539 results about "System error" patented technology

Methods and apparatuses for a system error-correction code function are presented. The apparatus includes a memory configured to communicate with a host via at least one data connection and at least one non-data connection. The memory includes a memory array. The memory array includes a first portion and a second portion. The memory is further configured to, in a first mode, store and output data in the first portion and the second portion of the memory array. The first portion is addressable by a first address, and the second portion is addressable by a second address. The memory is further configured to, in a second mode, receive ECC of the data from the host via the at least one non-data connection, store the data in the first portion of the memory array, and store the ECC of the data in the second portion of the memory array based on the first address.

The invention relates to the technical field of bridge health monitoring, in particular to a bridge dynamic deformation monitoring method and system. The method comprises the following steps: establishing a space coordinate reference network model of a bridge; obtaining a space-time registration result of multi-source heterogeneous data based on the space coordinate reference network model; according to the time-space registration result, performing hybrid error compensation and obtaining a compensation result; obtaining a deformation diagnosis result of the bridge structure according to the compensation result; and realizing dynamic deformation monitoring of the bridge through the deformation diagnosis result. According to the invention, through unifying the space-time reference, high-precision fusion of multi-source data is realized and a global deformation field is constructed; an environment noise and system error cooperative compensation mechanism is combined, so that the tiny deformation identification capability is greatly improved; a physical mechanism and a data driving model are fused, so that the diagnosis interpretability is remarkably enhanced; based on a dynamic three-dimensional visualization and self-adaptive early warning strategy, real-time accurate response to the health state of the bridge structure is realized.

The invention discloses a robust model prediction control method for mechanical arm trajectory tracking control, and relates to the field of multi-degree-of-freedom mechanical arm trajectory tracking control. According to the method, disturbance and parameter perturbation of a mechanical arm system are converted into lumped disturbance, a mechanical arm system error model is established, and feedback linearization is conducted on an error system based on an inverse dynamics method; based on a mechanical arm system error model, a nominal system model prediction controller is designed, a terminal area, a terminal control law and corresponding terminal cost are designed, and the feasibility of rolling interval optimization in each time step is guaranteed. According to the method, the uncertainty and disturbance of the mechanical arm system can be processed, it is guaranteed that system constraints are met, and high-precision trajectory tracking control and input torque optimization are achieved.

The invention discloses an automatic air cylinder clamping system and method, and belongs to the technical field of air cylinder clamping, and the system comprises a map drawing module which is used for drawing a three-dimensional clamping force distribution map; the risk prediction module is used for synchronously predicting a deformation deviation risk value in the clamping process; the model calculation module is used for generating a dynamic attenuation coefficient; and the air cylinder adjusting module is used for dynamically adjusting the opening degree of a multi-stage cushion valve and the servo pressurization rate of the automatic air cylinder set through a self-adaptive control strategy till the clamping force error is stabilized within a preset threshold value. The technical problems that in the automatic air cylinder clamping process, due to environment interference and system errors, the clamping force is unstable, and the workpiece clamping precision is low are solved, and the technical effects that the clamping force stability is improved through dynamic compensation and self-adaptive adjustment, the deformation risk is reduced, and the clamping precision is improved are achieved.

The invention provides a PID (Proportion Integration Differentiation) control method for an AGV (Automatic Guided Vehicle) wheel type driving system. The method comprises the following steps: acquiring a first parameter of an AGV; filtering the first parameter, and correcting error data in the first parameter to obtain a second parameter; setting a preset speed function based on a system error model of the AGV wheel type driving system and the second parameter, enabling the AGV to operate according to the preset speed function, and combining the error model to obtain a driving gain coefficient, a friction coefficient and a speedometer gain coefficient; substituting the plurality of coefficients into the fitness function, and carrying out optimal solution solving through a preset optimization algorithm to obtain an optimal solution; verifying the optimal solution to obtain an evaluation value, and when the evaluation value is smaller than a preset threshold value, outputting a target parameter corresponding to the optimal solution; and substituting the target parameters into an AGV wheel type driving system, and controlling a chassis motor servo driving device to cooperatively complete a specified action based on the control instruction. According to the invention, the adaptive capability of the AGV to various environments is improved.

The invention discloses a self-adaptive safety control method of a nonlinear MIMO system under intermittent DoS attack, which comprises the following steps: establishing a nonlinear MIMO system model suffered from intermittent DoS attack, and the system model is in a strict feedback form and comprises an unknown nonlinear function and bounded external disturbance; the DoS attack causes periodic interruption of a communication link from a sensor to a controller, so that actual measurement data is unavailable in an attack activation period; designing a switching type adaptive state estimator; coordinate transformation is carried out by introducing a filter, and a system error is decomposed into a tracking error and a dynamic surface filtering error; a Lyapunov function is constructed; performing stability analysis in stages; designing an adaptive law to update unknown parameter estimation values of the FLS; and a final event triggering controller is designed, and the event triggering controller is combined with a virtual controller to track an error compensation term and an FLS approximation term. The problem that the state cannot be measured is solved.

According to the mechanical arm control method based on multi-scale dynamic state estimation and error compensation, high-frequency and high-precision estimation of the state of a mechanical arm system is achieved through multi-sensor data fusion and an extended Kalman filtering algorithm, and the accuracy and reliability of state estimation are remarkably improved; on the basis, a multi-scale error analysis method combining a time domain and a frequency domain is provided, and an autoregressive moving average model is utilized to dynamically predict system errors, so that the sensing and pre-judging capability of the system on the errors is enhanced; a comprehensive control strategy including multiple links of feedforward, self-adaption and feedback is designed, a dynamic compensation and adjustment mechanism of errors is introduced, meanwhile, a real-time execution framework based on three-layer task scheduling is constructed, cooperative operation of all functional modules is coordinated, and the real-time performance and high efficiency of a control system are ensured. The method is suitable for multiple fields of industrial manufacturing, intelligent robots and the like, and provides technical support for precise control of mechanical arms in complex scenes.

The invention discloses a space robot finite time anti-interference trajectory tracking control method based on performance constraint and actuator saturation, and the method comprises the steps: firstly solving a state-space equation of a flexible joint free flight space robot, constructing a system error vector in combination with a finite time instruction filter and an anti-saturation auxiliary system, and carrying out the finite time instruction filter and anti-saturation auxiliary system; a novel asymmetric logarithmic obstacle Lyapunov function with an asymmetric fixed time constraint boundary is constructed; and finally, a novel finite time advanced activation anti-saturation auxiliary system and a finite time adaptive controller with a performance constraint effect are constructed, so that finite time anti-interference trajectory tracking control of the space robot is realized. According to the method, the influence of sudden disturbance is considered in the design of the flexible joint free flight space robot controller for the first time, and under the limitation of the saturation performance constraint of the actuator, the trajectory tracking rapidity and accuracy of the space robot are ensured; and the influence of sudden disturbance in a space robot system on the trajectory tracking transient performance can be effectively inhibited.

The invention relates to the technical field of hydraulic servo control systems, and discloses a high-precision hydraulic servo control system and a fault diagnosis method thereof.The hydraulic servo control system comprises a system control module, a hydraulic execution module, a data feedback module, an automatic compensation module and a feedback control module; the system control module is used for receiving and processing an input signal and outputting a control instruction; the hydraulic execution module is used for generating a corresponding hydraulic action according to the control instruction; the data feedback module is used for detecting the actual position and pressure of the hydraulic execution module and transmitting a feedback signal to the automatic compensation module; the automatic compensation module is used for eliminating system errors through a compensation algorithm; the feedback control module is used for comparing the difference between the system output and a set target, generating a deviation signal, and adjusting the input of the system by using the signal, so that the system output is closer to the set target; through the system, the control precision and stability of the hydraulic servo control system can be improved.

The invention provides a mechanical arm predefined time trajectory tracking control method and system based on time delay estimation, and relates to the technical field of multi-degree-of-freedom mechanical arm trajectory tracking control. Constructing a predefined time error conversion function to define a system error and conversion error system, designing a barrier function and a feedback control rate based on the conversion error system, and designing a Lyapunov function to prove the stability of the system so as to track an expected trajectory; according to the method, time delay information is deeply fused, dependence on a complex model approximate structure is not needed, stable convergence of the mechanical arm system state within predefined time can be achieved when a mathematical model is not accurate, the system control performance and robustness are effectively improved, and the requirement of modern industry for mechanical arm high-performance control is met.

The invention relates to the technical field of spraying control, in particular to an anti-corrosion steel pipe inner spraying track self-adaptive control system based on multi-sensor fusion. The feed-forward prediction module generates a feed-forward correction signal through a model compensator and a self-adaptive compensator and predicts and compensates system errors, the feedback control module comprises a track generator and a disturbance observer, a feedback control instruction is generated by comparing a correction track with an original track, an execution mechanism is dynamically adjusted, and the system error is predicted and compensated. The signal driving module adopts a frequency domain decoupling fusion technology, combines a feedforward correction signal and a feedback control instruction, generates a composite control instruction, and ensures the accuracy of trajectory tracking control. According to the invention, through a double-loop control architecture combining feedforward prediction and feedback disturbance suppression, high-precision and strong-robustness self-adaptive control of a spraying track under a complex working condition is realized.

The invention relates to the technical field of semiconductor manufacturing, in particular to a wafer pre-alignment device and method, and the device comprises a mounting platform, an XY motion platform, a rotating platform, a suction cup, and an edge detection assembly. The method comprises the steps of device correction, coarse scanning and circle center positioning, fine scanning and notch accurate positioning and alignment execution. Through software and hardware cooperation, online self-correction is used for eliminating system errors, high-order fitting and feature screening algorithms are used for restraining random errors and model errors, the repeated positioning precision, notch orientation precision and equipment consistency of the wafer pre-alignment device are improved, and the high-precision requirement for a wafer transmission system under the advanced manufacturing process can be met.

The invention relates to a processor and a terminal for positioning a subway train, and the processor is in communication connection with an inertial measurement unit of the train, and is used for receiving multi-source data, collected by the inertial measurement unit, of the train; in the processor, an initial correction module performs state estimation on original acceleration and angular velocity data of a train acquired by an inertial measurement unit based on an error Kalman filtering algorithm, and dynamically corrects measurement noise and system errors in combination with a motion model of the system and an actual measurement value of the inertial measurement unit; the curve generation module constructs a time-mileage curve of the train and corrects the time-mileage curve based on the first correction proportion; the turning recognition module calculates the total mileage in the turning section and corrects the total mileage in the turning section based on the second correction proportion of the turning section; the abnormal vibration recognition module recognizes abnormal vibration features based on the high-order change rate of the acceleration and the angular speed of the train and marks the abnormal vibration features in a visual mode.

The invention belongs to the technical field of unmanned aerial vehicle trajectory tracking control, and particularly discloses an unmanned aerial vehicle preset performance control method and system considering time-varying position constraints. According to the method, under the condition that the unmanned aerial vehicle has time-varying position constraint, model uncertainty, actuator fault and wind disturbance, a nonlinear mapping method is firstly adopted to convert a system with position constraint into an unconstrained system, then a preset performance control method and a self-adaptive backstepping technology are adopted to carry out controller design on the converted system, and therefore the control precision of the unmanned aerial vehicle is improved. The system error is kept in a preset boundary function range; the invention also designs a fuzzy logic system (FLS) and a nonlinear disturbance observer to estimate the uncertainty and wind disturbance of the model, thereby providing a trajectory tracking control scheme combining model mapping and preset performance. Under the control of the method, the quad-rotor unmanned aerial vehicle has good robustness, and even if an actuator breaks down, the tracking error can still be kept within a preset interval.

The invention relates to an information fusion method and device of a multi-source navigation system, computer equipment and a computer program product. The method comprises the following steps: constructing a preset number of sub-models according to sensor systems in the multi-source navigation system, taking the product of the expected credibility and the real-time credibility of the performance of each sensor system as the comprehensive credibility of each sensor system, and determining a state transition probability matrix of the sub-models according to the comprehensive credibility; state prediction updating and measurement updating are carried out on each sub-model, and a system error state and a system state covariance are calculated; and calculating a predicted posterior probability of each sub-model, determining an interactive weight between the sub-models according to the predicted posterior probability and the state transition probability matrix, and correcting and updating the system error state and the system state covariance according to the interactive weight to obtain a final system error state and a final system state covariance. By adopting the method, the robustness of information fusion can be effectively improved.

Providing issue resolution in a cluster system by monitoring system operation to detect occurrence of a system error, and automatically generating, upon detection of the error condition, a core file for a user node. The core file captures a current memory state of a respective node, where the current memory state comprises system statistics, system information, and logs. An intelligent core debugger extracts information from a core file to generate a core file report that is sent to a vendor for a quick determination of whether sufficient information is in the report to allow the vendor to recommend a fix, or whether further information from is required, including the core file itself, if necessary. This prevents the need to send an entire core file to a vendor in every instance of a system fault.

The invention relates to the field of thermotechnical intelligent control, and discloses a heavy-duty gas turbine rotating speed finite time error active-disturbance-rejection control method, which comprises the following steps of: establishing a heavy-duty gas turbine mechanism model according to a mass and momentum conservation law; mathematical processing is carried out based on the heavy duty gas turbine mechanism model, and then an error active-disturbance-rejection control strategy is designed; and designing a finite time expansion state observer based on the error active-disturbance-rejection control strategy so as to realize the active-disturbance-rejection control of the rotating speed error of the heavy-duty gas turbine. The invention relates to an active-disturbance-rejection control method for the finite time error of the rotating speed of a heavy-duty gas turbine. The method comprises the following steps: establishing a heavy-duty gas turbine component-level mechanism model; designing an error active-disturbance-rejection control strategy by considering internal and external disturbance in operation; a finite time expansion state observer is introduced, and a scheme and thought are provided for design of an intelligent control method of the heavy duty gas turbine in a system error convergence mode.

The invention discloses a PMSM (permanent magnet synchronous motor) control method for a mining electric locomotive. The method comprises a cooperative control framework of a fractional order sliding mode controller and a nonlinear extended state observer. Wherein the fractional order sliding mode controller constructs a novel sliding mode surface by introducing a terminal factor and a fractional order calculus theory, and introduces a state variable to design an adaptive fractional order reaching law, so that buffeting is effectively inhibited and controlled while system error convergence is accelerated; and the nonlinear extended state observer is used for estimating the uncertainty of a system model and the disturbance of an external load in real time, and introducing the uncertainty and the disturbance as feedforward compensation quantity into a control loop, so that the anti-interference capability of the system is remarkably enhanced. Through deep fusion of the fractional calculus theory, the sliding mode control and the nonlinear extended state observer, the problems of large buffeting, slow response, poor anti-interference capability and the like of a traditional control method of the mining electric locomotive under complex working conditions are solved, and the dynamic and steady state performance and the control precision of the system are remarkably improved.

The invention discloses an overlay error real-time correction system based on image recognition and dynamic fitting, and belongs to the field of semiconductor advanced packaging and manufacturing. The system comprises an image acquisition module, an error decoupling module and a dynamic compensation module to form closed-loop control. The image acquisition module adopts a high-resolution linear array CCD (Charge Coupled Device) and an AI (Artificial Intelligence) algorithm to realize high-speed and high-precision mark identification and error quantization; the error decoupling module performs decoupling and trend prediction on system errors and random errors by using PCA, a region weight matrix, LSTM and the like; and the dynamic compensation module realizes mu s-level partition graph compensation by a DMD optical system based on an error thermodynamic diagram through heterogeneous platform operation. The system supports the real-time processing of more than 100Hz, the average overlay error is less than or equal to 0.3 mu m and the response time is less than 5 seconds in the FOPLP and other processes, the problems of lag, low precision and the like of the traditional method are effectively solved, and the manufacturing yield and the production line efficiency are remarkably improved.

The invention provides a gas turbine IGV control optimization method combining a PID controller and a single neuron. The method comprises the steps of obtaining a first system error of the PID controller in a kth control period, a second system error of the PID controller in a (k-1) th control period and a third system error of the PID controller in a (k-2) th control period; generating a proportional control input item, an integral control input item and a differential control input item of the PID controller according to the first system error, the second system error and the third system error; the single neuron determines a first control increment of the PID controller in the kth control period based on a first weight coefficient, a proportional control input item, an integral control input item and a differential control input item, and the first weight coefficient comprises a first proportional coefficient, a first integral coefficient and a first differential coefficient in the kth control period; and adjusting the first weight coefficient according to the first system error and the first control increment to obtain a new weight coefficient. Therefore, online adaptive adjustment of the PID parameters can be realized.

The invention relates to the technical field of multi-beam sounding calibration, and discloses a multi-beam sounding online real-time calibration method. The method comprises the following steps: acquiring original water depth data and corresponding sensor attitude data in real time; generating a data quality evaluation result containing a data credibility index and a data abnormal distribution feature based on the original water depth data; triggering a dynamic calibration mechanism containing a calibration period adaptive adjustment strategy and a calibration parameter optimization rule according to the data credibility index; optimizing the target calibration rule parameters in combination with the sensor attitude data to generate an optimized calibration parameter set, and correcting the original water depth data in real time by using the parameter set to generate primary calibration data; and performing topographic feature consistency check on the primary calibration data, if the primary calibration data does not pass the topographic feature consistency check, starting a re-calibration process containing a rule base updating mechanism and a parameter iteration mechanism, and analyzing a deviation trend based on historical calibration records to generate a system error prediction model.

The invention discloses a geothermal water layer quality rapid identification method and system based on an algorithm model, belongs to the technical field of geological exploration, and aims to correct logging curve data of a thermal reservoir research well area, effectively avoid possible system errors in a logging data acquisition process, including improper operation modes or instrument self-measurement errors and the like. Standardized logging data can more remarkably reflect regional geologic features compared with original data, principal component analysis is conducted on sample data through a PCA method, an original sample space is reconstructed according to the contribution rate of principal components, the dimensionality of an input space is effectively reduced, meanwhile, the correlation between variables of the input space is eliminated, and the accuracy of the input space is improved. The identification precision is improved; according to the method, the recognition accuracy is greatly improved, a more scientific and reliable method is provided for well logging data thermal reservoir recognition, and the method has practical significance in exploring geothermal resource development.

The invention provides a laser ceilometer system error compensation method based on regression analysis, and belongs to the technical field of laser ceilometers. A measurement range is divided into four height intervals by constructing a layered height interval step regression equation set, and an independent nonlinear regression equation is established; designing a double-layer game optimization framework to realize collaborative optimization of global error minimization and local fitting precision maximization, executing historical data preprocessing and data set division, and implementing a double-layer game model collaborative optimization algorithm to determine a regression equation coefficient and a neural network parameter; a self-adaptive cloud height error correction model based on a Transform architecture is constructed to realize real-time error compensation, the optimal performance of the model is kept through sliding time window monitoring and automatic retraining, and the technical problem that the measurement precision of the laser ceilometer is affected by atmospheric environment parameters and equipment parameter changes, and consequently system errors are remarkable is solved.

The invention discloses a system manager, an error data processing method, error data processing equipment and a program product, and relates to the technical field of data process.A processor is used for writing error data corresponding to a system error into an error information storage area when the system error is detected, and a controller is used for writing the error data corresponding to the system error into the error information storage area when the system error is detected. And the alarm module is used for reading the error data stored in the error information storage area and generating alarm information when the error data meets a preset condition, so that transmission and efficient asynchronous communication of the error data are realized through the error information storage area and the interrupt register, the preset condition is added, and the alarm information is triggered when the preset condition is met. The processor is prevented from entering a system management mode, meanwhile, performance jitter of an operating system caused by frequent system management interruption is reduced, and the efficiency of processing error data by the system is improved.

The invention provides a hierarchical correction method, device and equipment for a satellite compass and a storage medium, and the method comprises the steps: firstly obtaining multi-source navigation data collected by a sensor group in real time, constructing short-term filter input, medium-term filter input and long-term filter input according to the multi-source navigation data, and carrying out the calculation of the short-term filter input, the medium-term filter input and the long-term filter input; calling a filter with a three-layer filtering structure to process the short-term filter input, the medium-term filter input and the long-term filter input, and performing weighted fusion on the output of the three-layer filter through adaptive weight to generate a comprehensive error estimation value; thirdly, applying a time weighting function based on data age and applying a time attenuation factor based on system use time, and performing weighting processing on the comprehensive error estimation value to generate a system error estimation value; finally, the system error estimation value is applied to the satellite compass course data in a smooth transition mode so as to automatically correct the installation error of the satellite compass, and the problem that the satellite compass course correction cannot be continuously and reliably carried out is solved.

Methods, systems, and devices for enhanced error handling in memory systems are described. A memory system may enter a read error handling procedure to recover data from a memory cell. For example, the memory system may perform multiple read operations at respective first read levels to identify a voltage valley associated with the data in the memory cell. Based on identifying the voltage valley, the memory system may read the data from the memory cell according to a second read level, where the second read level may be based on one of the first read levels, a first learning rate parameter, and a first momentum parameter. The memory system may determine whether to exit the error handling procedure or continue with the error handling procedure based on whether the data was successfully decoded in response to the read operation at the second read level.

The invention belongs to the technical field of PID control, and particularly relates to a PID parameter adaptive algorithm, system and program based on a multi-dimensional fuzzy rule. A PID parameter adaptive algorithm based on a multi-dimensional fuzzy rule comprises the following steps: normalizing system error data and an error change rate by taking a maximum change value as a normalization factor; performing fuzzification processing on the normalized error data and the normalized error change rate by adopting a triangular membership function of a plurality of linguistic variables to obtain fuzzy error data and a membership distribution matrix of the fuzzy error change rate; orthogonal design is carried out on the multiple linguistic variables, and a fuzzy rule base is constructed; and according to fuzzy error data in the membership degree distribution matrix and the distribution of the fuzzy error change rate, traversing the fuzzy rule base, forming a plurality of matched mapping results, performing weighted average calculation, and performing fixed amplitude limiting and dynamic amplitude limiting to obtain a final proportional gain # imgabs0 #, a final integral gain # imgabs1 # and a final differential gain # imgabs2 #.

The invention discloses a cable terminal crimping quality detection method, device and equipment and a storage medium, and relates to the technical field of cable testing, and the method comprises the steps: exciting a cable terminal through a current pin in the crimping process of the cable terminal, collecting the voltage of the cable terminal through a voltage pin, and obtaining a real-time voltage signal; and evaluating the crimping quality of the cable terminal based on the resistance change condition of the real-time resistance value in the crimping process. According to the invention, the detection behavior and the crimping process are carried out synchronously, and the real-time monitoring of the dynamic change of the resistance in the crimping process is realized, so that the method can be applied to the one-by-one full detection of a production line, and the measurement mode of separating current excitation and voltage collection is adopted. A system error caused by lead resistance introduced by a common lead of current excitation and voltage acquisition is avoided, and real-time performance, non-destructive performance and high precision are effectively considered when the crimping quality of the cable terminal is evaluated.

The invention relates to the technical field of stacking, in particular to a stacking regulation and control method, system and equipment of a stacking robot and a medium, and the stacking regulation and control method comprises the steps that system errors are eliminated by collecting and fusing data of multiple sensors, layered dynamic path planning is conducted to generate the optimal track of a mechanical arm, the posture of an end effector is updated, and stacking is executed through double-closed-loop control. The method comprises the steps of performing joint self-calibration and clamp self-adaptive adjustment, and realizing digital twin monitoring and group intelligent scheduling, and further comprises a corresponding system, electronic equipment and a computer readable storage medium. The effects of improving the cargo box stacking precision, stability and safety, adapting to the cargo boxes of different sizes, achieving state monitoring, optimizing task allocation and improving the overall operation efficiency and reliability of the stacking robot are achieved.

The invention discloses a set top box fault prediction method and device based on log data, a medium and equipment, and the method comprises the steps: obtaining the log data of a set top box in a set period, the log data of the set top box comprises system error and warning information, an application crash report, network connection state change, hardware health indexes, a user behavior mode and performance statistical data; preprocessing the log data; constructing a set top box fault prediction model, and training the model; and inputting the preprocessed log data into a trained set top box fault prediction model so as to predict the fault of the set top box. According to the invention, whether the set top box has a fault can be accurately predicted based on the log data.