172173 results about "Control engineering" patented technology

The invention relates to the technical field of cable operation state detection, and particularly discloses a multi-source data driven cable operation state comprehensive evaluation method, which comprises the following steps of S1, adopting a layered distributed sensing network architecture, and deploying three types of core sensors at key nodes of a cable, through space-time calibration of the multi-source heterogeneous sensor, data consistency is improved, fusion deviation is eliminated, the problem of data islands of a traditional system is solved, and a precise evaluation foundation is laid; noise suppression and dynamic correlation modeling are adopted, environmental interference is stripped, a vibration and displacement coupling relation is quantified, limitation of a single parameter is broken through, heterogeneous fault features are captured, and evaluation comprehensiveness and sensitivity are improved; a self-adaptive threshold mechanism is constructed based on environment weight and historical data, the bottleneck of a fixed threshold is broken through, an evaluation standard is corrected along with equipment aging and environment change, misjudgment is avoided, and diagnosis robustness in different scenes is enhanced.

Disclosed in the present invention are a fuzzy-logic-control-based coordination method and system for a power grid requirement response and an energy storage system, the method comprising: S1, collecting real-time power grid data and prediction data, and constructing a corresponding real-time power grid data set and a corresponding prediction data set; S2, using a fuzzy algorithm to convert the real-time power grid data set, the prediction data set and multi-dimensional renewable energy information into a fuzzy set; S3, customizing a power grid requirement response measure and an operation strategy of an energy storage system; S4, executing the strategy customized in step S3; S5, monitoring in real time the execution effect of the strategy and collecting operation data such as a power grid load matching degree, energy storage device response speed and efficiency, and a requirement response participation degree; and S6, periodically updating a decision model of a fuzzy logic controller. In the present invention, the fuzzy logic controller is used to process and analyze power grid data in real time, such that the uncertainty and ambiguity during power grid operation can be effectively handled, especially for the production capacity fluctuation of renewable energy and the rapid changes of power loads.

The invention discloses a hydraulic pressure test tube machine and a tube body clamping centering device, in the tube body clamping centering device, two guide rods and a telescopic oil cylinder are arranged on a base side by side; corresponding ends of the two guide rods are respectively provided with a first sliding block, corresponding other ends of the two guide rods are respectively provided with a second sliding block; a fixed end of the telescopic oil cylinder is fixed on one end of the base, the fixed end of the telescopic oil cylinder is arranged corresponding to the second sliding blocks; a telescopic end of the telescopic oil cylinder is connected with the first sliding blocks, and is used for driving the first sliding blocks to slide along the guide rods, one of two clamping blocks is fixed on the two first sliding block, and the other one of the two clamping blocks is fixed on the two second sliding blocks; the first sliding block on one of the two guide rods is provided with a first rack, the second sliding block on the other one of the two guide rods is provided with a second rack, both sides of a linkage gear are respectively meshed with the two racks, the first sliding blocks and the second sliding blocks are adjusted by the telescopic oil cylinder to move towards each other or depart from each other. The scheme can solve the problems of high control precision and slow regulation efficiency of a plurality of oil cylinders of the tube body clamping centering device.

The invention discloses an intelligent cable fault diagnosis and positioning method and system, and relates to the technical field of intelligent operation and maintenance of a power system. The method is used for accurately identifying and positioning high-resistance faults and external damage. According to the method, electric field, current, temperature and vibration signals are synchronously collected, a multi-source fusion enhanced signal flow is constructed, and multi-physical field features are extracted; based on a coupling mechanism of an electromagnetic-thermal field and a mechanical-electric field, generating a fault type label and a space coordinate; executing targeted impedance correction for different fault types, establishing a dynamic topology network, and inputting a time-space diagram neural network to output a preliminary positioning result; and multi-source verification is carried out by further fusing salinity dielectric, a harmonic thermal field, a vibration electric field and stress topological information, a high-confidence-coefficient fault positioning result is finally output, a closed-loop diagnosis mechanism is formed, and the fault recognition accuracy and the system adaptability under complex working conditions are improved.

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 present disclosure relates to the technical field of intelligent monitoring and management of power systems, and specifically relates to an intelligent collaborative power consumption regulation and control method, apparatus and system for source-grid-load-storage, an electronic device and a storage medium. Said system comprises an energy regulation and control center and energy regulation and control units provided in microgrids; the energy regulation and control units use a temporal attention mechanism-based LRCN dual-layer network combined model to predict power consumption amounts, so as to generate power consumption surpluses and shortages within a future preset time; and on the basis of the power consumption surpluses and shortages and latest current electricity prices of the microgrids, the energy regulation and control center uses a fusion multi-objective algorithm based on a Pareto front curve and a fuzzy algorithm to generate a microgrid collaborative power consumption regulation and control solution, and sends the regulation and control solution to the energy regulation and control units for execution, so as to ensure the balance of energy supply and demand of the microgrids. Therefore, the present disclosure achieves efficient, intelligent and refined energy management for microgrid clusters, reducing energy consumption and costs, and providing solid support for sustainable development of microgrids.

A dynamic optimization system for energy consumption of a refrigeration house based on digital twinning is characterized by comprising a data acquisition module used for acquiring basic structure data of the refrigeration house, technical parameters of a refrigeration system, real-time operation data and historical operation data, preprocessing the data and then outputting a standardized multi-dimensional real-time data stream; the model construction module is used for constructing a 3D geometric model, a thermodynamic transfer model and a refrigeration system mathematical model according to the multi-dimensional real-time data flow, performing machine learning calibration on model parameters through historical operation data, and performing fusion to construct a refrigeration house digital twin model; the prediction analysis module is used for predicting future energy consumption demand and load change according to the refrigeration house digital twin model and the real-time operation data, and outputting an energy consumption prediction result and a load analysis report; a strategy generation module; an execution feedback module; and a learning optimization module. Overall energy consumption of the refrigeration house is reduced, energy utilization efficiency is remarkably improved, and goods storage safety is guaranteed.

The invention discloses a multi-mechanical-arm space-time synchronization control method for coiled pipe welding, and belongs to the technical field of automation control, and the method comprises the steps: in a primary laser and TIG hybrid welding execution stage, carrying out real-time data acquisition on a welding area of a workpiece, carrying out real-time deviation detection, and once real-time welding deviation is detected, carrying out time-space synchronization on the welding area of the workpiece; a local correction mechanism is triggered immediately, and deviation is prevented from being accumulated in the single welding process; after one-time welding is completed, the workpiece enters a transition area, global contour reconstruction is conducted on a welded section of the workpiece through three-dimensional scanning equipment, global deviation recognition is conducted, and unprocessed hysteresis deformation and accumulative errors are covered and corrected in real time; on the basis of the analysis result of the global deviation recognition, double correction is conducted on secondary welding, and cooperative control over space track correction and technological parameter adaptation is achieved; and after secondary welding is completed, the correction effect is evaluated through a double-layer evaluation mechanism, and the stability of the correction process is ensured.

The invention discloses a stamping part forming precision dynamic monitoring system based on digital twinning, and particularly relates to the field of general monitoring and adjusting systems, and the stamping part forming precision dynamic monitoring system comprises a digital twinning modeling module, a twinning simulation prediction module, an intelligent function coupling decision module and a physical execution closed loop feedback module; the digital twinning modeling module collects stamping related data through a multi-source sensor, and constructs and dynamically updates a geometric, physical and behavior three-level digital twinning body after preprocessing; the twinborn body simulation prediction module is based on three-stage twinborn bodies and is combined with parameter initialization, double-source simulation, result fusion and threshold decision to realize pre-judgment of forming precision; the intelligent function coupling decision-making module generates a targeted regulation and control instruction through a basic regulation and control function and a dynamic coupling mechanism according to the precision out-of-tolerance signal and the physical data; and the physical execution closed-loop feedback module completes instruction execution, state perception and model correction through iterative loop, realizes dynamic monitoring of the forming precision of the stamping part, and improves the forming precision stability and the production efficiency of the stamping part.

The invention discloses a self-generating and self-using distributed photovoltaic power station power anti-countercurrent control method and system, and relates to the technical field of photovoltaic anti-countercurrent control, and the method comprises the steps: obtaining the real-time load power of a grid-connected point of a power grid and the output power of a photovoltaic inverter, and calculating the net load power deviation; when the net load power deviation is smaller than a preset countercurrent risk threshold value, it is judged that a countercurrent risk exists, and an anti-countercurrent adjusting instruction is generated; in response to the anti-countercurrent regulation instruction, dynamically correcting the maximum output power limit value of the photovoltaic inverter, and synchronously activating the charge and discharge compensation mode of the energy storage system; an anti-countercurrent control coefficient is calculated, and the power reduction proportion of the photovoltaic inverter and the compensation power of the energy storage system are synchronously adjusted based on the anti-countercurrent control coefficient, so that the power flow of the grid-connected point of the power grid is kept to be zero or forward flow; according to the method, dynamic and accurate cooperation of photovoltaic reduction and energy storage compensation power in time and magnitude is realized, and the efficiency of a self-generating and self-using distributed photovoltaic power station is improved.

The invention discloses a dynamic path planning and self-adaptive control method and system for a coating robot, and relates to the technical field of coating automation. The method comprises the following steps: acquiring point cloud data through three-dimensional scanning equipment, constructing a dynamically updated workpiece curved surface model, and extracting curvature, edge and high-curvature mutation region features; a spraying path is generated based on a reinforcement learning algorithm, and path density, speed and coating supply are dynamically adjusted for a high-curvature area; distance, force feedback and environment parameters are fused, and mechanical arm and spray gun parameters are dynamically adjusted; dividing operation sub-areas and distributing tasks based on robot capability characteristics to realize multi-machine cooperation; and fault redundancy control and multispectral imaging are added to optimize the coating quality. The system comprises a sensing module, a decision-making module, an execution module, a redundancy control module and a communication module. According to the invention, the uniformity of the complex curved surface coating, the multi-machine cooperation efficiency and the system anti-interference capability are improved, and the method is suitable for spraying large workpieces such as aircraft fuselages and high-speed rail vehicle bodies.

The invention relates to the technical field of power distribution network evaluation, and discloses a power distribution network bearing capacity evaluation system based on dynamic correction. The system comprises a dynamic data acquisition module, a multi-dimensional state space construction module, a security domain analysis module, a partition coupling degree calculation module and a bearing capacity evaluation engine module. The dynamic data acquisition module acquires a power injection quantity sequence, a voltage deviation ratio sequence and uncontrollable parameter fluctuation data of each partition node of the power distribution network; a multi-dimensional state space construction module performs dimension raising mapping on the sequence to generate a linearized power flow state space model containing a power-voltage Jacobian matrix; the security domain analysis module corrects the boundary of the model according to uncontrollable parameter fluctuation and generates a dynamic security operation constraint set; the partition coupling degree calculation module quantifies an electrical independence index by means of a spectrum radius; and the bearing capacity evaluation engine constructs a chance constraint optimization model, outputs the photovoltaic maximum accessible capacity of each partition and a safety guarantee supply control strategy set, and improves the evaluation accuracy and practicability.

The invention relates to the technical field of grinding wheel monitoring, in particular to a centerless grinding machine full-life-cycle grinding wheel state monitoring system and method.According to the centerless grinding machine full-life-cycle grinding wheel state monitoring system and method, through combined difference extraction of a main shaft current mean value fluctuation ratio and a power gradient continuous variation value and assisted by transverse comparison of periodic response intervals, effective recognition of time period boundaries of working condition states is achieved; according to the method, operation stage division does not depend on static threshold judgment any more, a track trend is established through linkage judgment of an amplitude kick point and a slope extreme value, a dynamic evolution path of signal response is clearer, the identification resolution of abnormal changes and local deviation trends is improved, response synchronous identification of parameter state changes is achieved, and the identification accuracy is improved. In multi-signal collaborative analysis, directivity comparison between a temperature rise mutation fragment and a current residual error response is carried out, so that the consistency analysis capability of response logic between different monitoring paths is enhanced, and the real-time performance, stability and anti-interference capability of state identification are improved.

The invention belongs to the field of data processing, and particularly relates to a dynamic cooperative control system and method for a gas turbine and a micro-grid, and the method comprises the steps: constructing a micro-grid real-time monitoring module, continuously collecting distributed energy real-time output, controllable load demands, bus voltage frequency and equipment state parameters, and carrying out the filtering and noise reduction through a preprocessing unit, thereby guaranteeing the data precision; calculating a real-time power difference value based on the preprocessed data, calling an adaptive neural fuzzy inference system, taking the power difference value, the bus voltage deviation and the frequency deviation as input, and judging whether the power difference value, the bus voltage deviation and the frequency deviation exceed a preset threshold value by means of a fuzzy rule base and a neural network model; if the threshold values are not exceeded, the current states of the gas turbine and the energy storage system are maintained; if any one exceeds the threshold value, a dynamic cooperative control instruction is triggered, precise cooperative control of the gas turbine and the micro-grid is achieved, and the operation stability, the operation efficiency and the reliability of the micro-grid are improved.

The invention relates to the technical field of sensor calibration, in particular to an environment adaptive multi-dimensional calibration method for an excavation type intelligent sensor, which comprises the following steps: acquiring temperature, humidity and gas data through environment monitoring, comparing with a threshold value, marking an abnormal state, evaluating the influence of each parameter on a sensor signal and analyzing fluctuation amplitude and intensity; combining sensor sensitivity and response rate to screen key factors, extracting factor fluctuation trend, optimizing signal disturbance weight and response path, correcting signals in real time, updating a calibration parameter set, monitoring signal values based on multi-dimensional parameters, and adjusting output to maintain a stable range. According to the invention, temperature and humidity and gas abnormity are dynamically identified through threshold comparison, a factor model is constructed based on sensitivity weight, a multi-dimensional calibration path is generated, a parameter set is updated in real time to decouple environment sudden change and sensor drift, signal self-adaptive convergence is realized through closed-loop control, multi-parameter interference errors are reduced, and the bottleneck of calibration delay is broken through. And the time-varying working condition data stability is ensured.

The invention discloses an error compensation inductance sensor calibration method and system, and the method comprises the steps: controlling a macro-micro composite driving platform to drive a moving part provided with a to-be-calibrated inductance sensor, and enabling the moving part to carry out the continuous displacement movement relative to a fixed standard target surface; measuring the actual displacement of the moving part to obtain a standard displacement sequence; the actual output voltage of the inductive sensor to be calibrated in the movement process is synchronously collected, and an actual measurement value sequence is formed; solving measurement deviation, generating a measurement deviation sequence, and dividing the sequence into not less than three compensation intervals according to a displacement range; a compensation value lookup table is established in each compensation interval, and calibration parameters used for correcting zero offset, gain drift and nonlinear characteristics are generated. Displacement measurement and sensor output synchronous acquisition are combined, zero offset, gain drift and non-linear errors can be corrected in a targeted mode, and compensation fineness and adaptability are improved.

The invention discloses a health degree evaluation system and method based on a photovoltaic string, and belongs to the technical field of photovoltaic operation and maintenance intellectualization, and the method comprises the steps: firstly collecting SCADA operation data of a plurality of strings of a photovoltaic power station, including voltage, current, assembly temperature, environment irradiance and power factors; secondly, constructing a whole-station feature reference model, and generating a state vector fusing environment and load characteristics through a nonlinear projection algorithm; aiming at the target group string, extracting a health reference track, calculating a disturbance propagation factor, and identifying a health abnormal state; when the deviation degree exceeds a threshold value, dynamically screening heterogeneous reference group strings, constructing a health score distribution model, and further calculating a confidence score and a prediction residual error; if the score is low and the residual error is unstable, determining that the string is in a sub-health or hidden fault state, and generating an intervention instruction; the method has high accuracy, strong adaptability and good interpretability, and can be widely applied to intelligent diagnosis and refined operation and maintenance management of the photovoltaic power station.

The invention relates to the field of numerical control machining, discloses a numerical control machining method and system integrating thermal expansion dynamic compensation, and aims to solve the problem that precision is out of control due to thermal deformation in high-speed cutting of motorcycle light alloy castings. The method comprises the steps that distributed temperature and displacement sensors are arranged in a key area of a workpiece, and a temperature field and thermal expansion amount are collected in real time; constructing a local thermal deformation prediction model based on a material expansion coefficient and a geometric structure, and correcting a tool path in real time through a coordinate system dynamic offset algorithm; and an on-line measurement feedback mechanism is introduced, and model parameters are adaptively optimized, so that closed-loop compensation is realized. The system comprises a sensing module, a thermal deformation modeling and compensation calculation module, a coordinate dynamic correction module, an online measurement feedback module and a parameter adaptive updating module. Through millisecond-level dynamic compensation and closed-loop self-optimization, the size precision and form and position stability of the complex thin-wall part under the high-temperature working condition are remarkably improved, the rejection rate and cooling energy consumption are reduced, and the method is suitable for a high-precision batch machining scene.

The invention discloses a fault ride-through hierarchical control method for a network-constructed converter, and the method comprises the steps: introducing a virtual resistor formed by fault current negative feedback into the voltage and current inner loop control of the network-constructed converter based on VSG control through a current inner loop dynamic amplitude limiting control layer after the converter enters a fault ride-through state, the size of the virtual resistor is adjusted in real time along with the amplitude of the fault current so as to carry out dynamic current limiting control on the virtual resistor; and the power outer loop dynamic compensation control layer dynamically adjusts an active power instruction value and a reactive power instruction value of the network construction type converter based on VSG control during the fault ride-through period according to the voltage drop degree of the power grid after the converter enters the fault ride-through state. And respectively performing active-frequency control and reactive-voltage control based on the active power instruction value and the reactive power instruction value. In addition, the method also comprises a state coupling and parameter self-adaption module. According to the method, the GFM converter can have better dynamic performance in the fault recovery stage.

The invention discloses an operation collaborative optimization method for an optical storage direct current flexible interaction system. Comprising the steps of collecting operation data such as photovoltaic output, an energy storage state, household load power and direct current bus transmission power, fusing power market price information, and constructing a multi-dimensional time series data set; then, predicting an adjustable load capacity interval of the system based on a coupled physical constraint neural network model embedded with DC bus power balance, voltage constraint and equipment operation limitation; further constructing a state-action space, solving a Pareto frontier by adopting a multi-objective optimization algorithm, and generating a light storage and home load collaborative scheduling strategy set; then combining the real-time operation state and the prediction deviation information, applying a voltage-power droop control mechanism to carry out strategy decoupling, and generating an energy storage power correction amount and a flexible load priority control instruction; and finally, a control instruction is issued to the optical storage direct flexible system, so that collaborative optimization operation with consideration of economical efficiency, safety and comfort of the system is realized.

The invention relates to the technical field of industrial automation control, and discloses a change gear assembly processing technological parameter intelligent optimization system, which comprises a multi-dimensional data synchronous acquisition unit, an angular domain static map construction unit, a phase lead constraint control unit and an angle trigger execution unit, according to the method, a static angle-load topological graph with a main shaft angle as an index is established, a phase lead angle is calculated by combining inherent physical response lag time of a servo system, and a parameter adjustment instruction sequence is directly injected into a servo current loop at a preset angle window before physical load impact occurs; according to the feed-forward control mechanism based on angular domain reconstruction and phase lead constraint, the problem of physical lag of traditional time domain feedback control in response to high-frequency intermittent cutting is avoided, and zero time difference of periodic load impact and even pre-judgment torque compensation are achieved.

The invention, which belongs to the technical field of intelligent breeding equipment, discloses an underwater bionic robotic fish school cooperative breeding environment dynamic monitoring and regulation system comprising a bionic robotic fish body core module, a group cooperative control core module and an intelligent regulation core module. The bionic robotic fish module simulates a real fish swimming mode and carries various water quality sensors to autonomously cruise, so that interference to cultured fishes is reduced; the group cooperation module utilizes a cluster intelligent algorithm and an underwater acoustic communication technology to realize coordinated movement of multiple robotic fishes and full coverage of a monitoring area; the intelligent adjusting module is based on an abnormal detection algorithm, and integrates a miniature oxygenation device, a pH adjusting device and the like to achieve precise regulation and control of the local environment. By the adoption of the system, dynamic sensing and active regulation and control of the culture environment are achieved, a bionic monitoring regulation and control solution is provided for modern aquaculture through intelligent cooperation of the robot fish school and the management system, and the system has the important value of improving the culture efficiency and improving the growth environment.

The invention provides an electric power inspection path planning method and device and electronic equipment, and relates to the technical field of unmanned aerial vehicle electric power inspection. The method comprises the following steps: acquiring obstacle information of an electric power facility environment, wherein the obstacle information comprises the type and position of an obstacle; based on the type of the obstacle and a preset safety distance coefficient, determining a differentiated safety distance, the type of the obstacle including a power transmission line, a transformer substation, a tower and other obstacles; based on the obstacle information and the differentiated safety distance, obtaining an initial global path through a path search algorithm; based on a preset multi-objective optimization function, the initial global path is optimized, a Pareto optimal path set is generated, and the multi-objective optimization function comprises a path length objective, a safety margin objective and an electromagnetic safety objective; and determining a target global path from the Pareto optimal path set based on a preset inspection task mode. According to the invention, the inspection efficiency and adaptability can be improved while the safety is guaranteed.

The invention discloses a precision compensation method of a composite numerical control machine tool, and particularly relates to the technical field of precision control of the numerical control machine tool, which comprises the following steps: synchronously acquiring temperature, vibration and force information under the driving of a unified clock through multiple types of sensors deployed at key nodes of the machine tool, and generating a synchronous multi-source sensing data set with aligned timestamps; inputting the data set into a multivariable coupling error model obtained through data driving training, and calculating and generating a comprehensive space volume error predicted value of a tool nose point of the tool; performing inverse calculation based on a machine tool kinematic chain model, and generating a multi-dimensional micro-compensation instruction set containing the compensation amount of each motion axis and the sequential relationship; and finally, dynamically writing the instruction set into a servo control ring of the numerical control system through a real-time data interface, and driving each motion shaft to execute synchronous compensation motion. According to the method, comprehensive compensation of multi-physics field coupling errors such as temperature, vibration and force load is achieved, and the machining precision and stability of the numerical control machine tool under complex working conditions are improved.

The invention relates to the technical field of heat management of energy storage systems, and discloses a heat management method of an intelligent distributed liquid cooling energy storage system. The method comprises the following steps: acquiring operation state parameters of a plurality of single batteries and optimizing timestamp synchronization; and processing the temperature data by adopting small-batch time series decomposition, and extracting change trend characteristics and abnormal hot spot positions. And based on the information, optimizing a heat exchange control rule base parameter set through a non-dominated sorting strategy, and generating optimized control parameters and priority rules. And in combination with inlet temperature and flow parameters of the liquid cooling system, a tabu search mechanism is utilized to decide a cooling liquid flow distribution scheme, and a branch control instruction is output. And finally, a flow constraint boundary is dynamically adjusted by adopting an approximate gradient projection technology, and a branch and bound strategy is fused to discretize and optimize a distribution path, so that the branch flow is finely regulated and controlled, and the thermal management precision and reliability of the energy storage system are improved. According to the method, the accuracy, adaptability and stability of thermal management of the liquid cooling energy storage system are improved.

The invention discloses a path planning and hierarchical cooperative control method and system for an unmanned aerial vehicle cluster. The system comprises a path planning module and a formation motion control module. The method comprises the steps that firstly, an improved RRT * algorithm is adopted by a path planning module, through a multi-strategy heuristic node expansion mechanism fusing target bias and artificial potential field guidance and comprehensively considering path length, channel volume and Z-axis height change, a center path and a three-dimensional safe channel which take into account safety and smoothness are planned for an unmanned aerial vehicle cluster; and then, based on the central path, the formation motion control module adopts a distributed model prediction control framework, designs different optimization targets for a navigator and a follower, and solves an optimal control instruction on line, so that a cluster is guided to complete trajectory tracking, collision avoidance among individuals and self-adaptive formation reconstruction in a secure channel. According to the invention, the navigation problem of the unmanned aerial vehicle cluster in a complex obstacle environment is solved, and the path planning efficiency and the robustness of cooperative control are improved.

The invention discloses a micro-grid intelligent scheduling method and system based on an AI large model, and the method comprises the steps: collecting and processing the real-time output data of a photovoltaic power station and a wind power station, and obtaining a standardized micro-grid operation data set; a discrete time micro-grid dynamic model is established and a recursive least square method is adopted to carry out system parameter online estimation so as to obtain a robust scheduling scheme oriented to uncertainty interference; and in combination with real-time operation state monitoring, real-time micro-grid intelligent scheduling is carried out by deploying edge computing nodes. According to the method, the Lyapunov stability theory and the control barrier function are combined, a safety reinforcement learning framework oriented to micro-grid dispatching is constructed, and the absolute safety of system operation in the dispatching process is ensured.

The invention discloses an energy storage power supply residual life prediction method and system based on dynamic weight distribution, and belongs to the technical field of energy storage power supply health management. The method comprises the following steps: acquiring voltage, current and temperature time sequence signals of energy storage power supply operation through a multi-source sensor, and constructing a degradation characteristic sequence by adopting a sliding window method; a degradation inflection point division stage is detected and identified by using a curvature extreme value, a historical degradation mode is matched based on a dynamic time warping algorithm, and an optimal model group is selected; respectively carrying out dynamic weight distribution on the time step length and the feature dimension by adopting a dual-channel attention network, and fusing a learnable coefficient with space-time attention output to generate a life prediction result; residual errors and feature drift are monitored and predicted in combination with an incremental learning mechanism, and model parameters are updated and a degradation knowledge base is expanded by adopting an elastic weight consolidation algorithm. According to the method, the key information capturing capability is enhanced through a space-time attention mechanism, the model adaptability is optimized in combination with incremental learning, and the energy storage power supply life prediction precision and the working condition generalization performance are remarkably improved.

The invention relates to the technical field of artificial intelligence welding, and discloses an automatic welding robot based on artificial intelligence and a welding system thereof. The system comprises a welding feature acquisition module, a process parameter generation module, a real-time regulation and control module and a motion planning module. The welding characteristic acquisition module is used for acquiring three-dimensional contour data, material component information and welding seam geometric parameters of a workpiece to be welded, and generating a welding characteristic spectrum through characteristic fusion; a process parameter generation module retrieves a matching template from a welding knowledge base according to the parameters, and outputs a reference welding process parameter combination in combination with an environment temperature and humidity compensation coefficient; the real-time regulation and control module dynamically corrects the reference parameters to generate an optimization instruction according to the molten pool form, thermal radiation distribution and electric arc voiceprint characteristics in the welding process; and the motion planning module calculates a motion track, attitude parameters and a speed curve of the welding execution mechanism according to the optimization instruction to form a robot control instruction set. The system can improve the intelligent level of welding, guarantees stable welding quality, and is suitable for welding scenes of multiple manufacturing industries.

The invention relates to the technical field of metallurgical raw material batching control, and discloses a bottom blowing smelting furnace automatic batching intelligent control method and system based on deep learning, and the method comprises the steps: achieving intelligent batching through multi-source data fusion, physical constraint modeling and dynamic optimization control; edge calculation is adopted to realize data space-time alignment and purification, and physical and economic mixed features are constructed; modeling a reaction path based on a graph neural network, and embedding conservation law constraint to synchronously predict key process parameters; and finally, in combination with gradient sensitivity analysis and reinforcement learning, constructing a differentiable optimization framework to realize multi-target dynamic ratio decision and real-time compensation control, and forming a perception-decision-execution closed loop. The system comprises a global sensing and data purification module, an intelligent decision-making and optimization batching module and a high-precision execution and closed-loop control module. According to the invention, the batching strategy is adaptively adjusted, and optimal resource allocation and maximum economic benefit are realized.