590 results about "Calibration algorithm" patented technology

The invention relates to the technical field of program control systems, in particular to a pentahedron machining center precision calibration method and system based on multi-sensor fusion, and the method comprises the steps: a sensor system construction and calibration module is used for field calibration, drift correction and redundancy deployment to ensure data precision, and achieves the whole-course traceability of a calibration process through a block chain technology; the multi-source data preprocessing and fusion module is used for time-space synchronization of heterogeneous data and dynamic fusion of multi-source information; the intelligent modeling and state prediction module is used for performing real-time and multi-task prediction on key states such as tool wear and thermal deformation; based on the prediction result, the adaptive compensation and path optimization module is used for dynamically optimizing the tool path; meanwhile, through an online learning mechanism, the calibration model is continuously updated by utilizing a processing result; the distributed cooperative control module executes data processing and calibration algorithms locally and makes a cooperative decision with a numerical control system, and low-delay and intelligent response to machining abnormity is achieved.

The invention discloses a multi-view panoramic point cloud splicing method, which comprises the steps of performing joint calibration on a binocular camera and a laser radar through a calibration algorithm, and aligning a coordinate system; a binocular camera is used for collecting a two-dimensional image and carrying out distortion correction, a depth map is generated based on parallax calculation, and three-dimensional point cloud reconstruction is carried out in combination with a triangulation principle; performing deep learning target detection on the two-dimensional image, and screening a line rod assembly area through non-maximum suppression; multi-view point cloud data are collected through a laser radar, point cloud segmentation processing is carried out, and a telegraph pole assembly point cloud subset is reserved; matching the two-dimensional pixel area of the telegraph pole assembly with a laser radar point cloud projection result, and screening point cloud data belonging to the telegraph pole assembly; and carrying out alignment and fusion on the multi-view point clouds through initial registration and fine registration by adopting an improved point cloud splicing algorithm to generate a panoramic point cloud image. According to the method, the point cloud splicing processing time is shortened, and the robustness, precision and integrity of point cloud splicing are improved.

The invention provides an image acquisition card multi-mode identification method and system based on intelligent security and protection. The method comprises the following steps: acquiring a multi-mode original data stream according to a global clock signal of an image acquisition card; performing space-time calibration on the multi-modal original data stream to generate a synchronous multi-modal data queue; extracting a multi-modal feature tensor of the synchronous multi-modal data queue through data preprocessing; performing hierarchical attention fusion on the multi-modal feature tensor to generate a fusion feature matrix; performing channel pruning on the fusion feature matrix through a lightweight convolutional neural network, and constructing a target detection model; and determining a detection result corresponding to the multi-modal original data stream according to the target detection model. Through the synergistic effect of a global clock signal and a dynamic space-time calibration algorithm, a time synchronization and space alignment compensation mechanism is constructed in a multi-modal data stream, and the problem of multi-modal information complementary advantage attenuation caused by space-time mismatch is effectively solved.

The invention relates to the technical field of computer vision and three-dimensional reconstruction, and discloses an exhibition hall three-dimensional modeling intelligent optimization system based on multi-modal data fusion, and the system comprises a data collection module which is configured to synchronously obtain laser radar point cloud data, a multispectral image sequence and inertial measurement unit data; the preprocessing module is used for receiving the output of the data acquisition module, aligning a multi-source sensor coordinate system through a space-time calibration algorithm, and separating a static scene from a dynamic interference element by using a dynamic segmentation network; and the multi-modal fusion module is used for receiving the preprocessed data and carrying out adaptive weighted fusion on the geometric features of the laser radar and the visual texture features through a cross-modal attention mechanism. According to the invention, through multi-modal data fusion and a dynamic scene adaptive mechanism, the modeling precision and the real-time updating capability in a complex exhibition hall environment are significantly improved.

The invention provides an intelligent water conservancy digital twinborn simulation system based on multi-source data, and belongs to the technical field of digital monitoring. Minute-level data acquisition and transmission are realized by constructing a space-air-ground three-dimensional sensing network and combining edge intelligent preprocessing, data acquisition, noise reduction and abnormity identification are realized by utilizing sensors such as millimeter wave radar and laser radar, and the system is used for realizing data acquisition and transmission. Dynamic data fusion and intelligent calibration are carried out, and second-level alignment and credible verification of data are realized by means of a space-time calibration algorithm, Kalman filtering and a block chain evidence storage technology; a hybrid simulation and intelligent decision model is established, a physical model, a machine learning architecture and a dynamic threshold decision tree are adopted, flood routing minute-level prediction and emergency response are realized, and the system improves water conservancy monitoring prediction precision and emergency response efficiency.

The invention discloses a railway intelligent construction site safety penetration type management messenger platform which comprises a multi-modal data fusion processing module, an edge computing node cluster module, a three-dimensional visual penetration type management interface module, an intelligent early warning and emergency response module, a self-adaptive network transmission module and the like. Real-time cleaning, alignment and correlation analysis are realized by using a dynamic data calibration algorithm, and a data island is broken; the edge computing node cluster carries out localization preprocessing and the like on data in a key area, so that the load of a central server is reduced; the three-dimensional visual interface is based on a digital twinborn construction model, supports drilling type viewing and realizes three-dimensional monitoring; the intelligent early warning system adopts a reinforcement learning model to automatically trigger multi-channel early warning; and the adaptive network transmission module dynamically switches communication modes to ensure low-delay transmission of key data. The platform realizes real-time acquisition and integration of construction site data and reduces manual intervention.

The invention discloses a high-precision current detection circuit calibration system, and belongs to the technical field of current detection circuit calibration. Comprising a shunt resistor, a Hall sensor array, a chopping amplifier, a low-noise operational amplifier, a temperature sensor, an ADC acquisition unit, an RC low-pass filter, a signal conditioning unit, a main control unit, an environmental parameter adaptive compensation module and a gain adaptive adjustment module. After an original signal is obtained from a multi-channel sensor, a dynamic error model is constructed in combination with temperature data, a long-term imbalance trend is extracted by using a moving average or median filtering algorithm, and real-time compensation is performed through a pre-calibrated temperature-drift function, so that the system maintenance period is remarkably prolonged.

The invention discloses a water quality detection system for hydraulic engineering and a monitoring method, and belongs to the technical field of water quality monitoring. The system is composed of a distributed sensor array, an edge calculation data acquisition module, a LoRa wireless communication module, a cloud analysis platform and an early warning execution terminal. The distributed sensor array adopts an anti-interference protective shell and a self-adaptive calibration algorithm. The monitoring method comprises the steps that water quality data are collected in real time and transmitted through a LoRa wireless network, edge nodes conduct data cleaning and abnormal value correction, a cloud analysis platform constructs a water quality dynamic model through a multi-source information fusion algorithm, and the pollutant diffusion trend is predicted in combination with deep learning. When the parameters exceed the standard, a grading early warning mechanism is triggered, regulation and control suggestions are synchronously generated, and the water treatment equipment is linked. All-weather continuous monitoring of the water quality of the water conservancy project is achieved, the problems that a traditional method is high in hysteresis quality and poor in reliability are solved, and the water quality abnormal response speed and the comprehensive treatment efficiency are remarkably improved.

The invention discloses an intelligent field management system based on an Internet of Things platform, and relates to the technical field of Internet of Things, and the intelligent field management system comprises a multi-source data fusion and analysis module, a waste recovery and resource utilization module, a knowledge graph and question and answer module, and a self-adaptive decision and regulation module. The multi-source data fusion and analysis module comprises a data acquisition sub-module and a data analysis and preprocessing sub-module; the data acquisition sub-module comprises a multi-source heterogeneous data acquisition unit and a real-time dynamic data acquisition unit; the multi-source heterogeneous data acquisition unit uses a wireless sensing technology, a GPS positioning technology, a sensor calibration algorithm and a data interpolation algorithm to acquire meteorological, soil, crop physiology and biological field data. By arranging the multi-source data fusion and analysis module, the problems that traditional agricultural data is difficult to obtain and not deep in analysis, precision agricultural development is difficult to support, and agricultural resources are wasted and crop yield and quality are unstable due to data quality problems can be solved.

The invention relates to the technical field of intelligent war game deduction, in particular to an intelligent war game deduction simulation system based on a digital twin fusion large model. Comprising a digital twin modeling unit which adopts a dynamic precision adaptive modeling mechanism; a large model decision unit; a deduction execution unit; and a dynamic interaction unit. A dynamic precision self-adaptive modeling mechanism is adopted by the digital twin modeling unit, the model precision can be dynamically switched on demand, the detail simulation demand and the computing resource consumption are balanced, and meanwhile, the sub-second-level dynamic synchronization of a virtual model and a war game deduction scene is realized through a time sequence calibration algorithm; and space confrontation, time sequence decision and rule triggering features are fused through the multi-modal feature analysis module, and the Wargame rule verification module is combined to embed entity performance boundaries and scene rule constraints, so that compliance confrontation decision parameters conforming to Wargame deduction logic can be generated, and the problem of insufficient model precision adaptation and decision constraints is solved.

The invention provides a gas data collection and analysis system. The gas data collection and analysis system comprises a data acquisition module, a data transmission module, a data storage module, a data analysis module and an alarm and visualization module. According to the system, gas operation parameters are collected in real time through flow, pressure, temperature and gas concentration sensors, the data accuracy is improved through a correction algorithm, data are transmitted to a cloud or a local server for storage based on the wireless communication technology, and data safety and efficient storage are guaranteed through a distributed architecture. According to the gas data collection and analysis system, gas demand prediction and anomaly detection are achieved through a time sequence prediction model and a clustering algorithm, leakage or abnormal operation conditions are accurately recognized, when gas leakage, flow anomaly or equipment faults are detected, an alarm is automatically triggered, related personnel are remotely notified, meanwhile, a data visualization interface is provided, and the system is convenient to use. And real-time monitoring and trend analysis are supported.

The invention discloses an RGB LED light source calibration mode optimization method and system, and the method comprises the steps: collecting the color coordinates and brightness data of a three-primary-color light source of an RGB LED, and recording the actual optical characteristics; based on the collected data, establishing a physical model of the light mixing effect of the RGB LED light source, and calculating an initial PWM duty ratio corresponding to a target color coordinate and brightness; inputting the initial PWM duty ratio into the intelligent calibration algorithm model, and optimizing and calculating the optimal PWM duty ratio; the optimized PWM duty ratio is written into an MCU, and the MCU controls mixed light output of the RGB LED according to the written duty ratio; and testing and verifying the calibration effect of the RGB LED light source, and adjusting the parameters of the intelligent calibration algorithm model according to the test result. According to the invention, the PWM duty ratio can be dynamically optimized to realize high-precision light mixing output, and rapid repair and real-time adjustment are supported. The invention belongs to the technical field of photoelectricity, and aims to solve the core problem of the existing RGB LED light source calibration technology and improve the consistency of the light source performance and the production efficiency.

The invention relates to the technical field of time synchronization, and discloses a hybrid time service method based on Beidou satellite navigation and a TSN, which comprises the following steps: acquiring network topology structure information and historical time service error data of each synchronization node in a target distributed system, and constructing a dynamic time delay state set for jitter identification; based on the dynamic time delay state set, a current global time reference is extracted through a time calibration algorithm, a time difference reference vector sequence is generated, and initial synchronization adjustment is carried out on the TSN main node; constructing an inter-node clock convergence function curve according to the slave node response stability, and judging whether the synchronous network is in a low jitter interval or not based on the time offset rate and the time drift trend change; in combination with the node time state after directional offset suppression, a multi-channel redundant time synchronization mechanism in a time window is applied, a weight adjustment coefficient is extracted, and an unbalanced calibration factor is introduced; and according to a regression correction result, evaluating a time service stability index in real time. The method has the advantage of improving the time service stability.

The invention relates to the technical field of material processing control, and discloses a method for efficiently cutting a vacuum brazing soldering lug. The method comprises the following steps: firstly, receiving material parameters and target geometrical shape data of a to-be-cut soldering lug, and generating and optimizing an initial cutting track based on a preset algorithm; in combination with real-time temperature field monitoring data, thermal stress distribution is calculated through a finite element analysis method, and the cutting speed and laser power parameters are dynamically adjusted; and then the cutting precision and the equipment energy consumption are synchronously optimized through a multi-objective optimization algorithm, and final cutting parameters are output to a vacuum brazing control system. In addition, signals are collected in real time during cutting to recognize abnormity, and algorithm weights are calibrated by comparing geometric parameters after cutting. The cutting precision and efficiency can be effectively improved, equipment energy consumption is reduced, cutting abnormity is treated in time, self-optimization of the cutting process is achieved, and the method is suitable for the production field with the high requirement for vacuum brazing piece cutting.

The invention relates to the technical field of image correction, in particular to a correction method and system for a low-distortion image of a panoramic camera. The method comprises the following steps: based on a second-hand vehicle scene acquisition demand, acquiring a panoramic image and executing basic preprocessing to obtain a preprocessed panoramic image; calling a lens distortion coefficient, analyzing a corresponding distortion parameter in combination with a checkerboard calibration algorithm, and generating a polar region distortion distribution diagram; carrying out segmentation and grid reconstruction on a polar region of the preprocessed panoramic image, and executing distortion elimination and geometric reduction to obtain a preliminary correction image; fine tuning is carried out on the pixel position of the preliminary correction image, color fault and edge dislocation of a multi-lens splicing seam are eliminated, and an optimized correction image is obtained; executing second-hand car scene customized enhancement processing on the optimized correction image to obtain an enhanced correction image; and automatically checking the enhanced correction image, and outputting a low-distortion final correction image. According to the invention, the correction efficiency of the panoramic low-distortion image of the second-hand vehicle can be improved.

The invention discloses a visual perception and dynamic anti-collision intelligent closed-loop control method for a central control mechanical arm in a vehicle, and the method comprises the steps: S1, loading an improved YOLOv11 network detection model, and reading a video stream of a camera; s2, preprocessing each frame of image in the video stream, inputting the preprocessed image into the model, and performing target identification; s3, inputting an identification result into a dynamic safe area collision model, and performing coordinate conversion through a hand-eye calibration algorithm; calculating a relative position and a motion trend in combination with depth sensor data, and judging a risk level to generate a corresponding protection instruction; and S4, executing a hierarchical control strategy according to the risk level, updating the action track of the mechanical arm, and returning to the step S1 through a closed-loop feedback mechanism to adjust the safety threshold and the control parameters to form a complete'perception-decision-execution-optimization 'autonomous evolution closed loop. According to the method, the visual perception robustness and the target detection precision under the complex illumination condition are improved, and real-time dynamic anti-collision response and millisecond-level rapid obstacle avoidance are realized.

The invention relates to the field of vegetation index monitoring, and discloses a method for monitoring solanum roselle based on a multi-spectral index change rate of an unmanned aerial vehicle, which comprises the following steps: acquiring multi-spectral image data of a target area under a continuous time sequence; extracting NDVI, GNDVI and red edge vegetation index change layers from the reconstructed time sequence multispectral image by adopting a self-adaptive spectrum calibration algorithm to obtain a vegetation index change rate initial map; the method aims at the spectrum confusion problem of the eggplant at different growth stages and peripheral plants in change rate expression. Carrying out deep fusion on the initial vegetation index change rate map and the growth fluctuation contour model, constructing a change rate anomaly response factor map layer, and outputting suspected distribution candidate regions of the solanum aureocauda var. Aureocauda var. Aureocauda; and combining the change rate evolution trend of the historical extension path with a neighborhood growth consistency index, and dynamically generating a high-confidence identification map of the solanum aureum. The method has the advantage of improving the change rate discrimination precision.

The invention discloses a pressure injury risk early warning method and equipment, and relates to the technical field of medical monitoring instruments. The pressure sensor array layout can be self-adaptive to the body shape of a patient, a key osseous process part is covered, no monitoring dead angle is ensured, a temperature compensation mechanism is built in, environmental interference is reduced, the measurement precision is improved, an abnormal pressure threshold value can be dynamically set based on individual medical record data, data verification is performed before early warning is triggered, false alarm is avoided, and the array structure is light, thin and flexible. The mask is comfortable to fit skin, supports long-term wearing and is easy to disinfect and maintain; environment temperature and humidity parameters and pressure data are subjected to fusion analysis, the influence of a high-temperature and high-humidity environment on skin microcirculation can be recognized, the risk of pressure sores can be evaluated in an auxiliary mode, a calibration algorithm is built in a sensor, environment fluctuation interference is reduced, and data reliability is ensured; and the early warning unit comprises sound-light alarm equipment which supports a multi-stage alarm mode and is used for triggering an early warning signal and pushing information when the risk index exceeds a threshold value, so that the early warning timeliness is ensured and the false alarm risk is reduced.

The invention discloses an underground space time-shifting transient electromagnetic unmanned monitoring device and method. The underground space time-shifting transient electromagnetic unmanned monitoring device comprises a mobile platform, a multifunctional transient electromagnetic detection device and a ground remote control terminal, the multifunctional transient electromagnetic detection device can actively adjust the distance between a transmitting coil and a receiving coil so as to realize a mutual inductance eliminating 8-shaped coil structure, and through cooperation with a calibration algorithm of a control center, the automation level and reliability of detection are remarkably improved, and it is ensured that the 8-shaped coil is in a mutual inductance eliminating state. In addition, the transient electromagnetic monitoring device can perform structure switching between deep layer detection and shallow layer detection, so that a high-efficiency detection mode that one circle has two purposes is realized, and the efficiency and the flexibility of detection operation are remarkably improved. In the actual detection process, the advantages that the 8-shaped coil is small in shallow blind area and large in overlapped loop detection depth are fully played, advantage complementation of two coil modes is achieved, and comprehensiveness and accuracy of a detection result are remarkably improved through a data fusion mode.

The invention discloses a current and voltage signal synchronous correction and output method and system, and relates to the technical field of power system analysis and control. Compared with a previous current and voltage signal correction method, the method solves the problems that traditional multi-channel signal acquisition depends on conventional clock control, and synchronization precision is insufficient; a conventional sampling circuit is weak in anti-interference capability. The problem that an existing scheme lacks efficient error prediction and dynamic optimization capability is solved. Through a multi-channel FPGA clock synchronization and double-differential sampling technology, complete acquisition of high-frequency transient characteristics of voltage and current signals is ensured; a dynamic calibration algorithm and multi-stage composite filtering are combined, so that common-mode interference and non-linear errors are effectively suppressed; and finally, through optical fiber isolation output and intelligent feedback control, signal full-link synchronous correction is realized, the electric energy metering error can be reduced, the fault positioning precision is improved, the characteristics of high precision, strong anti-interference and low energy consumption are realized, and a reliable data reference is provided for intelligent power grid and new energy grid connection.

The embodiment of the invention provides an optical measurement method and system based on camera calibration and a distortion model, and a medium. The method comprises the following steps: shooting a plurality of calibration images and distortion measurement images at different angles and positions based on a three-dimensional calibration workpiece; analyzing the calibration image and the distortion measurement image based on a calibration algorithm to obtain camera parameters and a distortion model; obtaining a to-be-measured object image based on the calibrated camera, and extracting features of the to-be-measured object image; performing distortion correction on the image features of the to-be-measured object based on a distortion model to obtain corrected image features of the to-be-measured object; performing three-dimensional reconstruction on the corrected image features of the to-be-measured object to obtain a measurement image, and analyzing parameter information of the to-be-measured object based on the measurement image to obtain a measurement result; parameter calibration and distortion correction are carried out on the camera, so that the distortion error is reduced, and the measurement precision is improved.

The invention discloses a visual tracking image acquisition method and device, electronic equipment and a readable storage medium, relates to the technical field of robot inspection, and accurately establishes a mapping relation between an actual image coordinate and a camera coordinate through a camera checkerboard calibration algorithm to obtain an internal reference matrix and a distortion coefficient. A shooting holder is controlled to collect a large-range image containing an inspection target, and a visual tracking algorithm is used to identify the contour of the target. And calculating a target magnification factor and a holder fine tuning parameter based on the contour coordinate data, the internal reference matrix and the distortion coefficient. And rotating the holder according to the fine tuning parameters, accurately positioning the inspection target to the center of the global image, and executing image magnification operation according to the target magnification times to complete target image acquisition. Through cooperation of calibration data support, algorithm contour recognition and parameter driving adjustment, the collection effect of a target image is not affected by repeated positioning precision of the robot, it is ensured that the collected target image is centered and clearly amplified, and a high-quality image basis is provided for subsequent detection.

The embodiment of the invention discloses a real-time monitoring method and equipment for a cloud computing server room and a medium, relates to the technical field of equipment supervision, and is used for solving the problems of multiple blind areas and low reliability and real-time performance in the existing monitoring. The method comprises the following steps: collecting multi-source data in real time through a heterogeneous sensor network deployed in a cloud computing server room, and preprocessing the multi-source data based on edge computing nodes to obtain multi-source data features; performing alignment processing on the multi-source data features based on a preset space-time calibration algorithm so as to construct a global dynamic topology matrix of the cloud computing server room based on a preset machine room digital twin model and the aligned multi-source data features; based on the historical operation data and the global dynamic topology matrix of the cloud computing server room, constructing a multi-dimensional prediction map of the cloud computing server room; and inputting the multi-dimensional prediction map into a preset space-time diagram convolutional neural network model to identify a current abnormal event of the cloud computing server room and carry out early warning.

The invention discloses a structure three-dimensional displacement monitoring method and device based on a monocular camera and a concentric annulus, and relates to the field of computer vision and structure health monitoring, a concentric annulus target is fixed at a to-be-monitored structure monitoring point, and the monocular camera carries out positioning and lens parameter adjustment; shooting a checkerboard image and calibrating the checkerboard image by adopting a camera calibration algorithm to obtain a distortion parameter and an internal reference matrix; collecting a video sequence containing a structure displacement process of the target; image distortion correction is carried out based on the distortion parameters and the internal reference matrix, a target is identified through a target detection algorithm, and target parameters are calculated through least square circle fitting; realizing multi-target tracking by a target point topology identification algorithm based on Y-X threshold sorting; calculating scale factors according to target parameters and actual physical sizes, and calculating in-plane orthogonal direction displacement and out-of-plane displacement to form three-dimensional displacement information of the structure; according to the invention, the monocular camera is adopted to realize the accurate measurement of the three-dimensional displacement of the structure and reduce the complexity and calibration difficulty of the system.

The present disclosure relates to a method and device for a camera calibration algorithm using an unbiased conic estimator considering distortion, and may be configured to calculate an ellipse in a normalized coordinate system from a circular pattern in a space, express a center of a distorted ellipse in a camera coordinate system as a linear combination of moments of the ellipse, and perform camera calibration from an image of the circular pattern using the center as a control point.

The invention discloses a liquid hydrogen container heat leakage prediction and boost characteristic detection method and system. The method comprises the following steps: establishing a three-dimensional heat flow field model of a liquid hydrogen container; calculating the heat flow density of a gas-liquid interface based on the three-dimensional heat flow field model so as to adjust the liquid hydrogen evaporation capacity; total heat leakage is obtained through finite element simulation and experimental data collaborative calibration; calculating a static evaporation rate according to the total heat leakage; establishing a nonlinear mapping model of the static evaporation rate and the boost rate through a response surface regression method; the wall temperature distribution of the liquid hydrogen container is monitored in real time, thermophysical parameters are calibrated through the LSTM neural network by adopting a dynamic calibration algorithm, and the output result of the three-dimensional heat flow field model is corrected. By establishing a three-dimensional heat flow field model of the liquid hydrogen container, determining a nonlinear response relationship between the evaporation rate and the pressure rising rate of the liquid hydrogen container, and constructing a cloud-edge collaborative detection system, the heat leakage performance of the liquid hydrogen container is accurately predicted, and efficient pressure rising rate detection is realized.

The invention discloses an optical body surface monitoring method and device applied to a radiotherapy room system and a medium, and relates to the technical field of optical body surface monitoring, and the method comprises the steps: employing a checkerboard calibration algorithm, and determining a positioning external parameter and a treatment external parameter; performing positioning monitoring on the user according to the positioning reference body surface, the body surface positioning real-time point cloud of the user under multiple visual angles and the positioning external parameters of each camera in the positioning camera group, and moving the treatment bed until the body surface positioning mark point of the user is aligned with the positioning isocenter of the radiotherapy machine; performing radiotherapy image verification monitoring on the user according to the treatment reference body surface, the first body surface treatment real-time point cloud and the treatment external reference, and moving the treatment bed until the body surface positioning mark point of the user is aligned with the treatment center of the radiotherapy machine; and performing treatment monitoring on the user in the beam treatment process according to the second body surface treatment real-time point cloud and the treatment external reference, and determining a treatment real-time error. The optical body surface monitoring can be completed based on the annular structure radiotherapy machine by determining the positioning external reference and the treatment external reference.

The invention discloses a button switch adaptive optimization method and a storage medium. The method comprises the following steps: acquiring a tactile signal through a high-density piezoelectric sensor, and extracting pressure distribution, gradient change rate and energy concentration degree characteristics in combination with a space-time convolution kernel and a dynamic baseline calibration algorithm; integrating time-frequency domain analysis of the historical operation sequence and neural differential equation modeling, predicting an operation probability and marking an anomaly; and an anti-interference control instruction is generated by fusing environmental parameters, and multi-source data are dynamically weighted by adopting a gating attention mechanism. The system constructs a multi-stage reliability protection mechanism, and fault immunity is realized through feature drift detection, digital twin residual verification and hybrid control switching. And responding to a real-time demand, dynamically optimizing heterogeneous computing resource allocation, executing three-dimensional storage reconstruction and precision bit width switching, and forming a closed-loop cognitive evolution link.

The invention relates to the technical field of display, in particular to a Mini LED backlight module intelligent control system and method of a distributed driving architecture, and the system comprises a processor unit, an LED driving chip, a Mini LED module and a sensor. The processor unit converts an RGB color space into a backlight brightness signal in an XYZ tristimulus value form based on a topological mapping theory; the LED driving chip receives the signal, controls the light intensity of the Mini LED module, and executes verification and compensation based on a Bayesian inference framework; the Mini LED modules are arranged in a distributed architecture, receive the brightness signal and output backlight brightness; a sensor collects backlight brightness signals of an RGB color space to form closed-loop feedback, a calibration coefficient matrix is generated through a polynomial fitting calibration algorithm optimized through matrix decomposition, and high-precision and high-reliability backlight control is achieved in combination with adaptive parameter adjustment and a dynamic compensation mechanism.

The invention discloses an in-service precision intelligent diagnosis and calibration industrial robot system and a diagnosis and calibration method. The system comprises a parameter acquisition module, a fault diagnosis engine module, a collaborative calibration algorithm module, a laser tracking calibration module, an execution calibration module and a result feedback module. According to the method, joint, tail end, load and environment parameters of the robot are collected through the parameter collection module and transmitted to the fault diagnosis engine module, and features are extracted and faults are classified through a specific technology; the collaborative calibration algorithm module calculates a calibration compensation amount in combination with the fault parameters, and the laser tracking calibration module tracks and measures the motion trail of the robot in real time; the execution calibration module adjusts controller parameters to complete calibration, and the result feedback module compares the parameters and adjusts the acquisition frequency and type. According to the system and the method, in-service real-time diagnosis and calibration of the robot are realized, the fault diagnosis accuracy and the calibration precision stability are improved, the production interruption is reduced, the flexible production requirement is met, and the operation precision and the production efficiency of the robot are guaranteed.