10008 results about "Automatic control" patented technology

The invention discloses an automatic control method and system for secondary granulation of a high-voltage zinc oxide resistor disc, relates to the technical field of intelligent manufacturing of power equipment, and solves the problems of out-of-control particle morphology caused by dynamic coupling parameter identification lag and control instability caused by multi-physical field parameter coupling in an existing method. According to the invention, a dynamic physical property parameter matrix is generated in real time based on multi-band dielectric relaxation spectrum analysis and terahertz wave tomography; predicting a fluidized phase change threshold value and an energy gathering area through multi-physics field coupling modeling; a time sequence attention deep reinforcement learning algorithm is adopted to generate a multi-field cooperative adjustment instruction; positioning a parameter conflict source and triggering decoupling compensation by combining a high-frequency vibration and acoustic emission combined monitoring module; performing closed-loop correction on the control network weight based on the laser spectrum data and a partial least squares regression model; the real-time performance of fluidization parameter identification, the stability of multi-field coupling control and the recovery efficiency of abnormal working conditions are remarkably improved, and meanwhile the batch consistency of the electrical performance of the resistor discs is guaranteed.

The invention discloses a smelting waste gas recycling optimization system based on fuzzy control, and relates to the technical field of automatic control. The problems of low control precision and response lag caused by the fact that an existing static membership function cannot adapt to multi-scale working condition changes in real time are solved. Comprising a sensing acquisition module, a membership reconstruction module, a feature decoupling module, a coupling compensation module, a rule optimization module and an execution control module. Standardized data are acquired through sensing acquisition and filtering, an adaptive membership function is constructed by adopting incremental fuzzy clustering, and closed-loop self-correction of a multi-modal control instruction is realized by combining wavelet packet decoupling, feed-forward compensation and double-depth Q network reinforcement learning online optimization; the waste gas desulfurization efficiency, energy consumption optimization and treatment system stability of waste gas treatment can be effectively improved.

The invention relates to the technical field of microbial fermentation control, in particular to an automatic microbial reaction fermentation control system and a control method. The method comprises the following steps: collecting real-time microbial environment parameters and identifying a data multi-axis structure, extracting master control environment interval data, eliminating redundant signals to generate standard microbial environment data, defining a fermentation degree contrast template through fermentation historical records, and constructing a microbial fermentation timeline framework. The method comprises the following steps: acquiring standard environmental data, identifying a fermentation state of the standard environmental data, constructing a simulated microbial environmental field by using the standard microbial environmental data, performing global microbial reaction simulation, determining fermentation reaction structure partitions based on the data, performing difference comparison to generate fermentation difference data, and performing compensation simulation by using the difference data. And automatic fermentation compensation control of microorganisms is realized. According to the invention, the intelligent automatic control and optimization of the microbial fermentation process are realized, the fermentation efficiency and stability are obviously improved, and the manual intervention is reduced.

The invention relates to an intelligent decision-making method, system, equipment and medium for sewage medicament addition, and the method comprises the following steps: detecting and preprocessing sewage water quality parameters, extracting feature vectors, and carrying out clustering analysis to obtain water quality categories. Historical dosing data is retrieved and subjected to statistical analysis, and an initial dosing scheme is generated; a water quality change curve is obtained by simulating the scheme, and then the optimal dosing scheme is obtained through optimization. And finally, generating and issuing an agent adding control instruction, and executing treatment, so that the technical problems that the traditional agent adding mode mostly depends on artificial experience or simple automatic control, and is difficult to cope with the complex working condition of dynamic change of water quality and water quantity, so that the agent adding is inaccurate, and the agent waste is possibly caused are solved.

The invention discloses a rail transit hub emergency regulation and control method and system based on real-time simulation, and relates to the technical field of intelligent traffic and rail transit emergency management, and the method comprises the steps: carrying out the time synchronization processing of real-time passenger flow data and train operation state data, and generating a hub state data set; constructing a multi-level simulation model, and simulating a passenger flow distribution evolution process by presetting passenger flow evacuation behaviors and train operation constraint conditions; based on an output result of the multi-level simulation model, positioning capacity limiting nodes in each evacuation path by adopting a bottleneck identification algorithm, calculating passenger flow carrying capacity, and generating an optimal path regulation and control parameter with shortest evacuation time as a target; and the optimal path regulation and control parameters are converted into a train automatic control system instruction and a passenger flow guide display instruction, the instructions are issued to each line control unit through a hub centralized dispatching system, and the train operation frequency and the passenger flow evacuation direction are adjusted. According to the invention, intelligence and collaboration of emergency regulation and control of the rail transit hub are realized.

The invention belongs to the technical field of automatic control, and relates to a self-adaptive control method based on multi-physical modeling. According to the method, by collecting multi-source data of a controlled object, a thermal, electric and force coupling relation used for control analysis is established so as to describe dynamic responses under different operation conditions. And calculating stress, motor power, energy consumption and temperature rise change in the operation process based on a coupling relation to obtain system performance data, verifying stability and safety of different control parameter combinations in a simulation environment, and obtaining performance indexes including operation retardation risk, overload safety margin and safety response time limit. And according to a simulation result, under the condition of meeting safety constraints, taking energy consumption and temperature rise as optimization targets, adjusting control parameters, generating optimized control parameter configuration data, and feeding back the optimized control parameter configuration data to a control unit, so that closed-loop adaptive control and performance optimization are realized. According to the invention, through multi-physical coupling modeling and simulation optimization, the adaptability and reliability of the automatic control system are improved.

The invention provides a robot control method based on Model-Based and RL, and relates to the technical field of automatic control. According to the method, a high-level task atlas model and a predefined task action set are constructed, a target task is mapped into a state node, graph search is carried out in combination with a current world state, an optimal task action path is planned, and a first action instruction is output. And calling a baseline strategy in the simulation environment to generate a control instruction, executing feasibility verification, compensating the deviation between the simulation and the real environment through the error prediction model, correcting the real control instruction, and then driving the robot to execute. And dynamically updating the success probability weight of state transition in the atlas based on the execution result, and feeding back the executed state for subsequent decision. According to the method, the reliability, the adaptability and the execution safety of migrating a simulation strategy to a real robot are improved, and closed-loop optimization and continuous self-adaption of a complex task are realized.

The invention discloses a high-precision laser positioning unpacking path planning system, and belongs to the technical field of robot automatic control and industrial automation. The system comprises a data synchronization module used for multi-sensor hardware synchronization and data alignment; the high-precision positioning module is used for outputting a precise pose based on environment skeleton characteristics and sliding window optimization; the semantic map construction module is used for fusing vision and laser data to generate a dynamic semantic grid map; the global path planning module is used for planning a smooth path in the skeleton channel by utilizing a mixed potential field improved A * algorithm; the motion control module is used for realizing closed-loop motion control and safety monitoring through model prediction and tracking; and the operation execution module is used for finishing millimeter-level precise stopping of an operation point by adopting visual servo and triggering unpacking operation. According to the method, the positioning robustness under dynamic shielding is improved through the environmental skeleton features, the safety and the high efficiency of the path are ensured by utilizing semantic understanding and intelligent planning, and the full-process automation from navigation to precise operation is realized.

The invention provides a dexterous hand based on multi-modal fusion control, and relates to the technical field of automatic control, the dexterous hand comprises a palm module, a palm center vision sensing module, a finger module, a fingertip force sensing module and a multi-modal control module; the palm center visual sensing module is used for acquiring visual information of the dexterous hand in a working area; the finger module comprises at least two fingers; the fingertip force sensing module is used for detecting information of contact force applied to the fingertip position; and the multi-mode control module is used for controlling the dexterous hand to grab a target object based on the visual information and the contact force information. According to the dexterous hand based on multi-mode fusion control, data of two modes of visual information and contact force information are fused, so that the dexterous hand can adapt to different grabbing environments, intelligent closed-loop control of visual guidance and force sense regulation and control is realized, and the environmental adaptability and flexibility of the dexterous hand are improved.

The invention discloses an automatic planning method and system for a curved surface path of a grinding robot, and the method comprises the following steps: S1, constructing a to-be-machined three-dimensional curved surface model and a triangular mesh model, and relates to the technical field of automatic control of robots for surface machining in the manufacturing industry. According to the method, a complex three-dimensional surface coverage path planning problem of a workpiece is converted into two low-complexity sub-problems of key point sampling of a two-dimensional path planning domain and three-dimensional shortest path searching on a triangular mesh model, a small number of sampling points are sampled only on the two-dimensional path planning domain, and the sampling process depends on a spatial index framework; according to the method, global traversal of disordered point clouds is avoided, three-dimensional coordinates are positioned through nearest neighbor search of a KD tree algorithm, adjacent three-dimensional ordered key points are sequentially connected through a graph search algorithm, and a continuous machining path is formed, so that the operand is greatly reduced, real-time response of path planning is facilitated, and the problems that traditional online planning is long in time consumption and difficult in real-time response are solved.

The invention relates to the crossing field of mechanical engineering and automatic control, in particular to a global path planning and anti-swing control method for a ship unloader, and aims to solve the problems of rigid path planning and poor synergism of out-of-control swing of a lifting appliance in traditional ship unloading operation. The method comprises the following steps: constructing a dynamic three-dimensional operation space model with fusion of a laser radar and binocular vision, and updating obstacle information in real time; an improved fast extended random tree algorithm is adopted to generate a high-smoothness initial path; establishing a six-degree-of-freedom lifting appliance swinging dynamic model and identifying parameters on line; path tracking and anti-swing torque are synchronously optimized through a model prediction controller, and the control period is smaller than or equal to 20 milliseconds; and closed-loop feedback correction is implemented by combining an encoder and an inertial measurement unit. According to the scheme, environment dynamic sensing, path-anti-swing depth cooperation and multi-disturbance self-adaptive compensation are achieved, the operation rhythm is improved by 30% or above, the system still operates stably under the 8-level wind condition, and high precision, high robustness and engineering implementability are achieved.

The invention relates to the technical field of industrial process automatic control, and discloses an intelligent manufacturing flexible production line cooperative beat control method. The method is used for solving the technical problem that dynamic adjustment of the real-time rhythm of the flexible production line is difficult to achieve under the extreme uncertainty condition in a traditional method. The method comprises the following steps: firstly, collecting operation data and external uncertainty signals of each station of the flexible production line, and carrying out preliminary integration through a central control module; classifying the data, and distinguishing internal deviation and external interference according to priorities; constructing a dynamic mapping relation based on a classification result, and associating uncertain factors to beat parameters to form a temporary adjustment framework; correcting station beat parameters step by step by using a frame, and transmitting an instruction from upstream to downstream; synchronously monitoring a response state during correction, and returning information to update a mapping relation; the updated mapping is applied to acquisition and processing of the next period to form closed-loop iteration; according to the method, the problem of real-time rhythm dynamic adjustment under the extreme uncertainty condition is solved.

The invention relates to the technical field of automatic control, in particular to a multi-parameter cooperative control method and device for a clamping system of a precision boring and milling machine. According to the method, an industrial camera is used for collecting and processing a workpiece image and extracting visual features, and the visual features are fused with process parameters of a workpiece and geometric distribution parameters of a workpiece key area obtained from a process database; generating a comprehensive state vector; based on the vector, outputting a target clamping force adjustment coefficient by a pre-trained MLP decision model, and calculating a final target clamping force in combination with a safe clamping force range; in the main clamping stage, vibration, temperature and pressure signals are synchronously collected, multi-modal disturbance characteristics are extracted, observation vectors are constructed, the observation vectors are input into a preset fuzzy rule base for reasoning, and compensation activation factors are obtained; and finally, a current correction instruction is dynamically generated according to the clamping force deviation and the compensation factor, and an electro-hydraulic proportional valve is driven to achieve accurate compensation. According to the invention, the stability and the machining reliability of the clamping system are obviously improved.

The invention discloses a dynamic feeding speed adjusting method self-adaptive to production line takt, and relates to the technical field of production line automation control, and the method comprises the following steps: 1, constructing a production line takt sensing system, setting sensing units at all key stations of a production line, and setting a plurality of monitoring points on a material transmission path of the production line; according to the invention, various sensing assemblies are arranged at key stations, various state information of materials in the process from station entering to transmission can be captured in real time in combination with monitoring points on a transmission path, and information reliability is ensured through data screening; therefore, the central processing unit can comprehensively master the actual operation condition of the production line, and firm data support is provided for follow-up adjustment. On the basis of characteristic parameters of multi-dimensional analysis, continuous correction is carried out in combination with historical data, and the defect that a traditional fixed model cannot adapt to beat fluctuation is avoided; in this way, the feeding speed can be adjusted on the basis of the current real production line takt all the time, and timeliness and pertinence of adjustment are ensured.

The invention discloses a pulse stable output control method and system for an ns-level high repetition frequency fast pulse power supply, and relates to the technical field of automatic control, the system is composed of a plurality of functional modules, and the system comprises a self-adaptive absorption module, a control module and an output control module, and the self-adaptive absorption module obtains power supply parameters through real-time sampling, and the power supply parameters comprise load impedance and switch tube parasitic parameters; dynamically optimizing R and C parameters of the RCD absorption circuit and the active clamping circuit based on power supply parameters; the feed-forward prediction module is used for extracting derivative state information according to the power supply parameters, acquiring load current feed-forward and resonant circuit states based on the derivative state information, modeling based on the load current feed-forward and resonant circuit states, and predicting the drain-source voltage valley bottom moment of the switch tube; and the multi-section variable impedance transmission module adopts a three-section variable impedance structure, performs broadband impedance matching based on R and C parameters of the optimized RCD absorption circuit and the active clamping circuit, inhibits pulse leading edge reflection distortion, and outputs undistorted pulse signals.

The invention relates to the technical field of temperature control, in particular to an enzyme reaction kettle self-adaptive temperature control system and method, and the system comprises a temperature detection module, a temperature control module, a heat conduction oil flow control module and an automatic regulation and control module; temperature data of a reaction kettle are collected in real time through a temperature detection module, a temperature control module adjusts parameters based on a PID algorithm and fuzzy logic to generate a control instruction, and a heat conduction oil flow control module adjusts flow by combining double-closed-loop cascade control with a feedforward compensation algorithm, so that thermal inertia lag is shortened. And the automatic regulation and control module generates optimized process parameters based on a reaction kinetic model and a genetic algorithm, and dynamically adjusts a temperature set value. The control method comprises the steps of temperature data acquisition, PID control instruction generation, conduction oil flow coordinated regulation, process parameter optimization and the like. The problems that in the prior art, temperature control precision is insufficient, conduction oil flow control is not accurate enough, and technological parameters cannot be automatically regulated and controlled are solved.

Systems and techniques are described for implementing autonomous control of powered earth-moving vehicles, including to automatically control movement of a vehicle's component parts on a job site to perform tasks. The techniques may include using an MPC-based Control System to perform a cycle of training and deployment of a predictive model specific to a particular earth-moving vehicle to control that vehicle's autonomous operations, and to further using resulting data in additional manners—such a cycle may include using a data gathering module on the vehicle to gather actual operational data of the vehicle during manual control of the vehicle on job site(s) by human operator(s) during performance of task(s), generating a 3D site map modeling the vehicle's surroundings, training a Model Predictive Control (MPC) model based on the actual operational data and 3D site map, and deploying the trained model to the vehicle for use in autonomous operations.

The invention relates to a painless gastrointestinal endoscope self-adaptive drug delivery method and system based on multi-source physiological signal fusion, and belongs to the technical field of medical artificial intelligence and automatic control. An artificial intelligence model is utilized to deeply extract features and quantitatively evaluate the sedation-analgesia level of a patient, a pharmacokinetics-pharmacodynamics model constructed by a big data platform is utilized to dynamically predict the drug effect according to the individual features and real-time state of the patient, and the anesthetic infusion rate is automatically and accurately adjusted through a closed-loop control algorithm, so that the patient can be accurately infused. Personalization and self-adaption of drug delivery are achieved, an independent safety monitoring mechanism is set, and real-time intervention is conducted on risk events such as respiratory depression. The problems that traditional anesthesia depends on artificial experience, single parameter monitoring is not comprehensive and individualized dynamic adjustment is lacked can be effectively solved, and the safety, comfort and medical efficiency of painless gastrointestinal endoscopy are remarkably improved.

The invention relates to the technical field of automatic control, and discloses a water affair treatment process optimization control method, system and equipment and a medium. The method comprises the steps of performing state judgment on sensor detection data through a multi-parameter state recognition algorithm to obtain a processing stage switching instruction; modeling a microbial activity change rule according to the switching instruction to obtain a staged prediction model; optimizing the prediction model through a switching model prediction control algorithm to obtain a multi-target control strategy; performing coordinated calculation on the set value according to the control strategy to obtain a distributed control instruction; and performing driving control on the equipment by using the control instruction through the field bus to obtain a coordinated operation state. According to the invention, the control precision and the multi-target coordination optimization capability of the water affair treatment process are improved.

The invention relates to the technical field of automatic control, in particular to a coating machine transverse closed-loop control method and system based on fuzzy PID. The method comprises the following steps: arranging a plurality of pressure sensors on the inner side of the boundary of a coating head to collect the pressure value of each measuring point in real time, and calculating the pressure difference value between the adjacent sensors to form a pressure gradient sequence; the abnormal degree of the boundary flow state is recognized according to the distribution mode of the pressure gradient sequence, when the pressure gradient exceeds a normal fluctuation range, a boundary disturbance event is marked, and boundary state monitoring data is obtained; and measuring the actual coating thickness of each transverse position, and comparing the actual coating thickness with a preset target thickness to calculate a thickness deviation value. According to the method, hysteresis and rigid constraints of traditional PID control are broken through, restrictions of difficulty in boundary disturbance modeling, control feedback delay and frequent manual parameter adjustment are overcome, and transverse closed-loop control based on data perception and intelligent judgment is realized.

The invention relates to the technical field of automatic control, and discloses a pressure closed-loop self-calibration method for an ultrahigh-speed jet injection process, and the method comprises the steps: initializing a system, and building a pressure and temperature combined compensation model; a high-precision sensor is used for collecting real-time pressure and temperature data; by calculating a pressure deviation intelligent switching control strategy, PID is combined with feed-forward compensation in a steady state, and a deep reinforcement learning algorithm is switched to realize quick response in a transient state; automatically triggering closed-loop self-calibration according to operation time or a deviation threshold value, executing zero point and full scale calibration and updating compensation parameters; and carrying out residual analysis by using a digital twinborn model to realize fault diagnosis and early warning. According to the invention, the problems of low control precision and poor long-term stability of an ultra-high-speed jet flow process under nonlinear and large-lag working conditions are solved, and high-precision stable control and system self-adaptive calibration under a wide speed range are realized.

The invention discloses an L-shaped conveyor belt path dynamic planning and congestion prediction system, which relates to the technical field of automatic control, and constructs a conveying path node diagram by collecting material weight, speed and spacing information of each node on a conveying path, and calculates a congestion index of each path section; the method comprises the following steps: combining a light-mass material jumping sensitivity model, identifying a high-risk node and marking the high-risk node as a congestion prediction area, constructing a dynamic optimization model based on path risk distribution and a material flow direction, generating and evaluating a plurality of path adjustment strategies, executing an optimal strategy according to priority, and realizing path speed adjustment, standby path switching and feeding rhythm control; through a feedback mechanism, a strategy effect is evaluated in real time, a path state is updated, operation data is synchronized to a historical database, a learnable conveying operation file is formed, the stability and the intelligent response capability of a conveying system can be effectively improved, and the method is suitable for high-speed sorting and automatic packaging scenes.

The invention relates to the technical field of fire-fighting facility detection, in particular to a fire-fighting pump room anti-freezing and moisture-proof system, which comprises a data acquisition module, a data processing module, a data processing module and a data processing module, and is characterized in that the data acquisition module is used for acquiring pump room environment parameters, outdoor environment parameters and indoor facility surface temperature parameters; the data processing module is used for preprocessing the pump room environment parameters, the outdoor environment parameters and the indoor facility surface temperature; the control decision module comprises an abnormity early warning unit which is used for calculating a freezing risk index and a moisture-proof risk threshold temperature, judging whether anti-freezing early warning or moisture-proof early warning exists or not and outputting an early warning result; and the automatic control unit is used for regulating and controlling the operation state of related equipment in the pump room according to the early warning result. According to the invention, through intelligent monitoring and dynamic adjustment of environment temperature and humidity and equipment surface temperature in the fire-fighting pump room, equipment in the fire-fighting pump room is effectively prevented from being damaged by freezing and humid environment.

The invention discloses a high-sensitivity miniaturized HOx free radical accurate measurement device and method, and relates to the technical field of atmospheric environment detection and automatic control, and the device is composed of a free radical generation and removal device, a light source module, longitudinal HOx double cavities, a gas distribution module and a collection and control system. The free radical generation and removal device can generate HOx free radicals or remove OH free radicals; the light source module can emit laser which enables OH free radicals to generate fluorescence and transmit the laser into the longitudinal HOx double-cavity; the longitudinal HOx double cavities are used for exciting OH free radicals to generate a large amount of fluorescence and collecting and converting the fluorescence into electric signals; the gas distribution module is used for injecting gas in the device and maintaining the flowing state of gas flow; and the control and acquisition system is used for controlling automatic operation and data acquisition and processing of the device. The device has the advantages of being high in detection sensitivity, small in size and capable of rapidly obtaining and removing OH free radical measurement interference, rapid calibration of similar concentration can be conducted on the actual HOx concentration in the actual measurement environment, accurate spectrum determination can be conducted on the laser wavelength, and therefore high-sensitivity accurate measurement of HOx free radicals is achieved.

The invention discloses a phase modifier unit state monitoring and load control method and system for reactive power compensation of a power grid, belongs to the technical field of automatic control of power systems, and mainly solves the problem that service life loss is aggravated or a control target is single due to the fact that the health state of equipment is not fully considered when an existing phase modifier unit responds to reactive power requirements of the power grid. The method comprises the following steps of: calculating reactive vacancy and extracting vibration frequency band energy characteristics by acquiring voltage and frequency of a power grid as well as vibration, temperature and exciting current signals of a unit in real time, and constructing a health state index HSI in combination with self-adaptive wavelet basis selection; taking minimum equipment life loss and optimal power grid voltage stability as multiple objectives, dynamically generating a load control instruction by adopting a particle swarm algorithm, realizing weight adaptive adjustment based on HSI, and introducing an online life loss model as a constraint condition; finally, reactive power output is adjusted through an excitation system, and cooperative control of power grid stability and equipment life extension is achieved.

The invention discloses a machine learning multi-objective optimization control method based on coal quality characteristics and combustion state feedback, and belongs to the technical field of industrial process automatic control. According to the method, collaborative optimization control over the combustion efficiency, NOx emission and combustion stability is achieved under the multi-working-condition operation condition of the combustor. According to the method, coal quality characteristic parameters and combustion state data are collected, working condition clustering is carried out after data fusion, and a historical combustion operation data set coupling coal quality characteristics and combustion states is constructed; a combustion state evaluation model and a combustion control model are established by adopting machine learning, on the basis of identifying working conditions in real time and evaluating combustion performance, a control parameter adjustment amount is generated in combination with trend extrapolation and a multi-objective cost function, and a combustion state feedback rolling correction model and a control strategy are utilized to realize combustion control. The self-adaptive closed-loop collaborative optimization control of the combustion process under multiple coal types and multiple working conditions is realized, the boiler efficiency is improved, the NOx emission is reduced, and the operation robustness is enhanced.

The invention relates to the field of power system automatic control and new energy grid-connected operation, and provides an energy storage-containing new energy field station frequency modulation optimization method based on deep reinforcement learning. The method comprises the following steps: firstly, establishing a frequency response model containing an energy storage new energy field station, constructing a frequency modulation problem into a Markov decision process, and defining a state space containing a charge state and frequency deviation, an action space taking a charge and discharge power instruction as an element, and a multi-target reward function fusing a frequency regulation effect and charge state management; and then designing an intelligent agent network based on a long short-term memory network and an improved soft actor-commentator algorithm, constructing an online training framework with priority experience playback and a sliding time window, and realizing adaptive optimization of the strategy. And finally, generating a robust frequency modulation instruction in real time based on the trained model. Simulation shows that the frequency long-term regulation and control capability and the intelligent level can be remarkably improved, and the frequency modulation performance and the frequency modulation margin are effectively coordinated.

The invention relates to the technical field of data processing, and discloses an automatic control platform operation data management method. The method comprises the steps of collecting industrial equipment data through a field bus, storing the industrial equipment data into a database in a classified mode and establishing an index table, extracting a control loop to construct a DCS model, generating a process abnormality diagnosis report, calculating control parameters by using a self-tuning algorithm and outputting a control instruction, optimizing a process formula based on an implementation effect and constructing a recommendation knowledge base. According to the intelligent data management method, the process abnormality is accurately diagnosed, the control parameters are automatically optimized, and the process knowledge is continuously accumulated, so that the control precision, the diagnosis efficiency, the parameter optimization capability and the knowledge reuse level of the industrial production process are improved.

The invention belongs to the technical field of intelligent energy and power system automatic control, and particularly relates to a cross-level collaborative regulation and control system integrating meteorological prediction, power market and equipment state, which comprises a central collaborative controller, a data acquisition unit, a communication network and an execution terminal, the data acquisition unit is used for acquiring meteorological data, power load data, electric energy consumption cost data and equipment operation state data in real time; the execution terminal at least comprises a photovoltaic inverter, an energy storage converter and a charging pile controller; the central cooperative controller is configured to execute three cooperative optimization closed loops, namely, a data fusion and prediction closed loop, a multi-target dynamic optimization closed loop and a multi-time scale control closed loop. According to the system and the method provided by the invention, the problems of real-time performance and accuracy of multi-source heterogeneous data fusion are solved; the balance optimization bottleneck of multiple targets such as economy, stability and environmental protection in the dynamic process is broken through; full-time-scale seamless cooperative control from second-level emergency response to hour-level economic dispatching is realized; and the self-adaptive capability and the overall performance of the system in different application scenes are improved.

The invention discloses a hoist automatic control method for intelligent water conservancy, and belongs to the technical field of intelligent water conservancy. The method specifically comprises the following steps: S1, constructing a multi-dimensional parameter monitoring system: deploying multiple types of sensors on a hoist and corresponding water conservancy facilities, collecting water conservancy environment parameters, equipment operation parameters and environment meteorological parameters in real time, performing cleaning, format conversion and space-time alignment on data, and synchronizing the processed data to an intelligent water conservancy cloud platform; s2, historical data preprocessing and depth feature extraction: obtaining historical data of the multi-dimensional data, performing multi-dimensional preprocessing, constructing depth feature extraction ore pulp based on the preprocessed historical data and the data acquired in real time, and extracting depth features capable of accurately reflecting a water conservancy scene rule and an equipment operation state. By constructing a multi-dimensional parameter monitoring system, three types of parameters of a water conservancy environment, equipment operation and environmental meteorology are collected in real time, and decision one-sidedness caused by only depending on water level data is avoided.