250 results about "Compensation strategy" patented technology

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 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 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 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 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 relates to the technical field of mechanical transmission, in particular to a reinforcement learning compensation method and system for gear multi-body dynamics constraint, and the method comprises the steps: building a multi-body dynamics digital twinborn model containing bearing rigidity and gear meshing force; constructing an adaptive reward function based on nonlinear manifold mapping, and dividing a system state space into a small deviation domain and a large deviation domain according to an angle deviation threshold value; training the multi-body dynamic model based on a depth deterministic strategy gradient algorithm to obtain a compensation strategy; the obtained compensation strategy is applied to a multi-body dynamic model, and an optimal compensation signal is obtained through self-adaptive fusion of static compensation and dynamic compensation, so that the training efficiency of a reinforcement learning algorithm in a complex nonlinear system is improved, the adaptability of the system to working condition changes is enhanced, the vibration of a gear system is remarkably reduced, and the reliability of the system is improved. The angle control precision is improved, and the service life of equipment is prolonged.

The invention discloses a gear coding error dynamic compensation method based on multi-sensor phase difference fusion, which belongs to the field of mechanical transmission, and comprises the following steps of: cooperatively acquiring gear phase information through a Hall sensor and a photoelectric sensor, and calculating phase difference data; constructing a rotating speed, temperature and load three-dimensional topology error space; training an error prediction model based on a seven-layer long-short-term memory network; a self-adaptive compensation strategy is executed, and when the error is larger than 0.1 degree, the driving rotating speed is increased; implementing closed-loop feedback control, and triggering model self-calibration when a difference value between an actual error and a predicted error is greater than 0.16 degree; a transfer learning mechanism is introduced, the bottom-layer feature extraction capability is reserved, and only upper-layer parameters are updated; the system state monitoring and collaborative optimization are realized, the working mode and resource allocation are dynamically adjusted, the precision and stability of the gear transmission system are remarkably improved, and the method has wide engineering application value.

According to the springback prediction compensation method in the precise stamping process of the high-pitch panel, the stamping force, the die temperature and the material thickness tolerance are collected through the multi-channel sensor, and the data quality is improved by combining standardization and abnormal value elimination; realizing parameter dimension reduction by adopting principal component analysis, obtaining working condition feature representation, quantifying working condition similarity by utilizing a weighted Euclidean-Mahalanobis distance, judging unseen working conditions in real time, and automatically triggering meta-learning model compensation; through model-independent element learning, rapid adaptive error prediction based on newest stamping data, and combination of confidence evaluation and dynamic adjustment compensation strategies, efficient prediction and robust compensation of springback errors are realized, and model parameters are continuously optimized in follow-up combination with actual compensation feedback. According to the method, the self-adaptive capacity and the forming precision to unknown stamping working conditions are improved.

The invention provides a data error compensation method for a biological net port flowmeter, and belongs to the technical field of ocean detection. A multi-dimensional sensor is arranged at a biological net port to collect fluid parameters, a deep learning model is used for recognizing multi-phase flow characteristics, and a corresponding compensation strategy is selected; based on a correlation analysis algorithm, compensation processing frequency is dynamically adjusted to achieve self-adaptive balance of precision and efficiency, the flow state change degree is monitored through the Euclidean distance, compensation parameters are hierarchically adjusted to ensure system stability, a parallel processing architecture is adopted to execute coarse compensation and fine compensation algorithms at the same time, and results are fused according to weights. Finally, dynamic and accurate compensation of the measurement error of the biological net port flowmeter in the complex multiphase flow environment is achieved, and the technical problem that in the prior art, the biological net port flowmeter cannot achieve dynamic and accurate error compensation in the complex multiphase flow environment is solved.

The invention discloses a real-time compensation control method and system for thrust fluctuation of a linear motor, and belongs to the technical field of linear motors, and the method comprises the steps: collecting a hybrid fluctuation source in the operation process of the linear motor, splitting the hybrid fluctuation source, and generating independent geometric nonlinear fluctuation signals; further screening the mixed fluctuation source, screening out a non-geometric nonlinear fluctuation signal, and preprocessing the non-geometric nonlinear fluctuation signal; establishing an independent compensation strategy, and respectively performing targeted compensation on geometric nonlinear fluctuation and non-geometric nonlinear fluctuation; and establishing a feed-forward compensation network, inputting the compensation strategy into the feed-forward compensation network, predicting a fluctuation component, and performing pre-compensation processing in advance. In the implementation process of the technical scheme provided by the invention, the pertinence and effectiveness of a compensation strategy are ensured through precise splitting and preprocessing of the hybrid fluctuation source, and meanwhile, the compensation amount is adjusted in real time by using the prediction function of the feedforward compensation network, the thrust fluctuation is remarkably reduced, and the operation stability of the linear motor is improved.

The invention discloses a hydropower station signal source side data anti-interference acquisition method and system. The method comprises the following steps: constructing a multi-source interference feature recognition network according to an environment state of a hydropower station, obtaining an interference source distribution feature spectrum, and obtaining an original signal through space-frequency domain joint filtering preprocessing based on the spectrum; performing recursion processing on the original signal by adopting an adaptive Kalman filtering algorithm to obtain a high-fidelity target signal; processing the high-fidelity target signal based on a multi-scale feature extraction and verification mechanism to obtain a credibility quantitative purification signal; and processing the credibility quantitative purification signal based on a compensation strategy to obtain a high-reliability output signal. According to the method, the influence of interference signals is gradually removed from interference source identification, filtering processing, signal reconstruction and quality evaluation to signal compensation, and finally, a result subjected to multiple anti-interference processing is obtained, so that various interferences in a complex environment of a hydropower station can be effectively resisted, and high-reliability data acquisition is realized.

The invention relates to the technical field of aero-engine quality inspection, and discloses an aero-engine part machining quality detection method based on data management. The method comprises the following steps: collecting multi-source machining data of a part, wherein the multi-source machining data comprises dimensional tolerance, surface roughness and material hardness data; key quality features are extracted according to a design drawing, and the key quality features and the multi-source processing data are subjected to time sequence alignment according to process stages; a three-dimensional mass space model with the process stage as the horizontal axis, the key quality features as the longitudinal axis and the multi-source machining data as the depth axis is constructed, and a mass distribution thermodynamic diagram is generated; identifying an abnormal process region based on the thermodynamic diagram, extracting a corresponding data subset, inputting the data subset into the dynamic convolution kernel quality analysis network, and outputting a process abnormal probability value; when the probability value exceeds a threshold value, triggering cross-process correlation analysis, and fusing upstream and downstream process data to generate a composite quality feature vector; and an abnormal type is judged through a cascade classifier, and a compensation strategy library is called to generate a machining parameter adjustment instruction set.

The invention provides a hard springback processing system and method for precision stamping parts, and belongs to the technical field of precision stamping part manufacturing, and the system comprises a springback value detection module, a data processing module, a multi-field cooperative execution module and a closed loop feedback module. The springback value detection module collects springback value distribution data of the surface of the stamping part in a non-contact mode through a multispectral camera, and a visual numerical matrix is generated by combining color scale bar calibration. And the data processing module fuses finite element simulation and a machine learning model, and calculates the refined compensation amount of each region. The method comprises the steps of detecting springback value distribution after demolding, generating a compensation strategy, performing multi-field cooperative compensation and performing real-time feedback optimization. According to the invention, accurate identification and dynamic compensation of the micron-sized springback value are realized, the problems of low detection precision, single compensation mechanism and poor adaptability in the prior art are solved, elastic springback is effectively inhibited, the shape and size precision of a workpiece are improved, manual correction links are reduced, and the production efficiency and the product qualification rate are improved.

The invention discloses a geometric error identification and compensation system and method for a numerical control machine tool, and belongs to the technical field of numerical control machine tools, and the method comprises the steps: collecting the position, temperature and cutting force data of the machine tool in real time, and calculating the components of a position error, a thermally induced equivalent error and a force induced equivalent error to construct a multi-dimensional state vector; according to the method, a compensation value is calculated through a dynamic compensation model, the core of the method is that a dynamic modulation factor is introduced, the factor integrates the heat effect, the force effect and the multi-axis coupling effect to dynamically adjust the compensation weight, in addition, a grading trigger compensation strategy based on comprehensive error amplitude evaluation is designed, and the compensation accuracy is improved. The reconstruction compensation can update model parameters on line to cope with long-term changes of the system, intelligent distribution of compensation resources and self-adaptive adjustment of system changes are achieved, time-varying errors are effectively restrained, and precision, efficiency and robustness are taken into consideration.

The invention discloses a numerical control machine tool error intelligent compensation method based on spatio-temporal feature fusion, and the method has the following advantages: through spatio-temporal feature fusion and a physical constraint model, decoupling analysis of multi-source errors such as thermal errors and force errors is realized, through a physical model, the thermal errors and the force errors are separated, and a data driving model corrects nonlinear residual errors; the modeling precision is improved, and the modeling error is reduced; based on a rolling optimization strategy of model prediction control, error compensation can be dynamically carried out on the numerical control machine tool in real time, compensation parameter updating can be completed in a polar period of time, and the method adapts to rapid changes of machining working conditions; a hybrid model of physical prior and data driving is combined, and all-working-condition positioning error control is carried out, so that the error is controlled within a precise error range, and the precision is greatly improved; the physical model provides mechanism explanation of error generation, a data driving model and correction of supplementary nonlinear errors, and the engineering credibility of the compensation strategy is greatly improved through the error compensation strategy combining the two.

The invention relates to a TBM attitude adaptive correction method and system, and belongs to the technical field of heading machines, and the method comprises the steps: respectively calling a multi-target genetic algorithm and a reinforcement learning algorithm to obtain a corresponding candidate correction strategy set and a corresponding compensation strategy set; selecting an initial deviation correction strategy from the candidate deviation correction strategy set according to the dispersion of the strategy set, and optimizing the initial deviation correction strategy by using the compensation strategy set to generate an optimal deviation correction strategy; inputting the optimal correction strategy into a pre-trained confidence evaluation model used for evaluating the credibility of the correction strategy to obtain the confidence of the optimal correction strategy; and determining the current deviation rectification mode corresponding to the confidence coefficient of the optimal deviation rectification strategy from a preset corresponding relation between the confidence coefficient and the deviation rectification mode, so that the TBM performs deviation rectification according to the current deviation rectification mode, the uncertainty caused by deviation rectification depending on a digital model is reduced, the TBM is subjected to direction adjustment through man-machine cooperation, and the deviation rectification efficiency of the TBM is improved.

The application relates to the technical field of machine tools and specifically discloses a gantry machine tool overall real-time thermal error compensation method based on CNN-BIGRU-A, which comprises the following steps: acquiring historical temperature data and historical thermal deformation data of a main shaft and a transmission shaft; constructing a thermal error prediction model, training the thermal error prediction model according to the historical temperature data and the historical thermal deformation data, so as to obtain a trained thermal error prediction model; transmitting current temperature data of the main shaft and the transmission shaft to the trained thermal error prediction model, so as to output a current thermal error compensation value of the main shaft and a current thermal deformation calculation value of the transmission shaft; a numerical control system of the machine tool calculates a current thermal error compensation value of a target position point on the transmission shaft according to the coordinates of the target position point; and the numerical control system of the machine tool compensates according to the current thermal error compensation value of the main shaft and the current thermal error compensation value of the target position point on the transmission shaft. The application can realize a compensation strategy with high frequency and high synchronization.

The invention relates to the technical field of power system dispatching and control, in particular to a fire coal-fused salt energy storage dynamic characteristic decoupling method based on multi-time scale control, which comprises the following steps: constructing a multi-physical field coupling model of a coal-fired unit and a fused salt energy storage system; dividing a minute-level adjustment scale and a second-level response scale based on the coupling model; aiming at the minute-level regulation scale, designing a power distribution strategy based on model prediction control, matching a load trend in a power grid, and reserving a power regulation margin for second-level response; aiming at a second-level response scale, designing a compensation strategy based on sliding mode control, and correcting power deviation to track a real-time instruction of a power grid; and associating the minute-level strategy with the second-level strategy through a dynamic weight coefficient to form a closed loop to cooperatively complete dynamic characteristic decoupling. The problem of power supply and demand mismatch of the coal-fired unit and the fused salt energy storage system in coupling operation can be solved, the response speed, the adjustment precision and the operation stability of the system are improved, and technical support is provided for efficient operation of a novel power system.

The invention relates to a ship assembly carrying docking precision control method, device and equipment and a storage medium. According to the method, the whole-process precision control in the ship assembly and carrying process can be realized by constructing the digital twin, collecting the data of the multi-source sensor, establishing the virtual-real mapping relation and combining deep learning prediction and real-time compensation, the butt joint state of the ship block can be monitored in real time, thermal deformation caused by welding can be predicted in advance, and the welding quality can be improved. And dynamic adjustment is conducted through a compensation strategy, it is guaranteed that the final welding precision reaches the standard, and therefore rework is reduced, the precision of butt joint errors can be greatly improved, the rework rate is effectively reduced, and the quality and efficiency of ship building are remarkably improved.

The invention relates to the technical field of semiconductor test equipment, in particular to a full-automatic high-precision probe station, which utilizes multi-physics field coupling sensing and compensation to remarkably improve the positioning precision: by comprehensively collecting multi-physics field data such as a temperature field and a stress field and combining a coupling algorithm to optimize a compensation strategy, multi-factor interference is effectively counteracted, and the accuracy of the probe station is improved. The positioning repetition precision can reach + / -0.05 [mu] m, and the test requirement of the advanced semiconductor technology is met; a dynamic three-dimensional modeling technology and dynamic adaptability enhancement are achieved, a dynamic three-dimensional modeling system with the size affected by multi-physical field data can update the form of a test target in real time, test deviation caused by target deviation or surface fluctuation is avoided, and the adaptability of equipment to a complex test scene is improved. The automation degree is high, the testing efficiency is improved, the whole process of data acquisition, modeling, compensation and testing can be completed without manual intervention, the calibration process is simplified, the testing period is shortened, and the method is suitable for batch detection scenes.

The invention discloses a springback compensation control system for a metal stamping part, and belongs to the technical field of metal plate forming. The system comprises a stress memory prediction module, a fractal compensation analysis module, a phase change compensation regulation and control module, a compensation decision fusion module and an execution feedback optimization module. The stress memory prediction module extracts stress evolution characteristics of the stamping process through a multi-scale memory network and a time decay attention mechanism; the fractal compensation analysis module adaptively adjusts the compensation grid density based on the fractal dimension to realize optimal configuration of computing resources; the phase change compensation regulation and control module predicts the phase change behavior of the material through the thermal-mechanical coupling constitutive model and calculates the compensation correction amount caused by phase change; the compensation decision fusion module intelligently fuses three compensation strategies by adopting Bayesian reasoning and a spatial modulation function; and the execution feedback optimization module realizes continuous optimization of the system through online learning. According to the method, the influence of three dimensions of stress history, geometric complexity and material phase change is comprehensively considered, accurate prediction and compensation of the springback behavior are achieved, and the forming precision and production efficiency of the metal stamping part are remarkably improved.

The invention relates to the technical field of bridge jacking construction, in particular to a large-area special-shaped continuous beam asynchronous jacking system and a jacking construction method. According to the method, jacking force is pre-distributed based on a finite element model and a historical data training machine learning algorithm, and high-precision deformation coordination control is achieved through a hydraulic jack group and a multi-sensor cooperative monitoring network (including displacement, strain, inclination angle and deflection parameters) placed on a special-shaped beam in combination with an iteratively optimized graded jacking and dynamic compensation strategy. Comprising the steps that the total jacking height is constructed in a graded mode, and in each stage of jacking, output of all fulcrums is adjusted in real time through a PLC so that the displacement synchronization error can be kept to be smaller than or equal to 0.5 mm; when it is monitored that local stress exceeds the limit, a dynamic compensation mechanism is triggered, and secondary balance of the load is achieved by jacking and accelerating an overstress point and jacking adjacent fulcrums in a reinforcement mode. According to the method, the problems of stress concentration, local support disengaging and the like caused by uneven stress in the jacking process of the special-shaped continuous beam are solved.

The embodiment of the invention provides a transfer robot clamping force control method and system, and relates to the technical field of transfer robot clamping force control, and the method comprises the steps: obtaining a clamping force signal of a first clamping force sensing element, a driving parameter of a clamping device driving mechanism, and displacement information of a clamping device jaw; obtaining a reference clamping force according to the driving parameters and the displacement information; detecting a deviation trend between the clamping force signal and the reference clamping force to obtain a target deviation; compensating the clamping force signal according to the target deviation and a preset online self-adaptive compensation strategy to obtain a compensated clamping force signal; and based on the compensated clamping force signal, the clamping force of the clamping device driving mechanism on the high-temperature metal blank is controlled. The forming quality and the production efficiency of the forge piece can be improved.

The invention discloses a multi-unmanned aerial vehicle cluster stochastic prediction control method and device based on anti-saturation and packet loss compensation, and the method comprises the steps: building a closed-loop control model of a stochastic system based on a pilot-following multi-unmanned aerial vehicle cluster architecture; through a Markov chain modeling data transmission packet loss process, a forgetting factor is introduced to design a packet loss compensation strategy after random data is randomly lost so as to correct a closed-loop control model of a random system; aiming at the corrected closed-loop control model of the following stochastic system, constructing an anti-actuator saturation control law through convex representation of a saturation function, and further constructing a saturation-limited following stochastic augmentation prediction model; solving a predictive control problem on line based on a random augmentation predictive model; and the control performance index is controlled through rolling optimization, the gain of the feedback controller is updated, and anti-saturation random prediction control following with random following in a packet loss environment is realized. According to the method, the coupling influence of data transmission packet loss and actuator saturation can be effectively solved, and the system performance weakness caused by single problem processing is avoided.

The invention provides an acquired data error compensation method for a small-sized thermohaline instrument, belongs to the technical field of electrical digital data processing, and realizes accurate error compensation by constructing a multi-stage neural network tree structure. According to the method, firstly, errors are classified into system errors, random errors and environment interference errors, and second-level and third-level subdivision is carried out to form a complete error classification system; based on the classification system, an optimal path selection function is adopted to dynamically determine a compensation strategy, and corresponding models are selected from a pre-trained miniature neural network library to form a compensation chain. All compensation model structures are subjected to optimization design, different network structures and parameter configurations are adopted for linear errors, nonlinear errors, noise errors, fluctuation errors, temperature errors and salinity mutual interference errors, the total parameter number is controlled to be at a low level, and efficient operation on small equipment is ensured. The technical problem that in the prior art, a small-sized salt thermometer is insufficient in measurement precision under the condition of low energy consumption is solved.