5283 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.

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 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 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 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.

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.

The invention provides an intelligent control system for pulse signal switching, which relates to the technical field of data processing and is used for acquiring main pulse signal data of a main reading head and auxiliary pulse signal data of an auxiliary reading head, calculating a differential sequence of the main pulse signal data and generating an abnormal signal indication. The method comprises the following steps: detecting duration and stability of an abnormal fluctuation signal, generating an abnormal confirmation instruction, calculating a numerical value difference between main pulse signal data and auxiliary pulse signal data, generating a switching trigger instruction, switching the main pulse signal data and the auxiliary pulse signal data, extracting an error sequence between pulse signals of a main reading head and an auxiliary reading head, and outputting the error sequence. The method comprises the following steps: acquiring an error correction formula, correcting switching signal data according to the error correction formula to obtain corrected signal data, calculating time offset, and performing time sequence alignment on the corrected signal data according to the time offset to obtain aligned signal data. According to the invention, switching of pulse signals can be intelligently controlled.

The invention discloses an industrial robot intelligent control system which comprises a master control terminal, the master control terminal is provided with an industrial-grade multi-core processor and is integrated with a motion control subsystem and an environment sensing subsystem, the motion control subsystem is connected with the master control terminal through a first communication interface, and the environment sensing subsystem is connected with the master control terminal through a second communication interface. According to the motion control subsystem, a distributed axis controller array and reinforcement learning strategy switching algorithm is adopted, so that the trajectory tracking precision and the energy consumption efficiency are improved; and the environment sensing subsystem realizes real-time anomaly detection through multi-mode sensor fusion and an LSTM-GAN fault prediction model. The system supports containerized deployment and edge computing collaboration and integrates AR human-computer interaction, and the adaptive capacity and safety of the industrial robot are remarkably improved.

The invention discloses a smart city street lamp intelligent control system and method. The method comprises the following steps: performing multi-aspect inspection on each hardware device through a comprehensive intelligent control module and a communication module; the environment monitoring module is used for collecting multi-source data in real time and integrating and analyzing the multi-source data through the data fusion module; generating a dynamic dimming strategy of the common road section and the high-priority road section according to the collected data and regional requirements; brightness adjustment and on-off control are executed one by one through the single lamp control module, and equipment operation parameters are collected through the real-time feedback and monitoring module. The remote management platform is used for monitoring the running state of the street lamp in real time and dynamically adjusting strategy optimization analysis; different operation scenes are simulated through a system simulation test, actual deployment is carried out in a pilot area, and parameters and operation logic are optimized. Through modular design, intelligent decision making and data driving modes, the management efficiency and the energy-saving level of urban street lamps are remarkably improved, and reliable technical support is provided for smart city construction.

The invention provides an LED lamp intelligent energy-saving control method and system, and the method comprises the steps: obtaining environment illumination intensity data, personnel flow density data and current working state parameters of an LED lamp, which are collected by a plurality of sensors, carrying out the preprocessing of the original data of the sensors, and obtaining a standardized environment perception data set; calculating power resources required by the intelligent control system are evaluated according to the LED lamp control parameter optimization scheme, the brightness output of the LED lamp is controlled according to the calculating power resources, and the current proportion of red, green and blue LED chips is adjusted according to the color temperature; whether the light beam angle needs to be adjusted or not is judged through matching analysis of the light beam angle and environmental perception data, if yes, the lens position of the LED lamp is changed, and the output spectral characteristics of the LED lamp are measured and determined through a spectrum analyzer; after sensor calibration parameters are updated, LED lamp power consumption data are collected, and if the deviation between actual power consumption and a theoretical calculation value is smaller than a preset percentage, it is determined that the energy-saving control effect reaches an expected target.

The invention belongs to the technical field of automatic assembly, and discloses an automatic assembly system and method for a large nut of a water turbine ball valve, and the system comprises a mechanical arm, an end effector and an intelligent nut tightening control system. The end effector integrates an electromagnet adsorption mechanism, a servo drive rotating mechanism, a floating compensation mechanism and a visual recognition mechanism, nuts of different specifications are grabbed in a self-adaptive mode through an electromagnet, submillimeter-level accurate positioning is achieved through a laser galvanometer stereo camera, and the assembling posture is automatically adjusted in combination with the floating compensation mechanism. The intelligent control system adopts a staged tightening strategy, performs real-time monitoring through a torque sensor, and has a dual protection mechanism. The hydraulic turbine ball valve large nut assembling machine achieves full-automatic assembling of hydraulic turbine ball valve large nuts, has the advantages of being high in assembling efficiency, high in positioning precision, high in adaptability, reliable in quality and the like, solves the problems that manual assembling is large in labor intensity, many in potential safety hazards, poor in quality consistency and the like, and remarkably improves the assembling quality and efficiency of key parts of a hydropower station.

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 discloses an efficient refrigerating machine room air conditioner energy-saving intelligent control system and method, and relates to the field of air conditioner intelligent control. The method comprises the steps that air conditioner operation parameters, machine room equipment operation data and machine room environment data are collected and preprocessed; building a deep neural network by using a deep Q-learning reinforcement learning algorithm to obtain a thermal load demand of the machine room; introducing a nonlinear dynamic compensation function to calculate an air-conditioner dynamic energy efficiency coefficient and air-conditioner refrigeration power, and calculating the rotating speed of an air-conditioner compressor by referring to air-conditioner operation parameters; a PID (Proportion Integration Differentiation) controller is used for carrying out primary frequency conversion on a machine room air conditioner, and a multi-stage adjusting mechanism is used for correcting a primary frequency conversion result in real time according to machine room equipment operation data, machine room environment data change and frequency conversion conditions. According to the air conditioner energy-saving control method, efficient air conditioner energy-saving control is achieved by combining the deep Q-learning reinforcement learning algorithm, the nonlinear dynamic compensation function and the PID controller.

The invention discloses an operation balance optimization method for a waste incineration thermal power plant. Efficient and environment-friendly waste treatment and green energy recovery are achieved through regulation and control of core parameters. Comprising an intelligent control system, a carbon cyclic utilization technology and a flue gas purification technology, an optimization model taking comprehensive net income maximization and carbon emission intensity minimization as targets is constructed, a garbage heat value, incinerator temperature, steam parameters, power generation efficiency, station service power consumption rate and the like are selected as core parameters, an operation balance optimization target function is constructed, and the comprehensive net income maximization and the carbon emission intensity minimization are realized. And through an intelligent control system, key parameters such as the incinerator temperature, the excess air coefficient and the flue gas treatment temperature are monitored and adjusted in real time, it is ensured that garbage is fully combusted in the incinerator, harmful gas emission is reduced, and the power generation efficiency is improved. Compared with the prior art, efficient operation of the waste incineration thermal power plant and maximum utilization of green energy are achieved, and remarkable economic and environmental benefits are achieved.

The invention discloses an efficient and energy-saving type intelligent management and control system and method for an integrated cold station, and particularly relates to the technical field of intelligent management and control of cold stations. A plurality of sensors are arranged in a cold station system, the operation state of each component and the external environment change are monitored in real time, historical data and external environment information are combined, and a load fluctuation prediction model is established by adopting a long-short-term memory network, so that the load demand is accurately predicted, and the operation parameters of each component of the system are adjusted in advance; meanwhile, on the basis of real-time monitoring data and load prediction deviation, dynamic optimization is conducted on the cold station system through fuzzy logic, and therefore the equipment response speed and the energy-saving efficiency are balanced; abnormal detection is carried out on the system through a machine learning model, potential fault risks are found in time, and early warning is given out; the system can quickly and accurately adjust the operation state when the load fluctuates, the stability, the energy efficiency and the fault prevention capability of the cold station system are improved, and the overall operation efficiency and the energy-saving effect are remarkably improved.