3174 results about "Intelligent control system" patented technology

The invention belongs to the field of artificial intelligence, particularly relates to an intelligent regulation and control system for an injection molding process of an industrial control system, and aims to solve the problem that high-precision cooperative regulation and control are difficult under material batch fluctuation, mold state change and environmental disturbance. The system comprises a multi-source sensing module, a dynamic modeling module, a self-adaptive decision-making module, an execution feedback module and a knowledge evolution module, and high-stability and high-adaptability intelligent regulation and control of the injection molding process are achieved through a mixed digital twin model integrating a physical mechanism and data driving, confidence-guided multi-objective optimization and continuous evolution of a process knowledge graph.

The invention discloses an urban traffic intelligent regulation and control system and method based on digital twinning. The method comprises the following steps: S1, collecting and fusing multi-source data in real time; s2, constructing a digital twinborn model; s3, traffic flow prediction and optimization; s4, multi-system cooperative control is carried out; the system comprises the following modules: a multi-source data real-time acquisition and fusion module which comprises a video data acquisition and processing unit, a geomagnetic sensor network construction unit and a floating car data integration unit; the digital twinborn model building module comprises a three-dimensional road network model building unit and a data fusion and calibration unit; the traffic flow prediction and optimization module comprises an intelligent prediction model unit and a real-time optimization control unit; and the multi-system cooperative control module comprises an emergency priority passing system unit, a reversible lane dynamic adjustment unit and a parking lot induction unit. The traffic efficiency can be remarkably improved, the cooperative response speed is greatly increased, and carbon emission is effectively reduced.

The invention discloses an internet of things multi-protocol fusion limo intelligent control system, which relates to the field of limo intelligent control and comprises a hardware irrelevant layer, a multi-protocol adaptation layer, a data abstraction layer, a programmable logic layer, a security and redundancy mechanism and an edge AI module. According to the invention, a physical bus is packaged into an independent module, an MCU / SoC and a protocol transceiver are built in, hot plugging is realized through a standardized PC I e / EdgeConn backboard, a core processing board only exposes a unified backboard interface, module drive localization execution is realized, master control firmware does not need to sense protocol details, only the module needs to be replaced when a protocol is newly added, and the master control firmware or upper software does not need to be modified. The defect that the whole vehicle firmware needs to be refreshed when a bus is replaced in a traditional system is overcome, each protocol corresponds to an independent drive micro-service deployed in a containerization mode, the drive micro-service analyzes a message into a unified data model and uploads the unified data model to a data abstraction layer, a downlink control instruction is packaged and issued according to a target protocol, and it is guaranteed that the protocols are diversified and interfaces are consistent.

The invention discloses a metal grinding machine tool grinding wheel wear compensation type intelligent control system, and relates to the technical field of mechanical manufacturing automation, and the system comprises a contact type reference detection module which is used for obtaining the initial contour and reference wear loss of a grinding wheel through discrete point measurement in a non-machining state; the multi-parameter non-contact real-time detection module is used for collecting acoustic emission, vibration and spindle current signals in the grinding process through three types of sensors; the grinding environment sensing module collects the temperature of a grinding area and the cutting fluid concentration; the intelligent control unit receives reference data and an environment signal, performs deep learning on a wear loss prediction model after multi-source data fusion, performs periodic reference calibration, analyzes the dynamic wear loss, and synchronously transmits the data to the data storage and tracing module; the executing mechanism adjusts grinding wheel feed amount compensation according to the dynamic abrasion loss; and the data storage and tracing module stores full-process data in real time. According to the system, high-precision and real-time wear compensation is achieved, the machining precision and consistency are improved, and high-end manufacturing requirements are met.

The invention relates to the technical field of industrial equipment control, and discloses an industrial equipment intelligent control system and method based on deep reinforcement learning. The system comprises an equipment state monitoring module, a reinforcement learning decision-making module, a control parameter adjustment module, an abnormity intervention module and an equipment performance optimization module. The equipment state monitoring module collects real-time operation data of equipment, analyzes state change by means of a deep reinforcement learning algorithm, and generates a state feature data set; the reinforcement learning decision-making module calculates a control strategy by using a deep reinforcement learning model and generates a control strategy parameter set; the control parameter adjusting module optimizes the operation efficiency according to the adjusted parameters and generates an optimized control parameter set; the abnormity intervention module monitors abnormity, recognizes intervention through deep reinforcement learning, and generates an abnormity intervention adjustment data set; and the equipment performance optimization module optimizes the performance indexes according to the parameters and generates a performance optimization parameter table. The system realizes accurate monitoring of equipment, intelligent optimization of strategies, timely intervention of abnormities and performance improvement, and meets intelligent management and control requirements of the equipment.

The invention relates to the field of industrial automation control, and discloses an intelligent control system and method based on multi-parameter coupling analysis and predictive maintenance. According to the method, flow, temperature and water quality parameters are collected in real time through a sensor network, and after data cleaning and standardization processing, a parameter coupling relation model is constructed; an LSTM neural network is adopted to predict a system energy consumption trend, and a time sequence regression model is combined to analyze equipment performance attenuation characteristics; and when the predicted value exceeds a threshold value, dynamically adjusting operation parameters and optimizing load distribution to form closed-loop control. Accurate energy-saving control under complex working conditions is achieved, energy consumption can be reduced by 8%-12% through tests, the equipment maintenance cost is reduced by 15%-20%, and the system operation efficiency is remarkably improved.

The invention discloses a water electrolysis hydrogen production intelligent control system and method based on artificial intelligence. The system and method are suitable for a large-scale water electrolysis hydrogen production scene under the power supply condition of fluctuating renewable energy sources such as wind power and photovoltaic. The system comprises a data acquisition layer, an edge calculation layer, an intelligent control layer and an execution layer. Dynamic modeling of the running state of the hydrogen production system is achieved through multi-parameter real-time monitoring and feature extraction. The intelligent control layer fuses an LSTM prediction module and a reinforcement learning controller, the LSTM prediction module is used for predicting future renewable energy input and hydrogen demand trends, and the reinforcement learning controller calculates an optimal current density set value based on a prediction result so as to maximize hydrogen production efficiency per unit energy consumption. And meanwhile, a digital twinning technology and a safety protection mechanism are combined, thermoelectric dual regulation and control of the electrolysis process are achieved, and the control response speed and the system stability are improved. According to the invention, energy consumption can be effectively reduced, the service life of the stack is prolonged, and the operation efficiency and reliability of the hydrogen production system under complex load are improved.

The invention relates to the technical field of intelligent regulation and control of fusion data, in particular to a rolled finished product-based size precision intelligent regulation and control system and method, which comprises a data acquisition unit, a data fusion unit, a deep learning prediction unit and a compensation execution unit, multi-source data such as temperature gradient, roll gap fluctuation and tension spectrum are collected in real time through a sensor array, thermal expansion compensation correction is carried out through a data fusion unit based on a rolling elasticoplastic deformation equation, an anti-interference coupling feature vector is generated by using adaptive Kalman filtering, and a deep learning prediction unit is used for predicting the rolling elasticoplastic deformation through a dual-channel space-time convolutional network. The thickness error and the width error are predicted by combining the constraint of a rolling force balance equation, the compensation execution unit dynamically generates the roll gap adjusting amount and the rolling force compensation amount according to a three-level response mechanism, phase lag compensation current is injected to suppress resonance, the high-end steel yield and the rolling precision are improved, and the rolling quality is improved. And an intelligent closed-loop control scheme is provided for high-precision rolling under complex working conditions.

The invention relates to an intelligent regulation and control system for multi-phase conversion of water quality of a gate-controlled river reach, in particular to the field of water treatment, and realizes accurate dynamic perception of the multi-phase conversion process of the water quality of the gate-controlled river reach by constructing a high-fidelity digital twinborn body and fusing a real-time data assimilation technology; by means of a time-space diagram neural network agent model based on an attention mechanism, the system can predict a complex time-space pattern of pollutant concentration field and phase evolution under different regulation and control strategies in an ultra-fast manner; on the basis, a multi-agent reinforcement learning algorithm is adopted to automatically generate a globally optimal gate group coordinated regulation scheme, and multiple targets of water quality improvement, ecological protection, engineering operation and the like are effectively coordinated; and finally, a theoretical strategy is reliably converted into a physical action through rolling optimization and a closed-loop execution mechanism, and adaptive adjustment can be performed according to environmental feedback, so that the emergency response speed of sudden water pollution events, the scientificity of regulation and control decision and the intelligent level of water quality management of the whole river network are comprehensively improved.

The invention provides a multi-parameter cold source intelligent control system and method based on big data, and the system comprises a data collection and control module which is used for collecting related parameters of a cold source system; the comprehensive energy consumption standardization module is used for converting the energy consumption parameters into unified standardized energy consumption indexes; the comfort degree algorithm construction module is used for generating a dynamic comfort degree index; the association rule mining module is used for mining a multi-dimensional association rule from historical data; the intelligent decision module is used for generating an optimal control strategy based on the index, the index and the association rule; according to the embodiment of the invention, based on a multi-parameter fusion cold source intelligent control technology of deep reinforcement learning and an improved FP-Growth algorithm, through deep mining and intelligent decision making of multi-source data, the optimal control strategy is converted into equipment regulation and control parameters, and the equipment regulation and control parameters are obtained. And high efficiency and energy conservation of the cold source system and accurate regulation and control of indoor environment comfort are realized.

The invention discloses an agricultural environment intelligent control system based on artificial intelligence and a control method thereof, and belongs to the technical field of agricultural intelligent control and artificial intelligence application. According to the method, agricultural sensor data and image data are jointly packaged into a standardized AI input structure by adopting prompt engineering and semantic modeling means; a general large language model or an image recognition model is accessed, and comprehensive evaluation of complex crop states is realized through customizing a Prompt semantic structure; constructing a natural language analysis engine, and converting the control suggestion returned by the AI into a structured command; the Agent module is used for completing control opportunity judgment, control path planning and effect feedback adjustment, and strategic intervention on the whole agricultural control process is achieved.

The invention provides a PLC-based biomass gasification industrial steam full-automatic intelligent control system and method. The system comprises a data acquisition module, a modeling identification module, a multi-objective optimization module, a rolling prediction control module, a coordination execution module, a self-adaptive correction module and an abnormal fault tolerance and safety control module. The system collects operation data of a feeding system, a gasification furnace, a boiler, a steam-water system and an environmental protection system in real time through a PLC platform, a dynamic coupling model of a combustion side, a steam-water side and an environmental protection side is established, and a multi-target constraint optimization model with stable steam pressure, up-to-standard emission and minimum energy consumption as targets is constructed. According to the invention, unattended operation of the biomass gasification industrial steam system is realized, the system has the advantages of high control precision, low energy consumption, stable emission and strong fault-tolerant capability, and the intelligence and safety of the industrial steam system are obviously improved.

The invention relates to the technical field of optical manufacturing and processing control, in particular to an intelligent control system of lens processing equipment, which comprises a process relationship pre-storage module, a pressure distribution acquisition module, a surface shape error prediction module, a pressure compensation decision module, an actuator control module and a processing effect verification module. Wherein the process relation pre-storage module is used for establishing a pressure-removal rate relation mapping table; the pressure distribution acquisition module is used for acquiring pressure distribution data; the surface shape error prediction module is used for predicting surface shape error data generated by the lens; the pressure compensation decision module is used for generating a regionalized pressure compensation amount; and the actuator control module is used for dynamically adjusting the pressure applied to different areas of the lens by the polishing head. According to the method, by constructing the pressure-removal rate mapping and closed-loop self-calibration mechanism, intelligent pressure regulation and control and surface shape error dynamic correction in the lens polishing process are achieved, and the machining precision and stability are remarkably improved.

The invention provides a large reciprocating compressor continuous stepless regulation system and method based on intelligent control, and the method comprises the steps: building a continuous stepless regulation model according to a load change trend and process fluctuation characteristics, and determining an initial parameter set of a regulation instruction through a preset load range regulation threshold value and a dynamic response speed requirement; according to the non-equilibrium stress adjustment scheme, dynamically adjusting operation parameters of a compressor execution mechanism, monitoring the abrasion state of key components through a real-time feedback control technology, and determining operation data of the components after stress equilibrium; and the operation data of the parts after stress balance are obtained, the operation parameters of the compressor are subjected to secondary calibration through an intelligent control system in combination with an energy efficiency improvement target, and the optimized optimal pressure ratio state parameters are obtained.

The invention discloses an intelligent control system for a high-precision emulsifying pot, relates to the field of intelligent control, and is used for collecting multi-dimensional data such as temperature field distribution, material rheological characteristics, liquid drop behaviors and mechanical states in real time through distributed sensors and monitoring the emulsifying process in real time. The system evaluates the phase stability risk level by adopting a multi-source data fusion technology, and dynamically adjusts the process parameters such as the stirring rotating speed, the emulsifier flow and the heating power according to the risk level. For a high-risk state, by analyzing the correlation between a temperature field and torque fluctuation, an abnormal reason is accurately identified, and a targeted regulation and control measure is triggered. A liquid drop coalescence monitoring mechanism is introduced into the system, and when liquid drop coalescence exceeds the standard, temperature compensation is automatically enhanced. The system has a self-learning capability, dynamically optimizes a risk judgment threshold according to historical data, and improves the control precision.

The invention relates to the technical field of industrial control, in particular to an industrial stacker intelligent control system based on machine vision, and the system comprises a state resolving module which is used for carrying out the feature region decoupling of original observation data, and resolving a position deviation matrix and a deformation state variable for a rigid subspace and a flexible subspace respectively; the dynamic modeling module is used for identifying the first-order inherent frequency and the damping ratio of the mechanical structure on line and establishing a second-order impedance contact model; the impedance control module is used for generating an expected speed based on the position deviation and adaptively adjusting an impedance parameter according to a deformation state so as to carry out compliant modulation on a speed vector; and the estimation shaping module is used for correcting system time lag by using a Kalman observer and inhibiting residual vibration in combination with a zero-vibration input shaper. Through multi-dimensional physical field fusion and dynamic modeling, the problem that a rigid control model is mismatched with a flexible operation object is solved, and self-adaptive compliant control and vibration-free accurate operation of the stacking machine in the unstructured environment are achieved.

The invention discloses a water-wind-light combined intelligent control system based on multi-energy coordinated control, and belongs to the technical field of multi-energy coordinated control, and the system comprises the steps: firstly collecting environment and operation parameters of wind power, photovoltaic and hydroelectric equipment, and carrying out the preprocessing of the environment and operation parameters to form a structured data set with a space-time label and a dynamic sampling identifier; performing space-time correlation analysis on the data set, constructing a wind power fluctuation and hydropower regulation margin real-time response matrix and a photovoltaic output attenuation and hydropower storage capacity compensation time sequence correlation model, and quantifying the multi-energy dynamic complementation degree and grading the data credibility through a two-dimensional coupling algorithm; then combining with a historical meteorological scene association rule base, constructing a water-wind-light combined power supply capability prediction model containing an energy characteristic mutual compensation coefficient, and generating a hierarchical regulation and control strategy with a space-time priority; and finally, executing a regulation and control strategy to complete parameter adjustment, continuously monitoring through closed-loop feedback, and triggering multi-energy collaborative complementary regulation when the single energy regulation capability is saturated, thereby realizing water-wind-light efficient collaborative control.

The invention discloses a photovoltaic power generation intelligent control system of photovoltaic panels in an optical storage direct flexible system, and relates to the technical field of photovoltaic power generation, the intelligent control system comprises a plurality of photovoltaic panel assemblies, the plurality of photovoltaic panel assemblies comprise main node assemblies, one main node assembly is arranged every 5-10 groups of photovoltaic panel assemblies, and the main node assemblies are connected with the photovoltaic panel assemblies; the master node assembly is configured with a high-precision double-axis sun sensor and an edge calculation module and is responsible for collecting a solar azimuth angle, a current inclination angle of the assembly and environment temperature and humidity data in real time, and the slave node assembly is configured with a single-axis inclination angle sensor and adjusts a tracking angle by receiving a master node instruction. Specifically, a master-slave node heterogeneous design is adopted, the slave node only needs a single-axis tilt angle sensor and a wireless communication module, the hardware configuration number is reduced, and cost control is achieved. According to the method for photovoltaic panel tracking optimization, the control instruction quantity of the system is reduced, the central control pressure is remarkably reduced, and the problems of cost and complexity during large-scale application of photovoltaic panel tracking are solved.

The invention discloses a high-pier large-span extra-large bridge suspended pouring beam hanging basket construction method, and belongs to the field of bridge engineering construction.The method comprises the following steps that hanging basket rails are installed, the precision is controlled, and a hanging basket comprising a double-channel-steel main truss, a manganese steel sling, an arc-shaped flow guide plate and a honeycomb formwork is assembled; grading pre-pressing is carried out according to the specified multiple of the design load, and the pre-camber of the template is corrected by adopting a BP neural network to ensure that the finished bridge linear error meets the requirement; a mechanical-hydraulic-electrical integrated intelligent control system is deployed, and the traveling speed and synchronism of the hanging basket are controlled through double-oil-cylinder driving, a 5G digital twin platform and an edge computing technology, so that rapid alarm is realized when the load is abnormal; segmental circulation construction is carried out, and when the environment wind speed reaches a set value, a hydraulic rail clamping device is automatically locked; a model is established by using ANSYS software, and simulation verification is performed on multiple types of key working conditions to ensure that the safety coefficient of the structure meets the specification requirement. The construction precision and safety are guaranteed, and the method adapts to complex environments.

The invention discloses an indoor gas leakage monitoring and intelligent joint control system, and belongs to the technical field of intelligent safety monitoring systems. Comprising a sensing layer, a network layer, a control layer and a platform layer, the sensing layer comprises a multi-sensor array and an edge computing node, the multi-sensor array is combined with a catalytic combustion sensor, an infrared sensor, a semiconductor sensor and a temperature and humidity sensor, and sensor data are fused through a weighting algorithm so as to reduce the false alarm rate; a temperature and humidity sensor dynamically adjusts a detection threshold value to adapt to different climate conditions, and an edge computing node is deployed with a lightweight machine learning model. Through multi-sensor data fusion and a weighting algorithm, the false alarm rate is reduced to be smaller than 0.1% per year, environment self-adaptive calibration and AI auxiliary decision making are performed, interference of alcohol, perfume and the like is effectively eliminated, different climate conditions are adapted, leakage judgment is completed at an equipment end through edge calculation, cloud delay is avoided, a multi-stage emergency strategy is provided, and intelligent safe ecology driven by the Internet of Things is constructed.

The invention relates to the technical field of metal rolling automation, and discloses a computer intelligent control system of a rolling mill, which comprises a rolling mill production line, and the rolling mill production line further comprises a tensiometer, a pressure sensor and a displacement sensor; and the computer intelligent control system is electrically connected with an executing mechanism and a sensor of the rolling mill production line and comprises a tension control module, an automatic thickness control module and an online model self-adaptive module. The method comprises the following steps: controlling strip steel tension through a tension control module; the strip steel outlet thickness is adjusted through the thickness automatic control module; and model parameters such as rolling mill rigidity and friction compensation are identified in real time in the rolling process through an online model self-adaption module, and continuous optimization of the control model is achieved. By introducing the online model adaptive module, real-time online correction of the core physical model of the control system is realized, and the problem of model mismatch caused by equipment wear and working condition change is solved.

The invention relates to the technical field of selenium-rich white tea production, and discloses an intelligent control system and method for automatic production of selenium-rich white tea. The system comprises a selenium-rich white tea growth environment regulation and control module, a tea growth stage recognition module, a selenium content self-adaptive supply module, a production equipment cooperative control module and a quality closed-loop feedback module. The growth environment regulation and control module collects tea garden multi-region environment parameters and establishes a dynamic monitoring set; the growth stage identification module divides growth stage identifiers in combination with the environment parameters, the bud and leaf morphological image sequence and the growth cycle time sequence data; the selenium content self-adaptive supply module generates a supply scheme and an equipment control instruction set according to the growth stage identifier and the soil selenium content data; the production equipment cooperative control module is linked with related equipment to execute a cooperative operation time sequence control process; and the quality closed-loop feedback module generates a correction amount according to the finished product detection data, and feeds the correction amount back to the growth environment regulation and control module for parameter iterative updating.

The invention discloses a reinforcing method for long-term automatic grouting repair of fractured rock mass.The reinforcing method comprises the steps that a pressure sensor is arranged at the tail end of a grouting opening, and data of the pressure sensor are obtained through an intelligent control system; when the intelligent control system monitors that pressure difference is generated due to pressure unbalance caused by surrounding rock fracture development, the intelligent control system sets grouting parameters and starts a grouting program; in the grouting process, pressure balance between equipment and surrounding rock is maintained through a pressure adjusting module, and it is ensured that grout is stably conveyed within the set pressure range all the time in the conveying process after a grouting program is started; grouting pressure and flow are controlled in the grouting process; in the grouting process, the bacterial liquid and the cementing liquid are input in a separated conveying and on-site gathering mode; and accumulated liquid in the pipeline is rapidly removed after grouting is finished. By adopting the method, cracks can be continuously monitored and periodically repaired, and the manual intervention requirement is reduced. The modular design is convenient for maintenance and replacement of equipment, and the maintenance cost is reduced.

The invention discloses a sorting manipulator intelligent control system based on big data, and the system comprises a multi-source data collection module which is used for synchronously collecting the real-time operation parameters of a sorting manipulator, the multi-modal feature data of a to-be-sorted object, and the dynamic environment parameters; the cross-modal fusion module is used for performing space-time alignment on the multi-modal feature data through a dynamic weighting algorithm; the double-domain cooperative training module is used for constructing a mapping relation between a virtual simulation domain and a physical entity domain, migrating initial model parameters trained by the virtual domain to the entity domain based on transfer learning, minimizing double-domain data distribution difference through a domain adaptation loss function, and generating an intelligent decision model adaptive to an entity scene; and the adaptive control module is used for generating a real-time control sequence through a model prediction control algorithm according to the sorting path planning result output by the intelligent decision model. According to the invention, more intelligent sorting manipulator control is realized.

The invention relates to the technical field of intelligent manufacturing, in particular to a fur processing laser cutting machine and an intelligent control system and a cutting method thereof. The fur processing laser cutting machine comprises a rack, a material transferring mechanism, a negative pressure stabilizing device, a laser cutting mechanism and an intelligent control device, the material transferring mechanism is arranged in the feeding direction of the rack and comprises a fur preprocessing part and a net-shaped conveying belt, the negative pressure stabilizing device is arranged below the net-shaped conveying belt, and the laser cutting mechanism is arranged below the negative pressure stabilizing device. The laser cutting mechanism is erected on the machine frame and located above the net-shaped conveying belt, and the intelligent control device cooperates with the control focus depth and the cutting path of a laser beam based on the fur thickness and deformation data. By optimizing cooperative work of the fur feeding system, the fur pretreatment part, the negative pressure stabilizing device and the intelligent control system of the laser cutting machine, the defects of an existing fur processing laser cutting machine in the aspects of cutting precision and wool stability are overcome.

The invention relates to the technical field of intelligent control of heavy equipment, in particular to a hydraulic synchronous intelligent control system for segmented turnover of an overweight steel structure, and discloses the hydraulic synchronous intelligent control system for segmented turnover of the overweight steel structure. According to the system, full-field strain data of a structure surface is acquired by deploying a flexible grid sensor array, and a deformation gradient topological graph is constructed to identify an instability risk area. And a dynamic fluid network model of the hydraulic system is established, fluid-solid coupling simulation is carried out in combination with deformation characteristics, and pressure pulsation transmission paths under different control instructions are predicted. And the system dynamically optimizes a data sampling strategy and network model parameters according to a prediction path, generates a hydraulic cylinder coordination action instruction subjected to pressure balance verification, and finally drives an execution mechanism to complete turnover operation. According to the scheme, full-field real-time sensing of structural deformation and dynamic coupling prediction control of hydraulic fluid are achieved, and the safety and control precision of the overweight component in the turnover process are improved.

The invention discloses an intelligent control system and method for shot blasting equipment based on multi-modal perception, and relates to the technical field of industrial surface treatment.The intelligent control system and method for the shot blasting equipment based on the multi-modal perception have the advantages that a technological parameter self-evolution decision-making mechanism with deep reinforcement learning as a core and real-time feedback of discharging quality as a system is established; the shot blasting process is converted from static experience setting to dynamic model driving, a process parameter optimization model serves as a deep reinforcement learning agent, instant reward functions of processing quality, energy consumption, material consumption and equipment loss are comprehensively considered, and the function guide model pays attention to whether the final quality reaches the standard or not, and also pays attention to the real-time reward functions of the processing quality, the energy consumption, the material consumption and the equipment loss. And meanwhile, the comprehensive economic benefits of the whole process are actively optimized, the quality evaluation and prediction model performs real-time and quantitative analysis on visual images of discharged workpieces, and immediately feeds back results to the decision model, so that the system has the learning ability, and autonomously explores and converges to an optimal process parameter combination for specific workpieces and specific states.

The invention discloses a partitioned multifunctional metal double-cavity vacuum coating process and system, and belongs to the technical field of material surface treatment. The process comprises the steps of base material pretreatment, double-cavity preparation, vacuum inspection, first cavity bottom layer deposition, vacuum transfer, second cavity functional layer deposition and cooling, the double cavities are provided with corresponding target materials respectively, and parallel operation and vacuum seamless transfer are supported; the system comprises double coating cavities, a vacuum transmission channel, a vacuum acquisition and control module, a coating deposition module, a workpiece bearing module and a process parameter regulation and control module, and each of the double cavities is provided with a multi-target mounting rack and carries a real-time monitoring unit and an intelligent control system. Integrated preparation of the composite film layer and synchronous production of multifunctional workpieces are achieved, operation gaps are eliminated, the compactness and adhesive force of the film layer are improved, parameter deviation and the reject ratio are reduced, and batch production requirements are met.

The invention discloses an AI-based raw water pretreatment dosing intelligent control system and method, and relates to the technical field of artificial intelligence, and the system comprises a sensor layer, an edge calculation layer, a cloud platform layer and an execution layer. The sensor layer consists of a distributed sensor network, comprises a turbidity meter, a pH electrode, an electromagnetic flowmeter, a COD (Chemical Oxygen Demand) online monitor and a distributed optical fiber temperature measuring device, covers a raw water inlet, a mixing tank and a sedimentation tank node, and transmits a signal to the edge calculation layer through an RS485 bus. According to the raw water pretreatment dosing intelligent control system and method provided by the invention, through multi-modal data fusion and a dynamic prediction model, the water quality of raw water is accurately sensed and predicted, the dosing amount is adjusted in advance, the hysteresis of a coagulation reaction is effectively compensated, the standard reaching rate of the water quality is improved, and by utilizing an NSGA-II multi-target optimization algorithm, the quality of the raw water is effectively improved. The medicament cost and the treatment effect are balanced, excessive dosing of medicaments in a traditional mode is avoided, and the operation cost is reduced.

The invention relates to the technical field of biological process intelligent control, and discloses a pomegranate leaf fermentation process optimization and intelligent control system based on digital twinning. According to the system, the Internet of Things and a visual sensor are integrated through a multi-source data sensing module, so that synchronous acquisition and fusion of temperature, pH, dissolved oxygen, flora density and image data are realized; the intelligent data preprocessing module extracts high-quality features by adopting a self-adaptive filtering technology; the digital twin simulation engine module drives the virtual model to dynamically update based on real-time data; the deep prediction module accurately predicts the fermentation trend through a deep learning algorithm; the intelligent diagnosis module realizes abnormal root cause analysis by using a knowledge graph; the reinforcement learning optimization module autonomously generates an optimal control strategy based on the diagnosis result; and the self-adaptive execution module accurately regulates and controls fermentation parameters. According to the system, multi-dimensional perception, high-precision prediction and adaptive optimization control of the fermentation process are realized, and the accuracy and stability of process control are effectively improved.