104 results about "Ambient intelligence" patented technology

The invention relates to the technical field of ocean dynamic environment intelligent forecasting, and provides a three-dimensional ocean dynamic environment intelligent forecasting method and device and electronic equipment, and the forecasting method comprises the steps: S1000, preparing 1 / 30-degree high-resolution ocean dynamic environment element data (three-dimensional temperature and salt flow and two-dimensional water level), and carrying out the normalization; s2000, selecting a corresponding model input sequence from the training set according to a forecasting strategy of the model; s3000, training a hierarchical feature projection network; and S4000: performing reverse normalization and generating future prediction data: performing reverse normalization on the feature-enhanced output data according to the maximum value and the minimum value of the ocean data, and finally generating a day-by-day prediction product of the sea surface height of the C sea, the three-dimensional sea temperature, the salinity, the latitudinal flow velocity and the longitude flow velocity with 1 / 30-degree high resolution in the future day, namely, the product is obtained by predicting the sea surface height of the C sea, the three-dimensional sea temperature, the salinity, the latitudinal flow velocity and the longitude flow velocity.

The invention discloses an intelligent monitoring method and system for the microenvironment of a power distribution cabinet, and the method comprises the steps: collecting the multi-parameter data of the microenvironment of the power distribution cabinet in real time through a plurality of sensors disposed in the power distribution cabinet, and enabling the multi-parameter data to at least comprise temperature, humidity, voltage and current; transmitting the multi-parameter data to a processing platform through an internet of things network; on the processing platform, performing data fusion processing on the multi-parameter data to obtain comprehensive environment parameters; based on the comprehensive environmental parameters, performing environmental state evaluation by using a fuzzy logic algorithm, and outputting a security level; based on the time sequence data of the comprehensive environmental parameters, using a time sequence prediction model to carry out fault prediction, and outputting a parameter change trend; and performing anomaly detection based on at least one of the comprehensive environmental parameters, the security level and the parameter change trend, and sending out an alarm signal when an anomaly is detected. The invention aims to realize real-time accurate monitoring of the microenvironment of the power distribution cabinet, early warn various potential risks in time, avoid the situations of terminal strip corrosion, poor contact of a secondary line, short circuit, lightning breakdown, false alarm of a switching signal, false operation of a primary switch and the like caused by the microenvironment problem, improve the power supply reliability and guarantee the safety of a power grid.

The invention relates to an intelligent regulation and control method and system for a health-care environment based on a neural network algorithm, and belongs to the technical field of intelligent regulation and control of the environment, and the regulation and control method comprises the steps: collecting environment sensor data, human physiological data and user interaction data, carrying out the space-time alignment and encryption desensitization processing, and generating an encrypted space-time matrix; respectively calculating a user physiological fitness index, an environment disturbance index and a behavior preference index based on the encrypted space-time matrix, and generating a three-dimensional feature vector; inputting the three-dimensional feature vector and historical sequence data into a neural network model, and outputting environment setting parameters; performing energy-saving adjustment on the environment setting parameters through a multi-objective optimization algorithm, and generating an equipment control instruction set; distributing the equipment control instruction set to an executor, and collecting equipment state data after execution; and calculating an error according to the equipment state data and the user feedback information, and performing parameter adjustment on the neural network model. The method can effectively meet the increasingly complicated health-care environment regulation and control requirements.

This invention provides an active flow control method and system based on intelligent algorithms. The method includes: determining the number of inlet parameter sets for a typical flight state point, constrained by simulation economy and sampling efficiency; performing numerical simulations according to the number of inlet parameter sets to obtain dataset 1, with inlet parameters as independent variables and flow field characteristics as dependent variables; obtaining dataset 2, with flight state as independent variables and flow field characteristics as dependent variables, through numerical simulation for a certain set of inlet parameters; constructing a proxy model of inlet flow field characteristics for a typical flight state point using deep learning based on dataset 1; constructing a proxy model of inlet flow field characteristics for a wide-speed-domain flight state based on the proxy model of inlet flow field characteristics for the flight state point, using a transfer learning algorithm and dataset 2; constructing an environment-agent interaction model based on a deep reinforcement learning algorithm, using the proxy model of inlet flow field characteristics for a wide-speed-domain flight state as the environment model for deep reinforcement learning; setting optimization termination conditions based on the environment-agent interaction model, and obtaining a control sequence with continuously optimal inlet performance under different flight states through deep reinforcement learning. This invention can stably adapt to flight scenarios with a wide speed range and a large airspace, and achieve real-time optimal control of the air intake flow field under a wide speed range, solving the problem of poor generalization ability of traditional methods under different operating conditions.

The application discloses a kind of wireless environment intelligent monitoring equipment and its working method, belong to environment intelligent monitoring equipment technical field, including lifting device and temperature and humidity monitor, still including connecting piece, support table, drive arrangement, surround movement device, stretching device, sprinkler and temperature regulating device, connecting piece is set in horizontal plane, lifting device is set in the lower end of connecting piece, support table is set on lifting device, drive arrangement is set in the lower end of support table, surround movement device is set on drive arrangement, stretching device is set on surround movement device, sprinkler is set in the upper end of support table, temperature regulating device is set in the upper end of support table and is connected with sprinkler, the device can drive temperature and humidity monitor to carry out around stretching detection, to improve detection range, and can adapt to different environment, and in the equipment that measures corresponding measure is carried out when monitoring that temperature is too high or too low.

The invention discloses a digital environment infrastructure system and method, and relates to the technical field of the Internet, and the system comprises a demand management module which is connected with a target user terminal, and is used for generating a demand list; the supply management module is used for collecting supply information of suppliers in real time and determining the supply state of each supplier; the supply and demand decision module is used for generating and executing an optimal solution based on the demand list and the supply information of the target supplier; the data management module is used for cleaning data and converting the cleaned data into data assets by using a block chain technology, wherein the data is data acquired and generated by each module; and the digital environment intelligent support management module is used for managing the operation state of each module in the implementation process. The invention aims to solve the problems that data splitting and data collaboration are difficult and cross-platform data element circulation and capitalization are difficult to realize due to the fact that an existing digital scheme mostly focuses on a single link or a specific scene.

The invention discloses a cloud native fault diagnosis method and system based on large model intelligent agent enhanced fine tuning, and belongs to the technical field of intelligent operation and maintenance and artificial intelligence. The system comprises a data preprocessing module which is used for actively injecting a fault into a cloud native environment and preprocessing multi-modal operation and maintenance data before and after the fault occurs; the scene construction module is used for constructing a fault scene sample based on the preprocessed multi-modal operation and maintenance data; the virtual environment module is used for constructing a fault diagnosis interaction environment; and the agent training module is used for optimizing a diagnosis strategy through model fine tuning and reinforcement learning. According to the method, high-accuracy, high-efficiency and interpretable fault root cause positioning of the cloud native application can be realized.

The application discloses a power distribution cabinet environment intelligent monitoring system and a monitoring method, and relates to the field of power distribution cabinet monitoring. The monitoring system comprises a total control computer, a total control programmable controller and a sub-control unit, the total control computer and the total control programmable controller are connected through a communication network cable to enable the two to communicate with each other, and the total control programmable controller is connected with multiple sub-control units through the communication network cable; the sub-control unit comprises a sub-station controller, a power supply tripping device, a sub-terminal and a power distribution cabinet, and the power distribution cabinet is provided with a fault signal acquisition module, a terminal row, a sensor module and a thermal imaging camera. The application aims to provide a power distribution cabinet environment intelligent monitoring system and a monitoring method, so that the environment in the power distribution cabinet can be detected in real time, and when a safety hazard occurs, maintenance personnel can be timely informed and handled.

The invention relates to an environment-adaptive intelligent logistics trolley, an environment intelligent sensing system and application, and belongs to the technical field of artificial intelligence, in particular to the technical field of logistics intelligent equipment. According to the technical scheme, the intelligent logistics trolley capable of self-adapting to the environment, the environment intelligent sensing system and the application of the environment intelligent sensing system are provided; the warehouse circulating mechanism and the pitching driving mechanism are arranged on the chassis to be matched with the mechanical arm to form the intelligent trolley capable of automatically and circularly changing the positions of all the warehouses and automatically achieving dumping type unloading, and the blank that traditional freight logistics is low in automation degree and troublesome in manpower is filled up.

The invention provides a USB measurement and control system collaborative verification platform building method based on the combination of System Verilog and a C language, and relates to the technical field of FPGA testing, the method is based on a Cadence IES simulation environment to build a verification platform of a layered tree topology architecture, the verification platform is independent of a tested design project file, a three-level system structure is adopted, and the verification platform is provided with a three-level architecture; the rtl design layer is used for storing a source code file of a to-be-tested USB measurement and control system baseband processing FPGA (Field Programmable Gate Array) described by adopting a register transmission level; and the tb test platform layer adopts a modular design and comprises a dutif interface definition module, a dutrap design packaging module, a tb test control module and a tbtop top layer integration module. According to the USB measurement and control system collaborative verification platform building method based on the combination of the System Verilog and the C language, intelligent generation of a verification environment is achieved through an automatic script, and the configuration complexity is remarkably reduced; and a parameterized configuration mechanism is adopted, so that different types of USB measurement and control systems can be quickly adapted, and excellent adaptability is shown.

The invention discloses an intelligent environment regulation and control method and system for concrete winter maintenance, and relates to the technical field of environment monitoring, the system comprises a control center, and the control center is connected with a building acquisition module, an intelligent processing module, a maintenance analysis module and a strategy execution module; the method comprises the following steps: constructing a virtual-real twin space, carrying out graph conversion on collected data, obtaining a concrete monitoring dynamic graph and an environment monitoring dynamic graph, carrying out curve extraction on the concrete monitoring dynamic graph, obtaining a local monitoring coefficient, carrying out anomaly capture, obtaining a raw material mutation monitoring section, and carrying out time checking on the environment monitoring dynamic graph according to the raw material mutation monitoring section. Obtaining an environment sudden change time period; performing optimization simulation through a virtual-real twin space based on the environment sudden change time period to obtain an optimization regulation and control strategy; parameters are accurately controlled, personal errors are reduced, the concrete quality is guaranteed, and the curing efficiency is improved.

The invention relates to the technical field of data center bus duct temperature monitoring, in particular to a data center bus duct operation environment intelligent monitoring method. The method comprises the following steps: firstly, monitoring temperature through distributed temperature measurement optical fibers laid on the surface of a large bus, and positioning a high-temperature section; calling data of the four-channel infrared temperature sensor at the connector joint of the high-temperature section, and identifying abnormal high temperature of the neutral line; main and branch harmonic data are called, whether harmonic exceeds the standard or not is evaluated through weighted calculation, and whether the high temperature of the neutral line is caused by the harmonic or not is judged. If it is judged that the heat source is caused by harmonic waves, a phase comparison method is adopted to lock a main harmonic source branch and the contribution ratio of the main harmonic source branch, harmonic spectrum characteristics of the branch are analyzed to identify an abnormal type, an automatic treatment strategy is generated, and then generation of the heat source is blocked from the source. According to the invention, deep perception, root cause diagnosis and active intervention of the running state of the bus duct are realized, and the reliability and the operation and maintenance intelligence level of a data center power supply system are improved.

The application discloses an industrial malodor online monitoring system based on big data, which comprises a data acquisition module, a pretreatment module, a feature fusion module, a multi-source perception weight analysis module, a dynamic risk assessment module, a self-adaptive correction module and a real-time feedback module; data interaction between the modules adopts a block chain encryption transmission protocol, and a feature evaluation mapping chain with a time stamp is established in a distributed database; the application builds a new paradigm of industrial malodor monitoring with environmental intelligent adaptability through the innovative design of heterogeneous sensor network collaborative perception, cross-modal feature organic fusion, pollution diffusion three-dimensional dynamic deduction and credible data governance architecture; four technical effects complementarily support from four dimensions of data acquisition credibility, feature modeling scientificity, risk assessment accuracy and system decision robustness.

The present disclosure relates to a game picture enhancement method and device, electronic equipment and medium, and belongs to the technical field of intelligent cockpit. The method comprises the following steps: determining the game type of a game to which a game picture belongs based on the game picture required to be displayed by a vehicle machine; obtaining in-vehicle environment information of a cockpit in which the vehicle machine is located; and performing picture quality enhancement on the game picture based on the game type and the in-vehicle environment information. By coordinating the game type with the actual in-vehicle environment information in the cockpit, different visual requirements of the game picture can be met when facing different game types, immersive fusion of the game world and the cockpit space is realized, and the display quality of the game picture is improved. Further, the game and the cockpit environment can be intelligently matched, the visual comfort of the game picture can be ensured without manual adjustment by the user, and the experience of the user is improved.

This invention relates to the fields of cloud computing and identity authentication security technology, and discloses a method and system for cross-domain intelligent identity authentication in a cloud environment. The method includes: collecting and preprocessing heterogeneous identity data from multiple cloud platforms to obtain a standardized identity dataset; constructing a unified identity semantic model based on the standardized identity dataset and generating feature embedding vectors; performing identity mapping transformation on the feature embedding vectors using a deep neural network to obtain mapped identity feature vectors; performing multi-factor adaptive trust assessment by combining the mapped identity feature vectors with real-time behavioral data to obtain dynamic trust assessment results; generating an adaptive security token based on the dynamic trust assessment results; and converting the adaptive security token into the protocol format required by the target system. This invention can solve the problems of heterogeneity and dynamic trust assessment in cross-domain identity authentication in a cloud environment, providing an effective solution for cloud computing security.

The invention belongs to the technical field of ecological environment protection and intelligent environment monitoring, and particularly relates to an ecological conservation and purification accurate real-time monitoring system integrated with an intelligent sensor, which comprises a multi-parameter monitoring module, a self-balancing sensing terminal structure, a self-adaptive surface purification unit, an algorithm center and a logic compensation control unit, the self-adaptive surface purification unit scours and peels attachments by utilizing high-frequency vibration or directional fluid; the algorithm center constructs an evaluation model based on a biochemical parameter coupling relationship, and executes data authenticity depth verification; and the logic compensation control unit performs data correction and process optimization according to the verification result. By the adoption of the technical scheme, long-acting high-precision sensing and intelligent feedback control of water quality parameters under the complex habitat are achieved.

This invention belongs to the field of intelligent thermal environment control technology in buildings, specifically disclosing a multi-parameter intelligent control method and system for the thermal environment of elderly living spaces. The method includes: establishing a priority sequence for thermal environment parameter control; collecting infrared temperature data from multiple points on the user's body, calculating comprehensive facial temperature parameters, and then calculating the current real-time operating temperature; determining whether the current operating temperature is within a preset acceptable range; if so, no operation is performed; if not, based on a preset thermal environment parameter comfort range, neutral parameter value, and thermal environment parameter control priority sequence, the thermal environment parameters and their corresponding appliances are adjusted to ensure thermal comfort is within an acceptable range, thus completing multi-parameter intelligent control of the thermal environment in elderly living spaces. This invention solves the problems of existing technologies not being specifically designed for the adaptive thermal comfort characteristics of the elderly and the invasiveness of existing physiological parameter testing equipment.

The application provides a temperature control culture device and related equipment, and belongs to the technical field of intelligent control. The device comprises a culture reactor, an intelligent controller and a monitoring and executing assembly. The culture reactor is used for simulating a natural environment according to environmental parameters. The intelligent controller is fixed on any side of the culture reactor and is used for generating a control signal of the culture reactor according to multiple environmental parameters output by the monitoring and executing assembly and environmental parameters of a target region obtained through a network. The monitoring and executing assembly is assembled in the culture reactor and comprises a monitoring assembly and an executing assembly. The monitoring assembly is used for monitoring multiple environmental parameters of the culture reactor and sending the monitored multiple environmental parameters to the intelligent controller. The executing assembly is used for adjusting the environmental parameters of the culture reactor according to the control signal output by the intelligent controller. The application improves the accuracy of the culture device in simulation culture, can provide data support for the generation of the control signal by remotely obtaining the environmental parameters of any target region, and can greatly improve the applicability and convenience.

The application discloses a kind of mine robot remote control system and method based on augmented reality, belong to the cross technical field of mine rescue and robot control. Through SLAM algorithm, the high-precision virtual-real registration of robot back data and mine digital twin model is realized, and the real environment and virtual information are integrated rendered in AR field of view;Intuitive man-machine interaction is realized by using depth camera and CNN neural network to identify natural gesture instruction;Based on UWB positioning network, centimeter-level accurate positioning and path deviation alarm are provided;Through global mini-map and gesture pointing, frame selection mechanism, seamless view switching and intelligent task collaboration of multiple robots are realized.The application overcomes the defects of the existing two-dimensional screen control system, such as lack of spatial perception, complex interaction, non-intuitive positioning and low collaboration efficiency, and constructs a man-machine-environment intelligent closed-loop control ecology, which can significantly improve the efficiency and safety of detection and rescue tasks in mine disaster environment.

The invention discloses a smart watch notification priority scheduling method based on context awareness, and relates to the technical field of smart device notification scheduling, and the method comprises the steps: initial queue filing, unified access of notifications, field filing and index establishment; carrying out conflict judgment based on the notification attribute parameters and the context sensing parameters, and dynamically mediating a notification sending strategy; performing grouping display, content summarization and throttling control on the plurality of notifications according to the two parameters; finally, a final delivery decision is made through a bridging gating strategy in combination with the terminal state and the environment, notifications can be intelligently scheduled according to the user state and the environment, invalid disturbances are reduced, and the intelligence of notification management and the man-machine interaction experience are improved.

This disclosure relates generally to system and method for ambient intelligence based user interaction. Prior methods for touchless user interaction are sensitive to ambient temperature in a lab environment, susceptible to noise from metallic surfaces and ambient radio waves and are dependent on ambient lighting. Embodiments of the present disclosure provides a multi-modal sensor fusion method which captures touchless gestures from a user or a group of users with their physical context information fused and tagged to these gestures for user interaction. Further pose graphs are generated for user interaction systems using a data association technique and Gaussian mixture model technique. The disclosed method provides a hands-free interface to operate instruments in a smart space, using principles of ambient intelligence.

The invention relates to the technical field of intelligent glasses, and discloses a video call assistance method and system based on intelligent glasses, and the system comprises the steps: enabling the intelligent glasses to collect the information of an environment in real time, and uploading the information to a cloud platform; the cloud platform performs scene recognition on the received environment information, judges whether the environment is in a complex environment, and triggers a remote assistance request if the environment is in the complex environment; screening the registered volunteers, and scheduling the volunteers to access the assistance task; the volunteer terminal accesses a video stream of the intelligent glasses, and provides navigation or obstacle prompt for a user through a voice instruction or audio feedback; the evaluation module records and analyzes interaction data in real time in the assistance process to generate volunteer service scores; and generating a volunteer score based on the volunteer service score, and counting the volunteer score into a volunteer incentive system. According to the application, intelligent environment identification is realized, volunteer matching optimization is realized, and an interactive behavior quantitative evaluation and service incentive mechanism is assisted.

This invention relates to the fields of photovoltaic agriculture and intelligent control technology, specifically to an intelligent control system for crop growth environment based on photovoltaic supports. It includes modules for multi-dimensional data acquisition, light and shadow geometry calculation, wind load risk assessment, and adaptive collaborative control. The system constructs a light and shadow coupling model by acquiring crop parameters, cable tension, and illumination data. Its core is to generate graded height adjustment commands based on the calculated unobstructed critical height and wind load risk level: precisely adjusting to the critical height when the structure is safe, and forcibly lowering to a safer height when in a risky range. This invention achieves a precise match between biological needs and mechanical adjustment, ensuring the safety of the support structure while guaranteeing crop photosynthesis.

The present disclosure provides an agent-based data flow real-time scheduling method, which comprises: according to the factory production environment, building an intelligent scheduling framework driven by reinforcement learning, which comprises an environment, an agent and a reward function; converting data flow characteristics and system state into feature vectors to construct a multi-dimensional feature reinforcement learning state space; the agent perceives the reinforcement learning state space and calls a prediction model to predict the data flow characteristic change trend and the system state change trend in the future preset time period, and generates a resource pre-allocation scheme based on the prediction result; the agent generates at least one scheduling action based on a multi-objective scheduling optimization algorithm to form a scheduling strategy according to the current production target, the resource pre-allocation scheme and the reinforcement learning state space. The present disclosure effectively avoids resource waste and data processing delay, significantly improves resource utilization, and ensures stable and efficient production of the intelligent factory production environment.

The invention belongs to the technical field of crossing of artificial intelligence and intelligent robots, and particularly relates to a ground-air robot storage environment intelligent sensing and anomaly detection system based on AI vision. The invention discloses an AI vision-based intelligent sensing and anomaly detection system for a storage environment of a ground-air robot, and aims to solve the problem of incomplete anomaly detection caused by isolated sensing and shallow data fusion of a ground-air platform. The system comprises a multi-modal acquisition unit, a ground-air collaborative mapping unit, a unified semantic cognition unit, a multi-scale anomaly detection unit and a dynamic task planning unit. Through fusion of air wide-area perception and ground fine observation data, a high-precision three-dimensional semantic map is constructed, comprehensive detection of macroscopic layout, microscopic state and time sequence logic abnormity is realized, and the ground-air robot is driven to cooperatively respond, so that the perception integrity and intelligent decision-making capability of a storage environment are improved.

The present application relates to the technical field of edge computing, and particularly relates to a mine environment intelligent monitoring method and system based on edge computing, which comprises: collecting multi-dimensional environment data under a mine; dividing a mine space into multiple local spaces based on three-dimensional coordinates of each sensor under the mine; determining an abnormal state value of a local space in a preset time period by using multi-dimensional environment data of the local space in the preset time period; obtaining a first initial category center point by using the abnormal state value and multi-dimensional environment data of the local space in a previous time period; obtaining a second initial category center point by using multi-dimensional environment data of the local space in a subsequent time period; iteratively updating the first and second initial category center points by using a Mini-Batch K-Means algorithm to obtain a risk value of the local space; and performing mine risk early warning based on the risk value. The present application can improve the real-time performance of underground security.

The application discloses a kind of distributed environmental parameter intelligent early warning methods based on total control net stream computing, it is related to activity launch platform environmental intelligent early warning technical field, this method is by dividing into multiple monitoring points to launch platform environment, and the running state data of environment and key equipment are collected;Calculate the priority coefficient of monitoring point, judge whether key safety parameter meets the requirement;Under the framework of stream computing, establish minute level and hour level sliding window, extract multi-time scale parameter fluctuation characteristics, calculate stability index and compare with threshold value analysis;For adjacent monitoring node, calculate multidimensional parameter consistency and obtain overall consistency index, realize single point link fault and global environmental anomaly discrimination;Automatic link traceability log is generated, which provides basis and parameter optimization correction for abnormal event tracing.The method can realize efficient monitoring and intelligent early warning of multi-point environment and equipment parameters of activity launch platform, improve the accuracy and timeliness of safety protection.

The invention provides an intelligent wood frog breeding environment regulation and control system, and relates to the technical field of agricultural product related Internet of Things control systems, the technical scheme is characterized in that the system comprises a multi-dimensional sensing module, an AI decision processing module, an execution module and a remote interaction module, the multi-dimensional sensing module is in communication connection with the AI decision processing module, and the remote interaction module is in communication connection with the AI decision processing module. The AI decision processing module is respectively connected with the execution module and the remote interaction module; the multi-dimensional sensing module is used for collecting environmental parameter data, wood frog state data, external interference data and micro-ecological data; the AI decision processing module generates specific regulation and control instructions based on historical data and real-time data, and the regulation and control instructions at least comprise an emergency relieving instruction, a temperature and humidity regulation and control instruction, a light intensity regulation and control instruction and a feeding instruction. The wood frog culture environment intelligent regulation and control system provided by the invention has the advantages that multi-dimensional perception is realized, and intelligent pre-judgment and real-time accurate refined regulation and control of the wood frog culture environment are realized.

The invention discloses a shunting operation environment intelligent sensing method based on vision and radar. The method comprises the following steps: generating a fusion point cloud by using binocular vision and radar; constructing a track topology reference point cloud according to line design data, and forming a safe reachable domain in combination with train power parameters; extracting train contour points and track adjacent points from the fusion point cloud, and sampling prediction track points according to a safe reachable domain to form a space-time four-point base set; searching a point set meeting the relationship between the distance ratio and the orbital sequence based on the improved Super4PCS, and solving rigid body transformation candidates; acting the rigid body transformation candidates on the fused point cloud and calculating a geometric residual error and a safety margin to obtain a collaborative score; selecting rigid body transformation with the highest score as a train registration pose and generating a residual risk adaptive weight field; and splicing the registration pose sequences into a train movement track, and generating an environment perception result and a safety protection instruction in combination with a safety reachable domain. According to the invention, the stability of shunting environment registration and safety perception is improved.

The invention relates to the technical field of environment intelligent control, in particular to a greenhouse environment intelligent control system and method based on multi-sensor fusion, and the method comprises the steps: obtaining greenhouse multi-dimensional space-time environment data, and constructing a time sequence evolution benchmark through the historical time sensor data, so as to generate an environment state dynamic coupling confidence coefficient; therefore, an adaptive weighted fusion gain matrix is reconstructed to output a global environment representation value, and an execution mechanism control dead zone threshold and a response delay time window are dynamically adjusted in combination with the deviation degree. Finally, a control instruction is sent through the proportional-integral-derivative controller only when the parameter limitation after dynamic adjustment is met; according to the method, data verification and control strategy adaptive adjustment based on a time sequence rule are realized, and the problems of environmental characterization distortion and frequent oscillation or response hysteresis of equipment are effectively solved.