9702 results about "Loop control" patented technology

The invention provides an intelligent workshop scheduling optimization system, method and device based on artificial intelligence and a storage medium. The system comprises a data acquisition module, a data fusion and preprocessing module, an intelligent scheduling model training module, a real-time scheduling decision module and an execution monitoring module. The data acquisition module collects production equipment operation parameters, material circulation information, production order details and other data, and transmits the data to the data fusion and preprocessing module for cleaning, feature extraction and labeling. And the intelligent scheduling model training module constructs an optimization model by adopting a deep reinforcement learning algorithm based on the annotation data. And the real-time scheduling decision module receives real-time data, inputs a model to generate a scheduling instruction, and performs risk assessment and adjustment. And the execution monitoring module controls workshop production according to the instruction, collects feedback data to form closed-loop control, and realizes production dynamic optimization. The method can improve the production efficiency, reduce the cost, and enhance the robustness and adaptability of the scheduling scheme.

The invention relates to the field of power plant control, discloses a virtual power plant control method, system and equipment based on a neural network, and is used for solving the core problems of high data dependence, low topology safety and difficulty in multi-scale collaboration in traditional virtual power plant control. According to the virtual power plant control method based on the neural network, a correction instruction set, a joint estimation value and a topology constraint matrix are input into a neural network controller, and a cooperative control signal is output through singular perturbation decoupling of a fast-varying subsystem and a slow-varying subsystem. And the cooperative control signal is issued to the distributed power supply inverter, the energy storage converter and the intelligent switch, and meanwhile, an execution result is monitored in real time and fed back to the phase space reconstruction module, so that closed-loop control is formed. By constructing the Lyapunov candidate function and calculating the virtual damping coefficient, the transient stability, real-time persistent homologous analysis and topology self-healing instruction generation of the system are enhanced, and the self-healing capability and the fault-resistant capability of the system are improved.

The invention discloses a machine tool control method and system based on mechatronics, and the method comprises the steps: outputting an optimal cutting track sequence according to the three-dimensional model features and material attribute parameters of a machined workpiece; based on the optimal cutting track sequence, motion interference among shafts of the machine tool is eliminated through dynamic weight distribution, and an anti-interference optimization machining instruction is generated; constructing a nonlinear vibration wave propagation model according to the coupling relation between the spindle rotating speed and the feeding speed, and generating a steady speed regulation and control signal; generating an energy efficiency optimal parameter set including motor torque, cooling power and lubricating frequency based on the energy consumption efficiency constraint condition and the steady speed regulation and control signal; and outputting a closed-loop control signal and synchronously updating the three-dimensional machining precision thermodynamic diagram according to the real-time data in the machining process and the predicted deviation of the digital twin model. According to the embodiment of the invention, global optimization, dynamic adaptation and efficient operation of the machine tool can be realized, and technical support is provided for intelligent upgrading of the modern manufacturing industry.

The invention relates to the technical field of engineering control, in particular to a hoisting control method of a safe and portable hoisting cage, which comprises a hoisting pre-stage, data pre-acquisition and analysis before hoisting, a hoisting control stage and hoisting landing control. In the prior art, position closed-loop control mainly depends on a stroke encoder and a position sensor, dynamic swing deviation caused by wind disturbance is difficult to accurately pre-judge and counteract in real time, and the defects of control lag and insufficient response exist. According to the scheme, the motion trail and posture of the cage are predicted in advance based on simple pendulum dynamic model numerical solution and the Runge-Kutta method, high-precision deviation detection is carried out in combination with a virtual-real vision calibration technology and AR enhanced display, a cage motion state control model based on feedforward prediction is established, trail prediction errors are greatly compressed, and the accuracy of the motion state of the cage is improved. The forward-looking inhibition of the movement of the cage, especially wind-induced swinging, is realized, and the accuracy and the response speed of an active swinging inhibition measure are obviously improved.

The invention provides a method and system for obtaining optimal parameters of a machine tool based on an industrial mechanism AI data model, belongs to the technical field of industrial machine tool application, and solves the problems that an existing machine tool cannot obtain the optimal parameters in real time, autonomous intelligent control of the machine tool cannot be achieved and the like. Comprising the following steps: multi-dimensional high-frequency data fusion acquisition and monitoring; performing data synchronization and storage management; analyzing and mining data; mixing intelligent decision engines; constructing an industrial knowledge base and a model base; training an industrial knowledge base and a model base; intelligent decision making and optimization; equipment is automatically controlled; user visual interaction is carried out; carrying out on-line adaptive parameter optimization; continuously optimizing and iterating; and the production business system works cooperatively. According to the method, online self-adaptive parameter optimization can be realized, a closed-loop control system with rapid sensing, intelligent decision-making and accurate execution capabilities is constructed, the method develops in the direction of sensing-decision-execution full-link autonomous optimization, and finally autonomous intelligent control of the machine tool is realized.

The invention relates to the technical field of data center heat dissipation, and particularly provides a single-phase and two-phase immersion liquid cooling method and system based on AI intelligent decision, and the method comprises the steps: injecting coupled data into a dynamic feature extraction engine, and outputting a thermodynamic state evolution tensor which comprises the characteristics of a temperature change rate, a load-heat flux density coupling coefficient and the like; the thermodynamic state evolution tensor is input into the deep neural network model, the temperature and pressure matched with the current thermodynamic state evolution tensor are calculated, and a closed-loop control instruction set capable of being executed by equipment is generated; a closed-loop control instruction set is injected into an execution mechanism set, execution mechanisms execute power reconstruction and flow channel switching according to instructions, gaseous fluorinated liquid is liquefied and flows back through an efficient condenser in a two-phase mode, and heat dissipation mode self-adaptive switching and heat cycle reconstruction are achieved. The system comprises a server, an AI algorithm controller, a cooling liquid storage device, a condenser, a circulating pump, an electric valve, a pressure release valve and a temperature sensor. The heat dissipation efficiency and the system reliability are remarkably improved.

The invention relates to the field of ring main units, in particular to an event-driven intelligent ring main unit and a control method, and the event-driven intelligent ring main unit comprises a cabinet body, a cabinet body built-in circuit breaker, an isolation grounding switch and a fusion sensing array. An array synchronously acquires mechanical and electrical quantities, an edge decision-making system takes a voltage zero crossing point as an absolute time mark nanosecond alignment event, a closed-loop control core generates a protection and topology reconstruction instruction in situ according to the event, and primary equipment is driven in a millisecond level; the intelligent communication interface dynamically schedules bandwidth according to the data priority, and key instructions are ensured to be issued in real time. According to the invention, the problems of fault misjudgment and local control delay caused by lack of a unified time sequence reference in a mechanical state and an electrical transient state of a traditional ring main unit are solved.

The invention relates to the technical field of intelligent operation and maintenance and data processing of an electric power system, in particular to an electric power inspection system and method. The method comprises the following steps: collecting equipment state data, constructing a time sequence evolution characteristic of a three-order state transition tensor field modeling equipment space state, and identifying short-term high-risk equipment based on a weighted abnormal score and a dynamic threshold strategy; a historical state peak value is recorded through a state-time matrix fusion exponential decay model, a forgetting risk factor is calculated in combination with the current silencing degree, and equipment which is seriously abnormal but is silencing recently is actively rechecked; constructing a dynamic evolution diagram structure, optimizing an inspection path by adopting a class genetic perturbation mechanism, and scheduling multiple teams in parallel through task cluster division and queue matching; and establishing an equipment abnormity linkage propagation model to predict a cascade risk, and automatically matching an optimal disposal scheme in combination with a strategy template library. According to the method, closed-loop control from anomaly recognition, path optimization to strategy execution is formed, and the accuracy and timeliness of power operation and maintenance are improved.

The invention relates to the technical field of intelligent manufacturing, in particular to a rolling method, system and equipment in a full-closed mode and a storage medium, and the method comprises the steps that plate thickness and temperature data are collected in real time through a thickness measuring device integrated with a temperature sensor; predicting a future thickness change trend by using a temperature-sensitive deep learning model and combining historical working condition parameters; calculating a dynamic compensation gap sequence based on the prediction result, the target thickness and the compression roller characteristics, and driving a compression roller control system to adjust the gap; and feedback data after rolling are synchronously collected, model parameters are updated in a self-adaptive mode, and closed-loop control is formed. According to the full-closed mode rolling method, high-precision rolling is achieved through closed-loop control, and the quality and efficiency are improved.

The invention discloses a dynamic leakage prediction method and system for an old oil and gas pipeline, and the method comprises the steps: collecting pressure, flow and temperature parameters in real time through a multi-source sensor, and recognizing abnormal fluctuation through the combination of time sequence analysis and frequency domain feature extraction; calculating a pipeline state evaluation result based on the material degradation model; establishing a leakage prediction model fusing a wall thickness degradation kinetic equation and an LSTM neural network, calculating a leakage probability by adopting a Monte Carlo method, and generating a diffusion velocity and a concentration gradient through CFD numerical simulation; when the diffusion prediction exceeds a safety threshold value, a control strategy is optimized through fuzzy logic and a genetic algorithm; the verification model is fed back after real-time adjustment, and online learning is carried out through Bayesian optimization; and finally, calibrating the model by using experimental data, and deploying and generating risk early warning. The system comprises a multi-source sensor array, a data processing platform and other modules, and full-chain closed-loop control from sensing to early warning is achieved.

The invention discloses an electrical fire monitoring method based on inherent residual current automatic compensation, and relates to the technical field of electrical safety monitoring, and the method comprises the following steps: S100, collecting a residual current signal, executing multi-component time-frequency deconstruction processing, extracting an energy distribution characteristic through wavelet packet transformation, and extracting a mutability index in combination with short-time spectrum entropy analysis, and constructing a preliminary distribution map of higher harmonic interference, and determining boundary features of interference signals in a time domain and a frequency domain. According to the method, high-frequency interference signals are accurately positioned through time-frequency deconstruction, wavelet packet analysis and spectral entropy indexes, non-fault harmonic components are effectively eliminated in combination with amplitude-frequency coupling recognition and a recursive rejection strategy, closed-loop control is constructed by introducing an adaptive compensation and stability backtracking mechanism, accurate recognition and dynamic correction of real electric leakage risks are achieved, and the method is suitable for large-scale popularization and application. The identification precision and the safety reliability of the monitoring system in a complex industrial environment are obviously improved, and the method has good engineering adaptability.

The invention relates to the technical field of nondestructive testing, solves the problems of insufficient flaw detection precision and adaptability of a metal component caused by restriction in the prior art, and particularly discloses a flaw detection system and method for the metal component. The flaw detection system comprises a multi-modal sensor group, a cross-modal feature alignment module, a self-adaptive weight distribution module, a defect decision module and a closed-loop control unit, the flaw detection method comprises the following steps: S100, synchronously acquiring data through the multi-mode sensor group; s200, performing space-time registration on the multi-source data of the metal component by using the dynamic calibration matrix; s300, evaluating the confidence coefficient of the sensor in real time, and generating a defect feature map; s400, inputting the defect feature map into a defect classification network, matching a defect map database, and outputting a defect type and a quantization parameter; and S500, automatically adjusting the detection motion track and the sensor according to the defect parameters. According to the multi-method combined detection for the metal component, the detection precision is improved, and a detection result with higher accuracy can be obtained through comprehensive evaluation according to multi-source data.

The invention discloses a stacking machine motion speed parabolic trajectory deviation correction comprehensive method, and relates to the technical field of stacking machine trajectory deviation correction, and the method comprises the following steps: carrying out the data collection of the real-time motion trajectory of a stacking machine through an acceleration sensor, an encoder and a visual recognition system, building a trajectory deviation detection model, and carrying out the calculation of the trajectory deviation detection model; the motion speed, the acceleration and the position information are extracted, and the deviation value of the current trajectory deviating from the ideal parabolic trajectory is calculated. According to the invention, through multi-sensor fusion and Kalman filtering, the accuracy and stability of track correction of the stacker are improved; an LSTM neural network is adopted to predict inertial errors, and correction failures are reduced in combination with an adaptive compensation strategy; a double-closed-loop control and anomaly detection mechanism is introduced, intelligent safety protection is achieved, the fault recovery capacity is improved, it is ensured that the stacking machine stably operates in a complex environment, and the reliability and working efficiency of an automatic warehousing system are enhanced.

The invention relates to the technical field of printed circuit board manufacturing and quality detection, in particular to a PCB forming detection method based on a hole site scatter diagram and SPC analysis, and the method comprises the steps: collecting the actual hole site data of a PCB substrate through an optical scanner or a laser measurement device, generating a scatter diagram containing coordinates, aperture and tolerance, and carrying out the detection of the hole site scatter diagram; an abnormal region is identified by using an improved density clustering algorithm; performing multi-dimensional analysis on the scatter diagram based on an SPC model, extracting characteristic parameters such as hole site density deviation, constructing a dynamic control limit, and calculating a CPK to judge a processing state; and when the CPK is lower than a threshold value, optimization instructions such as drilling path correction and drill bit wear compensation are generated and fed back to the equipment through the anti-saturation fuzzy PID controller to realize closed-loop control. According to the method, the hole site machining quality can be accurately detected, the machining parameters are adaptively adjusted, and the PCB forming precision and the production stability are effectively 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 a multi-stage scheduling architecture control method based on a TSN network. Efficient management of network resources is realized through modular cooperation. The time synchronization calibration module periodically calibrates the difference of multiple clock sources by using an improved network time protocol and jitter monitoring to ensure the time unification of the whole network; the priority analysis module divides data streams according to a reference value, and determines time slot distribution of key and non-key streams; and the traffic peak period scheduling module dynamically adjusts resources by using a time triggering algorithm to ensure key data transmission. Meanwhile, the real-time path optimization module deals with delay exceeding based on a Dijkstra algorithm, the intelligent load balancing module is combined with a random forest algorithm to deal with an overload problem, and the rapid fault switching and isolation module realizes rapid fault response through redundant paths and topology analysis. And iteratively adjusting the parameters according to the key indexes. According to the method, full-process closed-loop control from time reference unification to dynamic resource scheduling and fault processing is realized, and the transmission stability and the resource utilization rate of the TSN network are effectively improved.

The invention discloses a high-precision drilling control method and system for a multi-layer PCB, and relates to the technical field of intelligent control. The method comprises the following steps: scanning and analyzing to obtain layered material characteristics and structural size information of a multi-layer PCB, and carrying out clamping fixation and positioning hole processing on the multi-layer PCB to obtain a PCB positioning hole; a drilling machining three-dimensional coordinate axis is constructed; a PCB drilling strategy space is constructed, the PCB drilling strategy space is adopted to conduct drilling analysis on the layered material characteristics and the structure size information in sequence according to the PCB drilling machining requirement, and layered drilling strategy parameters are obtained; and the layered drilling strategy parameters are mapped to a drilling processing three-dimensional coordinate axis for space route fusion, layered drilling processing parameters are determined, and high-precision drilling and quality detection closed-loop control are executed on the multi-layer PCB. The technical problem that in the prior art, the positioning precision is low in the drilling process of the multi-layer PCB is solved, and the technical effect of improving the drilling machining precision of the multi-layer PCB is achieved.

The invention relates to the technical field of intelligent pianos, in particular to a piano automatic playing driving method and system, and the method comprises the steps: obtaining first external data which is used for describing the position change of keys at first playing time; converting the first external data into discrete data, wherein the discrete data is associated with a time sequence label; the discrete data is sent to a main control module, and the main control module forwards the discrete data to a control unit of the exercise device; monitoring second external data of the key at the first moment, and searching a corresponding discrete position through time sequence label association according to the first moment; and calculating a difference value between the second external data and the discrete position, and outputting an adjusting current value through the difference value by adopting a closed-loop control algorithm. The invention provides a lossless built-in driving technology and a driving method, and based on the driving method, a low-cost and miniaturized exerciser can be adopted, so that automatic playing of the piano keys is realized under the condition that the original framework of the piano is not influenced.

The invention discloses a fire-fighting monitoring and generator set collaborative optimization multi-target constraint solving method, which comprises the following steps: carrying out real-time acquisition and structured processing on multi-source data, acquiring safety monitoring and equipment operation data through a sensor network, and storing the data in a distributed real-time database after preprocessing; carrying out dynamic thermodynamic field modeling and risk area division, discretizing a plant area as a grid unit, solving a heat conduction equation in combination with heat source modeling and boundary conditions, and dividing three levels of risk areas; building a multi-objective optimization model, defining three types of core constraints, building a normalized objective function, and realizing multi-objective decoupling through dynamic weight adjustment; according to an asymmetric load migration strategy, differential load adjustment is implemented according to an optimization result, an auxiliary system is linked, constraints are verified in real time, and closed-loop control is formed. According to the technology, the collaborative goals of safety risk controllability, system efficiency maintenance and equipment redundancy optimization are achieved, and the method is suitable for fire prevention and efficient operation in an energy production scene.

The invention relates to a self-adaptive pressure regulation vacuum pump closed-loop control system and method. The system comprises a model building module which is used for acquiring real-time data flow of a vacuum pump and building a nonlinear dynamic model to obtain a pressure-flow-power feature mapping relation; the predictive analysis module calculates a pressure-flow predictive value based on the relationship and compares the pressure-flow predictive value with the real-time power data for analysis. And generating a parameter adjustment instruction once the deviation value exceeds a preset threshold value. And the control scheme generation module corrects the control parameters according to the parameter adjustment instruction and generates an optimized power adjustment scheme. And if the data is abnormal, generating a state update value. And the control scheme updating module fuses the state updating value and the pressure-flow prediction value to obtain an optimized model parameter so as to adjust a weight coefficient of an adaptive control strategy and generate a control scheme. The system effectively improves the accuracy of vacuum pump pressure control, greatly enhances the ability of the system to deal with abnormal conditions and equipment performance changes, and ensures long-term stable operation of the vacuum pump.

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.

The invention relates to the technical field of control, and particularly discloses a multivariable energy efficiency optimization control system for a heating furnace, which is used for solving the problems of local overheating, non-uniform temperature and difficulty in accurate positioning and compensation of heat loss in the operation of the existing cracking heating furnace. Comprising a parameter detection module, a multivariable coupling modeling and simulation module, an optimization control module and an execution and feedback module. According to the method, dynamic digital twinning is constructed through multi-modal online sensing and data assimilation, a Pareto frontier solution is generated based on model prediction control and improved NSGA-II parallel optimization, and the weight is adaptively adjusted; and when the hot spot / cold spot is triggered, a quadric surface fitting compensation strategy is implemented and issued for execution, so that high-precision simulation prediction, precise closed-loop control and real-time online energy efficiency optimization are realized.

The invention discloses a water conservancy operation and maintenance platform based on a digital twinning technology, and relates to the technical field of digital twinning, and the platform comprises a digital twinning model building unit which is used for building a water conservancy facility digital twinning model comprising a physical mechanism model; the health state evaluation unit is used for calculating theoretical performance based on the mechanism model and comparing the theoretical performance with actual performance to obtain a performance residual error so as to accurately quantify the health state of the equipment; the intelligent scheduling decision-making unit adopts a reinforcement learning framework, performs decision-making deduction by using a composite reward function fusing business rewards and quantitative health loss cost in a simulation environment associated with digital twinning, and generates an optimal scheduling instruction giving consideration to business targets and equipment long-term health; and the closed-loop control and feedback unit is used for executing the instruction and forming a dynamic optimization closed loop. According to the method, abstract health loss is converted into computable decision cost, and global optimization of an operation and maintenance strategy is realized.

The invention relates to the field of industrial automation and intelligent control, and discloses an intelligent temperature automatic control system for a digital glass mold. The method comprises the following steps: acquiring mold surface temperature and heat flow data through a thermocouple array and a thermal infrared imager, and constructing heat flux and a disturbance coefficient; forming state vectors are established in combination with the forming process parameters and the heat flow distribution, and forming stability levels are generated through support vector regression; building a heat balance deviation model based on the thermophysical parameters and the environment variables, and predicting a temperature trend; a temperature control instruction is generated through the fuzzy neural controller, and temperature dynamic closed-loop control is achieved. The system improves the temperature control precision and thermal field balance of the glass mold, and is suitable for intelligent temperature control management in the glass container forming process.

The invention discloses an equipment temperature adjusting method fusing a long short-term memory network, and particularly relates to the technical field of temperature control, which comprises the following steps: determining the deployment position of a sensor through thermal simulation, collecting multi-source heterogeneous data, dynamically adjusting the sampling frequency in combination with the temperature and the load current change rate, and adjusting the temperature of the sensor; after data preprocessing, an attention mechanism enhanced LSTM prediction model is constructed, a time sequence sample data set is divided according to equipment thermal response characteristics, training is carried out, and an optimal model is obtained through early stop mechanism optimization; predictive feedback double-closed-loop regulation and control is achieved based on the optimal model, outer loop PI control is combined with an integral separation mechanism to generate a basic control quantity, an inner loop outputs a correction quantity through fuzzification, reasoning and defuzzification, an actuator is driven after superposition, and safety linkage is triggered synchronously; constructing an incremental data buffer pool to screen effective samples, and adaptively updating model parameters by adopting a layered fine tuning strategy; the temperature regulation and control precision and the long-term self-adaptive capability are obviously improved, and the over-temperature risk of equipment is reduced.

The invention relates to the technical field of power monitoring, in particular to a moisture-proof environment-friendly ring main unit online monitoring system with continuous fault indication and a ring main unit, and the system comprises an electric parameter dynamic analysis module, an environment collaborative verification module, a closed-loop control execution module and a behavior path optimization module. According to the method, the current fluctuation and the power difference value are matched through dynamic time warping, the temperature rise rate and the condensation index are calculated through linear regression, multi-dimensional verification is formed through fuzzy logic judgment, operation parameters are adjusted in real time through PID control, strategy weight is optimized through a genetic algorithm, feature extraction is reversely corrected, and the fault recognition precision and the response speed are improved; closed-loop feedback is established to continuously optimize the system state, environment and electrical parameter coupling analysis is enhanced, the misjudgment probability of a single threshold value is reduced, parameters are dynamically corrected, the adjustment real-time performance and accuracy are improved, the adaptive capacity under the complex working condition is enhanced, a complete closed loop of collection, analysis and feedback is established, and the insulation degradation judgment reliability is improved.

The invention provides a power management chip pin state intelligent detection method and system. The method comprises the steps that temperature difference quantity is input into a gradient heat conduction compensation structure, and compensation current opposite to a temperature gradient vector is generated based on temperature field distribution in the copper-based composite material; superposing and coupling the voltage fluctuation signal and the compensation current to generate a reference voltage signal; performing frequency component separation processing on the reference voltage signal, and extracting electromagnetic interference characteristic data in a frequency component; performing time sequence correlation on the electromagnetic interference characteristic data and the temperature distribution data, dynamically adjusting an interference suppression parameter through closed-loop control, and acting the adjusted interference suppression parameter on an interference suppression process of a reference voltage signal to generate a pin state detection signal; and detecting the pin state of the power management chip according to the pin state detection signal. According to the invention, the pin voltage stability and the state detection precision of the power management chip are improved.

The invention provides a cold-rolled strip steel plate shape closed-loop control method and system based on dynamic feedback, and belongs to the technical field of cold-rolled strip steel production.The method comprises the steps that a dynamic incidence matrix and a plate shape deviation signal are generated based on cold-rolled strip steel transverse measuring point data, processing the dynamic incidence matrix to obtain a space correlation defect feature vector of the cold-rolled strip steel, and processing the strip shape deviation signal to obtain a multi-dimensional defect feature vector; determining the strip shape defect type of the cold-rolled strip steel based on the multi-dimensional defect feature vector and the space correlation defect feature vector; and strip steel production control parameters are adjusted based on the strip shape defect type. The plate shape quality stability of the cold-rolled strip steel can be improved.

The invention relates to the technical field of laser welding control, and discloses a laser welding head integrated with a push-pull wire and a control system of the laser welding head. The system comprises a push-pull wire action monitoring module, a laser energy modulation module, a welding seam trajectory tracking module, a welding quality feedback module, a parameter collaborative optimization module and a dynamic deviation correction execution module. Wherein the push-pull wire action monitoring module collects motion parameters in real time and generates a cooperative state index; the laser energy modulation module analyzes the laser power requirement according to the workpiece parameters and outputs a regulation and control instruction; the welding seam trajectory tracking module identifies welding seam characteristics and offset through a multispectral sensor and generates a dynamic tracking path; the welding quality feedback module evaluates defects and generates an abnormal index set; the parameter collaborative optimization module generates an optimization control parameter set based on the abnormal index set and the tracking path adjustment parameters; and the dynamic deviation correction execution module is used for correcting the track and generating a closed-loop control signal according to the matching action and the energy output time sequence.

The invention discloses a flowmeter-oriented embedded multi-parameter fusion calibration control method and system, relates to the technical field of industrial measurement and embedded control, and is used for solving the problem of unstable calibration caused by misalignment of multi-link flow signals, compensation lag and fusion oscillation. The method relates to synchronous acquisition, pretreatment and calibration of differential pressure type, ultrasonic wave and thermal type mass measurement links and environment parameters such as temperature, pressure and density. All-channel unified time scales are established through ternary binding, filtering and denoising, zero snapshot and alignment rules, a stability scoring mechanism is constructed in combination with offset behavior recognition, event classification and feature aggregation, and dynamic switching of a compensation path and a fusion strategy is driven. In a double-path fusion structure, table look-up compensation, model estimation, multi-algorithm parallelism and fusion track recording are adopted, version traceability and calibration result closed-loop control are achieved, and the stability and rechecking performance of mass flow output under complex disturbance are effectively improved.