47 results about "Deviation vector" patented technology

The application provides a kind of castings logistics package automatic marking system, it is related to automatic detection technical field, including data acquisition module obtains marking deviation detection parameter and verifies validity;Deviation characteristic calculation module calculates the instantaneous deviation distance of actual marking point, and calculates dynamic deviation ratio in combination with package size;Window construction module determines whether marking position is qualified according to dynamic deviation ratio, constructs deviation sliding window and inputs the instantaneous deviation distance and coordinate deviation vector of qualified event;Trend evaluation and early warning response module calculates system drift coefficient based on the data in sliding window, determines whether marking deviation exists systematic cumulative trend and generates early warning signal.The application eliminates the influence of package specification on deviation evaluation by dynamic deviation ratio, realizes from single-point qualification determination to long-term trend monitoring by sliding window and drift coefficient, identifies the cumulative drift of marking equipment, avoids batch marking deviation accident, and improves the quality control ability of marking process.

The application relates to the technical field of industrial control and artificial intelligence, and discloses an industrial intelligent full-stack domestication multivariate time sequence prediction and intelligent diagnosis system, which comprises the following steps: accessing heterogeneous operation data of equipment in a data source layer; shunting the operation data through a measuring point prefix routing mechanism in a backend service layer, and triggering a degradation fallback logic to return a simulation sequence when an interface is abnormal, and constructing a sliding window sample; extracting features from the sliding window sample through a multivariate model in an intelligent agent layer, outputting a prediction deviation vector, generating a dynamic abnormal threshold value based on historical residual errors; performing online abnormal detection on the dynamic abnormal threshold value in the backend service layer, injecting a prompt word into the prediction deviation vector when an abnormality occurs, outputting a diagnosis result by combining knowledge retrieval and fine-tuning of a large language model in the intelligent agent layer; and performing elastic computing scheduling and rendering in a front-end display layer. The application improves the abnormality capturing accuracy and the fault diagnosis professionalism.

This invention relates to the technical field of paper bag production, specifically to a machine learning-based method for paper bag gluing positioning and adhesion control, comprising the following steps: S1, generating original grayscale data; S2, generating the maximum structural response amplitude at multiple frequency levels and the local phase information of each pixel; S3, generating a high signal-to-noise ratio structural amplitude map; S4, generating a structural feature map; S5, generating glue path segmentation results; S6, generating control commands based on the deviation vector; by calculating the multi-frequency structural synchronization index through frequency domain phase analysis, systematic positioning deviations caused by anisotropic interference are eliminated; a texture-independent structural feature map is generated, so that the geometric segmentation network does not need to be retrained when changing paper batches, completely solving the domain drift problem; combined with fast adaptive direction decomposition and lightweight network design, the real-time closed-loop control requirements of high-speed production lines are met, significantly improving gluing accuracy and production line adaptability.

The application discloses a wafer high aspect ratio Bosch etching parameter self-adaptive regulation method, which comprises the following steps: step one, collecting wafer to-be-processed structure data, Bosch etching process data and stage profile data, and preprocessing to generate standardized data; step two, performing period phase segmentation to generate period state slices; step three, performing period memory constraint state decoupling to construct a state variable set and a control input vector; step four, performing mechanism coupling candidate expansion to generate a nonlinear candidate function library; step five, calculating state derivatives and identifying through SINDYc to generate a sparse dynamic control equation set; step six, rolling predicting profile evolution and generating a profile deviation vector; and step seven, correcting parameters according to the profile deviation and iteratively updating to output final process parameters and etching profile results. The application realizes precise regulation of high aspect ratio Bosch etching parameters, and improves profile stability and processing consistency.

Embodiments of the present application disclose a virtual object control method and device, electronic equipment and a storage medium; the method comprises: acquiring a direction of a sight line and a starting point of the sight line, and displacement and speed of a virtual object; calculating a deviation vector between the virtual object and the starting point of the sight line according to the starting point of the sight line and the displacement of the virtual object; decomposing the speed into speed components in a plurality of specified directions according to the deviation vector; calculating acceleration in the specified directions; fusing the acceleration in all the specified directions to obtain a target acceleration of the virtual object; and controlling the virtual object to move along the direction of the sight line based on the target acceleration. Based on simple speed decomposition and acceleration calculation, the control process of missile guidance is simplified, and the calculation amount in control is reduced, so that the missile guidance can be efficiently applied in games to realize control of the virtual object.

This invention discloses a smart park load forecasting method integrating seasonal and solar term features, comprising: generating a feature input vector and a meteorological-solar term deviation vector based on current input data; a trained weight calculation module outputting a weight vector and a bidirectional dynamic hidden state vector based on these two vectors; a trained gating network outputting a fuzzy probability distribution based on the weight vector and the bidirectional dynamic hidden state vector; and a trained fuzzy boundary hybrid expert network outputting park load forecast data for a preset future time period based on the weight vector, the bidirectional dynamic hidden state vector, and the fuzzy probability distribution. The expert network includes multiple trained seasonal and solar term sub-networks, each of which uses the knowledge learned during training to predict park load data. This invention can deeply capture the nonlinear spatiotemporal relationships of multi-source data, significantly improving the prediction accuracy, convergence speed, and robustness of the model under complex and variable climate conditions.

The application discloses a product reconstruction method and system based on three-dimensional scanning and CAD surface deviation distribution, and relates to the technical field of three-dimensional digital measurement. The method firstly carries out CAD pretreatment on each part to obtain a reference CAD model and a finite element model, and scans and acquires single-piece scanning point clouds. The point clouds are accurately aligned with the reference CAD model through single-piece registration, and then the projection point corresponding to each scanning point and the deviation vector thereof are calculated. After physical assembly, the part point clouds are positioned to a product coordinate system through product registration. Then, the surface nodes of the finite element model are classified based on the deviation vector. Finally, the corrected finite element model and the reference CAD model are repositioned to the product coordinate system, and a digital prototype and a high-fidelity finite element analysis model integrating actual geometric deviation and assembly information are output. The application significantly improves the convenience, precision of digital measurement and fidelity of finite element simulation.

The application provides a physical mechanism coupling data-driven fast prediction method, and belongs to the technical field of disaster prediction.The application extracts an invariant topological feature vector through topological data analysis driven phase space reconstruction and inputs a physical constraint identification model to obtain a conservation law deviation vector and a physical feasible region boundary parameter, adopts a multi-resolution adaptive grid technology to perform multi-scale decomposition, extracts long-term evolution trend features and spatial local features through a full connection layer network and a convolution layer network, combines the physical feasible region boundary parameter to perform projection gradient descent iterative optimization to generate a preliminary prediction field, performs Bayesian uncertainty quantization, adjusts an artificial intelligence model regularization coefficient according to a disaster precursor identification function value, and outputs a graded early warning result, so that the technical problem of insufficient prediction accuracy caused by insufficient coupling of a physical constraint and a data-driven model in a disaster prediction system is solved.

This invention relates to the field of process control technology, and discloses an automatic control method and system for actual drilling trajectory comparison deviation. The method includes: performing coordinate analysis on the inclination angle and azimuth angle at the well depth and the spatial coordinate sequence of the designed trajectory at the well depth to obtain a spatial coordinate sequence; performing sliding window comparison analysis on the spatial coordinate sequence and the spatial coordinate sequence of the designed trajectory at the same well depth to obtain a multi-scale deviation feature set; performing nonlinear mapping inference on the multi-scale deviation feature set to obtain a dynamic control target vector; performing correlation analysis on the tool face angle in the dynamic control target vector and the measured tool face angle to obtain a bottom-level drive command sequence; performing spatial registration evaluation on the spatial coordinate sequence and the preset design trajectory to obtain a spatial deviation vector sequence; and adaptively adjusting the bottom-level drive command sequence to obtain a target drilling scheme. This invention can improve the efficiency of automatic control of actual drilling trajectory comparison deviation.

This invention discloses a smart simulation method and system for urban flood control. The method includes: acquiring water level monitoring data from the urban flood control monitoring network and constructing a real-time deviation vector based on the model status; calculating the observation information value index characterizing the contribution of each monitoring point to parameter correction based on the uncertainty of model parameters and their sensitivity to monitoring points; performing online rolling correction of hydrodynamic model parameters using weighted or filtered indicators; calculating a dynamic risk index reflecting the degree of disaster threat based on the simulation results, and determining the grid spatial resolution of each region within the computational domain based on the observation information value index; and performing simulation on a grid with the corrected parameters at that resolution to output flood evolution results. This invention achieves spatiotemporal adaptive optimization of computational resources and correction strategies by actively sensing the value of observation information and dynamic risk distribution, solving the technical problems of low data utilization efficiency and loss of focus in simulation in traditional methods.

The application relates to the technical field of micro-nano structure replication manufacturing, and discloses a transparent working mold replication process window control and acceptance method and system, computer equipment and a storage medium. The method comprises the following steps: acquiring a local topological feature parameter set of a silicon master mold, establishing a topological and dose mapping model and calculating a spatial dose compensation field, preparing at least three verification samples with different characteristic depth-width ratios, synchronously replicating by adopting the same replication process parameters, regulating an exposure dose according to the spatial dose compensation field during the replication process, acquiring a deviation vector of each verification sample after replication, judging whether the deviation vector is located in a multidimensional process window, when the multidimensional process window is exceeded, identifying a process drift type according to a deviation direction and a deviation degree and triggering a corresponding compensation operation. Through the feedforward dose compensation based on the topological features of the master mold and the multidimensional process window judgment, fine control and drift type identification of the replication process are realized, and process consistency and controllability are improved.

This application relates to the field of digital twin technology, specifically to an adaptive testing method and system for urban monitoring components based on digital twins. The method includes: constructing a baseline matrix for the twin components; extracting signal quality parameters, response delay parameters, and drive loop impedance parameters of the physical components to form an initial healthy baseline vector and storing it in the baseline matrix; extracting endogenous degradation features via bypass to form a real-time degradation feature vector; identifying monitoring task modes to retrieve sensitivity vectors and binding them to form input samples; calculating a normalized deviation vector and weighting it with the sensitivity vector to obtain an effective degradation intensity vector; calculating a compensation vector and multiplying it with the original adjustment vector to generate a compensated command vector for execution; and collecting indicators and driving the self-evolution of the spectrum and the rolling calibration of the baseline matrix. This application effectively solves the problem of coexistence of dimensionality-selective overcompensation and undercompensation in scalar compensation coefficients due to the lack of awareness of task modes.

The application discloses a power transmission and transformation equipment multi-modal fusion state evaluation method based on digital twinning, which comprises the following steps: constructing a digital twinning operation model, outputting a theoretical prediction curve according to operation condition parameters; extracting multi-modal feature sequences of multiple monitoring systems, taking a reference component as a benchmark to calculate a time offset by dynamic time warping, and combining a load current change rate to perform constraint compensation to obtain multi-modal data on a unified time axis; comparing the multi-modal data with corresponding values of the theoretical prediction curve to obtain a state deviation vector; and establishing a graph model of equipment components and their physical correlation, combining a graph attention network to output a state classification result and an edge weight, and determining an abnormal source node. The application can improve the accuracy of multi-modal data fusion analysis and enhance the abnormal identification and abnormal source positioning capabilities.

This invention relates to the field of energy storage dispatching technology, specifically disclosing an end-to-end energy storage dispatching strategy generation system. The system includes a load forecasting module, a dynamic programming optimization module, and a rolling closed-loop dispatching module. The load forecasting module constructs historical daily load data into load feature vectors in a high-dimensional space, retrieves similar historical days by vector distance, and generates a baseline load forecast vector by weighted summation. The dynamic programming optimization module maps the dispatching problem to a two-dimensional state space of time period and state of charge, encodes charging and discharging actions by using the direction vector and amplitude vector of the state transition edges, and solves for the optimal path to obtain the charging and discharging power command sequence. The rolling closed-loop dispatching module constructs the deviation between the actual load and the forecast into a deviation vector, transforms it in the two-dimensional state space, projects it to correct the forecast for the remaining time period, and then calls the dynamic programming optimization module to resolve the problem and extract the current command for issuance. This invention achieves full-process vectorized processing of energy storage dispatching.

The complex component intelligent welding trajectory and posture optimization method and system disclosed by the application belong to the technical field of welding process control. By acquiring the weld geometry characteristics and obstacle information of a to-be-welded component, a spatial data set is established; the nominal and actual weld center lines are registered, and the nominal trajectory points are discretized; a tangent, a secondary normal and a normal local coordinate base are constructed at each nominal trajectory point, the deviation is decomposed into the three directions, and a deviation vector field is obtained; the nominal trajectory point positions are corrected and smoothed according to the deviation vector field, and a continuous corrected trajectory is obtained; candidate welding gun postures are generated at each corrected trajectory point, and the optimal welding gun posture sequence is solved through constraint judgment and cost evaluation; and the continuous corrected trajectory and the optimal welding gun posture sequence are converted into robot instructions. The application reduces the wire stick-out fluctuation, improves the arc centering accuracy and stable welding heat input, effectively reduces welding defects such as incomplete fusion, undercutting and spatter, and thus improves the weld forming consistency.

The application belongs to the technical field of supervisory control, and discloses a machining progress-based intelligent monitoring method and system for numerical control equipment. The method comprises the following steps: acquiring progress data, equipment state data, process execution data and alarm codes, and determining a program segment progress reference benchmark based on historical normal machining data, wherein the progress data comprises a program segment actual execution duration and a cumulative completion progress; time-aligning various types of data and associating them according to program segment numbers to obtain a fusion data set; constructing a causal directed graph comprising process execution nodes, equipment state nodes, machining progress nodes and alarm code nodes based on the fusion data set, combining with machining sequence constraints to limit edge directions, and establishing a benchmark association model; when detecting progress deviation or alarms, determining a deviation vector according to a current observation value and a prediction value and performing probability reasoning to output an abnormal root cause node and an abnormal propagation path. The application improves abnormal identification correlation, root cause positioning accuracy and disposal interpretability.

This invention relates to the field of metal processing quality inspection technology, specifically a machine vision-based metal processing quality inspection method. The method includes: acquiring continuous image frames of the processing process to obtain a contour deviation vector; inputting this vector into a physics-based inference engine; combining mechanical and process parameters to inversely deduce the abnormal cutting force fluctuation curve; inverting the unstable spindle speed state and the feed mechanism position drift trajectory; constructing a multi-dimensional quality anomaly feature space; identifying anomaly patterns through clustering; tracing anomaly time windows; extracting the pixel-level diffusion path and area growth rate of defects; and generating an inspection report by combining equipment status data. This method achieves a chain-like inversion from contour deviation to equipment status, captures the dynamic evolution characteristics of defects, integrates multi-dimensional data to form an inspection report, and optimizes the representation dimensions and correlation logic of metal processing quality inspection.

This invention relates to the field of robot control technology and discloses a robot feedback control system based on multimodal sensing and driving. The system includes a pose sequence storage module for recording pose trajectory data; an asynchronous sampling synchronization module for retrieving historical poses and determining the deviation vector based on visual sampling timestamps; a command-feedforward pose extrapolation module for fusing expected acceleration values ​​to correct the deviation vector and generate the target pose; a driving torque verification module for determining the matching state between the target pose and environmental constraints based on the torque residual vector; and a drive control module for fusing the position feedback loop and increasing the virtual damping ratio in case of mismatch. This invention utilizes acceleration feedforward to compensate for sensing delay and, in conjunction with dynamic consistency verification to trigger an energy dissipation mechanism, effectively solves the interactive instability problem caused by sensing phase lag.

This invention relates to the fields of big data analysis and artificial intelligence technology, and discloses a method for dynamic analysis of farmland quality based on multi-source sensor fusion. The method includes: acquiring multi-source distributed input branch data streams characterizing the operating state of a controlled system; calculating the mutual information entropy increment between each sampled branch and the global feature evolution trend; extracting a set of high-confidence branches through logical arbitration; constructing a local tangent space of the feature manifold using local tangent plane basis vectors; mapping the projection deviation vector of the damaged branch to this local tangent space and solving for the minimum value of the projection energy functional; and achieving logical reconstruction of the missing dimensions. This invention utilizes a manifold consistency reconstruction mechanism to eliminate the contradiction of topological drift from heterogeneous information sources, effectively improving the logical robustness of the analysis results of the controlled system under complex operating conditions.

The application relates to the technical field of power equipment manufacturing, and discloses an automatic box assembling method and system of an intelligent comprehensive distribution box and a storage medium. The method comprises the following steps: acquiring box frame coordinate point cloud data, constructing a geometric shape model and calculating a deformation deviation vector; comparing and generating a deformation distribution map, identifying a potential interference geometric constraint area; marking a high-risk area, simulating stress distribution and completing layout compliance verification; generating a correction load scheme, determining a correction path and priority; a mechanical arm executes correction and updates the model in real time; precision verification closed-loop iterative correction; verifying the structural stability and determining the assembly compatibility state. Through three-dimensional point cloud collection, stress simulation, mechanical arm automatic correction and parameterized model iteration, the application solves the problems of inaccurate box deformation correction, difficult interference identification and low assembly precision, and improves the box assembling automation level and assembly stability.

This application provides a graded pre-control construction method for ballastless track of long-span bridges based on dynamic deformation prediction, belonging to the field of track pre-control construction technology. The method includes: collecting a multi-dimensional feature dataset of the beam and generating a graded pre-control construction instruction table; collecting re-measurement data after the rough laying of the track slab; calculating the deviation vector from the original measurement data, and updating the spatial reference coordinate system when a preset convergence threshold is met; solving the target coordinate vector for fine-tuning the track slab; constructing a spatial state locking model for the track slab, converting the anti-buoyancy constraint force threshold into control parameters of intelligent tooling; and collecting track slab alignment data after grouting, calculating the measured deviation sequence, and incrementally correcting the beam deformation prediction model. This application can solve the technical problem of substandard track slab laying accuracy in the prior art, and achieves the technical effect of improving construction accuracy by realizing the technical goal of real-time monitoring and dynamic adjustment.

The present application relates to the technical field of crane fault detection, and more particularly to a fault diagnosis method and system for a crane motor. The method comprises: acquiring motor operation data in real time through a sensor, extracting time domain and frequency domain statistical features, and generating a working condition classification label; determining an operation mode according to the working condition classification label, determining a deviation vector of a standard health sequence and an abnormal deviation threshold; calculating a similarity matrix of a current and historical sequence, optimizing a weight set; forming a comprehensive diagnosis index, and determining whether a potential fault signal is generated; if a fault signal exists, updating a diagnosis model parameter, forming an optimized diagnosis framework, and analyzing operation data based on the framework to obtain a final fault diagnosis result. The present application solves the problems of low fault recognition accuracy, diagnosis lag and non-self-adaptive model of the crane motor under multiple working conditions, and realizes intelligent monitoring and dynamic precise diagnosis of the motor operation state.

The application provides a personalized model generation method, device and equipment based on multi-source feature cooperation and a medium. The method comprises the following steps: collecting a motion video stream of a target object based on a camera terminal; outputting a motion quantization index corresponding to the motion video stream based on a pose estimation model; acquiring external sensor data synchronized with the motion video stream; determining a prediction deviation vector between the motion quantization index and the external sensor data in response to a preset calibration trigger condition; and performing parameter compensation on the pose estimation model in the camera terminal by using an incremental learning algorithm according to the prediction deviation vector, so as to generate a personalized model adapted to the target object. The application gradually fits the personalized motion mode and physiological characteristics of the user, breaks through the parameter solidification bottleneck of the traditional static model, transfers the external sensor data to the visual model in a cross-modal manner, makes up for the limitations of single visual perception, realizes the deep fusion of multi-source heterogeneous data, and improves the reasoning accuracy and environmental adaptability of the model.

This invention discloses a smart home control method, device, equipment, and medium based on causal inference. The method includes: acquiring user behavior data, environmental data, and user profiles; determining user behavior deviation characteristics based on the behavior data, environmental data, and preset historical behavior data; determining user behavior deviation vectors based on the user behavior deviation characteristics, user profiles, and environmental data; inputting the user behavior deviation vectors into a preset causal inference model, performing causal reasoning on the user behavior deviation vectors through the preset causal inference model to determine the root cause of the user behavior deviation; determining smart home control commands based on the root cause of the user behavior deviation, and controlling the corresponding smart home devices according to the smart home control commands. This achieves smarter, more user-centric, and automated control of smart home devices, improving user experience and bringing energy-saving benefits.

This invention discloses an error compensation method and system for sheet metal. The method includes: acquiring measured deviation data of sheet metal and theoretical part digital models; generating an adaptive parametric mesh based on local curvature characteristics; mapping the deviation data and weighted fitting to construct an error field; synthesizing a set of deviation vectors and extracting the dominant deformation mode; inputting mold deformation and material fluctuation parameters; generating a predicted deviation field through finite element simulation; obtaining an optimized predicted deviation field through residual optimization; extracting compensation vectors and inversely mapping them to the mold surface; determining the spatial distribution of compensation amount; generating adjustment instructions; and obtaining a mold error compensation and correction scheme. The method and system of this invention can achieve accurate and efficient compensation of sheet metal forming errors, meeting the high-precision and high-consistency production requirements of sheet metal processing in the field of industrial control technology.

This invention provides a method and system for data acquisition and precision calibration of a guide shield adjustment process, relating to the field of automation control technology. The method includes: acquiring real-time height and position data of the guide shield during the saccharification and boiling process; combining this data with synchronously acquired process parameters to generate an initial state vector with process identifiers; comparing this initial state vector with a target state vector in a preset process formula to extract initial deviation values ​​in each dimension, forming a control deviation vector; determining the positions of multiple key sections of the guide shield based on the control deviation vector, and acquiring flow rate and temperature parameters at each key section to form a set of control base point data with spatial coordinates; and mapping the control base point data to a three-dimensional feature space composed of height, flow rate, and temperature to construct multiple dynamic control prisms. This invention enables accurate data acquisition and high-precision closed-loop calibration during the guide shield adjustment process.

This application provides an automatic verification method and system for ship entry and exit declaration information based on artificial intelligence, belonging to the field of artificial intelligence technology. This application acquires the entry and exit declaration information of the target ship and a historical declaration verification dataset. Based on the historical data, it constructs a historical declaration sequence and a historical declaration deviation sequence. The historical declaration sequence is input into a trained prediction model to obtain a predicted declaration habit vector. A causal discovery algorithm is used to analyze the historical declaration deviation sequence, constructing a declaration deviation causal graph where nodes represent declaration item deviations and edges represent causal relationships. The difference between the current declaration and the predicted habit is calculated to obtain the declaration deviation vector, which is then input into the causal graph as observation evidence. Target nodes are determined through causal inference. A deviation threshold is calculated based on the historical deviation sequence. The current deviation value of the target node is compared with this threshold to determine the corresponding declaration information as abnormal declaration information, effectively identifying systematic and correlated declaration deviations in entry and exit declaration information.

This invention discloses an AI-based dynamic game parameter optimization system and method, belonging to the field of game parameter optimization technology. The invention collects game operation data for each character in the current game; extracts game feature vectors through sliding window statistics; compares the game feature vectors with a pre-stored target fair state template to analyze similarity; analyzes the activation coefficients of typical imbalance modes based on deviation vectors; weights and sums the corresponding response matrices according to the activation coefficients of typical imbalance modes to generate a comprehensive response matrix; combines the deviation vector to obtain the original adjustment vector, and finally generates candidate parameter schemes after filtering by historical adjustment amounts; analyzes the response time constant and overshoot for each parameter to be adjusted; and collects feedback deviations after updates to correct the response matrix. This improves maintenance efficiency and reduces system complexity.

This invention provides a method, apparatus, device, and storage medium for optimizing ship motion model parameters, relating to the field of artificial intelligence technology. The method acquires the previous parameter vector and current input information matrix of the ship motion model at the previous moment, as well as the ship's current measured information vector. It obtains the current prediction information vector by multiplying the transpose of the current input information matrix by the previous parameter vector. It obtains the current prediction deviation vector by the difference between the current prediction information vector and the current measured information vector. It obtains the current iterative correction matrix based on the current input information matrix through a preset recursive relationship. It obtains the current error matrix based on the current input information matrix and the iterative correction matrix through a preset update relationship. Finally, it obtains the current corrected parameter vector by multiplying the current error matrix, the current input information matrix, and the current measured information vector. This invention can improve the accuracy of ship motion model prediction.