8866 results about "Intelligent control" patented technology

The invention belongs to the technical field of intelligent control of heating, ventilation and air conditioning systems, and particularly relates to an intelligent optimizing and energy-saving control method for a central air conditioner based on deep learning, which comprises the following steps of: acquiring operation data of a central air conditioning system in real time through an internet of things technology; the operation data comprises operation parameters of cold and heat source equipment, flow and lift parameters of a water pump, fan frequency parameters of a cooling tower, temperature and humidity data of an air conditioner terminal, environment temperature and humidity data, weather forecast data and the like. Through deep integration of Internet of Things perception, deep learning prediction and a multi-objective optimization technology, the limitation of a traditional control framework is broken through, meanwhile, accurate prediction of building cooling and heating loads is realized through construction of a hybrid deep learning model, an optimization objective of a full life cycle perspective is established in combination with an equipment performance degradation model, and the system performance is improved. A federal learning framework is innovatively introduced into region-level energy efficiency management, and the model generalization ability is improved on the premise of ensuring data privacy.

The invention relates to the technical field of data center intelligent management, in particular to a data center construction and intelligent operation and maintenance management system, which comprises a dynamic environment sensing module, a heterogeneous equipment protocol adaptation module, a multi-dimensional resource dynamic scheduling module, a hidden fault prediction module and an energy efficiency optimization execution module. Physical environment data such as temperature gradient, current harmonic component and optical fiber strain rate are acquired by deploying a multi-mode sensor, and an environment characteristic matrix is constructed; standard semantic mapping of the heterogeneous protocol is realized by using a semantic slot migration algorithm; establishing a resource topological graph based on the hypergraph neural network and dynamically updating the resource topological graph; a dual-channel space-time convolutional network is adopted to realize fault prediction; and combining the fault probability matrix to generate a dynamic tuning strategy of dimensions such as cooling, electric power, network and the like, and forming closed-loop optimization control. According to the invention, integrated collaboration of multi-source information fusion, equipment intelligent control and energy efficiency adaptive optimization is realized, and the intelligence, reliability and energy efficiency level of data center operation and maintenance are improved.

The invention discloses a wind turbine generator hoisting construction tower drum operation system and a construction method thereof, and relates to the technical field of intelligent control. The problems that in the prior art, a static tension balance mechanism cannot restrain bending moment abrupt change, steel-concrete interface stress concentration causes microcrack propagation, the wave dynamic load compensation capacity is insufficient, and dynamic rigidity attenuation early warning is lacked are solved. Comprising a dynamic load prediction module, a multi-mode vibration suppression module, an offset compensation module and a digital twinborn decision module, a hoisting load is solved in real time through a multi-physics field coupling model and an improved time sequence deep learning algorithm, and interface crack propagation is suppressed in combination with traveling wave offset control and sweep frequency vibration. An improved Morison equation is adopted to drive a two-stage hydraulic servo to compensate a wave dynamic load, and a digital twin closed-loop correction mechanism is constructed based on a 5G URLLC protocol; the tower drum hoisting precision, the structural safety and the operation reliability under the complex working condition are remarkably improved.

The invention relates to the technical field of intelligent control of test equipment, and discloses a semiconductor device test equipment control system and method based on industrial data processing, and the method comprises the steps: collecting multi-source heterogeneous data of semiconductor device test equipment, and carrying out the preprocessing; constructing a structural causal model, and performing root cause identification through anti-fact reasoning by using the structural causal model; constructing an abnormal test fingerprint and a knowledge base; generating an intervention scheme, evaluating the generated intervention scheme, and selecting an optimal intervention scheme; processing the detected anomaly, and generating and implementing a preventive control strategy based on historical anomaly data and a causal analysis result; according to the invention, by introducing innovative technologies such as causal inference, anti-factual inference, abnormal test fingerprint identification and Monte Carlo tree search, intelligent control of semiconductor test equipment is realized.

The invention discloses a multi-agent-based virtual power plant intelligent control method and system, and the method comprises the steps: dividing a virtual power plant into a plurality of sub-virtual power plants, deploying an agent in each sub-virtual power plant, collecting a local resource state through each agent, and predicting a load demand and the output of a distributed power supply, a hierarchical control unit is adopted to carry out collaborative optimization among the sub-virtual power plants according to a prediction result, and an upper-layer optimization control module constructs a linear programming model according to the prediction result and solves the linear programming model to obtain an initial scheduling scheme; and the lower-layer reinforcement learning control module performs local adjustment on the preliminary scheduling scheme according to a multi-agent depth deterministic strategy gradient algorithm to obtain a decision scheme. Based on a distributed control strategy of a multi-agent architecture, the fault-tolerant capability and reliability of the system are improved, a hierarchical control architecture is adopted, global optimization and local adjustment are organically combined, and efficient coordination and real-time adjustment capability of global resources are realized.

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 relates to the technical field of autonomous navigation and intelligent control of an ocean unmanned system, in particular to a dynamic obstacle avoidance system in unmanned ship path planning, which comprises a multi-source sensing module, a global path planning module, a remote obstacle avoidance decision module, a short-range dynamic obstacle avoidance module and a multi-stage cooperative control unit, and is also provided with a semi-physical verification module. The multi-source sensing module fuses multi-source data to construct a layered map; the global path planning module generates and optimizes a path by adopting an improved algorithm; the long-range and short-range obstacle avoidance modules cope with far and near obstacles based on different algorithms; the multi-stage cooperative control unit coordinates the output of each module; the functions of all the modules are achieved through specific algorithms and formulas, and the semi-physical verification module simulates a real environment to conduct system testing. According to the invention, the environment can be sensed in all directions, intelligent path planning and multi-stage obstacle avoidance decision are realized, an optimal instruction is output through cooperative control, the reliability is improved by combining virtual verification, and safe and efficient navigation of the unmanned ship in a complex water area is effectively ensured.

The invention relates to the technical field of program control, in particular to a servo position control method and system applied to an engraving and milling machine. The method comprises the following steps that real-time temperature data and real-time load data of lead screws of all shafts in the engraving and milling machine are obtained; collecting multi-node temperature distribution data of the engraving and milling machine; obtaining cutting force data and resonant frequency characteristic values in the engraving and milling machine; constructing a screw rod reverse clearance prediction model based on the real-time temperature data and the real-time load data of the screw rod, and generating a clearance compensation value; performing pre-compensation control before the servo position direction of each axis is changed based on the clearance compensation value to obtain a correction position instruction; and performing thermal deformation simulation on the engraving and milling machine based on the multi-node temperature distribution data to generate thermal deformation compensation amounts of all shafts. Through multi-source data fusion and intelligent control, comprehensive optimization of the servo position system of the engraving and milling machine is achieved, the machining precision, dynamic response and stability are effectively improved, and efficient and reliable operation of high-speed and high-precision machining is guaranteed.

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 relates to the technical field of transformer monitoring, and discloses an explosion-proof intelligent monitoring and early warning device for a transformer. The device comprises a multi-source sensing module used for collecting multi-dimensional heterogeneous data of transformer operation; the feature extraction module is used for fusing data cross-domain features to generate various feature representations; the anomaly detection module is used for generating an abnormal signal space-time incidence matrix based on a dynamic causal network construction model; the risk early warning module outputs a risk level and an early warning instruction through a multi-task decision-making mechanism; and the self-adaptive regulation and control module is used for optimizing monitoring parameters and hardware resource allocation according to instructions. The device also can carry out critical state identification and emergency intervention, and constructs an insulation degradation prediction model to correct an early warning threshold value. According to the device, omnibearing monitoring, accurate early warning and intelligent regulation and control of the transformer are realized, the operation safety and reliability of the transformer are effectively improved, and the fault risk and loss are reduced.

In some embodiments, a configured artificial intelligence system includes a plurality of intelligence models; a scoring system configured to generate know-your-model scores that quantify suitability for specific tasks of each model; a model execution system configured to provide standardized execution environment for the plurality of intelligence models; a training and reinforcement system configured to monitor outcomes relating to decisions or predictions made by the plurality of intelligence models and use outcome data as feedback to reinforce model performance; and a governance and analysis system configured to ensure model operations comply with governance standards. The intelligence controller may be configured to receive task requests, analyze task complexity, decompose tasks into manageable subtasks, and dynamically select appropriate models from the plurality of intelligence models to execute each subtask based on model suitability and performance characteristics.

The invention provides a laboratory energy-saving optimization control system and method based on energy efficiency model driving, and relates to the technical field of intelligent control, and the method comprises the steps: recognizing a core coordinate of a temperature overrun region of a hazardous chemical substance storage region based on a fusion data set, and taking the core coordinate as a first point, identifying the oxidation reaction rate peak position coordinate of the hazardous waste treatment area as a second point, and identifying the boundary centroid coordinate of the abnormal heat dissipation area of the high-energy-consumption equipment as a third point; constructing a key point coordinate set based on the first point, the second point and the third point, and connecting the key points to generate a static polygon monitoring domain; performing grid discretization processing by taking the static polygon monitoring domain as a boundary to generate structured grid data containing vertex coordinates and unit adjacency relations; deformation parameters of each unit are calculated, and a non-uniform deformation field scalar operator is generated through fusion; and taking the scalar operator as a physical field coupling weight, and generating an optimization instruction set. According to the invention, intelligent control of equipment is realized.

The invention provides an unmanned aerial vehicle sharing intelligent management method and system based on artificial intelligence, and relates to the technical field of intelligent control, and the method comprises the steps: constructing a space virtual geometric shape through a coordinate mapping algorithm; extracting a geometric center coordinate of the space virtual geometric shape, and calculating an offset between the geometric center coordinate and a communication coverage centroid as a first correction factor; analyzing signal coverage included angle distribution among the three position points, and generating a second correction factor in combination with a path loss model; calculating a multipath effect compensation coefficient as a third correction factor according to the signal attenuation gradient on the virtual shape boundary; fusing the first correction factor, the second correction factor and the third correction factor into a comprehensive correction value; and according to the comprehensive correction value, evaluating a communication interruption risk through a path planning neural network, and generating a multi-path transmission scheme containing a redundant relay node selection rule and a dynamic fragmentation recombination strategy. According to the invention, the communication reliability is improved.

The invention belongs to the field of data analysis and processing, and discloses a building facade defect detection system based on unmanned aerial vehicle exogenous thermal excitation compensation. Comprising the steps of generating a building facade three-dimensional model, predefining a heterogeneous modal joint optimization framework, selecting an automatic connection port according to a mode, and operating an intelligent control center; unmanned aerial vehicle formations are deployed based on a master-slave mode, and the formations share detection data through federal learning; the intelligent control center comprises the steps of deploying an airborne lightweight model, processing thermal imaging data and RGB images in real time at an unmanned aerial vehicle end, identifying a suspected defect area, generating a light spot distribution thermodynamic diagram through a pre-detection model deployed by the intelligent control center, and performing a heating task in a dynamic light-heat cooperative and integrated manner; the intelligent control center further comprises ground analysis centralized control, a cloud actuarial model is used for constructing a thermal diffusion space-time map and hollowing expansion trend prediction, a three-dimensional defect distribution map is finally generated and displayed through an interactive interface, and defect detection of the unmanned aerial vehicle on the building facade under exogenous thermal excitation compensation is achieved.

The invention relates to the technical field of intelligent manufacturing, and discloses an extreme manufacturing process technological parameter optimization method and system fused with machine learning. The method comprises the following steps: acquiring multi-source data from a manufacturing equipment sensor, and fusing to generate a material state vector; inputting a pre-training model to obtain a material coefficient transition trend; judging whether the trend fluctuation amplitude exceeds a preset threshold value or not, and if yes, marking key nodes and extracting feature parameters; for the key nodes, according to the characteristic parameters and the real-time data of the key nodes, a control algorithm is adopted to calculate the parameter adjustment amount; optimizing the control parameters based on the parameter adjustment amount, generating a control instruction sequence and transmitting the control instruction sequence to an actuator; and obtaining adjusted feedback data, comparing the adjusted feedback data with the transition trend, and if the deviation exceeds an allowable range, updating the pre-training model. Through deep fusion of predictive monitoring and intelligent control, accurate optimization and adaptive control of process parameters are realized, the stability of the extreme manufacturing process and the product quality are improved, and the energy consumption and the defect rate are reduced.

The present invention relates to the technical field of the control of wastewater treatment devices. Disclosed is an intelligent control method for wastewater treatment devices at a dry bulk cargo terminal, which is used for solving the problem of poor control of wastewater treatment devices at a terminal. The method comprises the following steps: installing a plurality of types of sensors at key locations of a dry bulk cargo terminal, and using edge computing nodes to perform real-time data collection and preprocessing; on the basis of historical features and temporal features, using a machine learning model to perform wastewater type classification, thereby realizing efficient dynamic adjustment of operating parameters of wastewater treatment devices; then, by means of weighted voting and confidence evaluation, integrating a plurality of classification results to ensure an optimal treatment effect; and analyzing actual wastewater treatment conditions to continuously optimize device control, thereby preventing faults, extending the service life of devices, and improving the wastewater treatment effect.

The invention discloses a ship control auxiliary method based on machine learning, and relates to the technical field of ship intelligent control, and the method comprises the steps of ship data collection, fuel oil energy consumption prediction, dynamic navigational speed planning and energy-saving feedback control. According to the method, the fuel energy consumption is predicted by adopting a prediction method combining multi-mode and disturbance self-adaption, modeling multi-dimensional data is unified, perception of environmental disturbance is enhanced, accurate prediction of the fuel energy consumption under complex navigation conditions is realized, and the reliability of subsequent dynamic navigational speed planning is effectively improved; dynamic speed planning is carried out by adopting a speed planning method combining multiple targets and segmented constraints, three core factors of fuel energy consumption, navigation time and safety risks are comprehensively considered, a multi-target cost function is constructed, segmented constraints are introduced, the physical feasibility and risk control of speed adjustment are guaranteed, the speed is optimized through a reinforcement learning mechanism, and the speed planning efficiency is improved. Intelligent self-adaptive navigational speed adjustment is achieved, and the energy-saving capacity, the navigation efficiency and the operation safety of the ship under the complex navigation condition are improved.

The invention discloses an inverter intelligent control method based on a self-adaptive algorithm, and relates to the technical field of inverter intelligent control, and the method comprises the steps: multi-source temperature collection and environment recognition, machine learning-based electric-thermal coupling prediction, self-adaptive inverter parameter optimization, and cooperative thermal management and fault diagnosis self-repairing. According to the high-temperature or low-temperature failure risk of the motor and the inverter under the extreme climate, temperature distribution and load information are obtained in real time, the temperature rise trend is predicted in advance, and the current, voltage and modulation strategy of the inverter are actively adjusted, so that safe derating or torque compensation is achieved; multi-sensor cross validation and observer fusion are carried out when sensors drift or devices are aged, stable operation and efficient energy utilization of the system are kept, and the drivability and the whole vehicle reliability in an extreme environment are remarkably improved; in the process, fault diagnosis and self-repairing can be further improved through online model updating, and the durability and economical efficiency of the electric drive system are improved.

The invention relates to the technical field of soft robots and intelligent control, in particular to an intelligent control method based on the Internet of Things. The method comprises the following steps: carrying out multi-modal data acquisition and preprocessing on a contact process of a gripper and an object to obtain a time synchronization data set; constructing a tactile feature tensor including pressure, friction force and strain force according to the time synchronization data set; performing stress gradient calculation and mapping according to the tactile feature tensor to obtain a stress gradient field and a region threshold mapping table; performing stress gradient threshold adaptive adjustment according to the regional threshold mapping table and the stress gradient field to obtain an adaptive threshold distribution map; and performing stress risk prediction according to the adaptive threshold distribution map to obtain a stress risk map. According to the method, the potential damage risk is predicted by sensing the contact state of the gripper and stress concentration, the adaptive capacity, robustness and success rate of the grabbing process are remarkably improved, and the method is particularly suitable for grabbing tasks of fragile and irregular objects which are difficult to prejudge.

The invention relates to the technical field of computer vision, and discloses a thyroid ultrasonic robot automatic scanning method, device and equipment based on RGB images and depth information and a medium, and the method comprises the steps: obtaining image information and depth information, coding the depth information, fusing and recognizing a target scanning area, determining an initial scanning point and an initial scanning direction, controlling the scanning probe to scan and collect a real-time scanning image, analyzing the real-time scanning image to recognize a preset target and an artifact area, adjusting a scanning posture and a scanning path based on a recognition result, monitoring a continuous existence state of the preset target, and stopping scanning when the preset target is not recognized continuously. The target area is identified by fusing the multi-modal image information, the scanning posture and path are dynamically adjusted in combination with real-time image analysis, scanning termination is intelligently controlled according to the target detection result, the positioning accuracy, image quality and standardization level of ultrasonic scanning are improved, and the method is suitable for automatic ultrasonic imaging of thyroid and superficial organs.

The invention relates to the technical field of reaction kettle temperature control, and discloses a fine chemical engineering reaction kettle temperature intelligent control method based on self-adaptive search, which comprises the following steps: S1, data acquisition: acquiring operation data of a reaction kettle, including temperature in the kettle, jacket inlet and outlet temperature, fluid flow and valve opening signals; s2, heat balance calculation: establishing a heat balance equation based on the collected data, and calculating reaction heat release and an equivalent heat transfer coefficient; s3, updating the model; s4, predictive control solution is carried out; s5, performing adaptive search; s6, carrying out security constraint; s7, stage identification; and S8, performing control execution. The reaction heat release amount and the equivalent heat transfer coefficient are calculated in real time through linkage of a heat balance calculation module and a model updating module, online parameter identification is achieved through a recursive least square algorithm, a heat model can be dynamically corrected along with working condition changes, it is ensured that a prediction model is consistent with the actual reaction process, and the prediction efficiency is improved. And the temperature prediction precision and the reliability of control response are obviously improved.

The invention relates to the technical field of robot intelligent control, in particular to a humanoid robot multi-modal environment sensing and self-adaptive chassis control method, which comprises the following steps: monitoring a contact force vector and a sliding trend in real time through a multi-dimensional touch sensor, mapping pose drift to a chassis coordinate system based on a Lie group constraint space-time synchronization layer, and performing multi-modal environment sensing and self-adaptive chassis control on the basis of a multi-dimensional touch sensor; a compensation vector is generated, a three-level tactile response layer dynamic switching force / bit mixed impedance mode is adopted, reverse translation of an omnidirectional chassis is combined to counteract drift, compensation parameters are optimized through a depth deterministic strategy gradient algorithm, and the system ensures control instruction time sequence alignment through a time-space stamp synchronization engine. The attitude oscillation in the compensation process is suppressed by using the inertial measurement unit, the obstacle avoidance interference domain is constructed based on the kinematics chain of the operation arm, and the compensation trajectory is smoothed by using the B-spline curve, so that the precision, stability and safety of the humanoid robot in the complex contact operation are improved, and an efficient and reliable solution is provided for a man-machine cooperation scene.

The invention relates to the technical field of intelligent control, and discloses a dyeing process optimization method and system based on intelligent control. The dyeing process optimization method based on intelligent control comprises the following steps: acquiring key parameters of a dyeing process to obtain an original data set; performing preprocessing and feature extraction on the data set to obtain a feature data set; constructing a deep learning prediction model by using the feature data set, and executing multi-objective optimization through a non-dominated sorting genetic algorithm to obtain an optimal process parameter combination; and performing scheme analysis based on the optimal parameters to obtain an intelligent dyeing scheme. Multi-parameter collaborative optimization of the whole process of the dyeing process is realized, dependence on human experience is reduced, dyeing quality consistency is improved, self-learning and self-adaptive capabilities are achieved, optimization strategies can be automatically adjusted according to different fabric characteristics and production conditions, and production efficiency is improved. Therefore, the problems of large quality fluctuation, high energy consumption, poor adaptability and the like in the traditional dyeing process are solved.

The invention relates to the technical field of metallurgical raw material batching control, and discloses a bottom blowing smelting furnace automatic batching intelligent control method and system based on deep learning, and the method comprises the steps: achieving intelligent batching through multi-source data fusion, physical constraint modeling and dynamic optimization control; edge calculation is adopted to realize data space-time alignment and purification, and physical and economic mixed features are constructed; modeling a reaction path based on a graph neural network, and embedding conservation law constraint to synchronously predict key process parameters; and finally, in combination with gradient sensitivity analysis and reinforcement learning, constructing a differentiable optimization framework to realize multi-target dynamic ratio decision and real-time compensation control, and forming a perception-decision-execution closed loop. The system comprises a global sensing and data purification module, an intelligent decision-making and optimization batching module and a high-precision execution and closed-loop control module. According to the invention, the batching strategy is adaptively adjusted, and optimal resource allocation and maximum economic benefit are realized.

The invention relates to the field of heat energy engineering and automatic control, and discloses a multi-mode optimization system for combustion efficiency of a thermal power boiler. The system comprises a multi-modal data perception and space-time alignment module, a tensor manifold modeling and physical constraint feature extraction module, a space-time coupling dynamic prediction and uncertainty quantification module, a quantum optimization decision and DCS cooperative control module and a combustion state derivative early warning and optimization feedback module. Through multi-modal data space-time alignment, five-order tensor physical constraint modeling, PDE deep network prediction, quantum optimization decision and a closed-loop feedback mechanism, space-time unified fusion and physical feature extraction of combustion data are realized, the reliability of combustion state prediction is improved, an optimal control instruction is efficiently solved, system parameters are dynamically corrected, and the reliability of combustion state prediction is improved. The problems that in the prior art, data fusion is difficult, modeling physical constraints are lacked, optimization real-time performance is poor, and adaptivity is weak are solved, and the combustion efficiency and the intelligent control level of the thermal power boiler are remarkably improved.

The invention discloses an operation and maintenance manipulator intelligent control method and system based on visual identification, and relates to the technical field of intelligent manipulator control, and the method comprises the steps: collecting RGB image data and depth image data of an operation and maintenance operation area, and obtaining a standardized image matrix and a mapping relation matrix; inputting the standardized image matrix into an improved ResNet residual network model, generating a comprehensive feature descriptor, and calculating a spatial position coordinate and an attitude angle of the target equipment based on the mapping relation matrix; based on the current joint angle state of the manipulator, an improved Jacobian matrix inverse kinematics algorithm is used for solving a target angle sequence of each joint, a preset operation mode library is matched based on the comprehensive feature descriptor, and a grabbing force parameter and a motion speed parameter are determined; and converting the target angle sequence into a control instruction, and sending the control instruction to each joint driver of the manipulator to drive the manipulator to complete action planning. According to the invention, full-process automation from environment perception to task execution is realized.

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 discloses a collaborative unmanned aerial vehicle cluster path planning and scheduling system, and particularly relates to the technical field of unmanned aerial vehicle intelligent control, and the system comprises a multi-mode sensing unit which is composed of a heterogeneous sensor array composed of LiDAR, binocular vision and millimeter wave radar, and an output dynamically updated three-dimensional Gaussian mixture map; the decision control unit is used for implementing double-layer optimization of mixed integer programming task allocation and artificial potential field path planning; the dynamic communication network adopts a hybrid networking protocol of TDMA backbone nodes and 802.11 ax terminal nodes; an energy management module; aiming at the insufficient environment perception and dynamic modeling capability in the prior art, the method achieves the effects that the centimeter-level positioning precision and the dynamic obstacle recognition rate are greater than 92%, the environment model is delayed and compressed to be within 200ms, the response speed is increased by 5 times by setting multi-modal sensor fusion, constructing a dynamically updated 3D Gaussian mixture map and combining an LSTM network to predict the obstacle trajectory in real time, and the dynamic obstacle recognition rate is greater than 92%. And the obstacle avoidance reliability in a complex scene is obviously enhanced.

An SCR flue gas denitration intelligent control method based on multivariable collaborative optimization specifically comprises the following steps: S1, collecting multi-key variable data of SCR flue gas denitration, processing the multi-key variable data, and storing the processed multi-key variable data into a historical database; s2, on the basis of the processed data, constructing a multivariable correlation model, and predicting operation states of the denitration system under different working conditions; s3, according to the actual operation condition of the system and the equipment performance, determining constraint conditions and optimization targets of SCR flue gas denitration; s4, performing multivariable collaborative optimization calculation in combination with the multivariable correlation model, the optimization target and the constraint condition, and searching an optimal control variable combination; and S5, intelligent control is implemented according to the optimal variable combination, and re-optimization is performed by monitoring feedback deviation in real time. The system can keep stable control precision in different operation scenes, and denitration efficiency fluctuation or parameter adjustment lag caused by sudden change of working conditions is avoided.

Integrated intelligent system for beverage production, including distilled spirits and various brewed or fermented products, combining traditional methods with advanced technologies. System incorporates controlled fermentation unit, advanced distillation apparatus, and accelerated aging unit using novel methods like ultrasonic waves and thermal cycling. Real-time analysis system employs multiple sensors for continuous chemical composition monitoring throughout production process. Intelligent control mechanism optimizes processes and adjusts parameters dynamically. Chemical fingerprinting system enables precise quality control and authenticity verification. Flexible interface allows for customization of beverage profiles based on desired flavor characteristics and market demands. Specialized unit creates complex non- alcoholic alternatives. System adapts to different production scales and beverage types. Represents significant advancement in beverage production technology, offering unprecedented control, consistency, efficiency, and rapid product development across various beverage categories.