2893 results about "Dynamic control" patented technology

The invention relates to the technical field of manufacturing process control, and discloses a multi-dimensional process data co-simulation control method and system and a storage medium. The method comprises the following steps: firstly, collecting a multi-dimensional process parameter set of a plurality of process equipment in a manufacturing production line, carrying out cross-dimensional feature extraction, and generating a comprehensive feature matrix containing a time sequence feature, a spatial distribution feature and an energy consumption feature; according to feature relevance of different dimensions in the comprehensive feature matrix, a process parameter dynamic coupling model is constructed, co-simulation is carried out on interaction between process equipment, and a process state evolution sequence is output; extracting abnormal fluctuation characteristics in the sequence, and generating a process parameter adjustment instruction set; and finally, according to the adjustment instruction set and real-time process feedback data, dynamically correcting model simulation parameters, generating an optimized process control strategy, and executing the optimized process control strategy. According to the method, collaborative analysis and dynamic control of multi-dimensional process parameters are realized, and the accuracy and adaptability of process control are improved.

Various systems and methods are directed to an electro-mechanical dice shaker gaming system designed to enhance fairness, transparency, and security in wager-based gaming environments. The system includes an electro-mechanical random number generator (RNG) assembly that physically isolates the dice shaking mechanism from the player terminal, preventing external interference from affecting game outcomes. The dice shaker mechanism is housed in a transparent enclosure, allowing players and casino operators to visually verify each roll. Integrated sensors, including tilt and vibration detectors, monitor environmental conditions to detect and prevent tampering. A camera-based monitoring system captures images of each dice roll, facilitating automated outcome verification and compliance auditing. The system supports modular configurations for multi-player gaming, real-time streaming for remote participation, and AI-driven fraud detection. Additionally, an adjustable mirror enhances dice visibility, and automated mechanisms allow dynamic control over the number of dice in play, offering a scalable and secure gaming solution.

The invention relates to a tower crane operation control system based on complex scene three-dimensional real-time modeling. According to the system, a lifting hook is coarsely positioned through a lifting hook positioning and state sensing unit, a real-time position is positioned by combining a laser radar point cloud clustering algorithm with historical pose data, and visual tracking is synchronously performed by means of a tower top camera AI; converting the real-time point cloud data into a 3D voxel grid map, generating a global path by using a 3DA algorithm, and outputting a hoisting track after smooth processing and track optimization; establishing a sling-lifting hook double-pendulum dynamic model, predicting a state sequence based on a model prediction control algorithm, and adjusting a control signal through a feedforward compensation item and a feedback correction item; and the man-machine interaction and monitoring unit is used for displaying the cantilever angle, the lifting hook height and the three-dimensional map of the tower crane in real time and remotely intervening the operation state of the tower crane. According to the system, multi-source data are fused to construct a high-precision three-dimensional map, lifting hook positioning and full-view tracking are achieved, and lifting safety and trajectory tracking precision are improved through path planning and dynamics control.

The invention relates to the technical field of geothermal drilling monitoring management, in particular to a construction monitoring system and method for geothermal drilling. The method comprises the following steps: constructing a state variable set, quantifying structural disturbance intensity by adopting a disturbance derivative method, normalizing the structural disturbance intensity, and defining a state transition tensor mapping behavior influence coefficient; in combination with the high-order derivative of the disturbance trend function and the behavior collaborative change matrix, an offset risk factor is generated through fusion for early warning; dividing a well drilling area into three-dimensional grids, calculating local stress potential energy, adjusting weight through a dynamic sensitive belt, and generating a risk distribution diagram through smooth filtering; performing closed-loop dynamic regulation and control by using risk potential energy centroid coordinates and gradient field inversion intervention vectors for medium and high risk areas; and through multi-period risk potential energy tensor analysis, calculating the matching degree of a self-healing index and a stable cone region, evaluating the self-healing capability of the system and feeding back a regulation and control strategy. According to the invention, a closed-loop monitoring and active control system for nonlinear risks of geothermal drilling is formed.

The invention relates to the technical field of audio and video control, and discloses an audio and video player control method based on a voice instruction. The method comprises the steps that an original voice instruction stream of a user is collected, the instruction stream comprises a time domain audio signal sequence, an environment noise spectrum and user pronunciation characteristic parameters, and voice information can be comprehensively captured; multi-modal instruction analysis processing is carried out on the original voice instruction stream, a structured control instruction set containing acoustic control intention identification, semantic operation object description and context correlation parameters is generated, and the analysis precision is improved; then executing player state adaptation based on the set, generating a dynamic control response sequence containing an equipment state adjustment command, a media content positioning parameter and an interface interaction logic identifier, driving a player to execute a multi-dimensional control operation and generating real-time play control effect feedback data; and finally, multi-modal analysis parameters are optimized according to feedback data, a self-adaptive instruction analysis strategy is generated, and the control experience of a user on the audio and video player is optimized.

The invention relates to the technical field of industrial flue gas purification, and discloses a flue gas denitration pollution reduction and carbon reduction method and system based on model prediction control. The method comprises the following steps: collecting flue gas data through a sensor to obtain a flue gas distribution state; the state is processed through a preset model, and a nitrogen oxide concentration predicted value is determined; judging a regulation and control demand based on the predicted value and generating an adjustment coefficient sequence; calculating an opening value of an ammonia spraying distributor by adopting a particle swarm optimization algorithm, and determining ammonia spraying amount distribution; a control instruction is sent according to ammonia spraying amount distribution, and a temperature uniformity index is evaluated based on temperature feedback data; adjusting optimization algorithm parameters according to the indexes, and generating an optimized ammonia spraying strategy; updating the predicted value and determining the stable range of the denitration efficiency; verifying the ammonia escape concentration in the stable range to obtain a system performance index; and forming a continuous regulation and control sequence according to cyclic feedback of the indexes. According to the invention, accurate dynamic regulation and control of ammonia spraying amount are realized, denitration efficiency and system stability are effectively improved, and the risk of ammonia escape is significantly reduced.

The invention relates to the technical field of biomedicine, and discloses a female private rehabilitation training system integrating a breathing method and neural motor control, and the system comprises a data collection module which is used for collecting physiological data of a user; the personalized training scheme generation module is used for generating a personalized nerve-muscle-fascia training scheme; the nerve-muscle synchronous resonance training module is used for constructing a time sequence matching model and generating an adaptive training dynamic control instruction; the space-time adaptability training module is used for adjusting training duration, intensity and stimulation frequency; the multi-dimensional nerve-muscle feedback fusion module is used for constructing a feedback response model; and the fascia synchronous activation and relaxation module is used for coordinating the tension relationship between the fascia and the muscle system. According to the method, feature matching is carried out on an electric activation heat map matrix through the convolutional neural network, analysis of a myoelectricity response state transition probability is carried out in combination with a hidden Markov model, and stimulation parameters and respiratory rhythm are optimized in real time according to a training process.

The invention provides a distributed intelligent cooperative energy-saving control method and device for an industrial air conditioner, electronic equipment and a computer readable storage medium, and is applied to the field of data processing application. The method comprises the following steps: acquiring industrial air conditioner operation related data including environment parameters and equipment state data; an intelligent cooperative control mechanism is constructed based on multi-device cooperation and hierarchical adjustment requirements, and the intelligent cooperative control mechanism comprises a multi-device linkage strategy for dynamically distributing refrigeration resources according to regional cooling and heating loads and a hierarchical adjustment algorithm of a chilled water system and a cooling system, and a control framework for achieving global optimization is formed; processing the environment parameters and the equipment state data to generate a dynamic control strategy; generating target energy-saving control information based on the data analysis result and the dynamic control strategy; and all operation data and control effects generated based on the target energy-saving control information are integrated, a high-energy-consumption link optimization scheme is generated, energy consumption data credible tracing is achieved, and the distributed air conditioning system is aggregated to participate in power grid frequency modulation.

The invention relates to the technical field of mine environment control, and discloses an intelligent environment monitoring and control method in a mine laneway. The method comprises the steps that a real-time environment parameter set of a mine laneway is collected, the set contains gas concentration data, dust concentration data and temperature and humidity data, and real-time capture of laneway environment multi-dimensional parameters is achieved; then, based on the collected real-time environment parameter set, calculating an environment safety evaluation index so as to scientifically and objectively reflect the roadway environment safety condition; a dynamic control strategy is generated according to the environmental safety evaluation indexes, and the strategy comprises ventilation equipment regulation and control parameters and early warning level parameters and can be flexibly adjusted according to environmental changes; and finally, the ventilation equipment regulation and control parameters are sent to a mine ventilation system, and roadway environment regulation and control are executed. According to the method, environmental parameters can be comprehensively monitored, safety risks can be accurately evaluated, ventilation equipment can be flexibly regulated and controlled, the early warning function can be perfected, energy waste can be reduced, labor intensity can be reduced, and underground operation safety and mining efficiency can be guaranteed.

According to the intelligent grotto excavation stability analysis system based on the BIM, through deep fusion of the BIM model and multi-source monitoring data, whole-process dynamic control over the grotto excavation stability is achieved. The system adopts a lightweight model processing technology to remarkably improve the calculation efficiency, and an intelligent inverse algorithm effectively solves the subjectivity problem of traditional parameter determination. A three-dimensional numerical simulation and real-time monitoring data interactive verification mechanism greatly improves the reliability of an analysis result, and a dynamic risk assessment system realizes the accuracy of risk early warning through multi-index fusion analysis. A final support scheme optimization module combines engineering experience with an intelligent algorithm, safety is ensured, economical efficiency is taken into account, and an intelligent solution capable of being self-adaptive to engineering condition changes is formed. According to the whole system, seamless connection of all links is achieved through modular design, and full-chain technical support from data to decision making is provided for safety construction of underground engineering.

The invention provides a thermal power generating unit intelligent start-stop process multi-level linkage cooperative control optimization method, which relates to the field of thermal power generating unit control, and comprises the steps of collecting equipment group operation parameters, establishing a multi-level dynamic control reference, constructing a cascade response mechanism to determine a target operation interval, subintervals are divided according to the parameter coupling degree, compensation adjustment coefficients are set for hierarchical adjustment, and a linkage control link is constructed to coordinate the adjustment process of each level of equipment group. According to the method, accurate cooperation among equipment in the starting and stopping process of the thermal power generating unit is achieved, the safety and efficiency of the starting and stopping process are improved, and energy consumption is reduced.

The invention discloses a safety monitoring system of a high transverse support system for cable-stayed bridge man-shaped tower column construction, and relates to the technical field of bridge construction monitoring, the system comprises a creeping formwork integrated sensing module used for generating a point cloud model by using a mechanical arm integrated on a hydraulic creeping formwork platform and a laser radar scanning tower column curved surface, combining with a preset building information model coordinate, adaptively adjusting the mounting posture of the sensor, and outputting the mounting coordinate position of the sensor; the optical fiber sensing network module is used for collecting original strain and temperature data by using a distributed optical fiber sensor deployed along a main stress path of the support truss; an inertial navigation fusion positioning module; a digital twinning early warning module; and an edge calculation relay module. According to the invention, a full-process monitoring chain from data acquisition to risk early warning is constructed through cooperation of multiple modules, full-period and multi-dimensional dynamic control of construction of the human-shaped tower column high transverse support system is realized, and timely perception and overall control of potential risks are ensured.

The invention relates to wide-range multi-component sensor calibration equipment and a wide-range multi-component sensor calibration method. The method comprises the following steps: firstly, checking equipment parts and setting a target temperature range and a compensation model of each temperature interval; then, dynamic temperature control is started, jig deformation is monitored in real time, a transient temperature gradient suppression technology is applied, multi-physical-quantity calibration is executed, and a compensation measurement value is output; then setting multi-environment coupling calibration test conditions to simulate actual working conditions, and monitoring and recording calibration data of each physical quantity; and finally, calculating a calibration error by combining a calibration result and a reference standard value. According to the process, by precisely setting parameters and combining dynamic control, real-time monitoring and multi-physical-quantity calibration, the transient temperature gradient influence is effectively inhibited, and the measurement precision of the sensor in a complex environment is ensured; the temperature control performance is improved through the micro soaking film and the intelligent temperature control strategy, the actual working condition is simulated through multi-environment coupling testing, the accuracy and reliability of the calibration result are remarkably improved through compensation and error calculation, and guarantee is provided for precise application of the sensor.

The invention belongs to the technical field of intelligent regulation and control, and discloses an aquaculture pond water quality remote intelligent regulation and control system based on edge calculation. The method aims at solving the problems that in traditional water quality regulation and control, due to data transmission delay, model simplification neglects the coupling effect, and equipment regulation and control lack collaboration. Water quality parameter data are obtained in real time through a parameter acquisition module, a micro-coupling analysis module constructs a parameter micro-coupling matrix at edge nodes, a short-term fluctuation relation is analyzed, a dynamic regulation and control weight is generated, and then a local regulation and control instruction is formed. And the uploading coupling prediction module uploads the data to a cloud end, constructs a long-term coupling prediction model and generates a global regulation and control strategy. And finally, the edge node generates a final regulation and control instruction in combination with the local instruction and the global strategy, and optimizes the working states of the oxygenation equipment, the bait casting equipment and the water circulation equipment. Real-time accurate regulation and control are realized through edge calculation, and the risk of abnormal water quality is reduced; the efficiency is improved through equipment collaborative optimization, and resource waste is reduced.

The invention relates to the technical field of sewage treatment, in particular to an aerobic aeration treatment system for plateau domestic sewage treatment, which comprises a pretreatment unit, a biological treatment unit, an advanced treatment unit and a recycling unit which are connected in sequence, the system further comprises a data acquisition module, an aerobic aeration intelligent regulation and control module, a biochemical environment constant-temperature maintenance module, a carbon-nitrogen proportion and backflow dynamic regulation and control module, a digital twinning and sludge characteristic simulation module and an energy collaborative scheduling module. Compared with the prior art which adopts a standard aeration strategy and cannot adapt to the special environment of low air pressure and low oxygen partial pressure of the plateau, the system can accurately adapt to the plateau environment by integrating real-time atmospheric pressure and water temperature monitoring, dynamically calculating the oxygen saturation solubility and correcting the aeration control target according to the oxygen saturation solubility, so that the aeration control target is optimized. Stable supply of dissolved oxygen required by biochemical reaction is ensured, and the technical bottleneck of low efficiency of traditional sewage treatment in plateau regions is fundamentally overcome.

The invention relates to the technical field of agricultural intelligent equipment, and discloses a multi-mode sensing fusion control system for an aerogel greenhouse heat preservation quilt. An environment multi-mode monitoring module of the system collects internal temperature and humidity distribution, illumination intensity gradient, soil moisture content profile and external meteorological change data of a greenhouse in real time to generate an environment comprehensive monitoring data set; based on the data set, the thermal insulation mode decision module analyzes the difference between the thermal energy loss rate and the thermal insulation demand of different areas in combination with the physical characteristic parameters and historical regulation and control records of the aerogel thermal insulation quilt, matches a dynamic thermal insulation strategy and generates a multi-stage partition regulation and control instruction; the cooperative parameter optimization module calculates a combination scheme of the unfolding angle, the coverage area and the thickness of the heat preservation quilt, and adjusts a partition cooperative control parameter set; the multi-mode execution controller generates a heat preservation quilt dynamic regulation and control map; the anomaly fusion diagnosis module monitors anomalies and generates a set of regulation error correction factors. The system realizes fine, dynamic and intelligent regulation and control of greenhouse heat preservation.

The invention provides a dynamic obstacle avoidance path planning control method for a redundant mechanical arm, which adopts a self-adaptive artificial potential field dynamic obstacle avoidance algorithm based on a second-order dynamic control barrier function. The problems that a traditional artificial potential field method cannot reach a target, is prone to falling into a local minimum value and cannot effectively deal with a dynamic obstacle are solved. The efficient and safe obstacle avoidance of the mechanical arm in a dynamic complex environment is realized by constructing a self-adaptive velocity repulsion field and a virtual obstacle model, combining a high-order dynamic control barrier function as a safety constraint and utilizing a quadratic programming optimization algorithm. Experimental results show that the method can effectively guide the mechanical arm to escape from the local minimum point, the target point can be reached, meanwhile, the safe distance between the mechanical arm and a dynamic obstacle is guaranteed, the movement efficiency and track quality of the mechanical arm are remarkably improved, and the real-time performance and safety requirements of a dynamic obstacle avoidance task are met.

The invention provides a dynamic control method and system for hot-pressing plugging process parameters of a network cable end electronic wire, which realizes real-time optimization of the process parameters through a dynamic compensation technology, dynamically calculates an initial heating temperature and a riveting depth based on a wire diameter, an insulating layer thickness, a copper alloy hardness and an environment temperature, and realizes the hot-pressing plugging process parameters of the network cable end electronic wire. A compensation formula is combined with a conductor diameter compensation coefficient, a hardness compensation coefficient and an environment temperature compensation coefficient, heating temperature fluctuation and riveting pressure displacement deviation are monitored in real time, Z-axis displacement is adjusted by controlling a servo motor, a temperature-pressure coupling compensation model is established, and heating power and riveting pressure are dynamically corrected. And the online quality detection module corrects subsequent batch parameters in a self-adaptive manner at a learning rate eta = 0.2 through continuous five batches of data, so that the riveting depth precision and the connection strength are ensured to reach the standard, intelligent dynamic control of the hot-pressing plugging process is realized, and the product percent of pass and the production efficiency are improved.

The invention provides a coal-bed gas well production dynamic analysis method and system based on AI time sequence prediction, and the method comprises the steps: firstly collecting a production dynamic data sequence of a coal-bed gas well, a corresponding well position geological condition data sequence and a surrounding environment data sequence, and constructing a ternary time sequence dynamic association network comprising node dynamic time sequence segments and a connection line real-time association relationship; and then calling a pre-trained AI time sequence coupling deviation evolution mining model to process the ternary time sequence dynamic association network to obtain a ternary time sequence coupling deviation result. And a deviation conduction closed-loop path is determined according to a ternary time sequence coupling deviation result in combination with forward conduction positioning and reverse tracing verification, and a deviation conduction analysis report is generated. And finally, generating a dynamic regulation strategy based on the deviation conduction analysis report in combination with a production dynamic prediction threshold value, outputting the dynamic regulation strategy to a production control terminal, and meanwhile, feeding back regulation data to update a network node association relationship, thereby realizing accurate analysis and regulation of the production dynamic state of the coal-bed gas well.

The invention discloses a furnace body temperature control system, which belongs to the technical field of industrial automation control, and comprises the steps of monitoring and collecting multi-dimensional data of a physical furnace body in real time, performing preprocessing, generating a state vector, setting a working condition judgment method, classifying various working conditions, outputting working condition labels and characteristic parameters, and establishing a health degree evaluation model; the state of the actuator is monitored in real time, and frequent maintenance and short service life caused by overuse of equipment are prevented; constructing a furnace body virtual model, setting a real-time prediction method, and simulating furnace body temperature distribution so as to predict a future temperature trend, judge whether to start a rehearsal mode, output an optimal scheme, and realize advanced intervention of prediction and decision making; a working condition identification and temperature prediction result is read, a decision engine method is set, and a dynamic control instruction is generated and executed; and comparing data of the physical furnace body with data of the digital twinborn body, setting a self-evolution method, and carrying out online calibration and correction on the virtual model.

The invention relates to the field of factory intelligent cooling, and discloses an MPC-based large factory intelligent cooling system and a cooling control method, the system comprises a data acquisition module, an AI control module, a cooling tower control module, a cooling water pump control module and a cooling-water machine control module; the data acquisition module acquires outdoor environment temperature and humidity, cooling tower outlet water temperature, cooling water pump inlet and outlet water temperature difference and flow and water chiller operation state data in real time. The AI control module constructs a system model through real-time data, predicts and optimizes by using an MPC algorithm, and calculates optimal values of the running frequency of a cooling tower fan, the running frequency of a cooling water pump and the power of a cooling-water machine compressor by adopting sequential quadratic programming, so as to realize cooperative dynamic control of equipment in the system; the overall operation efficiency of the cooling system under the outdoor temperature and humidity change condition is effectively improved, and the overall energy consumption of the system is minimized in real time.

The invention relates to the technical field of transfer robots, in particular to an intelligent warehouse logistics automatic transfer robot. The robot comprises a scraper and a suction cup which are arranged on a robot body, the scraper is provided with specially-designed groove pressing teeth, an arc-shaped groove with a guiding function can be formed in the surface of a box body, and it is ensured that a frost layer effectively falls off along a preset path through the design. The system collects temperature gradient, surface appearance and contact stress data in real time through the integrated multi-mode environment sensing unit, the intelligent decision-making unit establishes a dynamic control model based on the data, and accurate evaluation and self-adaptive processing of the frost layer state are achieved. The whole system constructs a complete closed-loop control chain, the limitation of a traditional defrosting method is overcome, the adsorption stability is kept, meanwhile, the operation efficiency is improved, the system is particularly suitable for the low-temperature storage environment of-25 DEG C and below, and a reliable solution is provided for cold-chain logistics automatic operation.

The invention discloses a truss structure wind-induced dynamic response prediction method and system based on physical enhancement. The method comprises the following steps: carrying out feature extraction and alignment fusion on input data containing condition parameters and wind speed time sequence data by utilizing a long short-term memory network and a physical enhancement attention mechanism; extracting multi-scale features from the fusion features through expansion convolution, and performing weighted aggregation on the multi-scale features; the physical priori knowledge of structural vibration is fused into position coding and a self-attention mechanism so as to carry out response prediction; and integrating physical model information of the truss structure and a dynamic control equation into a loss function, and calculating physical information residual loss so as to improve the physical interpretability of a prediction result. According to the method, data heterogeneity can be eliminated, complementary information can be fused, the multi-scale characteristic of wind-induced response is coped with, the accuracy and efficiency of wind-induced dynamic response prediction of the truss structure are effectively improved, and the physical interpretability and generalization ability are enhanced.

The invention relates to the technical field of distributed energy storage monitoring, and discloses a distributed energy storage monitoring system based on an Internet of Things data platform. The system comprises a multi-dimensional state evaluation module, a load response strategy generator, a collaborative optimization decision module and a dynamic control instruction compiler. The multi-dimensional state evaluation module obtains real-time Internet of Things monitoring data of the energy storage unit and extracts dynamic operation characteristics, and the data comprises a voltage fluctuation curve, temperature gradient distribution and the number of charge and discharge cycles; the load response strategy generator identifies the operation mode through a charging and discharging mode classification algorithm, and generates a state vector set containing operation mode identification and load capacity score mapping; the collaborative optimization decision-making module generates a fusion result containing node energy interaction characteristics and capacity balance coefficients based on the set; and the dynamic control instruction compiler inputs the fusion result into a strategy optimization model, and outputs a control instruction element containing a load priority time sequence instruction and a parameter self-correction coefficient.

The invention discloses an autonomous charging dynamic control system for an inspection robot, and relates to the technical field of inspection robot management, and the system comprises a condition triggering module, a robot sensing module, a task decision module, a dynamic charging control module, a safety monitoring module and a log recording module. When a new inspection task packet is issued to a waiting or charging robot, a condition triggering module starts judgment, and a robot sensing module collects real-time electric quantity, task attributes and a charging pile state and encapsulates data; the task decision module calculates a task emergency degree and constructs a priority, and compares a charging basic priority to trigger a temporary or safe charging strategy; the dynamic charging control module dynamically adjusts charging parameters or calls safety parameters according to the dynamic charging parameters; the safety monitoring module monitors and intervenes in the charging process in real time, the log recording module records key operation and abnormity, and the system can dynamically adapt to task requirements, improves response efficiency and safety and is suitable for complex inspection scenes.

The invention relates to the technical field of capacitor manufacturing and state monitoring, in particular to a capacitor aging curve optimization method based on deep learning, which comprises the following steps: step 1, multi-modal data acquisition and standardization processing; 2, modeling and training a physical constraint neural network; 3, executing a dynamic control and maintenance strategy: dynamically adjusting the switching frequency of the inverter based on a comparison result of the residual life prediction value output in the step 2 and a preset threshold value, and enabling the ripple frequency to deviate from a capacitor impedance sensitive frequency band; and according to the aging mode classification result output in the step 2, triggering a corresponding temperature sampling frequency adjustment or bus voltage reduction operation. Through fusion of multi-modal data, a physically constrained neural network model and a dynamic control strategy based on a prediction result, the aging process of the capacitor is successfully optimized, the use efficiency of the capacitor is improved, and the life cycle of the capacitor is effectively prolonged.

The invention discloses a glass fiber particle assembly line-based magnetic suction quick-release vibration disc system and a detection method, and relates to the technical field of production and processing, the system comprises a data acquisition module for acquiring data of a vibration light-emitting chassis, a magnetic suction column, an assembly line and particles by means of a distributed sensor network, and transmitting the data to an intelligent self-adaptive control module after signal processing; the intelligent self-adaptive control module realizes intelligent dynamic regulation and control of the parameters of the magnetic suction quick-release vibration disc, optimizes the production process of the glass fiber particles, and automatically generates an accurate control instruction based on a data analysis result by integrating multi-source information and performing deep fusion and analysis, so that the production efficiency of the glass fiber particles is improved. Parameters such as the vibration frequency and amplitude of the vibration light-emitting chassis and the magnetic attraction strength of the magnetic attraction column are adjusted to meet the requirements in different production states, so that the system can quickly respond to changes in the production process, defective particles are reduced, and the production efficiency is improved.

The invention discloses a multivariable self-adaptive collaborative optimization control system and method for a high-alkali coal boiler, and relates to the technical field of high-alkali coal boiler optimization control, the system is based on real-time operation data acquisition and multi-working-condition state recognition, and dynamic evaluation and collaborative adjustment of combustion heat transfer and physical coupling effects are achieved; by collecting a first data set, a second data set and a third data set, a high-alkali coal quality and load mapping relation is constructed, and a multi-working-condition state label is output; based on the label, the multivariable cooperative adjustment module calculates a combustion heat transfer coupling coefficient and performs efficiency judgment; the physical coupling effect evaluation module evaluates the boiler internal structure risk; and the dynamic control optimization module integrates multiple indexes to realize control response optimization. According to the system, through threshold value judgment and strategy feedback, the combustion efficiency of the high-alkali coal boiler is improved, structural safety guarantee and dynamic regulation and control optimization are achieved, and the operation stability and economical efficiency of the high-alkali coal boiler are remarkably improved.

The invention provides an intelligent agricultural system and an agricultural greenhouse humidity intelligent control method. The method comprises the following steps: firstly, acquiring a humidity sampling data sequence in a specified monitoring time window before a current regulation and control period in an agricultural greenhouse, and calculating a humidity change trend index; comparing the humidity change trend index with a dynamic trend threshold value, distributing a weight for each humidity sampling point, and performing weighted fusion based on the weights to obtain a weighted fusion result; and then generating a humidity regulation and control reference value of the current regulation and control period based on the weighted fusion result, the current real-time humidity and the external environment parameters. And finally, the deviation between the target humidity of the crop growth model and the current real-time humidity and the humidity regulation and control reference value are input into a controller to generate a dynamic regulation and control instruction, and operation parameters of a humidity execution mechanism are regulated according to the dynamic regulation and control instruction. According to the scheme, the robustness of accurate regulation and control of the humidity of the agricultural greenhouse can be improved.

The invention discloses a dynamic thermal management and risk pre-judgment system for a lithium battery energy storage system, and relates to the technical field of lithium battery energy storage, and the system comprises a thermal field monitoring module which generates a real-time three-dimensional thermal field distribution map of a battery pack; the dynamic regulation and control module is used for self-adaptively regulating the heat dissipation power and the refrigerant flow in combination with the charging and discharging states of the battery and the environmental parameters; the risk assessment module is used for constructing a thermal runaway risk grade assessment model and generating a risk probability value; the early warning response module triggers a multi-stage early warning mechanism according to the risk probability value, and is linked with the dynamic regulation and control module to execute an emergency heat dissipation strategy to generate a risk disposal scheme; and the data archiving module is used for storing data. According to the invention, the whole-process intelligent design of monitoring, regulation and control, evaluation, early warning and archiving is provided, the thermal management precision, the risk prevention and control capability and the operation economy of the lithium battery energy storage system are comprehensively improved, and a key technical guarantee is provided for large-scale energy storage application.