349 results about "Compensation strategy" patented technology

The invention relates to the technical field of electronic manufacturing equipment, in particular to an adaptive thermal compensation system and method for a high-precision mounting head of a chip mounter, and the system comprises a temperature-deformation sensing unit, a thermal-mechanical coupling analysis unit, a dynamic compensation control unit, and a closed-loop execution unit. The temperature-deformation sensing unit collects temperature and deformation data of multiple parts of the mounting head in real time, the thermal-mechanical coupling analysis unit reconstructs a three-dimensional temperature field based on a finite element algorithm, the thermal expansion distribution quantity is dynamically calculated, the problem of rough model of traditional single-point temperature measurement is solved, and the measurement precision is improved. The dynamic compensation control unit predicts the thermal drift amount in the future 5 ms through online parameter identification and a long-short-term memory network model, a compensation strategy is adaptively adjusted in combination with the motion working condition, the closed-loop execution unit decomposes the compensation amount into displacement and torsion correction instructions, accurate offset of thermal deformation is achieved, and a whole-process thermal compensation closed loop is constructed. The precision stability of the mounting head in a complex thermal environment is improved, and the production efficiency is improved.

The invention provides a printing piece defect detection method and system based on machine vision, and relates to the technical field of machine vision, and the method comprises the steps: determining an optimal detection scale through image enhancement processing, multi-scale gradient pyramid construction and mutual information calculation, extracting key points through a curvature value, and carrying out the shape discrimination. And finally, generating a laser etching compensation path based on defect characteristics and performing layered compensation processing. According to the method, the automobile part surface printing flaws can be accurately identified, the compensation strategy is adaptively generated according to the defect types, and the defect repairing precision and efficiency are improved.

The invention belongs to the technical field of robot control, and particularly relates to a mechanical arm RBF network dynamic self-adaptive control method under the constraint of a time-varying mechanism, which comprises the following steps of: constructing a mechanical arm dynamic model, determining a system specified time convergence standard, combining a joint motion reference trajectory of a mechanical arm, defining a trajectory tracking error, and determining a mechanical arm dynamic model based on a dynamic nominal model. Constructing a stable robust control law of the nominal dynamical model under the specified time; and selecting a radial basis function, and designing an RBF network adaptive control law in a specified time to dynamically fit a comprehensive nonlinear disturbance term in the system. The problem that the convergence time of a traditional method is uncontrollable is solved, the RBF neural network is adopted to dynamically approach comprehensive disturbance, the network weight is updated online through the adaptive law of the time-varying gain, the anti-jamming capability is remarkably improved, a model driving method and a data driving method are combined, and the convergence time of the model driving method and the convergence time of the data driving method are greatly improved through a dynamic model decoupling and feedforward compensation strategy. And more accurate and low-delay trajectory tracking control is realized.

The invention provides a crane remote instruction response delay detection and in-advance compensation method and system, and relates to the technical field of cranes, and the crane remote instruction response delay detection and in-advance compensation method comprises the following steps: adopting an adaptive space-time alignment algorithm to map real-time operation data to a dynamic knowledge graph, and generating a feature vector; inputting the feature vector into a depth map neural network integrated with a causal reasoning mechanism to generate an incidence matrix; a multi-head attention network with a residual structure is adopted to extract time sequence features; constructing a hybrid decision system based on the delay prediction tensor, and outputting an optimal compensation strategy; and establishing a double-closed-loop evolution system with an online learning capability, and dynamically optimizing a prediction and compensation strategy according to a compensation effect. Through the dynamic knowledge graph, causal reasoning, the multi-head attention network and the double-closed-loop evolution system, the remote instruction response delay can be accurately predicted, effective compensation is carried out, and the real-time performance and safety of remote control of the crane are improved.

The invention relates to the technical field of precise injection molding control, and discloses an injection molding process parameter self-adaptive compensation system which comprises a multi-source sensing module used for collecting mold cavity pressure, melt temperature and screw displacement information in the injection molding process in real time; the data fusion module is used for performing time sequence synchronization, space registration and noise suppression on the multi-source sensing data; the dynamic modeling module is used for constructing a dynamic model of an injection molding process state based on the fused process data and outputting a state estimation value; and the stability analysis module is used for carrying out system stability analysis based on the dynamic model and generating a control compensation strategy. Cooperative control of precise positioning and high-pressure response is realized through a composite driving framework and a dynamic disturbance compensation technology; an incremental learning algorithm and a multi-dimensional quality evaluation system are combined, a process parameter self-evolution mechanism is constructed, material characteristic drift and complex working condition disturbance are effectively dealt with, and closed-loop optimization from process control to quality feedback is formed.

The invention provides a servo motor real-time adaptive control method and system based on artificial intelligence, and the method comprises the steps: injecting a tiny excitation signal into a servo motor, collecting temperature and electromagnetic response data under different working conditions, and constructing a multi-dimensional information data set; performing real-time reasoning on the relationship between the temperature gradient and the magnetic path characteristics by using deep transfer learning to obtain multi-dimensional parameter deviation vectors such as stator flux linkage unbalance degree, permanent magnet flux weakening degree and torque coefficient change; generating a multi-scale compensation strategy through a physical enhancement reinforcement learning method and the multi-dimensional parameter deviation vector; and carrying out Lie group transformation processing on the control law under a differential geometric framework, and realizing accurate compensation on the temperature gradient effect through a dual-channel-dual-time scale framework. According to the method, heterogeneous sensing and multi-dimensional compensation strategies are adopted, the problem that the control precision of a traditional method is insufficient under the complex temperature gradient condition is solved, and the high-precision motion control performance is maintained under the dynamic working condition that the temperature distribution of the motor is rapidly changed.

The invention discloses a numerical control machine tool wear monitoring and intelligent compensation control method based on deep learning, and relates to the field of numerical control machine tool wear monitoring. Multi-source data are collected through a sensor, and are preprocessed and fused through edge calculation; deep features are extracted and enhanced through an improved network, the abrasion state is evaluated through a mixed expert model, and life is predicted in combination with survival analysis; based on enhanced transfer learning, generating an intelligent compensation strategy according to a processing target, and executing the intelligent compensation strategy after verification in a virtual environment; a closed-loop system is constructed, all modules are acquired, fed back and optimized, and full-process intelligent management of functions such as knowledge graph early warning and multi-machine-tool cooperation is integrated. According to the method, the wear monitoring accuracy is improved, and early wear is accurately recognized; machining parameters are intelligently compensated and optimized, precision is improved, and the service life of a tool is prolonged; closed-loop control and multiple technologies are fused, the response time is shortened, and shutdown is reduced; the operation efficiency and reliability of the numerical control machine tool are improved, and the cost is reduced.

The invention discloses a dynamic compensation intelligent formwork real-time regulation and control method and system, and relates to the technical field of building intelligent control. According to the method, pulse signal sensing and cooperative compensation between nodes are realized by constructing a distributed neural unit network, and a three-level compensation mechanism is triggered when displacement or stress is detected to exceed a threshold value. A generative adversarial network is innovatively adopted to construct a virtual scene library to simulate extreme working conditions, a cross-scene migratable strategy library is generated through feature alignment, and intelligent strategy matching of a new construction site environment is realized. A full-structure rigidity field is dynamically generated based on an elastic mechanical model, a supporting arm rigidity switching path is optimized in combination with an ant colony algorithm, and directional dissipation of wind vibration energy is achieved through triangular rigid network construction. The system adopts a biological heuristic self-organizing algorithm to realize redundant network reconstruction when nodes fail, a supporting arm adopts a shape memory alloy and carbon fiber composite structure, and a micro strain sensor is integrated to realize directional release of corrosion stress. According to the method, the adaptive capacity of the formwork system under the complex working condition is effectively improved, and the structural failure rate is reduced through multi-modal compensation strategy fusion.

The invention discloses a load control method based on a charging pile ad hoc network, and relates to the technical field of charging pile ad hoc network load control, and the method comprises the following steps: firstly, carrying out the real-time collection of various parameters in the operation process of a charging pile, guaranteeing the obtaining of high-quality original data, and providing a solid data basis for the subsequent analysis and model evaluation; according to the invention, through real-time data acquisition, intelligent feature extraction, deep learning evaluation and adaptive dynamic regulation and control, the problems of harmonic resonance, power distribution imbalance and power grid instability in the operation of the charging pile are solved. Charging pile parameters are collected in a high-precision mode, harmonic key features are extracted through a detection window mechanism, intelligent evaluation is conducted through a deep learning model, and the harmonic abnormal trend is predicted. When the resonance risk is detected, the power of the charging pile and the reactive compensation strategy are intelligently adjusted, the load distribution is optimized, the harmonic resonance risk is reduced, the power supply quality of the power grid is improved, and the intelligent charging network has higher stability, reliability and scheduling capability.

The invention relates to the technical field of pulsed ion beam processing, and discloses a positioning system for a pulsed ion beam processing optical element, which can accurately predict nonlinear structure deformation in a complex thermal environment and eliminate compensation deviation caused by a traditional linear model. A multi-sensor data fusion mechanism effectively suppresses the interference of local measurement noise on a control decision, and improves the reliability of a compensation strategy. And a closed-loop control system realizes real-time verification and dynamic optimization of the compensation effect, and machining precision reduction caused by error accumulation is avoided. The submicron executing mechanism ensures that the thermal drift compensation amount is accurately converted into platform pose adjustment, the high-precision optical element machining requirement is met, and the problem that a traditional positioning system is incomplete in model input due to the single temperature field information collection dimension is solved. Meanwhile, the dynamic characteristics of the temperature-displacement coupling relation are verified through high-precision displacement data.

The invention provides a titanium alloy thin-wall part high-precision milling method and system based on dynamic compensation, and the method comprises the following steps: constructing a multi-physics field coupling model based on the constitutive relation and milling process parameters of a titanium alloy material; according to the method, a multi-physical field coupling model represented by dynamic parameters is constructed, structural feature recognition and coupling weight dynamic allocation are combined, and the model is iteratively optimized through a detection result after processing, so that the problem that a traditional prediction model is disjointed from an actual working condition is solved; by building a real-time monitoring and feedback system and combining a dynamic compensation closed-loop mechanism, non-interruption linkage of monitoring-adjustment in the machining process is achieved, and the problem that the hysteresis of a traditional compensation method is remarkable is solved; the initial compensation amount is optimized through a multi-objective genetic algorithm, a self-adaptive processing path strategy is adopted, dynamic parameter adjustment of structure adaptation is combined, a traditional single compensation strategy is replaced, and the problem of dynamic rigidity change in the complex curved surface and material removal process can be flexibly solved.

The invention relates to the technical field of audio optimization, and discloses an audio data offset detection and compensation method and system, a storage medium and equipment, and the method comprises the steps: building a timestamp synchronization mechanism with nanosecond precision, and distributing a unique time identifier for each floating-point audio sampling point; detecting time sequence offsets of input and output signals through a self-adaptive sliding window, and positioning and determining an offset value based on a cross-correlation function extreme value; a compensation strategy is dynamically selected according to different intervals where the offset absolute value is located, and meanwhile a dynamic cache management mechanism is established to support real-time offset compensation operation; constructing a time sequence relation model through inter-channel signal correlation analysis in the multi-channel system, and implementing coordinated offset compensation based on a correlation analysis result and the compensation strategy; a performance monitoring index system is established to evaluate the offset compensation effect in real time, compensation parameters are adaptively adjusted according to audio content characteristics and system performance feedback, and accurate time sequence control over audio data is achieved through the method.

The invention provides an electric energy quality compensation method and system based on power electronics, and relates to the technical field of power grid dispatching, and the method comprises the steps: collecting the voltage, current, harmonic content, reactive power, three-phase unbalance degree and other real-time multi-mode data of a power grid, and extracting key frequency spectrum features through wavelet transform denoising and windowing interpolation Fourier transform; and constructing a power quality feature vector. A self-adaptive fuzzy logic algorithm and power grid operation parameters are combined, a threshold value of a power quality index is dynamically adjusted, a multi-stage optimization strategy is divided based on the number of standard-exceeding indexes and importance analysis, and a dynamic compensation priority and a compensation strategy are generated. An improved particle swarm optimization algorithm is introduced, an inertia weight is dynamically adjusted, a constraint penalty function is introduced, output parameters of a power electronic compensation device are optimized, and effective compensation of power quality is realized by taking minimization of total active loss as a target.

The invention discloses a bridge crane anti-swing control method based on a neural network algorithm, and relates to the technical field of crane control, and the method comprises the following steps: collecting suspended load swing state variables of a bridge crane and establishing a dynamic model; the dynamic model is used for predicting the suspended load swing trend of the bridge crane, and a swing trend prediction result is obtained; feed-forward compensation control is carried out according to the swing trend prediction result, and a feed-forward control signal is obtained; performing real-time feedback correction on the movement of the bridge crane trolley according to the feedforward control signal to obtain a real-time correction control signal; and performing nonlinear compensation calculation according to the real-time correction control signal to obtain a final anti-swing control result. According to the invention, the method achieves the precise estimation of the future swing state, improves the prediction accuracy of the future swing state, achieves the advanced compensation, improves the response speed of a control system, enables a compensation strategy to adapt to different operation environments, and improves the control precision and robustness.

The invention relates to a permanent magnet synchronous motor optimization method and system based on a novel topological structure. The method comprises the steps that parametric modeling is carried out through preset magnetic pole block parameters, preset magnetic barrier groove geometric parameters and preset asymmetric air gap parameters, and a variable topology model library is generated; through multi-physics field coupling simulation, dynamic eddy current distribution, a local demagnetization risk coefficient and a high-frequency vibration mode of the permanent magnet synchronous motor are calculated; constructing a multi-target optimization model for optimization through dynamic eddy current distribution, a local demagnetization risk coefficient and a high-frequency vibration mode, and generating a Pareto optimal solution set; and based on the manufacturing tolerance sensitivity parameter and demagnetization risk coefficient distribution in the solution set, constructing a topology fault-tolerant structure and obtaining a dynamic current compensation strategy, and generating a final optimization scheme. According to the method, through comprehensive application of multi-physical field coupling simulation, multi-objective optimization and a dynamic compensation strategy, the precision and scientificity of motor optimization are improved, and effective technical support is provided for design and manufacturing of the motor.

The invention relates to the technical field of reactive compensation, in particular to a reactive compensation control system and method based on artificial intelligence autonomous learning, the reactive compensation control system comprises a data acquisition layer, an AI decision layer, a compensation execution layer and a feedback optimization layer, the data acquisition layer comprises a sensor module and a data acquisition module, the sensor module is used for collecting current and voltage data in the circuit. According to the method, firstly, a reactive power prediction model can be established through AI autonomous learning, and dynamic optimization of a compensation strategy is achieved; secondly, production load types are matched in real time, that is, products of the same kind are judged when the deviation is smaller than 0.05, the accuracy of a compensation strategy is ensured, in addition, instantaneous reactive power pre-judgment of the SVG and steady-state compensation cooperative control of the TBB / TSC can be achieved, and the response speed and accuracy of hybrid compensation are improved; and finally, through verification after compensation and a model updating mechanism, the adaptability and stability of long-term operation of the system are ensured.

The invention provides a self-adaptive variable-pitch control method and system based on turbulence intensity dynamic compensation, and relates to the field of wind power generation. Firstly, a real-time turbulence data set in the operation environment of a wind turbine generator is obtained, and the real-time turbulence data set comprises multi-dimensional turbulence characteristic units which are continuously collected and provided with time sequence marks; performing feature extraction on the real-time turbulence data set to obtain turbulence feature distribution containing spatial distribution features and dynamic change features, then generating a dynamic compensation strategy containing a related compensation parameter set according to the turbulence feature distribution, and adjusting control parameters of a preset variable pitch control model based on the dynamic compensation strategy to obtain the variable pitch control model. And finally, a pitch angle adjusting mechanism of the wind turbine generator is controlled through the corrected parameters, self-adaptive adjustment of the pitch angle is achieved, and therefore the operation stability and the output power stability of the wind turbine generator under the complex turbulence working condition can be improved, the damage risk of parts can be reduced, and the service life of the wind turbine generator can be prolonged.

The invention discloses a multi-agent cooperative control system based on adaptive learning, and belongs to the technical field of multi-agent system control, and the system specifically comprises the steps: collecting and analyzing the wind field data of the position of each agent; distinguishing two typical working conditions of a global uniform wind field and a local disturbance wind field based on a consistency analysis result; for a global uniform wind field, a model prediction control method is adopted to generate a wind field compensation strategy for keeping formation stability; for a local disturbance wind field, an accurate airflow disturbance state is obtained through environment three-dimensional reconstruction and fluid simulation, then the expected track deviation of each agent is predicted, and a collaborative avoidance instruction set is generated in combination with a relative position relationship. According to the method, adaptive cooperative control of multiple agents under different wind field working conditions is realized, the hidden conflict problem caused by local wind field disturbance is effectively solved, and the flight safety and task reliability of an agent cluster in a complex environment are remarkably improved.

The invention discloses a force sense feedback control method for an intelligent mechanical arm, which comprises the following steps of: 1, acquiring data through a multi-modal sensor and fusing the data to obtain a multi-dimensional perception vector; 3, calculating a force sense tracking error and a change rate and triggering an event-driven control decision mechanism; 4, designing a nonlinear compensation control rule and outputting a control torque instruction, wherein a control system comprises a multi-mode sensing module, a dynamic prediction module, an event-driven control module and a cooperative calculation module; according to the method, multi-mode sensing information is fused with the lifting force sense representation capacity, advanced adjustment is achieved in combination with a dynamic force sense prediction mechanism, event-driven control is used for reducing calculation redundancy, robustness to complex interference is enhanced through a nonlinear compensation strategy, and finally high-precision and low-delay force sense control of the mechanical arm in a dynamic interaction scene is achieved.

The positioning error of a feeding system obviously influences the machining precision, especially under the high-speed and high-load working condition. An existing positioning error compensation system often faces the problems of high calculation cost and complex sensor configuration and is difficult to be widely applied to the industry. Therefore, a simplified model needs to be established and an economical and efficient sensor integration and real-time compensation scheme needs to be developed urgently. Therefore, the invention discloses a dynamic positioning error compensation system of a numerical control machine tool feed driving system, and provides an error suppression system fusing a mechanism driving positioning error prediction model and a real-time compensation strategy. The positioning error prediction model is constructed as a hyperbolic cosine function of temperature and nut position, and the residual error-based dynamic compensation model can effectively process unmodeled errors and system dynamic changes. The dynamic positioning error compensation system shows excellent adaptability and precision under the dynamic working condition, and an extensible practical solution is provided for positioning error suppression in the high-precision manufacturing fields of aerospace, automobiles, electronics and the like.

The invention relates to the technical field of mining permanent magnet synchronous motor sensorless control, and particularly discloses a composite sliding mode control and adaptive phase-locked loop collaborative optimization method. Aiming at the technical bottlenecks of a traditional control strategy in the aspects of rotating speed fluctuation suppression, load disturbance compensation and rotor position observation precision, a collaborative architecture of a time-varying integral terminal sliding mode control module, a sliding mode disturbance observation module and a self-adaptive phase-locked loop module is constructed, so that the dynamic performance optimization of the system is realized. According to the method, through the dynamic coupling design of the time-varying gain nonlinear reaching law and the fast integral terminal sliding mode surface, the convergence speed and the buffeting suppression requirement are balanced while the disturbance dynamic influence is eliminated; multi-dimensional decoupling observation of external load disturbance is realized based on a hierarchical disturbance observer architecture, and a disturbance real-time suppression path is established in combination with a feed-forward compensation strategy; and constructing an error self-adaptive correction mechanism of rotor position observation by dynamically adjusting a phase-locked loop proportionality coefficient and a phase compensation parameter. The limitation of parameter solidification and observation coupling in traditional control is broken through, the rotating speed tracking stability, the position observation precision and the anti-interference capability under complex working conditions are remarkably improved, and an innovative solution is provided for a high-precision servo driving system.

The invention relates to the technical field of electric power engineering automation, in particular to a low-voltage equipment power factor automatic adjusting system and method based on end compensation, and the system comprises a load monitoring module, a compensation strategy generation module, an end compensation execution module, a dynamic balance correction module and an effect evaluation module. According to the invention, the sequential relationship between the load fluctuation and the compensation demand can be accurately identified by continuously monitoring the current waveform, the voltage waveform and the phase difference change trend of the low-voltage equipment, and the compensation strategy is dynamically formulated in combination with the response duration and the compensation demand matching rule, so that the compensation response agility and the matching property of the fluctuating load are improved; dynamic scheduling is carried out according to the capacitor switching capacity and the combination relation in compensation execution, switching weight configuration is analyzed and optimized through the power factor change rate before and after compensation and the load synchronization characteristic, the dynamic balance of the compensation process is enhanced, the power factor stability and the load fluctuation matching capacity after compensation are effectively improved, and the compensation efficiency is improved. And the adaptability and continuity of the adjusting effect are enhanced.

The invention discloses a three-phase imbalance compensation method and system based on negative sequence current and a medium, and relates to the technical field related to power imbalance compensation, and the method comprises the steps: executing the three-phase current, voltage and reactive power data collection of a power distribution network, and carrying out the three-phase imbalance recognition through negative sequence current analysis; carrying out additional acquisition of a load mode and environment data on the power distribution network; taking the data acquisition result and the additional data set as input data to execute negative-sequence current change trend prediction; a double-layer compensation device is configured for cooperative control optimization; power distribution network balance compensation is executed through a time sequence compensation strategy, and time sequence window feedback is established; and carrying out adaptive strategy correction. The technical problems that in the prior art, a three-phase imbalance compensation strategy lacks perspectiveness and self-adaptive capacity and cannot cope with a dynamically-changing power grid scene, and consequently compensation precision and stability are poor are solved, and the technical effects that perspectiveness and predictive compensation is achieved, and three-phase imbalance compensation precision and stability are improved are achieved.

The invention relates to the technical field of thermal management control, and particularly discloses a thermal management controller regulation and control method and system based on dynamic thermal inertia prediction. According to the method, the dynamic thermal inertia model is constructed through system identification, and the future temperature field evolution and thermal accumulation risks of the system are accurately predicted. On the basis, a multi-layer nested compensation strategy composed of pre-compensation, main compensation and residual compensation is generated in three dimensions of a compensation angle, a compensation order and a compensation domain, and prospective accurate intervention is achieved. In order to evaluate the control effect, an artifact intensity entropy value is introduced as a quantitative index, and a model and a strategy are corrected in real time through an outer ring optimization mechanism to form self-correction. When continuous thermal unbalance caused by hardware design defects is detected, the system can further start hardware cooperative regulation and control, local heat capacity and a heat dissipation path are optimized fundamentally, cross-level cooperative optimization from a control algorithm to a physical structure is achieved, and finally high-precision, low-energy-consumption and self-adaptive system-level thermal management is achieved.

The invention provides a self-adaptive algorithm and system for improving frequency measurement precision and response speed of a speed regulator. The method comprises the following steps: Step 1, signal acquisition and preprocessing; step 2, dynamically adjusting filtering parameters, and suppressing power grid signal noise; 3, extracting frequency characteristics in real time, and improving the frequency measurement resolution; 4, a synchronous clock is adopted as a reference frequency source, a clock reference value is periodically and automatically corrected, and long-term drift is eliminated; 5, a nonlinear dynamic model of the hydroelectric generating set is constructed, the nonlinear relation between the valve opening degree and output of the speed regulator is compensated in real time, and the frequency modulation precision is improved; and step 6, outputting the instantaneous frequency. According to the scheme, through the self-adaptive frequency measurement algorithm, high-precision clock calibration, a hardware acceleration mechanism and a nonlinear compensation strategy, a multi-dimensional optimization system is constructed, high-precision frequency measurement is carried out, a rapid effect can be achieved, and a hydroelectric system has long-term stability and high adaptability.

The invention discloses a torsion test method based on a Kalman filtering algorithm, and relates to the technical field of torsion test.According to the torsion test method, a dynamic noise model and a material feature library are combined to enable a filter to automatically adapt to acoustic characteristics of different substrates, and the problem of misjudgment caused by fluctuation of material parameters in a traditional method is solved; secondly, a sub-band processing mechanism can restrain high-frequency noise and meanwhile completely reserve transient characteristics before the material is broken, and effective signal loss is avoided; the transfer learning model eliminates batch differences through adversarial training, and the generalization ability of dynamic threshold judgment is ensured; the real-time compensation strategy generates a refastening scheme considering both mechanical safety and assembly precision in emergency.

The invention relates to a machining path planning system, in particular to the technical field of intelligent manufacturing and numerical control machining, and overcomes the defect that a thermal coupling effect is ignored in traditional geometric planning by constructing an evolution model fused with a physical law. A dynamic decoupling algorithm is utilized to extract a real cutting load from multi-source data and reconstruct a rigidity field changing along with material removal in real time, the problem that a physical boundary is difficult to perceive under a complex working condition is solved, and the system effectively eliminates size out-of-tolerance caused by heat accumulation through transient deformation advanced prediction. According to the method, the non-linear error is actively counteracted in cooperation with a reverse compensation strategy based on sensitivity analysis, the machining precision and surface quality of the thin-wall weak-rigidity part are remarkably improved, the rejection rate and trial cutting cost are reduced, and intelligent closed-loop control over the manufacturing process is achieved.

The invention discloses a special-shaped pipe forming compensation method and system based on digital twinning, and belongs to the technical field of computer-aided manufacturing and intelligent control. The method comprises the steps that multi-source sensing data in the special-shaped pipe forming process is obtained, and time-space fusion processing is executed; generating a time-space aligned multi-source sensing data set, carrying out dynamic calibration on the parameterized virtual digital twin, generating a calibrated virtual digital twin, carrying out simulation calculation on the forming process of the special pipe in a future time window, outputting predicted deviation data, and combining with a preset compensation strategy to generate a compensation instruction through logical judgment and calculation; and obtaining an adjustment result after the compensation instruction is applied, and updating the compensation strategy by using a reinforcement learning algorithm. According to the method, prospective simulation prediction is carried out by constructing the dynamically calibrated virtual digital twins, closed-loop optimization is carried out on a compensation strategy by utilizing a reinforcement learning algorithm, and active, high-precision and self-adaptive compensation control on the special pipe forming process can be realized.

The invention discloses a multi-stage collaborative protection simulation verification method for a subway power supply system, and the method comprises the steps: building an integrated simulation model which comprises an automatic reclosing switch, a spare power automatic switching switch and an automatic change-over switch based on an MATLAB / Simulink platform, and especially carrying out the precise modeling of an advanced compensation function of ATSE. The model is verified by a power-off test, a simulation result and actually measured data are highly consistent in voltage recovery time, an error of advanced compensation action time of ATSE is less than 2ms, and dynamic characteristics of an actual system are accurately reproduced. Model simulation shows that the advanced compensation strategy of the ATSE can effectively control the voltage sag amplitude within 15%, the power supply reliability of the system is remarkably improved, and through strict'simulation-actual measurement 'double-track verification, the model accurately reproduces the dynamic characteristics of the actual system, and shows remarkable advantages in the aspect of improving the power supply reliability.

The invention relates to the technical field of machine tool machining precision control, and discloses a portal frame milling machine error compensation system and method for achieving high-precision machining, and the system comprises a multi-mode sensor module, a data processing module, a feature extraction module, a deep reinforcement learning module and a compensation module; the method comprises the steps that error data in the machining process of the portal frame milling machine are collected in real time through a multi-mode sensor, and the data are preprocessed; and then, extracting features by using a convolutional neural network, generating a feature vector, optimizing the feature vector based on a deep reinforcement learning algorithm, and generating an optimal error compensation strategy. Finally, the machining parameters are dynamically adjusted according to the compensation strategy, and machining errors are corrected in real time. Through an intelligent error compensation strategy, the machining precision and the automation level of the portal frame milling machine are remarkably improved, dynamic changes in the machining process can be adapted in real time, and the machining quality and the production efficiency are effectively improved.