensation risk index value to realize active defense of condensation. According to the method, the condensation phenomenon with the space-time dynamic characteristic is quantitatively described through a mass transfer and heat transfer coupling equation, a grading prevention and control strategy and a cooperative control algorithm are developed by calculating the condensation risk index, setting the threshold condition and judging the condensation formation risk level, the prevention and control intensity is dynamically adjusted according to the risk level, and the control accuracy is improved. And the risk response speed and the resource utilization efficiency under the complex working condition are obviously improved.

The invention provides a greenhouse environment adaptive regulation and control method and system, and relates to the technical field of environment control, and the method comprises the steps: collecting multi-dimensional environment parameters in a greenhouse; based on the environmental parameters, a preset crop growth period database and weather prediction data, taking minimization of a preset cost function as a target, and adopting a model prediction control algorithm to generate an equipment linkage instruction set in a future preset time period, the preset cost function fusing environmental regulation and control deviation and operation cost; issuing the equipment linkage instruction set to each execution equipment, and executing linkage regulation and control; wherein when the equipment linkage instruction set is generated, a conflict resolution mechanism based on a dynamic priority is adopted to determine an execution sequence of a plurality of equipment instructions; the adaptive regulation and control method integrating multi-source data verification, multi-scale prediction, dynamic priority conflict resolution and multi-target cost optimization improves the accuracy, economy and crop suitability of greenhouse environment regulation and control.

The invention discloses a seed quality evaluation system and device based on multi-modal data fusion and a control method, and relates to the technical field of agricultural engineering. The system comprises a data acquisition module, a data processing module, a quality evaluation module and a control module. The data acquisition module acquires multi-modal data; the data processing module performs preprocessing, feature extraction and fusion on the multi-modal data to generate a comprehensive feature vector; the quality evaluation module performs seed quality evaluation based on the comprehensive feature vector by using a machine learning model; the control module controls all the modules to operate and outputs results. The device comprises a closed box body, a temperature and humidity control unit, an electronic scale, a hyperspectral imaging unit, a visible light imaging unit, a mechanical arm and a main control unit. The method realizes rapid, accurate, lossless and automatic evaluation of seed quality, overcomes the defects of low efficiency, strong subjectivity, easy damage of seeds and the like in a traditional method, and improves the accuracy and efficiency of seed quality evaluation.