1012results about How to "Good condition" patented technology

The invention relates to the technical field of electric power energy storage management, in particular to an independent electrochemical energy storage integrated management system, which comprises a load prediction analysis module, a charge and discharge response control module, a dynamic load threshold adjustment module, an energy storage response optimization module, a PID (Proportion Integration Differentiation) adjustment module and an abnormal state monitoring module. According to the method, the change rate and fluctuation amplitude of the load are adjusted in real time, the power demand change is accurately predicted, the stability and response speed of the system are improved, the fluctuation period and sudden change value of the load are analyzed, the demand trend is accurately predicted, the charging and discharging rate is adjusted, the actual demand is matched, the energy utilization efficiency is improved, the service life of equipment is prolonged, and the response threshold value is dynamically adjusted; the operation time interval is optimized, so that the system is maintained in an optimal state under variable loads, the energy consumption and the operation and maintenance cost are reduced, power output and equipment states are monitored in real time, abnormities are handled in time, safety and stability of the system are ensured, and the overall efficiency and reliability of the power system are remarkably improved.

The invention provides a digital twinning method and system based on industrial automation, computing equipment and a storage medium. The method comprises the following steps: acquiring real-time operation parameters from a physical system; based on the real-time operation parameters and a preset physical model, constructing a digital twinborn body of the physical system in a virtual environment; simulating the operation state of the physical system by using the digital twin, and predicting the performance of the physical system under different working conditions to generate a simulation result; comparing the simulation result with the obtained actual operation data of the physical system so as to evaluate the digital twin body and obtain an evaluation result; adjusting the physical model according to the evaluation result so as to update the digital twin; and performing fault prediction and / or optimizing operation parameters of the physical system by using the updated digital twin. According to the technical scheme provided by the invention, the digital twin can be continuously optimized and the physical model can be adjusted.

The invention provides an intelligent garden environment monitoring data analysis method and system. Sensors are deployed to collect temperature, humidity and other data in real time to form a data set. Analyzing an environment parameter change mode of each region by adopting a time sequence clustering algorithm, and determining characteristics and trends; and carrying out dimension reduction on the data by using a multi-dimensional scale transformation algorithm to a three-dimensional space for visualization, and generating an environment state map. And evaluating the plant fitness in combination with a plant physiological ecology knowledge base, and generating a scoring matrix to guide the preliminary layout scheme. And dynamically adjusting the layout by applying reinforcement learning, selecting an optimal scheme, and predicting a future environmental evolution path based on the optimal scheme and historical data. An automatic control system framework is constructed, a self-adaptive control algorithm is designed, intelligent regulation and control of plant layout are realized, garden management efficiency is improved, and ecological protection and sustainable development are promoted. Therefore, full-chain intelligent management from environment monitoring to plant layout optimization is realized, and the intelligent level of garden environment monitoring and management is remarkably improved.

The invention discloses an efficient flight control system of a low-altitude manned unmanned aerial vehicle based on tilting composite wings, and relates to the technical field of unmanned aerial vehicle control systems. The low-altitude manned unmanned aerial vehicle efficient flight control system based on the tilting composite wings comprises an initialization and pre-check module, a vertical take-off and landing control module, a transition flight control module, an obstacle avoidance and path planning module and a landing control module. Through integration of vertical take-off and landing, smooth transition to cruise, intelligent obstacle avoidance, path planning and other modules, autonomous and efficient flight in a complex environment is realized, and the application scene of the unmanned aerial vehicle is greatly widened. Meanwhile, the accurate flight control and data post-processing analysis capability not only guarantees the safety and comfort of passengers, but also promotes the continuous optimization of the system performance through the real-time performance evaluation and feedback mechanism. In addition, the aerial refueling / charging technology is supported, the cruising ability is prolonged, and the task execution continuity and flexibility are enhanced.

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

The invention relates to the related technical field of refrigeration energy-saving control, in particular to an energy-saving control method and system for refrigeration equipment, and the method comprises the steps: achieving the state linkage of an air valve, a compressor and an air feeder through control logic based on a refrigeration equipment unit; after starting, an initial strategy is formulated according to refrigeration requirements to manage a plurality of microclimate spaces; in combination with video monitoring, personnel tracks are captured, behavior modes are analyzed, a local customized control strategy is generated, and a comprehensive refrigeration control strategy is formulated, so that the technical problem of unnecessary energy waste caused by incapability of accurately matching refrigeration requirements and equipment operation states due to lack of real-time sensing analysis on actual operation conditions is solved; the overall refrigeration demand balance is considered, the local area is considered, the refrigeration intensity is flexibly adjusted, accurate matching between the refrigeration demand and the equipment operation state is achieved, distribution of refrigeration resources is optimized, energy waste is effectively reduced, the overall operation efficiency is improved, and the technical effect of energy-saving, intelligent and efficient management of the refrigeration equipment is achieved.

The invention relates to the technical field of power grid planning, in particular to a charging pile planning method based on dynamic analysis. The method comprises the following steps: obtaining electric vehicle use data in a target area, and carrying out charging demand prediction analysis according to the electric vehicle use data to obtain a charging demand prediction model; based on the charging demand prediction model, performing multi-scene simulation to obtain charging demand distribution map data, multiple scenes including different traffic flows, different driving modes and different charging demand scenes; obtaining geographic information data of the target area; and constructing a spatial analysis model according to the geographic information data, and evaluating the candidate position of the charging pile according to the charging demand distribution map data, thereby obtaining position suitability score data. According to the method, different scenes are simulated, so that the adaptability and robustness of planning are enhanced.

The invention discloses a tunnel wall surface deformation monitoring method and system based on computer image recognition. The method comprises the following steps: acquiring a tunnel area image and a tunnel wall surface image of each image acquisition point; tunnel wall surface deformation identification and wall surface deformation prediction are carried out on the tunnel area image and each tunnel wall surface image, and deformation image identification data and a wall surface deformation prediction result are correspondingly obtained; obtaining deformation image identification data and spatial position data corresponding to a wall surface deformation prediction result, and generating a plurality of deformation monitoring units in a deformation monitoring area of the tunnel; taking each deformation monitoring unit as a basic unit, acquiring sensor monitoring data and a corresponding monitoring threshold interval, and comparing the sensor monitoring data and the corresponding monitoring threshold interval to obtain a sensor monitoring judgment result; based on all sensor monitoring judgment results, the corresponding deformation image recognition data and the wall surface deformation prediction result, space matching analysis is carried out, and a tunnel wall surface deformation monitoring result is correspondingly obtained; according to the invention, the monitoring effect of tunnel wall surface deformation monitoring is improved.

The invention discloses an intelligent control system and method for production and processing of a photovoltaic panel, and relates to the technical field of intelligent control. During operation of the system, key data involved in the production process of the photovoltaic panel is collected, a light-transmitting effect data set, a durability data set and an electrical performance data set are generated and preprocessed, and a multivariate data analysis algorithm is applied to obtain a data set; and after calculation, obtaining a quality index Zlzs of the agricultural photovoltaic panel, receiving an analysis result of the data processing module, and performing real-time quality monitoring on the photovoltaic panel subjected to production process optimization by adjusting a material ratio, production environment parameters and a process flow. A quality index Zlzs of an agricultural photovoltaic panel is compared with a preset first quality qualification threshold value M and a preset second quality qualification threshold value N, potential quality problems in production are identified, a quality evaluation strategy scheme is obtained, an evaluation result is fed back to a production process optimization module and a data processing module, and a closed-loop control system is formed. And continuous optimization and quality improvement are realized.

The invention relates to a method and system for matching a driving circuit with a light source module, and relates to the technical field of vehicle control, and the method comprises the steps: determining an optimal modular component combination for a vehicle lamp system of a vehicle based on a compatibility scoring function; constructing a dynamic power management function by combining fuzzy logic and an adaptive control algorithm, and determining the output power of the vehicle lamp system at the current moment based on the dynamic power management function; constructing an ambient light adaptive function, and adjusting the light source output brightness of the vehicle lamp system in the current environment at the current moment based on the ambient light adaptive function; predicting an optimal parameter set of the vehicle lamp system at the next moment through the deep learning model, and controlling the vehicle lamp system at the next moment based on the optimal parameter set; calculating a current comprehensive performance score of the vehicle lamp system; and optimizing a driving circuit and a light source module of the vehicle lamp system according to the comprehensive performance score. The overall performance of the vehicle lamp system can be improved.

The invention belongs to the technical field of fluid control, and relates to a rotary flow distribution adjusting device and method for aviation. Comprising an inlet shell, an annular impeller, a torsion spring, a valve element, a magnetic damper and an outlet shell. The flow distribution adjusting device is arranged at the pipeline branch position of a liquid pipe network branch system, fluid flows through the annular impeller, the annular impeller generates torsional force to drive the valve element to rotate to change the overflowing sections of the two outlets, continuous adjustment of the flow distribution proportion of different branches is achieved, and the larger the fluid flow speed is, the larger the stress of the annular impeller is. And the valve core is driven to select a larger angle to correspond to different outlet flow distribution conditions. According to the flow distribution adjusting device, the complexity of an adjusting system can be reduced, the flow resistance caused by flow distribution adjustment is reduced, and then the energy consumption of a power source in a liquid system is reduced.

The invention relates to the technical field of ammonia-nitrogen wastewater degradation, in particular to a deamination reactor optimization method based on CFD and BP neural networks. The method comprises the following steps: performing three-dimensional modeling according to geometric parameters of a deamination reactor, and obtaining a sample set; training a neural network through the sample set; and predicting the cavitation volume fraction and length in the deamination reactor by using a neural network. According to the deamination reactor optimization method based on the CFD and the BP neural network, the cavitation intensity of the deamination reactor is predicted based on a CFD numerical simulation result in combination with the neural network. CFD numerical simulation has the defects of low simulation speed, tedious operation steps and large workload, the cavitation intensity in the device with different parameters is calculated by constructing a neural network model, the amount of information to be processed is large, the calculation speed is high, complex problems can be rapidly processed, and the calculation efficiency is improved.

An energy-saving optimization method for a building cold source system comprises the following steps: S1, acquiring a topological graph of a target building cold source system, analyzing a system structure, and collecting historical operation data of the system; s2, constructing a cold source system energy consumption model, and performing parameter identification on the cold source system energy consumption model to improve the precision; s3, energy consumption is calculated through the cold source system energy consumption model by means of the cold load and the system operation parameters, and the optimal operation state of the system is searched for with the minimum system energy consumption as the target through an LECALO algorithm; and S4, outputting the minimum energy consumption and the optimal operation state of the building cold source system, and issuing the minimum energy consumption and the optimal operation state to the building cold source system software control platform. According to the method, the operation efficiency of the building cold source system can be remarkably improved, and energy consumption and carbon emission are effectively reduced.

The invention, which relates to the technical field of artificial intelligence, discloses a warehouse-in and warehouse-out path planning method and system for intelligent warehousing, and the method comprises the steps: generating an optimal operation path of a to-be-operated carrier through a path planning model; the method comprises the following steps: predicting whether a current carrier has a collision risk with all carriers in a warehouse in a time dimension or not through a time block, predicting whether the current carrier has a collision risk with all carriers in a warehouse in a space dimension or not through a space block, and when the time block and the space block simultaneously detect that the current carrier has the collision risk at a certain moment, judging whether the current carrier has the collision risk; the decision block judges that the current carrier has a collision conflict, and determines an optimal avoidance strategy through an action value function; according to the optimal avoidance strategy, the avoidance waiting carrier is determined, the total waiting time and the waiting space position of the avoidance waiting carrier are calculated, and the avoidance strategy is executed. According to the technical scheme, the problem that in-out garage path planning and collision processing cannot be intelligently and accurately carried out in the prior art can be solved.

The invention discloses a storage chip energy consumption test method and device based on multi-objective optimization. The method comprises the following steps: S1, initializing a test environment; s2, setting a multi-objective optimization model and a fuzzy logic system; s3, constructing an initial test data set; s4, calculating the membership degree of each test target; s5, generating a group of initial optimization parameter combinations; s6, dynamically adjusting the test parameters on the basis of the initial optimization parameter combination; s7, generating an optimized test parameter combination; s8, repeating the steps S6 and S7 until a preset optimization convergence condition is reached, and generating a final test parameter combination; s9, obtaining final test result data; and S10, analyzing the final test result data, comprehensively evaluating the performance of each test target through a fuzzy logic system, and generating a multi-target optimization test report of the storage chip. According to the method, the problems of multi-target test balance, test process optimization and balance between efficiency and accuracy in the prior art are effectively solved.

The invention discloses an AI auxiliary screening method based on a mammary gland molybdenum target image, and relates to the technical field of image data identification processing, and the method comprises the following steps: S1, image collection: collecting mammary gland molybdenum target image data of a patient, carrying out the standardization processing, removing the noise in the image, standardizing the gray value, and enhancing the local contrast, and obtaining a structured image data set. According to the AI auxiliary screening method based on the mammary gland molybdenum target image, multiple machine learning algorithms are fused for focus classification, so that the classification accuracy is improved; multiple classification models are constructed in an integrated learning mode, prediction results are fused through a weighted average mechanism and a voting mechanism, model prediction confidence is calculated for weighted fusion, the advantages of all the models are brought into full play, and the limitation of a single model is reduced; the screening accuracy and efficiency of the mammary gland molybdenum target image are improved, the focus detection and classification difficulty is reduced, and the condition that the model has more false negative or false positive results is improved.

The invention discloses an AI large model driven full-process intelligent industrial equipment system and an application method thereof, which are characterized by being applied to industrial equipment. Comprising an integrated AI large model, a data acquisition and integration module, a real-time monitoring and analysis module, an automatic control module, an intelligent maintenance and predictive maintenance module, a supply chain optimization module and a user interaction module. The integrated AI large model performs self-learning and iteration according to new data and user feedback information acquired from the industrial equipment through the data acquisition and integration module, the real-time monitoring and analysis module, the automatic control module, the intelligent maintenance and predictive maintenance module and the supply chain optimization module; according to the integrated AI large model, the operation accuracy and efficiency of the integrated AI large model are improved, comprehensive intellectualization of the production process is achieved, the production efficiency and the product quality are improved, and the operation cost is reduced.

The invention relates to the technical field of unmanned aerial vehicle path control, and particularly discloses an unmanned aerial vehicle power grid inspection operation path planning method based on Beidou navigation, and the method comprises the steps: obtaining the historical inspection contribution value of each region of a power grid, and combining the actual operation state contribution value of each region of the power grid; determining an unmanned aerial vehicle power grid inspection operation priority value of each region of the power grid, determining an unmanned aerial vehicle power grid inspection operation initial path, determining an unmanned aerial vehicle power grid inspection operation height of each region of the power grid, and obtaining obstacle position coordinates of a connection path of each region of the power grid based on Beidou navigation; according to the method, the problems that a traditional path planning method cannot meet efficient and intelligent routing inspection requirements and is insufficient in dynamic optimization capability in combination with real-time data are solved, the routing inspection effectiveness is improved, and the routing inspection efficiency is improved. The flight risk of the unmanned aerial vehicle is reduced and the inspection task is smoothly carried out.

The invention relates to the field of medical equipment, and discloses a medical intelligent multi-layer protective gas conveying system, method and device and electronic equipment, and the system comprises a data collection module which is used for collecting gas flow, pressure, temperature and leakage state information in real time; the path optimization module is used for dynamically optimizing a gas conveying path; the control execution module adjusts the opening degree of the valve and the pumping power according to the optimization result; the feedback updating module feeds back the execution effect after path switching to the optimization model, and dynamically adjusts optimization parameters; the monitoring platform module visually displays the operation state of the system and provides alarming and fault positioning functions under abnormal conditions. Through closed-loop optimization and intelligent regulation and control, the energy consumption and leakage risk are reduced, the gas conveying efficiency and stability are improved, meanwhile, the real-time requirement in a complex medical scene is met, and the system is suitable for conveying scenes of medical gas such as oxygen and anesthetic gas.

The invention relates to the technical field of automatic chip removal, in particular to a control method and system for a centralized chip removal machine of a numerical control machine tool, in the control method and system, the adjusting process of compression pressure is optimized through accurate calculation of the chip expansion rate, the cooling rate and the density change, and the influence of temperature change on the compression effect can be more accurately controlled. Through the combination of the expansion coefficient and the cooling time, the temperature fluctuation is monitored in real time, and the compression process is always kept in the optimal state under different machining conditions according to the deviation adjustment of the chip form and the compression pressure. The compression pressure is adjusted on the basis of the chip form data, so that the compression device has higher adaptability to iron chips of different shapes and types, unnecessary energy waste and machine damage are reduced, and the overall operation efficiency is improved. In addition, by dynamically calculating the chip removal flow speed, the conveying resistance and the periodic fluctuation, the chip removal path can be optimized in real time in the machining process.

The invention relates to the technical field of hotel management, in particular to a smart hotel guest room control system and method and a storage medium, and the method comprises the steps: obtaining the booking information of a guest room through a continuous check-in judgment module, and judging whether the guest room is continuously checked in; the travel and demand acquisition module acquires travel information and self-defined cleaning demand data of the guests after confirming continuous check-in, and determines a cleaning time period. And the cleaning time management module detects the state of the guest according to the cleaning time period and the cleaning interval time and judges whether the guest is cleaned or not. And the reminding and feedback module is used for sending information and receiving feedback when the guests do not go out. And when the guest goes out or is allowed to clean, the cleaning and quality inspection module collects a cleaned guest room image through the hotel robot, inputs the guest room image into the trained guest room image semantic extraction model, generates first semantic information, analyzes the guest room image semantic extraction model, compares the analyzed guest room image semantic extraction model with a guest room arrangement rule, and notifies a cleaner if the guest room image semantic extraction model does not pass the guest room arrangement rule. According to the invention, the satisfaction degree of the guests is improved through the control of the hotel guest room cleaning mode.

The invention belongs to the technical field of intestinal flora transplantation, and discloses an intestinal flora enrichment microsphere as well as a preparation method and application thereof and an enrichment method of intestinal flora. The intestinal flora enrichment microsphere provided by the invention specifically comprises a calcium alginate gel substrate and a porous adsorbent doped on the calcium alginate gel substrate, and the intestinal flora-enriched microspheres have a special pore size distribution situation, namely the pore size is gradually increased along the direction from the center to the outer layer. When the intestinal flora enrichment microspheres are applied to enrichment of intestinal flora, active flora cells can be effectively separated from small impurities such as intestinal contents and flora metabolites while the proportion of the high-activity flora cells is kept, and the intestinal flora enrichment microspheres have an excellent intestinal flora enrichment effect.

The invention relates to the technical field of vehicles, in particular to a vehicle torque control method and device, a vehicle and a storage medium, and the method comprises the steps that when the vehicle enters a gear shifting working condition, a torque request of a transmission of the vehicle is received; the actual output torque of an engine of the vehicle is obtained, and the minimum torque value of the engine in the current operation state is determined; and according to the target torque value corresponding to the torque request and the minimum torque value, the target torque response action of the engine is determined, the actual output torque is adjusted according to the target torque response action, and the vehicle obtains the output torque value meeting the preset gear shifting condition. According to the embodiment of the invention, the actual output torque of the engine can be adjusted according to the target torque response action of the engine, so that the vehicle obtains the output torque value meeting the gear shifting condition, the risk of system response delay is effectively reduced, and the driving experience of a user is improved.

The invention provides an intelligent cold station control system based on machine learning, which relates to the technical field of energy and power engineering and is divided into an infrastructure part and an intelligent auxiliary part, the infrastructure part comprises a data acquisition subsystem, a data analysis and preprocessing subsystem, a machine learning model training subsystem, an intelligent control strategy generation subsystem, a real-time monitoring subsystem, a fault diagnosis and early warning subsystem, a man-machine interaction subsystem, an energy management subsystem and a communication subsystem. The intelligent auxiliary part comprises a self-adaptive optimization subsystem, a load prediction subsystem, an equipment health assessment subsystem, an intelligent cooperation subsystem, a virtual simulation subsystem, a green energy access subsystem and a safety protection subsystem, and the energy management subsystem generates output of the subsystems according to an intelligent control strategy and performs optimization management on energy consumption of the cold station. Through precise load prediction, intelligent control strategy generation and energy management optimization, the energy consumption of the cold station is reduced, and energy-saving operation is realized.

The invention relates to the technical field of nodular cast iron, in particular to a silicon-molybdenum-chromium-nickel-niobium nodular cast iron material and a preparation method thereof. The material comprises the following chemical components in percentage by mass: 3.0%-3.5% of carbon, 4.3%-4.7% of silicon, 0.1%-0.3% of manganese, less than or equal to 0.05% of phosphorus, less than or equal to 0.02% of sulfur, 0.7%-1.0% of molybdenum, 0.6%-0.8% of chromium, 0.4%-0.7% of nickel, 0.3%-0.6% of niobium, 0.03%-0.06% of magnesium, 0.05%-0.1% of rare earth, 0.006%-0.012% of antimony and the balance of iron. The preparation method comprises the steps of smelting, first-time spheroidizing and inoculation, second-time spheroidizing, second-time inoculation and heat treatment in sequence. According to the obtained nodular cast iron material, the form of graphite and the number of graphite nodules are improved, carbon elements are separated out as many as possible in the form of high-carbon phase-spheroidal graphite, generation of pearlite is reduced, and the mechanical strength and high-temperature resistance of nodular cast iron are improved.

The invention relates to the technical field of cosmetics, and particularly discloses a composition with after-sun repairing and whitening effects and a preparation method thereof. According to the present invention, various efficient components such as the ergothioneine compound, the acetylchitosamine, the nicotinamide, the sodium hyaluronate, the inositol, the aloe vera extract, the glycerophosphoinositide choline salt, the tetrahydromethylpyrimidine carboxylic acid, the oat beta-glucan compound, the citrus peel extract compound and the skin brightening agent are scientifically matched; according to the present invention, the excellent after-sun repairing and whitening effect is achieved, the comprehensive after-sun skin care is achieved, the skin state can be improved, and the skin health degree can be improved. The application creatively finds that when the glycerophosphoinositide choline salt, the citrus peel extract compound and the skin brightening agent are compounded, the effects of the composition in the aspects of after-sun repairing and whitening can be improved at the same time, and the comprehensive effect is relatively good.

The invention relates to the technical field of production management, and discloses an intelligent packaging line production management system and method, and the method comprises the steps: obtaining a product information vector V1 of a to-be-boxed product and a box body information vector V2 of a target packaging box; dividing the to-be-boxed products into three types of products according to the product information vector V1; generating a three-dimensional geometric model and a virtual box body model, and constructing a three-dimensional boxing simulation environment; an optimal boxing scheme is obtained through a boxing scheme learning model; the packaging efficiency and the product safety can be improved.

The invention relates to the technical field of microorganisms, in particular to application of pediococcus acidilactici ZJUIDS13 in improvement of liver diseases, including improvement of alcoholic liver diseases and improvement of fatty liver diseases related to metabolic dysfunction. The pediococcus acidilactici ZJUIDS13 has relatively strong capability of protecting liver injury induced by alcohol, and can improve the states of liver injury and lipid metabolism disorder caused by high fat diet.

The invention relates to the technical field of feed processing, and discloses impurity removal equipment for feed raw material processing and an impurity removal method thereof.The impurity removal equipment comprises an impurity removal mechanism, the impurity removal mechanism further comprises an impurity removal barrel, a rotating rod is rotationally connected to the top of the inner wall of the impurity removal barrel, and three placement frames are arranged in the impurity removal barrel; a motor is started to drive a rotating rod to rotate, a placing frame is driven to rotate through a sliding ring, a roller brush is made to move, the roller brush is subjected to the influence of friction force between the roller brush and a circular screen, the roller brush revolves, and a round ball rod on the left side of the roller brush slides in a concave-convex annular plate in the rotating process of the placing frame; when the raw materials are separated, the roller brush is pushed to move transversely, the roller brush rubs soil attached to the raw materials, the sliding frame extrudes air in the exhaust groove, the raw materials are sprayed out through the air outlet holes, and therefore the soil on the raw materials is rapidly separated from the raw materials, the soil separation effect is enhanced, and the surfaces of the raw materials are kept clean.

The invention relates to the field of intelligent production control, and provides an intelligent control system and method for a rock wool production line, which adopts a data analysis method based on artificial intelligence to analyze thermal infrared images of a cupola furnace at a plurality of time points so as to understand melting change characteristics of raw materials. And meanwhile, the feeding speed data of the feeding system at multiple time points are analyzed to obtain feeding speed time sequence characteristics, then the melting change characteristics of the raw materials and the feeding speed time sequence characteristics are fused and optimized, and finally the adjustment direction of the feeding speed is obtained through a classifier. Thus, by accurately controlling the feeding speed, it can be ensured that the raw materials reach the optimal state in the melting process, and therefore the quality and consistency of products are improved.