264 results about "Sensitivity coefficient" patented technology

The invention discloses a focusing control method and related equipment, and the method comprises the steps: synchronously collecting the data of a multi-modal sensor, extracting features and parameters, inputting a multi-modal fusion model subjected to joint optimization to generate a motor control instruction, and achieving the focusing through the hierarchical control of rough adjustment, fine adjustment and dynamic tracking. The dynamic weight fusion network deploys the weight of each modal data according to the environmental sensitivity coefficient, and combines the historical memory and trajectory pre-judgment of the alignment trajectory prediction network to reduce invalid calculation and repeated search and greatly improve the focusing speed; the multi-mode sensor works cooperatively to replace a special hardware function, so that the cost and the power consumption are reduced; the dynamic weight fusion network can adjust the modal confidence according to the environment and enhance the anti-interference capability in the complex light environment; the alignment trajectory prediction network and the fuzzy self-adaptive PID controller are in hierarchical cooperation, the problem of erroneous judgment of a complex scene is solved, and the micro-distance and dynamic tracking precision is improved; the model is subjected to pruning, distillation and quantitative optimization, light weight is achieved, and low-cost equipment is adapted.

The invention relates to the technical field of data processing, and provides a resistance line-associated underwater bench blasting numerical simulation error correction method, which is characterized in that land and underwater measured data are matched through the same resistance line and similar material parameters, and the resistance line and the water depth are used as basic input of an error prediction model; a reference basis is provided for error correction of subsequent numerical simulation; calculating a target result in combination with a prediction error after initial simulation, comparing a simulation result with the target result during iterative simulation to generate an iterative error, and analyzing and determining sensitivity coefficients of different error classifications in a resistance line and the iterative error so as to adjust material parameters to reduce an invalid iterative process; simulation results are processed according to stress areas in a zoning mode through an equidistant slicing method, adjustment precision is verified in combination with the root rate and the damage area, and the reliability of the simulation results is improved; and meanwhile, parameter data passing verification are input into a back propagation neural network, a correction parameter mapping library is constructed, and the design efficiency of an underwater blasting scheme is improved.

The invention provides an intelligent optical cable performance prediction method and system for a data center, and relates to the technical field of data center performance analys.The intelligent optical cable performance prediction method comprises the steps that firstly, an optical cable microstructure disturbance quantization model is constructed, and an optical cable is divided into a plurality of virtual quantization units in the longitudinal direction; each unit packages a real-time stress-strain state parameter and a response sensitivity coefficient of the basic structure unit, inputting a Brillouin scattering spectrum frequency shift data flow collected in real time into the model for disturbance source analysis, generating a physical excitation decomposition sequence, driving a virtual quantization unit to perform state evolution iterative operation according to the physical excitation decomposition sequence, and obtaining a Brillouin scattering spectrum frequency shift data flow model; a stress-strain state evolution track set is obtained, a time-space correlation network of optical cable link disturbance propagation is constructed according to the stress-strain state evolution track set, network flow characteristic analysis is conducted on the time-space correlation network, an early warning node set is extracted, the future evolution trend of the early warning node set is calculated, and serialized optical cable performance abnormity early warning information is generated. The optical cable performance abnormity can be predicted in advance, and stable operation of the data center optical cable is guaranteed.

The invention relates to the technical field of earthquake monitoring, and discloses a comprehensive earthquake prediction method, which comprises the following steps of: acquiring a continuous micro-motion data sequence to reconstruct an empirical green function sequence, and dividing the empirical green function sequence into a compression phase subset and a stretching phase subset by utilizing a synchronously acquired theoretical earth tide stress phase; respectively extracting a causal branch waveform corresponding to the positive time axis and a non-causal branch waveform corresponding to the negative time axis, and determining a weight index for inhibiting the non-structural interference by calculating a reciprocity residual error between a causal wave velocity offset and a non-causal wave velocity offset; correcting a wave velocity offset mean value by using the weight index to obtain a stress sensitivity coefficient reflecting the stress state of the earth crust medium; according to the method, a physical criterion is established by using an elastic wave propagation reciprocity principle, decoupling of medium attribute change and noise source spatial drift is realized, and visual wave velocity offset induced by environmental interference is eliminated.

The invention discloses an HPC curtain wall safety intelligent monitoring method and system, and the method comprises the steps: obtaining curtain wall multi-domain monitoring data and a cable climbing robot scanning record, recognizing a vibration abnormal component, generating a defect sensitivity coefficient, and constructing a damage response field domain; scanning and generating a modal capacity contour surface, setting a resonance early warning trigger node, extracting frequency response distribution to determine a resonance dangerous area, and generating a high-sensitivity monitoring area and a sensor control strategy; an environment load sequence is collected and projected to a damage response field domain to form an effective load bandwidth, and a safety control range is determined by combining structure bearing requirements; performing path search by applying a layered monitoring and adjusting scheme, identifying a stress concentration position through keel deformation modulation, and generating a structure control anchor; analyzing a structure control anchor to generate a fatigue accumulation rate, forming a damage development speed in combination with a scanning record, and extracting collaborative deformation parameters; and comparing the cooperative deformation parameter with a preset value to form a control deviation signal, and generating a self-adaptive control sequence to realize intelligent monitoring and active adjustment of structure safety.

The invention relates to the technical field of artificial intelligence, in particular to a pavement quality detection method and device based on artificial intelligence, and the method comprises the following steps: obtaining pavement images and load data, extracting crack boundary coordinates through edge detection, calculating a displacement difference to obtain crack distribution, calculating an extension rate in combination with time, and carrying out the weighted analysis of a direction difference. The method comprises the following steps: extracting crack boundary displacement difference, identifying defect evolution parameters, calculating stability and environmental sensitivity coefficients through cross coupling, judging degradation types in combination with load fluctuation, calculating defect grades through classification comparison threshold values of a support vector machine, and outputting a pavement overall quality result. Angle difference variation recognizes evolution direction difference, coupling extension rate and direction consistency depicts stability, environmental sensitivity and load fluctuation linkage calculation is introduced to present external action association, and grading is completed based on parameter and threshold difference to realize dynamic grading recognition of defect development rules.

The invention discloses a shaft system alignment key process identification and optimization method based on an error flow model, and the method comprises the steps: constructing an error source set in a shaft system assembly process, and building a related space coordinate system based on an assembly topological relation; establishing a nominal pose transfer equation based on an infinitesimal rigid body coordinate transformation principle; constructing a joint surface contact error vector by combining the diameter of the flange on the basis of a flange opening value and a dislocation value measured on site; performing first-order linearization processing on the nominal pose transfer equation based on the joint surface contact error vector, and obtaining a sensitivity matrix; constructing a state equation for describing error accumulation according to the sensitivity matrix and the error source set; a sensitivity coefficient is constructed based on a state equation and is compared with a judgment threshold, key control characteristics are identified, accurate errors can be predicted by quantifying a transmission mechanism of each error source, and key procedures are accurately identified in combination with the sensitivity coefficient, so that a reasonable strategy is adopted for optimization, and the product quality is ensured to be qualified.

The invention relates to the field of energy storage and reactive compensation capacity optimization configuration, in particular to a new energy pooling station energy storage and reactive compensation configuration method based on big data analysis. The method comprises the following steps: firstly, acquiring data, respectively obtaining a sensitivity coefficient of active power and a sensitivity coefficient of reactive power through a disturbance response sampling linearization identification method, and introducing a dynamic active-reactive decoupling voltage improvement evaluation algorithm based on sensitivity weighting to obtain a voltage improvement amplitude of a node; then, based on the voltage improvement amplitude of the node, constructing a target function; and carrying out iterative solution on the constructed objective function, and outputting the optimal active power regulation quantity of each energy storage unit and the optimal reactive power regulation quantity of each reactive compensation device. The technical problems that the sensitivity coefficient is difficult to update in real time, the calculation scale is huge, the response speed of real-time optimization scheduling, unnecessary active loss and battery life loss are seriously influenced, and the physical balance of the voltage improvement amplitude between the nodes is ignored are solved.

The invention relates to a port area illumination energy saving and visual performance comprehensive evaluation method based on multi-objective optimization, and the method specifically comprises the following steps: deploying an illumination sensor and an intelligent electric meter at a key position of a port area, and collecting illumination and energy consumption parameters to form a training data set containing a performance label; defining a sensitivity coefficient and a normalization index for each data feature and completing normalization calculation; constructing a comprehensive evaluation model, inputting a normalized value, and then realizing classification prediction through a non-linear interaction kernel illumination-energy consumption feature fusion enhancement module and a double-branch attention feature extraction deep neural network backbone module; fusing multiple constraints to construct a composite multi-objective loss function to calculate model loss; iteratively optimizing the model by adopting a gradient descent algorithm in combination with the training data set until a stop condition is met; and normalizing new data, inputting the normalized new data into the trained model, and selecting the highest probability category as an evaluation result. According to the invention, the energy-saving and visual performance of harbor lighting can be accurately balanced, and effective guidance is provided for optimization of a harbor lighting system.

The invention provides a steel plate internal defect identification method and device, and relates to the technical field of steel plate defect identification, and the specific steps are as follows: synchronously acquiring ultrasonic and electromagnetic signals through integrated detection equipment, and aligning to obtain bimodal original data; collaborative denoising and numerical calibration are carried out on the data, and preprocessing is completed; extracting acoustic features and electromagnetic features, determining a feature sensitivity coefficient based on a historical defect sample, and performing weighted fusion according to the feature sensitivity coefficient to generate fused feature data; and finally, inputting the fused feature data into a pre-trained feature-defect mapping model, and outputting the specific type, the position coordinate and the risk level of the defect, thereby realizing accurate and automatic identification and evaluation of the internal defect of the steel plate.

The invention provides a curing process online monitoring method for a variable-thickness composite material component, and relates to the technical field of structural health monitoring of composite materials, and the method comprises the steps: collecting original monitoring data of each optical fiber sensing point in the variable-thickness composite material component; based on a preset temperature sensitivity coefficient and a preset strain sensitivity coefficient, performing temperature-strain double-parameter decoupling on the original monitoring data to obtain chemical shrinkage strain; according to the chemical shrinkage strain and the change rate, a gel point, a maximum heat release rate point and a glass transition interval in the resin curing process are obtained, and curing state characteristics are constructed; and determining a curing stage corresponding to the variable-thickness composite material component based on the curing state characteristics, calculating a curing degree gradient, and when the curing degree gradient is greater than or equal to a preset process safety threshold value, sending a closed-loop regulation and control instruction to curing equipment so as to adjust the curing process of the variable-thickness composite material component, according to the invention, the curing monitoring precision and stability can be improved.

The invention relates to the technical field of fire monitoring, in particular to an early warning method based on correlation analysis of electric power parameters and fire parameters. Comprising the following steps: synchronously sampling power load and environment characteristic data, and obtaining fire behavior evolution parameters through sliding window residual analysis; projecting an environment evolution track by using the evolution mapping matrix; calculating a quantitative causal matching degree through the trend integrating degree, and identifying and eliminating non-electrical interference; the electric energy dissipation characterization quantity and the thermal dynamics evolution parameters are fused to construct an early warning decision operator; and correcting the sensitivity coefficient and combining the continuous energy characteristics to execute grading judgment to complete closed-loop early warning. According to the method, the problem of monitoring dimension isolation is solved by establishing the joint measurement logic from source abnormity to environment response, and the identification accuracy and response speed of the fire incubation period are remarkably improved.

The invention provides a digital deduction-based prestressed beam prestress loss evaluation method, and relates to the technical field of concrete structure performance evaluation, and the method comprises the following specific steps: firstly, building a finite element model, dividing a beam body into a plurality of sub-regions, and calculating region sensitivity coefficients by combining material attributes and environmental parameters of each region; secondly, actual measurement data are collected to invert the calibration model, and hole channel friction resistance and anchorage device deformation parameters are optimized; inputting a historical environment and a load sequence, calculating the time-varying loss of each sub-region through time-step simulation, and summarizing to generate a global comprehensive loss index; further, through independent deduction, loss components caused by concrete shrinkage, creep and prestressed tendon relaxation are quantified respectively; finally, the loss development trend is predicted based on future working conditions, and a reliable basis is provided for long-term performance evaluation and maintenance decision-making of the structure.

The invention discloses an industrial chain electricity consumption sensitivity feature extraction method, system and device and a storage medium, and the method comprises the steps: obtaining multi-dimensional electricity consumption data, carrying out the feature extraction and dimension reduction of the multi-dimensional electricity consumption data, and mapping an original high-dimensional feature into a low-dimensional feature; carrying out regression analysis on the low-dimensional features, calculating a feature sensitivity coefficient, reversely associating original high-dimensional features, calculating the total influence degree of the original high-dimensional features on power consumption, and screening to obtain key features; based on the characteristic sensitivity coefficient and the key characteristic, the deviation degree of the low-dimensional characteristic and the normal working condition distribution is calculated, abnormity is detected according to the deviation degree, when abnormity is detected, a dynamic optimization mechanism is triggered, extraction of the electricity utilization sensitivity characteristic is achieved, and online learning and dynamic optimization requirements in a super-large scale time sequence scene are supported.

The invention discloses an engine hood determination method and device and electronic equipment. The method comprises the following steps: acquiring a plurality of first target points on a first engine hood model, and performing head collision simulation on the plurality of first target points to obtain strain rate ranges corresponding to the plurality of first target points respectively; determining a plurality of target strain rates according to the strain rate ranges corresponding to the plurality of first target points; material parameters corresponding to the multiple candidate aluminum alloy materials are determined according to the multiple target strain rates; the first engine hood model is adjusted according to the multiple candidate aluminum alloy materials and the corresponding material parameters with the minimization of the head damage index as the target, a target engine hood model is obtained, and the strain rate sensitivity coefficient of the candidate aluminum alloy materials used by an inner plate in the target engine hood model is a negative value. The technical problem that in the prior art, an engine hood is designed based on the material performance of a single strain rate, and the head injury index is high in pedestrian head collision is solved.

This invention discloses a method and system for monitoring abnormal data in power grid dispatching. The method includes: embedding multidimensional perturbation codes containing first-type (time-domain delay) and second-type (frequency-domain oscillation) features into dispatching commands; target devices generating physical ripples with specific time-domain fingerprint features in response to dispatching commands, and dynamically selecting a set of responding devices based on node impedance matrices and sensitivity coefficients; target devices and the set of responding devices collaboratively triggering responses with specific frequency-domain fingerprint features, and achieving spatial multiplexing verification through a time synchronization protocol; extracting time / frequency-domain fingerprint features from feedback data, and performing spatiotemporal consistency verification using a dynamic transfer function model and multivariate mutual information entropy. Furthermore, by periodically injecting false interference data, the survival verification of the monitoring system status is achieved. This invention can effectively monitor system failures and identify covert data injection attacks, significantly improving the security defense capabilities of power grid dispatching.

The application provides a myopia occurrence risk assessment method based on multi-source data, and relates to the technical field of myopia prevention and control, comprising: collecting physiological parameters and dynamic behavior data of a user through a multi-sensor, and preprocessing and labeling; applying a sliding window algorithm to detect short-term trends of behavior, generate a myopia risk index change process, identify false risk reduction intervals and delay window periods, mark myopia risk index rebound events and cumulative rebound risk peaks; when the behavior continues to meet the standard, starting a physiological parameter verification process, generating a closed-loop intervention suggestion based on the rebound warning point and collecting intervention feedback; comparing the score change with the measured physiological data direction, pausing the process and generating a fuse log when a conflict event is triggered; extracting abnormal data of prevention and control compliance to update a causal graph, nonlinearly adjusting model weights and sensitivity coefficients according to comprehensive feedback indicators, and iteratively optimizing the model until the warning is removed to output a report.

The invention relates to a strain detection method and system, electronic equipment and a medium, and belongs to the technical field of optical fiber sensing, and the method comprises the steps: carrying out the linear fitting of a first central wavelength drift distance of a temperature sensing optical cable and a strain sensing optical cable which are laid in parallel in a first time window, and obtaining a ratio of the temperature sensitivity coefficients of the two optical cables; acquiring the actual strain of the strain sensing optical cable in a second time window, a second central wavelength drift distance and a third central wavelength drift distance of the temperature sensing optical cable; the product of the ratio and the third central wavelength drift distance is subtracted from the second central wavelength drift distance, and the strain-induced central wavelength drift distance of the strain sensing optical cable is obtained; performing linear fitting on the actual strain and the strain-induced central wavelength drift distance to obtain a strain sensitivity coefficient of the strain sensing optical cable; and performing strain detection based on the ratio and the strain sensitivity coefficient through the strain sensing optical cable. The temperature or strain sensitivity coefficient does not need to be calibrated in advance, and the practicability is higher.

This invention relates to the field of structural health monitoring of nuclear power plant equipment, and in particular to a method for testing the performance of high-temperature resistant long-wire strain gauges. The method includes: Step 1: Determining the temperature step for strain testing of the strain gauge; Step 2: Installing the strain gauge on a measuring beam; Step 3: Conducting room-temperature performance testing of the strain gauge; Step 4: Increasing the temperature of the high-temperature loading furnace, and repeating Step 3 under different temperature step conditions, respectively measuring and collecting the strain output, strain gauge lead-end resistance, and lead resistance of the strain gauge at each temperature step; Step 5: Calculating the actual strain; Step 6: Calculating the loaded strain; Step 7: Calculating the relative sensitivity coefficient. This invention enables the testing and calibration of the thermal output and sensitivity coefficient of high-temperature resistant long-wire strain gauges as a function of temperature. It provides accurate test data for the accurate testing and calibration of the thermal output and sensitivity coefficient of high-temperature resistant long-wire strain gauges as a function of temperature, and is of great significance.

The present application relates to the technical field of federated learning, in particular to a federated learning-based information big data privacy protection collection method and system, comprising the following steps: calculating a privacy sensitivity coefficient according to data attributes, perturbing and recombining a privacy gradient according to the coefficient, calculating gradient bias variance to construct a node contribution table, performing secure weighted aggregation based on the contribution table and the privacy gradient, calculating an encryption parameter to generate a global ciphertext parameter set, updating a global model and constructing a privacy feedback table.In the present application, the sensitive coefficient is used to replace the original feature field operation by regulating the perturbation limit in the training stage, the underlying sample hidden distribution is maintained to completely block the security collapse caused by the aggregation of plaintext, the evaluation system is constructed by calculating the cross-period bias variance to perform weighted aggregation, the weight of the heterogeneous terminal is balanced to correct the evolution direction deviation, the dynamic encryption parameter is injected into the global sequence to establish a ciphertext lossless defense line to resist flow disassembly interception and monitoring.

The invention discloses an in-operation OPGW icing tension monitoring method based on a micrometeorological temperature demodulation BOTDR. The method comprises the following steps: measuring through a Brillouin optical time domain reflectometer to obtain OPGW Brillouin frequency shift data; calibrating a Brillouin frequency shift temperature sensitivity coefficient and a Brillouin frequency shift tension sensitivity coefficient of the OPGW by using the data of the microclimate icing monitoring terminal; according to the Brillouin scattering principle, all Brillouin frequency shifts are converted into Brillouin equivalent temperatures; the external environment temperature of the OPGW is measured through the micro-meteorological icing monitoring terminal and is defined as temperature data of the micro-meteorological icing monitoring terminal; calculating a difference value between the Brillouin equivalent temperature and the temperature data of the microclimate icing monitoring terminal; and when the temperature difference value is greater than or equal to the temperature measurement precision of the Brillouin optical time domain reflectometer, the icing tension is calculated. According to the invention, the Brillouin frequency shift temperature sensitivity coefficient and the Brillouin frequency shift tension sensitivity coefficient can be directly calibrated on line, and the calibration result and the icing tension monitoring result are more accurate.

The invention discloses a vehicle sensing boundary self-adaptive adjustment method and device, a product and a medium, and relates to the technical field of intelligent driving. The method comprises the following steps: acquiring kinematics state data, environmental meteorological data and detection data of a vehicle; recognizing an obstacle according to the detection data, determining the spatial region position of the obstacle relative to the vehicle body, and determining a corresponding region sensitivity coefficient; determining a source sensor which detects an obstacle from the plurality of sensors, and determining a sensor sensitivity coefficient; determining a group of environment correction factors according to the environment meteorological data, and calculating a dynamic boundary expansion amount according to the kinematics state data; carrying out fusion calculation based on the regional sensitivity coefficient, the sensor sensitivity coefficient, the environment correction factor and the dynamic boundary expansion amount, and generating a dynamic sensing boundary for the obstacle; when it is determined that the obstacle is located within the dynamic perception boundary, a vehicle safety response instruction is generated. The method has the effect of improving the safety of the vehicle in a complex environment.

The invention provides a dual-core hemispherical inertial navigation voltage sensitivity coefficient online identification and self-calibration method, which comprises the following steps: respectively constructing equivalent virtual angular rate excitation under electrostatic driving for each hemispherical resonator gyroscope, and determining a driving voltage for calibration; excitation is applied to the two coaxial hemispherical resonator gyroscopes according to a preset rule, and the output angular velocity and the standing wave azimuth angle are collected; establishing a difference observation model; establishing a Kalman filtering equation based on the difference observation model, wherein the Kalman filtering equation comprises an observation matrix, observed quantity and state quantity; kalman filtering is carried out on angular velocities and standing wave azimuth angles output by the two coaxial hemispherical resonator gyroscopes based on the established Kalman filtering equation, and a voltage sensitivity coefficient and a zero offset coefficient are solved. And writing the calculated voltage sensitivity coefficient and zero offset coefficient into a calibration register of the corresponding hemispherical resonator gyroscope. According to the technical scheme, the technical problem that in the prior art, a single gyroscope is prone to external interference, and consequently calibration precision is poor is solved.

The invention relates to the technical field of supply chain intelligent optimization, in particular to a supply chain optimization management system and method for big data, and the method comprises the steps: collecting multi-source heterogeneous data of a whole link of a supply chain, carrying out the fusion processing, and constructing a space-time correlation knowledge graph; and mining implicit dependence and risk conduction paths among entity nodes in the graph by using a graph neural network, and generating a supply chain network state quantitative view containing node vulnerability scores and edge disturbance sensitivity coefficients. Meanwhile, in combination with historical orders and market public opinion text data, the demand and supply capability fluctuation interval of each node in multiple periods in the future is deduced. And comprehensively quantifying the view and fluctuation interval information, constructing and solving a dynamic optimization model under multiple constraints, and outputting a global collaborative optimization scheme covering purchase, production, inventory and logistics. According to the method, deep quantitative insight of supply chain implicit risks and robust optimization based on uncertainty interval prediction are realized.

The application relates to the technical field of industrial Internet of Things, and discloses a multi-channel time sequence data error bound quantification method and system based on dynamic graph grouping and error distribution, which comprises the following steps: S1: acquiring multi-channel time sequence data collected at the same collection point; S2: determining a scale factor, a point-by-point maximum absolute error upper bound, normalized channel time sequence data, and normalized error bounds of all channels; S3: calculating correlation measurement values of any two channels, and constructing a channel correlation graph; S4: determining a grouping threshold value, and forming an anchor point set from selected anchor point channels; S5: constructing a cross-channel correlation prediction model, and constructing an innovation sequence; S6: determining an error sensitivity coefficient, and determining a quantified error bound; S7: obtaining normalized reconstruction values of the anchor point channels and the innovation sequence; and S8: obtaining normalized reconstruction values of non-anchor point channels, and performing inverse normalization on the normalized reconstruction values according to the scale factor to obtain reconstruction values of the channels under the original dimension. The application can limit compress and highly restore original data.

The invention relates to the technical field of HPLC (High Performance Liquid Chromatography) system state monitoring and intelligent control, in particular to an HPLC mobile phase gradient intelligent proportioning online purification system. Comprising a data acquisition module for acquiring a baseline signal of a high performance liquid chromatography detector in real time; the first processing module is used for determining noise fluctuation parameters; the second processing module is used for calculating a pollution entropy based on the noise fluctuation parameter and a preset system pollution sensitivity coefficient; the third processing module is used for calculating an actual pollution entropy accumulation rate and determining an abnormal index in combination with a preset pollution entropy accumulation rate baseline model; the decision module is used for generating an operation mode decision based on the abnormal index; and the system control module responds to the operation mode decision to execute the corresponding operation mode, and determines whether to switch back to the optimal analysis efficiency mode after the diagnostic survival mode is executed. According to the invention, the long-term operation stability and reliability of the HPLC system and the accuracy of an analysis result are improved, and experiment interruption caused by accidental pollution is avoided.

This application discloses a feedback control method for the material removal rate during sapphire substrate polishing, relating to the field of precision machining control technology. The method pre-establishes a tool head wear compensation model and a mapping model between the material removal rate and process parameters, generating a priority list of adjustment variables based on the sensitivity coefficients of each process parameter. During polishing, the measured material removal rate is obtained after detecting spacing changes using a non-contact displacement sensor array and compensating for tool head wear. The deviation between this rate and the target value is calculated cyclically according to the sampling period. Parameter adjustments are implemented using an incremental control law based on the priority list of adjustment variables. When the adjustment variable reaches its range boundary, the process automatically switches to the next level of process parameters. Simultaneously, the thickness of the removed material is accumulated, and polishing stops when the target value is reached. This application overcomes rate drift caused by polishing slurry degradation, tool head wear, and environmental disturbances, significantly improving the batch-to-batch thickness consistency and removal accuracy of sapphire substrates.

The application relates to the technical field of intelligent sensor calibration and characteristic parameter quantification, and discloses a sensitivity coefficient quantitative calibration method of an intelligent sensor. Original output voltages are collected in sequence at different inclination positions and direct current bias is removed, a multilayer exponential smoothing kernel is adaptively constructed based on the maximum span and the minimum adjacent difference of the voltage sequence, and the signal is subjected to multiscale smoothing; the gradient of each layer of the smoothed voltage sequence is extracted by adopting central difference and end point difference respectively, and the maximum gradient and the corresponding inclination value are extracted in each layer as the local sensitivity; the sensitivities of the layers are arithmetically averaged to obtain the comprehensive sensitivity coefficient; finally, the prediction output and the real inclination residual are calculated by combining the verification data, and whether the calibration is qualified is judged according to the preset root mean square error threshold value; when the calibration is unqualified, sample points are supplemented or the smoothing parameter is adjusted to perform iteration.

The invention relates to the field of handle calibration, in particular to a handle equipment calibration method and device, equipment and a storage medium. The method comprises the following steps: carrying out process-level deep scanning on a game program, carrying out comprehensive evaluation on the control difficulty, and generating a scene control difficulty coefficient; performing adaptive sensitivity calculation based on the scene control difficulty coefficient to obtain an adaptive sensitivity coefficient; a handle operation log is extracted, player control style analysis is carried out, and a personalized control portrait is constructed; and carrying out dynamic configuration calibration based on the personalized control portrait and the adaptive sensitivity coefficient, carrying out calibration effect monitoring optimization, and constructing an equipment dynamic calibration mechanism. Based on different game scene analysis, the gamepad control parameters are dynamically and accurately calibrated, and the user experience and the adaptability to various games are improved.

The application provides a rock creep model construction method and system based on cyclic water flooding-stress coupling, and relates to the technical field of rock mechanics and engineering.The initial physical and mechanical properties of a rock sample are tested through a permeation experiment and a stress permeability experiment to obtain water permeability and stress permeability, a covariance matrix of characteristic parameters is obtained by using principal component analysis, the principal components related to water permeability and stress permeability are screened out, rock fracture sensitivity coefficients and stress sensitivity coefficients are calculated, a water migration equation is constructed based on Darcy's law, and is coupled with the sensitivity coefficients to form a rock creep relationship and construct a rock creep prediction model, clustering analysis is performed by using historical data to obtain clustering intervals of different risk levels, and the risk level of the rock creep prediction value is determined.The application realizes accurate prediction of the creep behavior of rocks in a complex environment by coupling water permeability and stress permeability.