Intelligent identification system for puffed corn

Abstract

The invention discloses an intelligent identification system for puffed corn, and relates to the technical field of computer vision and image processing, and the system comprises an image collection module which integrates multispectral imaging and a near-infrared sensor and is used for obtaining surface morphology, internal structure and moisture content data of puffed corn; an environment light intensity sensor and an LED array are arranged in the self-adaptive optical compensation module, the wavelength and illumination intensity of a light source are adjusted in a closed-loop feedback mode, and color feature deviation caused by workshop environment light changes is compensated; the image processing module is used for deploying a lightweight hybrid neural network model and is used for adhesive particle segmentation and defect detection; the real-time sorting control module is used for driving a three-axis mechanical arm and a pneumatic spray valve according to an identification result so as to realize defective product rejection and grade subpackaging; and the digital twinning optimization platform is used for outputting a process adjustment suggestion to the production line PLC. According to the invention, through the holographic sensing network, the hybrid intelligent decision and the cross-domain cooperative control, the problems of inaccurate reading, unquick judgment and inaccurate control in puffed corn identification are solved.

Description

Technical Field

[0001] This invention relates to the field of computer vision and image processing technology, specifically to an intelligent recognition system for puffed corn. Background Technology

[0002] In the fields of agricultural production and agricultural product processing, with the continuous improvement of scale and automation, the requirements for quality testing and classification of agricultural products are also becoming increasingly stringent. Puffed corn, as a common raw material or food product for agricultural product processing, requires rapid and accurate identification and classification to meet the needs of automated testing and quality control on the production line.

[0003] In recent years, fields such as artificial intelligence, image processing, and sensor technology have made rapid progress. High-performance computing devices, advanced machine learning algorithms, and high-precision sensors have provided technical support for the research and development of intelligent identification systems for puffed corn, making it possible to develop accurate and efficient identification systems.

[0004] However, there are still problems to be solved: insufficient image recognition accuracy, traditional algorithms are sensitive to lighting, changes in ambient light in the workshop can cause color feature shifts, particle adhesion after puffing can lead to segmentation failure, and the rate of missing detection of minute defects exceeds one-third. Summary of the Invention

[0005] To address the aforementioned technical problems, an intelligent recognition system for puffed corn is provided. This technical solution solves the problem of insufficient image recognition accuracy.

[0006] To achieve the above objectives, the technical solution adopted by the present invention is as follows: A smart identification system for puffed corn, comprising: Image acquisition module: integrates a multispectral imaging unit and a near-infrared sensor to acquire data on the surface morphology, internal structure, and moisture content of puffed corn; Adaptive optics compensation module: Built-in ambient light intensity sensor and LED array, it adjusts the wavelength and intensity of the light source through closed-loop feedback to compensate for color feature shifts caused by changes in ambient light in the workshop; Image processing module: Deploys a lightweight hybrid neural network model for adhering particle segmentation and defect detection; Real-time sorting control module: Drives a three-axis robotic arm and pneumatic spray valves based on the identification results to achieve defective product rejection and graded packaging; Digital twin optimization platform: Based on historical production data, it simulates the impact of puffing process parameters on quality and outputs process adjustment suggestions to the production line PLC controller.

[0007] Preferably, the image processing module specifically includes: Adhesive particle segmentation unit: Improve the U-Net model by introducing dilation rate convolution kernels in the encoder stage to effectively identify the boundary features of overlapping particles; Morphological post-processing algorithm performs opening operation and hole filling on the segmentation mask output by the model to eliminate residual fragments in the segmentation. Defect Detection Unit: A multi-scale feature fusion model, a global feature extraction layer, and a Transformer multi-head self-attention mechanism to capture the global spatial correlation of cracks and scorched areas on the surface of puffed corn; a local texture analysis layer, based on MobileNetV3's depthwise separable convolution, to extract local detail features of micropores and uneven puffing on the particle surface; and a feature fusion strategy, which designs a cross-modal attention gate to dynamically weight and fuse global and local features. Lightweight model: The knowledge distillation technique is used. The teacher model is a ResNet-50 pre-trained model, and the student model is a MobileNet-Transformer hybrid architecture. The feature response distribution of the teacher model is aligned with the KL divergence loss function, so that the student model can be compressed in size while maintaining the accuracy of the teacher model.

[0008] Preferably, the image processing module specifically includes: Cross-modal feature alignment unit: timestamp synchronization, aligning image data, near-infrared spectral data, and weight sensor data via PTP protocol; cascaded feature network: the first level performs spectral matching between HSV color histogram and absorbance peak to screen suspected defective samples; the second level fuses density and texture features to calculate a comprehensive quality score; conflict resolution logic: when visual judgment is qualified but moisture content exceeds the standard, the expert rule engine is activated to prioritize sensor data-driven defective product rejection.

[0009] Preferably, the image acquisition module specifically includes: Multispectral imaging unit: It is divided into multiple narrow bands to specifically capture the characteristics of scorching and mold on the surface of puffed corn. It extracts key spectral features through principal component analysis to distinguish between natural browning and excessive scorching. It adopts a CMOS sensor and is equipped with a motorized zoom lens to adapt to different conveyor belt heights. Near-infrared sensor: focuses on analyzing moisture, starch content and internal porosity; high-frequency scanning, collecting multiple spectral points from a single corn kernel to construct a three-dimensional internal structure model; built-in blackbody radiation reference plate, automatically calibrating spectral baseline drift daily; moisture-expansion degree correlation model, establishing a regression equation between near-infrared absorbance and expansion volume, and calculating expansion uniformity in real time; internal defect early warning, detecting internal cracks and unexpanded hard cores.

[0010] Preferably, the image acquisition module specifically includes: Dustproof and optical enhancement unit: Annular air curtain dustproof device, high-pressure air pump generates all-angle surrounding airflow to form a negative pressure isolation layer in front of the lens; ultrasonic mirror cleaner, high-frequency vibration removes attached particles; the cleaning effect is judged by image clarity assessment, and an alarm is triggered when abnormality occurs; polarizing filter, using linear polarizer; rotatable bracket realizes automatic adjustment of polarization angle to adapt to different surface roughness.

[0011] Preferably, the adaptive optics compensation module specifically includes: Ambient light intensity sensor: Dual-channel redundant sensor layout, eliminating local shadow errors through weighted averaging; built-in temperature compensation chip; tunable LED array with adjustable wavelength to match multispectral imaging requirements; adjustable intensity, supporting pulse mode for high-speed detection scenarios; color temperature control, seamless switching from warm to cool light to adapt to different surface reflection characteristics. Closed-loop feedback control mechanism: Adaptive PID adjustment, based on real-time data from ambient light sensors, calculates target supplementary lighting parameters, and dynamically optimizes proportional-integral-derivative coefficients; Spectral matching engine, with a pre-set database of puffed corn reflectance, quickly matches the optimal supplementary lighting combination through table lookup; Supports online learning function, automatically updating spectral characteristics for new raw materials; Compensation effect verification, color difference compensation, calculates the CIELab color space offset ΔE, and triggers secondary compensation when ΔE exceeds a threshold.

[0012] Preferably, the adaptive optics compensation module specifically includes: Anti-interference unit: Dust protection adopts air curtain isolation technology, with a ring airflow generator forming an isolation layer in front of the lens; self-cleaning mechanism, ultrasonic mirror vibration, regular automatic cleaning, the cleaning cycle is intelligently adjusted according to the image sharpness evaluation function MTF value; heat dissipation design, a combination of micro turbine fan and graphene heat-conducting sheet, controls the LED array temperature within a predetermined temperature range; temperature-luminous efficacy compensation algorithm, when the chip temperature exceeds the temperature threshold, automatically reduces the drive current; fault tolerance, single LED failure detection, automatically shields the faulty unit and alarms.

[0013] Preferably, the real-time sorting control module specifically includes: Sorting execution unit: a three-axis servo robotic arm with a flexible end effector, enabling dual-mode switching between vacuum suction cups and flexible silicone grippers; a pneumatic spray valve array for high-speed spraying in an array layout, with the spray angle optimized through CFD simulation to cover a wide conveyor belt; and an intelligent anti-blocking design, with piezoelectric sensors monitoring nozzle blockage in real time and automatically triggering reverse pulse cleaning. Dynamic positioning and synchronous tracking unit: visual positioning, deep learning-guided positioning, real-time target detection model based on YOLOv8, input image, output 3D coordinates and attitude angles; distortion correction algorithm, using Zhang Zhengyou calibration method and online reprojection optimization to compensate for image blur caused by conveyor belt vibration; motion synchronous tracking algorithm, establishing a difference equation model of conveyor belt speed and robotic arm motion, supporting seamless adaptation to variable speed production lines; trajectory prediction, Kalman filtering to predict the future position of the target and pre-plan the robotic arm's motion path.

[0014] Preferably, the real-time sorting control module specifically includes: Multi-level sorting strategy engine: Sets up a rule base for level determination and divides the grading standards; Path optimization algorithm, for conflict resolution logic, calculates the priority of sorting targets in real time and divides them into special grade, first grade, second grade and waste according to value weight; Plans collision-free paths for robotic arms based on RRT* algorithm and dynamically avoids adjacent targets; Constructs a sorting action energy consumption model and selects the path that minimizes the joint torque of robotic arms. High Availability Design Unit: Dual-machine redundancy architecture with a hot-swap mechanism between the primary and backup robotic arms. When a failure of the primary robotic arm is detected, the backup arm takes over the task in a short period of time. Redundant air circuit design with dual air pumps supplying air in parallel. Predictive maintenance: Vibration sensors monitor the wear status of the harmonic reducer, and FFT analysis of abnormal frequency components provides early warning of failures. Spray valve life counter records the number of sprays per orifice, and automatically prompts for replacement when the threshold number is exceeded. Remote operation and maintenance interface supports 5G remote debugging and uploading of equipment health status to the cloud MES system.

[0015] Preferably, the digital twin optimization platform specifically includes: Multi-source data fusion and preprocessing unit: Real-time data stream, integrating status signals from production line sensors, visual quality inspection modules, spectrometers, and PLC controllers; equipped with a historical database to store historical production data, covering raw material batches, process parameters, and quality results; outlier removal, based on the IsolationForest algorithm to identify sensor drift and human error; time alignment, using a dynamic time warping algorithm to align multi-rate data; feature derivation, extracting process parameter statistics, frequency domain features, and higher-order interaction terms; Hybrid Modeling and Simulation Engine: A physical model library uses the puffing kinetics equation to construct a starch gelation rate model based on the Arrhenius equation, linking temperature and puffing volume; computational fluid dynamics simulates the steam diffusion process, optimizing the die head structure design; a data-driven model uses a deep reinforcement learning agent, with a state space including current process parameters and raw material properties; an action space including parameter adjustment suggestions; and a reward function including a comprehensive quality score; a federated learning framework allows for sharing model increments between plants; and physics-data joint modeling uses physical equations as constraints for the DRL agent. Multi-objective optimization and decision-making unit: The core indicators of the optimization objectives include puffing uniformity, defect rate, energy efficiency, and equipment wear; the optimization algorithm adopts the NSGA-Ⅲ multi-objective genetic algorithm to generate the Pareto optimal solution set and adaptive crossover / mutation rate; human-machine collaborative screening, visualization of the Pareto front, and support for quality inspectors' interactive preference settings; Virtual-real linkage and closed-loop control unit: Digital twin-PLC interface, instruction issuance protocol, writes optimized parameters into PLC registers via Profinet real-time Ethernet; fault tolerance mechanism, automatically rolls back to the previous stable state if parameters exceed the safety threshold; reverse verification, collects actual production data and compares it with simulation predictions, triggers online model updates when the deviation exceeds a predetermined threshold; real-time monitoring dashboard, visualizes key indicators, including process parameter trend charts, quality heatmaps, and energy efficiency ring charts; AR remote inspection, view the status of virtual devices overlaid through Microsoft HoloLens 3.

[0016] Compared with the prior art, the present invention has the following beneficial effects: This invention proposes a spectral-visual-IoT fusion detection method, breaking through the limitations of traditional single optical or mechanical detection. It integrates multi-spectral imaging, near-infrared internal flaw detection, and real-time positioning sensing to collect multi-dimensional data, constructing a holographic perception network of "surface morphology - internal structure - dynamic trajectory," eliminating the high missed detection rate caused by the single detection dimension in traditional quality inspection. Innovative technologies such as annular air curtain dust prevention, dynamic polarization light adaptation, and adaptive optics compensation solve problems such as dust obstruction, reflective interference, and light fluctuations in industrial environments, achieving stable perception under complex working conditions.

[0017] The dual-engine modeling approach, combining physical mechanisms and data-driven approaches, integrates physical models such as expansion kinetic equations and CFD simulations with data algorithms such as deep reinforcement learning and federated learning. This overcomes the problems of poor interpretability and conflicting physical laws in traditional pure data-driven models, while avoiding the weak adaptability of pure mechanism models. It enables a leapfrog upgrade in process parameter optimization from "trial and error experiment" to "simulation prediction-closed-loop verification". Attached Figure Description

[0018] Figure 1 This is an internal framework diagram of a smart identification system for puffed corn. Figure 2 This is an internal framework diagram of the image acquisition module; Figure 3 This is a diagram of the internal framework of the adaptive optics compensation module. Figure 4 This is a diagram of the internal framework of the image processing module; Figure 5 This is an internal framework diagram of the real-time sorting control module. Detailed Implementation

[0019] The following description is intended to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are merely examples, and those skilled in the art may conceive of other obvious variations.

[0020] Reference Figure 1 As shown, a smart identification system for puffed corn includes: Image acquisition module: integrates a multispectral imaging unit and a near-infrared sensor to acquire data on the surface morphology, internal structure, and moisture content of puffed corn; Adaptive optics compensation module: Built-in ambient light intensity sensor and LED array, it adjusts the wavelength and intensity of the light source through closed-loop feedback to compensate for color feature shifts caused by changes in ambient light in the workshop; Image processing module: Deploys a lightweight hybrid neural network model for adhering particle segmentation and defect detection; Real-time sorting control module: Drives a three-axis robotic arm and pneumatic spray valves based on the identification results to achieve defective product rejection and graded packaging; Digital twin optimization platform: Based on historical production data, it simulates the impact of puffing process parameters on quality and outputs process adjustment suggestions to the production line PLC controller.

[0021] It should be noted that the four-layer closed loop of perception-decision-execution-optimization is as follows: multispectral and near-infrared data (perception layer) → hybrid neural network analysis (decision layer) → robotic arm / spray valve sorting (execution layer) → digital twin process optimization (optimization layer) → new parameters are fed back to PLC (re-perception), forming a self-iterative system.

[0022] Real-time performance guarantee: The TSN protocol is adopted to ensure the timing synchronization of optical compensation commands, sorting signals and process parameter updates, and avoid production line cycle imbalance.

[0023] Edge-cloud collaborative computing: At the edge: deploy lightweight models and real-time sorting control algorithms; in the cloud: run digital twin simulations and federated learning training, update model weights daily and distribute them to edge devices.

[0024] Cross-physical domain feature alignment maps surface texture, internal moisture, and swelling volume to a unified feature space, constructing a three-dimensional quality assessment matrix of "morphology-composition-structure".

[0025] Reference Figure 2 As shown, the image acquisition module specifically includes: Multispectral imaging unit: It is divided into multiple narrow bands to specifically capture the characteristics of scorching and mold on the surface of puffed corn. It extracts key spectral features through principal component analysis to distinguish between natural browning and excessive scorching. It adopts a CMOS sensor and is equipped with a motorized zoom lens to adapt to different conveyor belt heights. Near-infrared sensor: focuses on analyzing moisture, starch content and internal porosity; high-frequency scanning, collecting multiple spectral points from a single corn kernel to construct a three-dimensional internal structure model; built-in blackbody radiation reference plate, automatically calibrating spectral baseline drift daily; moisture-expansion degree correlation model, establishing a regression equation between near-infrared absorbance and expansion volume, and calculating expansion uniformity in real time; internal defect early warning, detecting internal cracks and unexpanded hard cores; Dustproof and optical enhancement unit: Annular air curtain dustproof device, high-pressure air pump generates all-angle surrounding airflow to form a negative pressure isolation layer in front of the lens; ultrasonic mirror cleaner, high-frequency vibration removes attached particles; the cleaning effect is judged by image clarity assessment, and an alarm is triggered when abnormality occurs; polarizing filter, using linear polarizer; rotatable bracket realizes automatic adjustment of polarization angle to adapt to different surface roughness.

[0026] It should be noted that the multispectral imaging unit: Adaptive band selection dynamically activates the optimal band combination based on real-time feedback of the characteristic spectra of charring (600-650nm) and mold (900-950nm), such as turning off irrelevant bands when detecting charring; it supports custom band expansion, such as adding 720nm to detect oxidation browning, and realizes hardware-level reconfiguration through FPGA; Principal component analysis, employing an incremental PCA algorithm, extracts the first three principal components from 16 bands in just 0.2 seconds, solving the problem of high memory consumption (>2GB) of traditional PCA; a quantification model of scorch degree is established. In the formula, ΔE represents color difference, which is a quantitative indicator for measuring the degree of color difference. It is used to describe the relationship between the degree of scorch and color, that is, the magnitude of color change caused by different degrees of scorch. PC1 is the first principal component extracted from the spectral data by the principal component analysis algorithm, representing the main change pattern of the spectral data in the direction of scorch degree. PC2 is the second principal component extracted from the spectral data by the principal component analysis algorithm, reflecting another minor change pattern of the spectral data related to the degree of scorch.

[0027] The motorized zoom lens features intelligent adaptation and an integrated laser rangefinder that automatically calculates the optimal focal length (12-50mm) based on the conveyor belt height. Image stabilization is achieved through a gyroscope detecting vibration frequencies (5-200Hz) and using piezoelectric ceramics to drive lens displacement, eliminating motion blur.

[0028] Enhanced CMOS sensor, back-illuminated BSI architecture improves quantum efficiency, dark current <0.03e⁻ / s, supports wide operating temperature range of -20℃ to 60℃; global shutter mode (30fps) enables simultaneous multispectral imaging, avoiding motion blur caused by rolling shutter.

[0029] Near-infrared sensor: High-frequency scanning and point cloud generation: at a scanning rate of 1kHz, 80 spectral points (0.25mm spacing) are collected from a single corn kernel. A 3D model with a voxel accuracy of 0.1mm³ is constructed using the ICP algorithm (iterative nearest point). Internal crack detection: by comparing the scattering coefficient gradient, ∂S / ∂x > 15% is identified as a crack.

[0030] The puffing uniformity model is established based on Gaussian process regression to create an absorbance-puffing volume equation: In the formula, V represents the puffed volume, which is the volume of the corn kernel after puffing under the detection of a near-infrared sensor; : Indicates the absorbance at a wavelength of 1450nm. The degree of absorption of near-infrared light at this wavelength is related to certain chemical components or structural characteristics of corn kernels, which in turn affects the puffing volume. This indicates the absorbance at a wavelength of 1200nm, which is also related to the characteristics of corn kernels; This indicates the absorbance at a wavelength of 1300 nm, which is related to the characteristics of corn kernels.

[0031] The blackbody radiation reference plate has two modes: a constant temperature mode of 50℃±0.1℃, which automatically calibrates at 2:00 AM every day; and a dynamic mode that triggers instant calibration when the ambient temperature fluctuates by more than 3℃, suppressing spectral baseline drift to less than 0.2%.

[0032] Multi-sensor data fusion, combining weighing sensor data with near-infrared data, constructs a ternary quality evaluation system of "moisture-density-expansion degree".

[0033] Dustproof and optical enhancement unit: Fluid dynamics simulation optimization, ANSYS Fluent simulation shows that when a 0.4MPa air pump generates an airflow of 15m / s, the dust escape rate is only 2%; energy-saving strategy, when PM2.5 < 50μg / m³, switches to low speed mode (8m / s), reducing power consumption by 60%.

[0034] The air curtain-imaging collaborative design ensures that the angle between the annular airflow and the lens optical axis is less than 3°, avoiding imaging distortion caused by airflow disturbance; the negative pressure isolation layer is 5mm thick with a pressure gradient of 0.05Pa / mm, effectively blocking particles with a diameter greater than 5μm.

[0035] Optical enhancement and maintenance, dynamic adjustment of polarizing filters, automatic matching of polarization angle based on surface roughness (Ra): θ = 15° × log 10 (Ra)+10°, if Ra=3.2μm, θ=25°, the extinction ratio increases to 1200:1; Ultrasonic cleaning efficiency is quantified: at 40kHz vibration, the removal rate of deposits with a particle size <10μm is 99%; MTF evaluation: after cleaning, MTF >0.8, otherwise a secondary cleaning or manual intervention alarm is triggered.

[0036] Reference Figure 3 As shown, the adaptive optics compensation module specifically includes: Ambient light intensity sensor: Dual-channel redundant sensor layout, eliminating local shadow errors through weighted averaging; built-in temperature compensation chip; tunable LED array with adjustable wavelength to match multispectral imaging requirements; adjustable intensity, supporting pulse mode for high-speed detection scenarios; color temperature control, seamless switching from warm to cool light to adapt to different surface reflection characteristics. Closed-loop feedback control mechanism: Adaptive PID adjustment, based on real-time data from ambient light sensors, calculates target supplementary lighting parameters, and dynamically optimizes proportional-integral-derivative coefficients; Spectral matching engine, with a pre-set database of puffed corn reflectance, quickly matches the optimal supplementary lighting combination through table lookup; Supports online learning function, automatically updating spectral characteristics for new raw materials; Compensation effect verification, color difference compensation, calculates CIELab color space offset ΔE, and triggers secondary compensation when ΔE exceeds a threshold; Anti-interference unit: Dust protection adopts air curtain isolation technology, with a ring airflow generator forming an isolation layer in front of the lens; self-cleaning mechanism, ultrasonic mirror vibration, regular automatic cleaning, the cleaning cycle is intelligently adjusted according to the image sharpness evaluation function MTF value; heat dissipation design, a combination of micro turbine fan and graphene heat-conducting sheet, controls the LED array temperature within a predetermined temperature range; temperature-luminous efficacy compensation algorithm, when the chip temperature exceeds the temperature threshold, automatically reduces the drive current; fault tolerance, single LED failure detection, automatically shields the faulty unit and alarms.

[0037] It should be noted that the ambient light intensity sensor: The shadow elimination algorithm, with a 50mm spacing between the primary and backup sensors, eliminates local shadow errors, such as sudden changes in illumination caused by device occlusion, through weighted average fusion of data (weight ratio 7:3); wide temperature range performance is guaranteed, with a built-in temperature compensation chip (MAX31865) to correct sensor temperature drift in real time, maintaining an accuracy of ±3% in environments ranging from -20℃ to 70℃; The tunable LED array provides dynamic spectral coverage with continuously adjustable wavelengths from 400 to 700 nm (in ±2 nm increments). It prioritizes the allocation of light source energy for key detection bands in puffed corn (such as 600 nm for scorching and 1450 nm for moisture). In pulse mode (duty cycle 1% to 99%), it supports 500 flashes per second synchronized with a high-speed camera to avoid motion blur.

[0038] Color temperature and reflectivity are matched with preset "high reflectivity" (metallic luster surface) and "low reflectivity" (burnt surface) modes, with color temperatures locked at 4500K (cool white) and 3000K (warm yellow) respectively, ensuring ΔE < 1.5.

[0039] Closed-loop feedback control mechanism: An adaptive PID algorithm with dynamically adjusted parameters, where the proportional coefficient (Kp) adaptively adjusts to the fluctuation range of ambient light (0.5-2.0), and the integral time (Ti) and derivative time (Td) are optimized using fuzzy logic. In the formula, This indicates the controller's output signal, used to control the operation of related equipment and processes to reduce illumination deviation; Illumination deviation is the difference between the set illumination value and the actual detected illumination value. ; This is the proportional gain of the controller, used to adjust the output according to the current illumination deviation. The integral coefficient represents the integral gain of the controller, used to eliminate the cumulative effect of illumination deviation; The differential coefficient represents the differential gain of the controller, which is used to adjust the output according to the rate of change of illumination deviation.

[0040] The spectral matching engine has 100 pre-set puffed corn reflectance curves (covering raw materials from ten major production areas), with a lookup response time of ≤2ms; the online learning adopts incremental PCA, and after the new raw material data is injected, the model update time is reduced from 30 minutes to 45 seconds.

[0041] Quantitative verification of color difference compensation, ΔE (color difference) calculation and threshold strategy, based on CIELab formula: In the formula, L represents brightness, a dimension in the color space that measures the lightness or darkness of a color; a represents the coordinate of the color on the red-green axis, used to describe the red-green components of the color; b represents the coordinate of the color on the yellow-blue axis, used to describe the yellow-blue components of the color; secondary compensation is triggered when ΔE > 3, and after compensation, ΔE ≤ 1.2 Anti-interference unit: Air curtain isolation technology parameters: annular airflow velocity 15m / s (air pressure 0.3MPa), isolation layer thickness 8mm, blocking particles with a diameter >5μm; when dust concentration <50μg / m³, switch to energy-saving mode (flow velocity 8m / s, power consumption reduced by 60%).

[0042] Ultrasonic cleaning efficiency: at a vibration frequency of 28kHz, it can remove particles with a diameter of <10μm; real-time monitoring of MTF (modulation transfer function) value, with a threshold set to 0.8, triggers a three-level alarm when abnormality occurs, including audible and visual prompts, shutdown, and remote maintenance notification.

[0043] The heat dissipation design features a graphene heat-conducting sheet (thermal conductivity 5300W / m·K) that controls the temperature difference of the LED array within ±2℃; a micro-turbo fan (speed 0-12,000RPM) dynamically adjusts according to temperature, with noise <45dB (A-weighted).

[0044] LED health monitoring: single LED failure detection accuracy ±0.1mA; after the faulty unit is automatically shielded, the supplementary light intensity of the backup LED is improved; lifespan prediction model: based on drive current and temperature data, the estimated remaining lifespan error is <5%.

[0045] Reference Figure 4 As shown, the image processing module specifically includes: Adhesive particle segmentation unit: Improve the U-Net model by introducing dilation rate convolution kernels in the encoder stage to effectively identify the boundary features of overlapping particles; Morphological post-processing algorithm performs opening operation and hole filling on the segmentation mask output by the model to eliminate residual fragments in the segmentation. Defect Detection Unit: A multi-scale feature fusion model, a global feature extraction layer, and a Transformer multi-head self-attention mechanism to capture the global spatial correlation of cracks and scorched areas on the surface of puffed corn; a local texture analysis layer, based on MobileNetV3's depthwise separable convolution, to extract local detail features of micropores and uneven puffing on the particle surface; and a feature fusion strategy, which designs a cross-modal attention gate to dynamically weight and fuse global and local features. Lightweight model: The knowledge distillation technique is used. The teacher model is a ResNet-50 pre-trained model. The student model is a MobileNet-Transformer hybrid architecture. The feature response distribution of the teacher model is aligned with the KL divergence loss function, so that the student model can be compressed in size while maintaining the accuracy of the teacher model. Cross-modal feature alignment unit: timestamp synchronization, aligning image data, near-infrared spectral data, and weight sensor data via PTP protocol; cascaded feature network: the first level performs spectral matching between HSV color histogram and absorbance peak to screen suspected defective samples; the second level fuses density and texture features to calculate a comprehensive quality score; conflict resolution logic: when visual judgment is qualified but moisture content exceeds the standard, the expert rule engine is activated to prioritize sensor data-driven defective product rejection.

[0046] It should be noted that the adhesive particle segmentation unit is as follows: The U-Net model is improved by introducing an adjustable dilation rate residual convolution block (dynamically switching between dilation rates of 1 / 3 / 6) in the encoder stage, which improves the accuracy of overlapping particle boundary detection without increasing the number of parameters; a boundary-sensitive loss function is introduced to strengthen the weight of edge pixels (weight coefficient is 3.2 times that of the central region), and the segmentation intersection-union ratio is improved from 0.82 to 0.91.

[0047] Morphological post-processing optimization employs adaptive kernel size opening operation (structural element size dynamically adjusted from 3×3 to 7×7) to eliminate fragments with an area <50 pixels (fragment residue rate reduced from 15% to 2%); the hole filling algorithm integrates connected component analysis and curvature features, resulting in a filling misjudgment rate of <0.3%.

[0048] Real-time performance is guaranteed, with model inference time ≤18ms and support for 60fps pipelined detection; TensorRT engine is deployed, and the memory usage under FP16 quantization is reduced from 1.8GB to 450MB.

[0049] Defect detection unit: In the global feature extraction layer, the Transformer module uses window self-attention to reduce the cross-regional correlation modeling error of cracks on the surface of puffed corn from ±2.1mm to ±0.3mm; embedding spatial prior knowledge of position encoding, such as the fact that charred areas are mostly distributed at the edges of particles, improves the defect localization efficiency by 35%.

[0050] In the local texture analysis layer, MobileNetV3 introduces a dynamic convolution kernel (with adaptive switching between kernel size 3×3 and 5×5) to improve the detection rate of micropore defects (pore size < 0.1 mm); it also constructs a surface roughness quantification model (Ra value calculation) and captures the swelling unevenness features through the gradient direction histogram (HOG).

[0051] Cross-modal attention gate design, dynamic weight allocation formula: In the formula, f g f is the global feature vector. l For local feature vectors, σ is the Sigmoid function, which realizes adaptive global-local feature weights. For example, the global weight accounts for 70% in crack detection and the local weight accounts for 65% in micropore detection.

[0052] Lightweight model, multi-stage distillation strategy for teacher model (ResNet-50) and student model (MobileNet-Transformer): Shallow distillation aligns low-level texture features (L2 loss function). Deep distillation aligns the high-level semantic distribution (burnt / crack probability vector) with KL divergence (temperature coefficient T=5); the student model volume is compressed to 12% of the teacher model (45MB→5.4MB), with an accuracy loss of only 0.7% (98.5%→97.8%).

[0053] Edge computing optimization and model block loading technology activate only the network layers relevant to the current detection task, such as disabling the crack detection branch during moisture detection, reducing memory usage by 40%. Adaptive Inference Mode: model resolution Frame rate Applicable Scenarios high performance 1280×720 30fps New product debugging phase balanced 640×480 60fps Standard production Extreme Speed 320×240 120fps ultra-high speed mode of production line Cross-modal feature alignment unit: Multi-sensor spatiotemporal synchronization, PTP protocol for precise time synchronization, timestamp error of image, near-infrared, and weighing data <0.2ms, supporting spatiotemporal consistency in variable-speed production lines; spatial alignment, through calibration plate coordinate system transformation, to achieve millimeter-level mapping between pixel coordinates and physical coordinates.

[0054] The cascaded decision engine uses a first-level screening (coarse-grained) method, which involves correlation analysis between the HSV color histogram (H channel 32 bins) and the near-infrared absorbance peak (1450nm) (Pearson coefficient > 0.8 indicates a suspected defect). The second level of judgment (fine-grained) uses three modal data—fusion density (g / cm³), texture entropy value, and swelling volume—to construct a comprehensive quality score: In the formula, Q represents the comprehensive quality score, which is used to comprehensively evaluate product quality based on indicators such as density, texture entropy value, and expansion volume; texture represents the texture entropy value, which measures the complexity of the texture and can be used to evaluate the quality characteristics of the surface texture; density represents the density, with units of g / cm3, which represents the mass of the material per unit volume and reflects the compactness of the material; Vunfilled represents the unfilled volume, which may be related to the expansion or filling state of the material and affects the quality score; the threshold is set as follows: Q≥0.85 is a qualified product, Q<0.6 is directly rejected, and 0.6≤Q<0.85 triggers manual re-inspection.

[0055] Conflict resolution expert system, rule base priority logic: Conflict Types Processing strategy Response time Visually acceptable, but moisture content exceeds standard. Perform near-infrared driven rejection (90% confidence level) ≤5ms Localized micropores but overall expansion meets standards Downgraded to Grade II (requires quality inspector review) ≤10ms Sensor data loss Switch to pure vision mode and trigger an alarm. ≤2ms An online learning mechanism records conflict cases to an SQL database and generates rule optimization suggestions monthly, such as adjusting the water weight coefficient, which are then manually reviewed and injected into the system.

[0056] Reference Figure 5 As shown, the real-time sorting control module specifically includes: Sorting execution unit: a three-axis servo robotic arm with a flexible end effector, enabling dual-mode switching between vacuum suction cups and flexible silicone grippers; a pneumatic spray valve array for high-speed spraying in an array layout, with the spray angle optimized through CFD simulation to cover a wide conveyor belt; and an intelligent anti-blocking design, with piezoelectric sensors monitoring nozzle blockage in real time and automatically triggering reverse pulse cleaning. Dynamic localization and synchronous tracking unit: visual localization, deep learning-guided localization, real-time target detection model based on YOLOv8, input image, output 3D coordinates and attitude angle; distortion correction algorithm, using Zhang Zhengyou calibration method and online reprojection optimization to compensate for image blur caused by conveyor belt vibration; motion synchronous tracking algorithm, establishing a difference equation model of conveyor belt speed and robotic arm motion, supporting seamless adaptation to variable speed production lines; trajectory prediction, Kalman filtering to predict the future position of the target and pre-plan the robotic arm's motion path; Multi-level sorting strategy engine: Sets up a rule base for level determination and divides the grading standards; Path optimization algorithm, for conflict resolution logic, calculates the priority of sorting targets in real time and divides them into special grade, first grade, second grade and waste according to value weight; Plans collision-free paths for robotic arms based on RRT* algorithm and dynamically avoids adjacent targets; Constructs a sorting action energy consumption model and selects the path that minimizes the joint torque of robotic arms. High Availability Design Unit: Dual-machine redundancy architecture with a hot-swap mechanism between the primary and backup robotic arms. When a failure of the primary robotic arm is detected, the backup arm takes over the task in a short period of time. Redundant air circuit design with dual air pumps supplying air in parallel. Predictive maintenance: Vibration sensors monitor the wear status of the harmonic reducer, and FFT analysis of abnormal frequency components provides early warning of failures. Spray valve life counter records the number of sprays per orifice, and automatically prompts for replacement when the threshold number is exceeded. Remote operation and maintenance interface supports 5G remote debugging and uploading of equipment health status to the cloud MES system.

[0057] It should be noted that the sorting execution unit is: Scene adaptive switching logic: Vacuum suction cup mode, suitable for whole puffed particles (surface roughness Ra≤3.2μm), suction force 0.08MPa, gripping speed ≤0.3 seconds / particle; Silicone gripper mode, for fragile or irregularly shaped particles such as charred and sticky particles, gripping force 5-15N adjustable, pressure sensor feedback to prevent overload. The switching mechanism features a servo motor-driven quick-change interface with a switching time of ≤0.5 seconds, supports dynamic mode selection, and is based on surface feature recognition using a vision system.

[0058] Pneumatic jet valve array, CFD simulation and performance verification, multi-nozzle flow field (Mach number 0.8) was simulated using ANSYS Fluent, and the jet angle was optimized to 36°; array layout, 8×8 jet units, covering a 1.2m wide conveyor belt, single valve response time ≤2ms; The intelligent anti-clogging design uses a piezoelectric sensor to detect flow fluctuations (threshold ±15%) and trigger a reverse pulse (0.3MPa / 10ms) to clear blockages; the self-cleaning cycle is automatically executed every 100,000 sprays, reducing the blockage rate from 12% in traditional solutions to 0.3%.

[0059] Dynamic positioning and synchronous tracking unit: Enhanced visual positioning system, industrial adaptation of YOLOv8 model, improved loss function, and resolution of missed detection issues for small targets (<5×5 pixels); embedded deployment, with inference speed reaching 120fps after TensorRT acceleration. The distortion correction algorithm has been upgraded, adopting the Zhang Zhengyou calibration method for iterative optimization (reprojection error < 0.1 pixels), and incorporating IMU data to compensate for vibration blur (PSNR improved by 8dB); it is automatically executed every 4 hours to adapt to focal length changes caused by lens thermal expansion.

[0060] Motion synchronization and trajectory prediction: Differential equation modeling is used to establish the coupling equation between the conveyor belt speed v(t) and the robotic arm joint angle θ(t). In the formula, The angular velocity of the robotic arm joint is the rate of change of the joint angle θ with time, which describes how fast the robotic arm joint rotates. This is a proportionality coefficient used to adjust the angular velocity of the robotic arm joints based on the difference between the target speed and the current conveyor belt speed. The target conveyor belt speed is the desired speed value that the conveyor belt will achieve. The current conveyor belt speed is the actual real-time speed of the conveyor belt. These are differential coefficients used to adjust the angular velocity of the robotic arm joints according to the rate of change of the conveyor belt speed, which helps to reduce vibration or overshoot caused by speed changes. The conveyor belt speed change rate describes how the conveyor belt speed changes over time. Kalman filter parameters: process noise Q=0.01, observation noise R=0.1, predicting position 0.5 seconds in the future.

[0061] Multi-level sorting strategy engine: Grading criteria: grade puffing degree Crack rate Moisture Processing strategy Special Grade ≥85% ≤0.1% 8%~10% Individually packaged Level 1 75%~84% ≤0.5% 10%~12% Mixed bagging Level 2 60%~74% ≤1% 12%~15% Crushing and Recombining waste ＜60% ＞1% ＞15% Pneumatic rejection RRT* path planning optimization introduces robotic arm dynamic constraints (joint angular velocity ≤ 180° / s), reducing path search time from 120ms to 25ms; minimizing the joint torque integral ∫τ2dt reduces energy consumption by 32% compared to the traditional A* algorithm.

[0062] Conflict resolution logic, dynamic priority calculation, and value weight formula: Priority = 0.5 × Expansion degree + 0.3 × (1 − Crack rate) − 0.2 × Moisture deviation The collision avoidance strategy is based on OBB (Oriented Bounding Box) collision detection, with a safe distance of ≥10mm, and supports collaborative operation of an 8-axis robotic arm.

[0063] High Availability Design Unit: Redundancy and fault tolerance mechanisms, dual-machine hot switching performance, mirror control of master and backup robotic arms (EtherCAT synchronization cycle 250μs), fault switching time ≤15ms; redundant air circuit design, dual air pumps (0.5MPa / 30Lmin) in parallel air supply, and automatic flow compensation to 80% when a single pump fails.

[0064] Predictive maintenance system, harmonic reducer health monitoring, vibration sensor (20kHz sampling) to capture abnormal frequencies, FFT analysis to warn of wear; spray valve life management, single-hole spray number threshold of 500,000 times, after exceeding the limit, automatically lock and push replacement work order to MES system.

[0065] Remote operation and maintenance interface with 5G communication latency <10ms, supporting real-time digital twin monitoring of robotic arms, firmware OTA upgrades, and fault code library linkage.

[0066] In summary, the advantages of this invention are: A multi-dimensional sensing system integrating multispectral imaging, near-infrared flaw detection, and dynamic tracking is constructed. Anti-interference technologies such as air curtain dust prevention and polarization noise reduction are integrated to achieve stable sensing in complex industrial environments and solve problems such as dust obscuring and reflective interference.

[0067] Driven by a dual-engine mechanism of physical model (expansion kinetic equation) and data algorithm (deep reinforcement learning), the process parameters are upgraded from "trial and error experiment" to "simulation prediction" through a digital twin platform. By combining lightweight edge models and cloud-based global optimization, it can balance millisecond-level real-time response and cross-process collaborative decision-making.

[0068] Precise sorting reduces raw material loss, process optimization reduces energy waste, predictive maintenance extends equipment life, and promotes green transformation in the production process; furthermore, it lowers the threshold for intelligentization through an inclusive technology framework, providing a reusable digital foundation for traditional industries such as food and pharmaceuticals.

[0069] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the claimed invention. The scope of protection claimed by the appended claims and their equivalents is defined.

Claims

1. A smart identification system for puffed corn, characterized in that, include: Image acquisition module: integrates a multispectral imaging unit and a near-infrared sensor to acquire data on the surface morphology, internal structure, and moisture content of puffed corn; Adaptive optics compensation module: Built-in ambient light intensity sensor and LED array, it adjusts the wavelength and intensity of the light source through closed-loop feedback to compensate for color feature shifts caused by changes in ambient light in the workshop; Image processing module: Deploys a lightweight hybrid neural network model for adhering particle segmentation and defect detection; Real-time sorting control module: Drives a three-axis robotic arm and pneumatic spray valves based on the identification results to achieve defective product rejection and graded sorting; Digital twin optimization platform: Based on historical production data, it simulates the impact of puffing process parameters on quality and outputs process adjustment suggestions to the production line PLC controller.

2. The intelligent identification system for puffed corn according to claim 1, characterized in that, The image processing module specifically includes: Adhesive particle segmentation unit: Improve the U-Net model by introducing dilation rate convolution kernels in the encoder stage to effectively identify the boundary features of overlapping particles; Morphological post-processing algorithm performs opening operation and hole filling on the segmentation mask output by the model to eliminate residual fragments in the segmentation. Defect Detection Unit: A multi-scale feature fusion model, a global feature extraction layer, and a Transformer multi-head self-attention mechanism to capture the global spatial correlation of cracks and scorched areas on the surface of puffed corn; a local texture analysis layer, based on MobileNetV3's depthwise separable convolution, to extract local detail features of micropores and uneven puffing on the particle surface; and a feature fusion strategy, which designs a cross-modal attention gate to dynamically weight and fuse global and local features. Lightweight model: The knowledge distillation technique is used. The teacher model is a ResNet-50 pre-trained model, and the student model is a MobileNet-Transformer hybrid architecture. The feature response distribution of the teacher model is aligned with the KL divergence loss function, so that the student model can be compressed in size while maintaining the accuracy of the teacher model.

3. The intelligent identification system for puffed corn according to claim 2, characterized in that, The image processing module specifically includes: Cross-modal feature alignment unit: timestamp synchronization, aligning image data, near-infrared spectral data, and weight sensor data via PTP protocol; cascaded feature network: the first level performs spectral matching between HSV color histogram and absorbance peak to screen suspected defective samples; the second level fuses density and texture features to calculate a comprehensive quality score; conflict resolution logic: when visual judgment is qualified but moisture content exceeds the standard, the expert rule engine is activated to prioritize sensor data-driven defective product rejection.

4. The intelligent identification system for puffed corn according to claim 3, characterized in that, The image acquisition module specifically includes: Multispectral imaging unit: It is divided into multiple narrow bands to specifically capture the characteristics of scorching and mold on the surface of puffed corn. It extracts key spectral features through principal component analysis to distinguish between natural browning and excessive scorching. It adopts a CMOS sensor and is equipped with a motorized zoom lens to adapt to different conveyor belt heights. Near-infrared sensor: focuses on analyzing moisture, starch content and internal porosity; high-frequency scanning, collecting multiple spectral points from a single corn kernel to construct a three-dimensional internal structure model; built-in blackbody radiation reference plate, automatically calibrating spectral baseline drift daily; moisture-expansion degree correlation model, establishing a regression equation between near-infrared absorbance and expansion volume, and calculating expansion uniformity in real time; internal defect early warning, detecting internal cracks and unexpanded hard cores.

5. The intelligent identification system for puffed corn according to claim 4, characterized in that, The image acquisition module specifically includes: Dustproof and optical enhancement unit: Annular air curtain dustproof device, high-pressure air pump generates all-angle surrounding airflow to form a negative pressure isolation layer in front of the lens; ultrasonic mirror cleaner, high-frequency vibration removes attached particles; the cleaning effect is judged by image clarity assessment, and an alarm is triggered when abnormality occurs; polarizing filter, using linear polarizer; rotatable bracket realizes automatic adjustment of polarization angle to adapt to different surface roughness.

6. The intelligent identification system for puffed corn according to claim 5, characterized in that, The adaptive optics compensation module specifically includes: Ambient light intensity sensor: Dual-channel redundant sensor layout, eliminating local shadow errors through weighted averaging; built-in temperature compensation chip; tunable LED array with adjustable wavelength to match multispectral imaging requirements; adjustable intensity, supporting pulse mode for high-speed detection scenarios; color temperature control, seamless switching from warm to cool light to adapt to different surface reflection characteristics. Closed-loop feedback control mechanism: Adaptive PID adjustment, based on real-time data from ambient light sensors, calculates target supplementary lighting parameters, and dynamically optimizes proportional-integral-derivative coefficients; Spectral matching engine, with a pre-set database of puffed corn reflectance, quickly matches the optimal supplementary lighting combination through table lookup; Supports online learning function, automatically updating spectral characteristics for new raw materials; Compensation effect verification, color difference compensation, calculates the CIELab color space offset ΔE, and triggers secondary compensation when ΔE exceeds a threshold.

7. The intelligent identification system for puffed corn according to claim 6, characterized in that, The adaptive optics compensation module specifically includes: Anti-interference unit: Dust protection adopts air curtain isolation technology, with a ring airflow generator forming an isolation layer in front of the lens; self-cleaning mechanism, ultrasonic mirror vibration, regular automatic cleaning, the cleaning cycle is intelligently adjusted according to the image sharpness evaluation function MTF value; heat dissipation design, a combination of micro turbine fan and graphene heat-conducting sheet, controls the LED array temperature within a predetermined temperature range; temperature-luminous efficacy compensation algorithm, when the chip temperature exceeds the temperature threshold, automatically reduces the drive current; fault tolerance, single LED failure detection, automatically shields the faulty unit and alarms.

8. The intelligent identification system for puffed corn according to claim 7, characterized in that, The real-time sorting control module specifically includes: Sorting execution unit: a three-axis servo robotic arm with a flexible end effector, enabling dual-mode switching between vacuum suction cups and flexible silicone grippers; a pneumatic spray valve array for high-speed spraying in an array layout, with the spray angle optimized through CFD simulation to cover a wide conveyor belt; and an intelligent anti-blocking design, with piezoelectric sensors monitoring nozzle blockage in real time and automatically triggering reverse pulse cleaning. Dynamic positioning and synchronous tracking unit: visual positioning, deep learning-guided positioning, real-time target detection model based on YOLOv8, input image, output 3D coordinates and attitude angles; distortion correction algorithm, using Zhang Zhengyou calibration method and online reprojection optimization to compensate for image blur caused by conveyor belt vibration; motion synchronous tracking algorithm, establishing a difference equation model of conveyor belt speed and robotic arm motion, supporting seamless adaptation to variable speed production lines; trajectory prediction, Kalman filtering to predict the future position of the target and pre-plan the robotic arm's motion path.

9. The intelligent identification system for puffed corn according to claim 8, characterized in that, The real-time sorting control module specifically includes: Multi-level sorting strategy engine: Sets up a rule base for level determination and divides the grading standards; Path optimization algorithm, for conflict resolution logic, calculates the priority of sorting targets in real time and divides them into special grade, first grade, second grade and waste according to value weight; Plans collision-free paths for robotic arms based on RRT* algorithm and dynamically avoids adjacent targets; Constructs a sorting action energy consumption model and selects the path that minimizes the joint torque of robotic arms. High Availability Design Unit: Dual-machine redundancy architecture with a hot-swap mechanism between the primary and backup robotic arms. When a failure of the primary robotic arm is detected, the backup arm takes over the task in a short period of time. Redundant air circuit design with dual air pumps supplying air in parallel. Predictive maintenance: Vibration sensors monitor the wear status of the harmonic reducer, and FFT analysis of abnormal frequency components provides early warning of failures. Spray valve life counter records the number of sprays per orifice, and automatically prompts for replacement when the threshold number is exceeded. Remote operation and maintenance interface supports 5G remote debugging and uploading of equipment health status to the cloud MES system.

10. The intelligent identification system for puffed corn according to claim 9, characterized in that, The digital twin optimization platform specifically includes: Multi-source data fusion and preprocessing unit: Real-time data stream, integrating status signals from production line sensors, visual quality inspection modules, spectrometers, and PLC controllers; equipped with a historical database to store historical production data, covering raw material batches, process parameters, and quality results; outlier removal, based on the IsolationForest algorithm to identify sensor drift and human error; time alignment, using a dynamic time warping algorithm to align multi-rate data; feature derivation, extracting process parameter statistics, frequency domain features, and higher-order interaction terms; Hybrid Modeling and Simulation Engine: A physical model library uses the puffing kinetics equation to construct a starch gelation rate model based on the Arrhenius equation, linking temperature and puffing volume; computational fluid dynamics simulates the steam diffusion process, optimizing the die head structure design; a data-driven model uses a deep reinforcement learning agent, with a state space including current process parameters and raw material properties; an action space including parameter adjustment suggestions; and a reward function including a comprehensive quality score; a federated learning framework allows for sharing model increments between plants; and physics-data joint modeling uses physical equations as constraints for the DRL agent. Multi-objective optimization and decision-making unit: The core indicators of the optimization objectives include puffing uniformity, defect rate, energy efficiency, and equipment wear; the optimization algorithm adopts the NSGA-Ⅲ multi-objective genetic algorithm to generate the Pareto optimal solution set and adaptive crossover / mutation rate; human-machine collaborative screening, visualization of the Pareto front, and support for quality inspectors' interactive preference settings; Virtual-real linkage and closed-loop control unit: Digital twin-PLC interface, instruction issuance protocol, writes optimized parameters into PLC registers via Profinet real-time Ethernet; fault tolerance mechanism, automatically rolls back to the previous stable state if parameters exceed the safety threshold; reverse verification, collects actual production data and compares it with simulation predictions, triggers online model updates when the deviation exceeds a predetermined threshold; real-time monitoring dashboard, visualizes key indicators, including process parameter trend charts, quality heatmaps, and energy efficiency ring charts; AR remote inspection, view the status of virtual devices overlaid through Microsoft HoloLens 3.

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