6840 results about "Intelligent control system" patented technology

The invention discloses an intelligent control system for a current transformer, and particularly relates to the technical field of power system monitoring, which comprises a fluctuation identification unit, a redundancy acquisition unit, an error fitting unit, a protection delay unit and a fault response unit, realizes real-time identification and classification of current fluctuation, and has standby path high-speed sampling and fusion signal construction capabilities. The fluctuation trend is judged through error regression, a protection strategy is dynamically set, response is delayed to avoid misoperation, finally, a high-level protection instruction is triggered and a fault event is output when offset overruns or alignment fails, and the data accuracy and the system response stability in the power supply switching process are guaranteed; according to the method, accurate identification of current fluctuation is realized through multi-dimensional feature extraction, data reliability is enhanced by using redundant sampling and error fitting, and by combining delay control and a hierarchical response mechanism, the risk of misoperation is effectively reduced, the fault judgment accuracy and the system protection stability are improved, and the method is suitable for a complex power supply switching scene.

An automatic surfacing device, comprising a welding execution system, a visual identification system and a welding intelligent control system. The present invention further relates to a surfacing control method, comprising: establishing a corresponding rule table between welding process parameters and welding bead topography relationship parameters; establishing coordinate systems, planning a welding trajectory, and presetting welding parameters; starting welding, and compensating for the welding width and welding reinforcement of a welding bead in real time; and performing scanning and detection, and providing a reminder to repair the position of a defect and a reminder for depth information. In the method, on the basis of an MIG welding procedure, highly automated welding is performed on a plurality of welding layers (an underlying layer, a transition layer and a wear-resistant layer) of a surfacing roller sleeve, thereby improving the production efficiency. During a welding process, closed-loop controlling is performed on the quality of each welding link, thereby guaranteeing the quality consistency of welded finished products.

The invention provides an electromagnetic-assisted nickel-based alloy laser melting deposition crack inhibition method and system, relates to the technical field of automatic control, constructs an intelligent control system, carries out deep fusion and real-time prediction on multi-source heterogeneous sensing data, generates a prediction result, sets a reward function and an action-value network, and improves the performance of the nickel-based alloy laser melting deposition crack inhibition system. Solving to obtain an optimization control strategy and analyzing and outputting the optimization control strategy; and constructing a three-dimensional electromagnetic field distribution matrix. In combination with real-time feedback of the shape of the molten pool, the molten pool is stimulated to flow directionally and oscillate non-linearly according to the resonance effect, oscillation parameters are obtained, thermodynamic behaviors corresponding to the molten pool are predicted according to the oscillation parameters, and a thermodynamic prediction result is fed back to the central control unit; the method comprises the following steps: detecting the surface appearance of a part, extracting defect characteristic parameter vectors, inverting defect influence factors, generating a dynamic regulation and control curve, dispersing the dynamic regulation and control curve into multi-dimensional control instruction vectors, updating a preset regulation and control model, outputting optimized regulation and control model parameters, and carrying out crack suppression operation again.

The invention provides an intelligent high-performance collaborative welding robot system, which belongs to the technical field of welding robots and comprises a multi-degree-of-freedom welding robot body, an intelligent control system, a high-performance welding power supply and auxiliary equipment. A welding process control module firstly obtains three-dimensional information of a workpiece based on visual inspection to construct a welding path; then selecting matched process parameters according to material characteristics and welding seam shapes; establishing a welding error matrix to monitor the welding process in real time; constructing a parameter error adjustment matrix for dynamic adjustment; analyzing parameter fluctuation by adopting a neural network to identify molten pool characteristics; the pre-trained neural network is used for evaluating the welding seam quality; and finally, a multi-robot collaborative welding strategy is integrated to achieve efficient operation, and the technical problem that in the prior art, welding process parameters in a welding robot are difficult to adjust accurately in real time so as to adapt to complex working condition changes is solved.

The invention discloses an integrated cold station intelligent control system based on multi-modal data fusion, and relates to the technical field of data fusion, an intelligent decision and optimization module adopts reinforcement learning and model prediction control based on fused data to realize dynamic optimization of start and stop of a water chilling unit, chilled water temperature setting and water pump frequency adjustment; the fault detection and adaptive optimization module evaluates the data fusion accuracy by calculating an environment entropy value and cold station dynamic response delay time, and dynamically adjusts a control strategy based on a prediction deviation rate and the data fusion accuracy; the closed-loop feedback module analyzes and adjusts control parameters through operation data, intelligent control models between different cold stations are optimized through transfer learning, intelligent deployment of a new cold station is accelerated, and the generalization ability of the models is improved. More accurate, more energy-saving and more stable intelligent control of the cold station is realized, and the operation efficiency and reliability of the cold station system are improved.

The invention provides a comprehensive and intelligent mariculture net cage management and control system. The system integrates a multi-parameter sensor module, an underwater robot module, an underwater camera module, a central control module, a data storage and analysis module, an early warning module, a remote communication module and an automatic execution module. By monitoring multiple environmental parameters such as water temperature, salinity, dissolved oxygen and the like in real time and utilizing the underwater robot and the underwater camera to carry out autonomous inspection and fish behavior observation, the system can accurately master the culture environment condition. The AI algorithm and the intelligent model are combined, the system dynamically adjusts the bait casting amount, the oxygenation frequency and the net cage position, and the breeding benefits are effectively improved. Meanwhile, the early warning module carries out graded early warning on risk events such as typhoon and diseases, and the breeding safety is ensured. The remote communication and visual human-computer interface further enhances the remote monitoring and management capability of the system.

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 exhibition hall intelligent control system and method based on the Internet of Things technology, and the method comprises the following steps: collecting environment data in an exhibition hall in real time through a self-adaptive multi-mode sensor network, and dynamically adjusting the sampling frequency of a sensor through a multi-dimensional data sudden change monitoring algorithm by the system, the central processing module is combined with historical and real-time data, time sequence multi-dimensional regression analysis is adopted, an environment change prediction model is generated, then equipment control parameters are generated, the system dynamically adjusts the operation states of temperature equipment, illumination equipment and the like in the exhibition hall according to behavior data of visitors and the environment parameters, the experience of the visitors is optimized, and the experience of the visitors is improved. The intelligent anomaly detection module monitors the operation state of equipment in real time and identifies and isolates fault equipment in time, and the system preferentially guarantees equipment operation and reduces energy consumption when visitors are dense through a long-term energy consumption optimization learning algorithm, so that energy-saving management and long-term self-optimization of the exhibition hall are realized.

The invention relates to the technical field of intelligent control of transformer substations, in particular to a standardized intelligent control system of a transformer substation, which realizes real-time response to the running state of equipment by acquiring the current, voltage, power factor, temperature and humidity, operation state, insulation state and environment temperature and humidity of a power transformation unit in real time. A gradient boosting decision tree is combined with a variational auto-encoder and a self-attention mechanism, state vector features are extracted, complex abnormal states are accurately recognized, potential faults are detected in an early stage, and the monitoring efficiency and the early warning precision are greatly improved. The future power demand is accurately predicted by combining the long-short-term memory network model with historical and real-time load data, and the limitation of traditional static load management is overcome. Based on the predicted power demand, the reinforcement learning model is adopted to dynamically adjust the operation number of the power transformation units, optimal configuration of resources is achieved while the demand is met, and the overall operation efficiency of the transformer substation is remarkably improved.

The invention provides an intelligent all-weather autonomous cruise unmanned rescue boat and a rescue control method thereof.The intelligent all-weather autonomous cruise unmanned rescue boat comprises a boat body and a multi-source energy supply system, an environment and target sensing system, an intelligent control system, a propelling system, a satellite positioning system and an autonomous navigation system which are arranged on the boat body. The rescue boat can autonomously acquire environment data and drowning person information, and dynamically plan an optimal cruise route through a deep reinforcement learning algorithm. The intelligent control system is responsible for controlling cruise, identifying drowners, generating rescue strategies and executing rescue tasks. After rescue is completed, the system can automatically re-plan a cruise route to continue the task. By means of the design, all-weather and full-autonomous water cruise and rescue are achieved, the rescue coverage range and efficiency are remarkably improved, manpower consumption and safety risks are reduced, the rescue response time is shortened, the rescue success rate is increased, and particularly the rescue capacity under the complex environment is remarkably improved.

The embodiment of the invention discloses a multi-mode adaptive industrial instrument intelligent control system based on deep learning, and relates to the technical field of industrial instrument intelligent systems. The system comprises a multi-modal data acquisition module used for acquiring multi-modal data of an industrial instrument in real time, the multi-modal data comprising at least two of image, sound, temperature and vibration data; the heterogeneous data fusion module is used for carrying out space-time alignment and feature fusion on the multi-modal data; the deep reinforcement learning control module is used for dynamically optimizing a control strategy and generating a control instruction; the self-adaptive human-computer interaction interface is used for providing visual operation and real-time feedback; and the predictive maintenance subsystem is used for predicting faults and generating maintenance suggestions based on the equipment state. The multi-modal data fusion technology of dynamic weight distribution is adopted, the problem that a traditional single sensor loses efficacy under the complex working condition is solved, and the performance of an industrial instrument in the aspects of measurement precision, self-adaptability and operation and maintenance efficiency is improved.

The present invention relates to the technical field of oil and natural gas exploitation, and discloses a real-time monitoring and intelligent control system for hydraulic fracturing cracks in oil reservoirs, including: a real-time monitoring module, which is used to collect crack geometric morphology parameters and construction operation parameters in real time during crack expansion; a nonlinear modeling module, which is connected to the real-time monitoring module, and is used to construct a nonlinear dynamic model of crack expansion according to the collected crack expansion parameters and construction operation parameters, and output a predicted value of crack morphology; an optimization control module, which is connected to the nonlinear modeling module, and is used to calculate the optimized construction parameters according to the nonlinear dynamic model, including injection pressure, injection flow rate and viscosity of fracturing fluid. The present invention realizes precise control of crack expansion, intelligent optimization of construction parameters and efficiency improvement through the closed-loop linkage of real-time monitoring, nonlinear modeling, optimization control and feedback execution.

The invention discloses an electromechanical installation engineering automatic debugging method and system based on intelligent control, and the method comprises the steps: enabling an intelligent control system to monitor the energy consumption of equipment in real time, optimizing an operation mode, adjusting the working state according to data, and enabling the equipment to work cooperatively through the intelligent control system; deploying temperature and humidity, pressure, current and voltage sensors for stable wiring, and configuring parameters by adopting an intelligent debugging tool; based on the preliminary data, optimizing the system by applying an adaptive control algorithm, adjusting a strategy, fusing multi-dimensional data, and identifying performance bottleneck and abnormality; the centralized monitoring platform monitors the equipment state in real time and sends out an early warning signal; after multiple rounds of debugging, the energy-saving effect and performance are evaluated, a control algorithm is optimized, and optimization is continuously carried out according to historical data and environmental changes; according to the electromechanical installation engineering automatic debugging method and system based on intelligent control, the adaptive control algorithm and the machine learning technology are introduced, and the strategy is dynamically optimized according to the equipment operation state, the environment change and the historical data.

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 relates to the technical field of intelligent control, in particular to a composite material forming process intelligent control system which comprises a micro-scale deformation detection module, a stress offset correction module, a temperature gradient regulation and control module, an interlayer curing balance module and a forming intelligent control execution module. According to the method, a deformation rate sudden change area is accurately positioned through micro-scale detection, and stress concentration is found in advance, so that stress adjustment is more targeted, the local stress overload influence is reduced, real-time pressure feedback is combined with target pressure offset analysis to optimize a loading mode, uniform stress in the curing process is ensured, and the defect risk is reduced; high-precision temperature gradient monitoring and thermal diffusion rate analysis improve temperature control precision and improve interlayer temperature equilibrium, curing rate change rate calculation and heat conduction path optimization dynamically adjust a heat dissipation channel and promote uniform curing, stress-temperature distribution equilibrium degree analysis is combined with a deformation trend to optimize heating and pressure parameters, and uniform curing is achieved. And the forming stability and the quality consistency are improved.

The invention relates to the technical field of water pump intelligent control, in particular to a vacuum pump and water supply pump intelligent control system. A water supply pump liquid conveying adjusting module collects operation parameters and liquid state information of a water supply pump and analyzes the boundary layer thickness of an inner wall area of a water supply pump connecting pipeline; and judging the influence of the boundary layer thickness change on the liquid flow fluctuation, adjusting the rotating speed of the water supply pump and the valve opening, and generating a water supply pump initial operation adjustment result. According to the method, the operation parameters of the water supply pump and the liquid state information are collected in real time, the flow velocity and the flow velocity gradient of the liquid on the inner wall of the pipeline are measured to calculate the thickness of the boundary layer, meanwhile, the relation between the thickness and the pressure gradient is analyzed, the regulation and control capacity of fluid flow fluctuation is effectively improved, and the liquid conveying stability is enhanced. In the liquid conveying process, the influence of liquid viscosity is analyzed through temperature changes, the conveying state is monitored in combination with the pressure difference, the abnormal condition can be rapidly recognized and marked, and the safety and the fault response speed are improved.

The invention relates to the technical field of data transmission, in particular to a meteorological data compression and transmission intelligent control system which comprises a network state evaluation module, a data priority evaluation module, a data compression and encryption module and a data transmission control module. According to the invention, network conditions can be effectively predicted and key data can be transmitted preferentially by evaluating network volatility and data emergencies, the reliability and efficiency of data transmission are improved, delay and packet loss in the data transmission process are reduced through targeted data compression and encryption processing, and the data transmission efficiency is improved. Meanwhile, the safety of data in the transmission process is guaranteed, especially in a bandwidth-limited network environment, quick and accurate transmission of key meteorological information can be guaranteed by adjusting data transmission bandwidth allocation and selecting a proper transmission protocol, resource allocation is optimized, the adaptive capacity of the system to complex network conditions is enhanced, and the service life of the system is prolonged. Therefore, the system can work efficiently under the condition that the network load is large or unstable.

The invention discloses an intelligent control system for a compression heat zero-gas-consumption dryer, and relates to the technical field of compressed air drying. The intelligent control system comprises a data sensing module, an intelligent decision module and an execution module; the data sensing module acquires humidity, temperature, pressure and flow data of compressed air in real time, and can dynamically adjust acquisition frequency according to working conditions to provide a data basis for subsequent decision making; according to the invention, the over-integrated data sensing module monitors the humidity, temperature, pressure and flow of compressed air in real time, and dynamically adjusts the acquisition frequency according to working conditions, thereby providing an accurate data basis for intelligent decision making; the intelligent decision-making module adopts an advanced data processing technology and a VAE model to repair and supplement the data, so that the accuracy and integrity of the data are ensured; in this way, the system can accurately control the drying process, reduce energy waste and achieve the zero-gas-consumption drying effect, and therefore the operation cost is remarkably reduced.

The invention relates to the technical field of oil filter control, in particular to an oil filter digital intelligent control system based on sensors, which comprises a fusion monitoring unit, an oil temperature control unit and a feedback regulation unit. The oil temperature control unit calculates the optimal heating rate and the target temperature range through a self-adaptive algorithm according to the environment and the initial temperature of oil, compares the optimal heating rate and the target temperature range with the actual oil temperature in the heating process, adjusts the heating power through pulse heating according to the result, judges the working load according to the specific heat capacity of oil and adjusts the frequency of a vacuum pump. And the feedback regulation unit evaluates the system stability based on the target operation frequency and the heating power of the vacuum pump, generates an optimization strategy and feeds back the optimization strategy to the oil temperature control unit, so that accurate oil temperature control is realized, the equipment stability and the energy utilization efficiency are improved, and efficient operation of the oil filter is guaranteed.

The invention relates to the technical field of automatic control, in particular to a paint spraying process intelligent control system which comprises an airflow dynamic monitoring module, an entropy optimization regulation and control module, an acoustic quality monitoring module and a visual precision correction module. According to the invention, through the integration of real-time monitoring and regulation and control technologies, the quality and efficiency of the paint spraying process are obviously optimized, the real-time adjustment of spraying parameters is realized by using fine airflow and particle behavior data, the paint is ensured to be uniformly distributed, the material waste is greatly reduced, the thickness and uniformity of a coating can be accurately evaluated through the analysis of sound wave signals, and the real-time monitoring and control of the paint spraying process are realized. The quality deviation is timely found and corrected, the coating quality is improved, the heavy work and rejection rate are reduced, the overall production efficiency is improved, meanwhile, the spraying process is accurately corrected through real-time image analysis, it is ensured that each step of operation is executed under the optimal condition, and the high-quality and high-efficiency spraying result is achieved.

The invention provides a hammer crusher and a crushing method, and relates to the technical field of material crushing, the hammer crusher comprises an intelligent control system, and the intelligent control system comprises a crushing parameter regulation and control analysis module, an energy-saving optimization module and a regulation and control execution module; the crushing parameter regulation and control analysis module analyzes the characteristic information of the to-be-crushed material to obtain crushing parameters; the energy-saving optimization module performs analysis according to historical data and real-time monitoring data of equipment operation to obtain an energy-saving adjustment coefficient; and the regulation and control execution module automatically adjusts the rotating speed of the driving mechanism and the striking force of the breaking hammer by using the adjusted breaking parameters. Material characteristic information is obtained in real time through the material characteristic detection module, adaptive crushing parameters are calculated through the crushing parameter regulation and control analysis module, an energy-saving regulation coefficient obtained through the energy-saving optimization module is combined, and the rotation speed of the driving mechanism and the striking force of the crushing hammer are automatically regulated through the regulation and control execution module; accurate crushing can be carried out according to different material characteristics, and energy consumption is effectively reduced.

The invention relates to the technical field of body-equipped intelligent robots and intelligent equipment, in particular to a self-adaptive flexible tightening body-equipped intelligent robot which comprises a body-equipped moving platform, the top of the body-equipped moving platform is provided with a body-equipped lifting mechanism, and the top of the body-equipped moving platform is provided with a body-equipped articulated arm module. And a tightening actuator and a clamping jaw actuator are arranged at the tail end of the outer part of the body articulated arm module. According to the self-adaptive flexible tightening intelligent robot and the tightening process method, sensing and self-adaptive adjustment of external environment changes are achieved through multi-module integration, and the tightening process method based on a multi-mode sensing system and an intelligent control system is constructed; vision, force sense and touch sense data are fused, real-time monitoring and dynamic adjustment of the tightening process are achieved through a machine learning algorithm, and the intelligent level and quality stability of tightening operation are effectively improved.

The invention relates to the technical field of protein enzymolysis, and discloses an intelligent control system and method for an animal protein enzymolysis production process, and the method comprises the steps: obtaining key attribute parameters of animal protein raw materials, building a raw material grading model, and obtaining initial grades of the raw materials; matching a preset grading corresponding relation library according to the initial grading to obtain an initial stage optimal production parameter range; collecting a first production parameter before enzymolysis to obtain an adjusted production parameter; starting enzymolysis according to the adjusted parameters, collecting process key parameters in real time, inputting an optimal production parameter prediction model, and predicting an optimal production parameter at the next moment; if the optimal production parameters exceed the safety range, correction is carried out; continuously performing enzymolysis according to the corrected parameters, and continuously predicting and correcting the parameters until the enzymolysis is finished; through raw material grading and parameter real-time optimization, the process parameters can be dynamically regulated and controlled according to different batches of raw materials, the production efficiency is improved to the maximum extent while the product quality is guaranteed, and remarkable intelligence and adaptability are achieved.

The invention discloses an intelligent control system and method for gas-liquid photoelectric slip ring injection molding machining, and relates to the technical field of injection molding process control. The data acquisition module is used for acquiring multi-dimensional production data by using a sensor array and an advanced optical technology; the data analysis and processing module fuses and optimizes data through deep learning; the decision-making module combines reinforcement learning and a knowledge graph to generate a strategy; the execution control module adopts an advanced control algorithm to realize accurate control; the monitoring and early warning module guarantees production by using technologies such as digital twinning; the man-machine interaction module applies a leading-edge interaction technology; the communication module adopts a high-speed communication architecture; all the modules cooperate to achieve intellectualization and precision of injection molding machining. The gas-liquid photoelectric slip ring injection molding processing level is greatly improved, the product percent of pass is improved, the production efficiency is improved, the unit product energy consumption is reduced, meanwhile, intelligent interaction and remote management are achieved, and the production benefit and quality are improved.

The invention relates to the technical field of automatic control, and further relates to a water and fertilizer integrated multi-objective optimization intelligent regulation and control system based on artificial intelligence driving, and the system comprises a water and fertilizer load analysis unit and a water and fertilizer combined regulation and control criterion generation unit. The water and fertilizer load analysis unit is used for acquiring meteorological data from a meteorological monitoring system; simultaneously receiving data from the soil sensor and the leaf surface sensor, acquiring growth state data of the crops, and evaluating water demand and fertilizer demand of the crops in unit area at each time; and the water and fertilizer combined regulation and control criterion generation unit is used for dynamically calculating the water application amount and the fertilization amount of the crops in unit area at each time according to the soil state and the root system absorption capacity of the crops by using an AI dynamic resource optimization engine. According to the invention, the utilization efficiency of moisture and nutrients is effectively improved, the waste of resources is reduced, and the intelligence, adaptability and robustness of regulation and control are enhanced.

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 relates to a part burr removing technology, in particular to an intelligent control system for removing burrs of machined parts, which is used for solving the problems that manual burr removing operation is poor in stability, parts are easy to damage, part of hidden burrs are difficult to perceive and remove, and the burr removing efficiency is low. Automation of the burr removing process is achieved through a plurality of modules arranged in the program controller, manual intervention is reduced, the production efficiency is improved, the path planning module conducts optimal planning on a burr treatment path according to a burr detection result, the efficiency and effect of burr removing work are improved, and the labor intensity of workers is lowered. The real-time monitoring module can monitor the working state of the deburring tool in real time, it is ensured that the deburring tool operates according to the optimal path, whether the deburring tool can continue to execute deburring work or not can be judged in real time, the whole control system is intelligently controlled through a program, dependence on manual deburring operation is reduced, the production cost is reduced, and the working efficiency is improved. And the safety risk possibly existing when the burrs are manually removed is avoided.

The invention relates to the technical field of electric power system control, in particular to an electric power resource intelligent control system based on digital twinning, which comprises a data acquisition module, a digital twinning model module, an electric power dispatching module, a fault early warning module and a three-dimensional visualization module. According to the method, the load demand prediction model is established based on the hybrid RNN, the RNN and full-connection network hybrid neural network model is constructed, the historical time sequence, the time feature, the historical statistical feature and the similarity feature are learned, and the problem that the power load demand is difficult to predict due to renewable energy source grid connection and power demand increase is solved; a power dispatching decision is made through a multi-objective function optimization algorithm based on an improved particle swarm, the particle swarm optimization algorithm is improved by adding a variation strategy for high-fitness particles, the problem that the particle swarm optimization algorithm is prone to falling into a local optimal solution in the practice process is solved, and therefore the problem that power dispatching of multi-objective optimization is difficult to achieve is solved.

An automatic control method and system for an electrostatic precipitation system. The method combines a neural network control algorithm with a PID regulation control algorithm, and uses the neural network control algorithm to carry out preprocessing on the control of the electrostatic precipitation system and uses the PID regulation control algorithm to carry out initialization processing when the electrostatic precipitation system is out of control, thereby avoiding the uncontrollable state of the electrostatic precipitation system caused by PID control overshoot or oscillation; moreover, the PID regulation control algorithm is used to carry out real-time tracking regulation on the electrostatic precipitation system, so that the real-time response speed of intelligent control of the electrostatic precipitation system can be effectively increased. Therefore, the energy conservation and efficiency improvement of the electrostatic precipitation system are better realized while the operation stability and response rapidness of an electric precipitator are ensured and controlled. By combining the two control algorithms, once intelligent control is used, the automatic control method effectively prevents the electrostatic precipitation system from being temporarily out of control due to excessive overshoot, thereby ensuring the stable operation of the electrostatic precipitation system, and implementing intelligent control.

The invention relates to the field of rail transit control systems, and discloses a rail transit intelligent control system and method based on the Internet of Things, and the system comprises a sensing layer which is integrated with a visual sensor, a thermal imaging sensor, a sound sensor, a pressure sensor, a temperature and humidity sensor, a CO2 sensor and other sensors, the sensing layer collects various data of the rail transit system in real time, and the sensing layer performs joint optimization processing on sensor data by adopting deep learning and reinforcement learning algorithms through an adaptive data fusion method so as to realize high-precision real-time monitoring, data quality screening and anomaly detection; and the network layer comprises a wireless communication module and adopts an intelligent routing mechanism based on real-time network load prediction. A self-adaptive data fusion method is adopted by the sensing layer, and the sensing capability of the rail transit system is greatly improved through intelligent data processing, so that the system can realize real-time and accurate response to a complex traffic environment.