34087 results about "Autonomation" patented technology

The invention discloses a material grabbing offset real-time compensation method and device based on multi-sensor data fusion, relates to the technical field of automatic production, and discloses a material grabbing offset real-time compensation method and device based on multi-sensor data fusion. The three-dimensional offset is generated in real time in combination with the neural network model, mechanical arm compensation is controlled, the problems that a single sensor is large in positioning error and insufficient in compensation precision are solved, the material grabbing accuracy can be improved, and then the stability of an automatic production line is enhanced.

The invention provides a wafer-level test yield prediction and process optimization method and system based on big data, and relates to the technical field of wafer-level tests.The wafer-level test yield prediction and process optimization method comprises the steps that wafer manufacturing process data is obtained, a time-sequence-sensitive multi-modal data fusion network is constructed, a LSTM and GNN combined mixed architecture is adopted, and a multi-modal data fusion network is constructed; self-adaptive weight distribution is carried out through an attention mechanism to predict the yield; and when the predicted yield is lower than a threshold value, triggering a layered progressive process optimization system, and adjusting process parameters through an equipment parameter optimization layer and a process level optimization layer. According to the invention, the accurate prediction of the wafer test yield and the automatic optimization of the process parameters are realized, the production efficiency is improved, and the manufacturing cost is reduced.

The invention discloses an industrial automation control method and system, and relates to the technical field of data processing, and the method comprises the steps: collecting the operation data of production equipment, constructing a digital twin model, and forming a virtual production network; obtaining resource state data of each factory in the virtual production network in real time, dynamically generating an optimal cross-factory scheduling scheme, and simulating the scheduling scheme in the virtual production network for simulation verification and scheme adjustment; and deploying the scheduling scheme to a physical factory, controlling equipment to execute tasks through an automatic control system, and summarizing production data and simulation results for storage and backup. According to the method, the accuracy and efficiency of industrial automation control are remarkably improved, equipment operation data can be processed more efficiently, equipment dynamic behaviors are accurately simulated, a cross-factory optimal scheduling scheme is generated in real time, a closed-loop optimization mechanism is formed through simulation verification and feedback adjustment, the dynamic adaptability and global optimality of the scheduling scheme are achieved, and the scheduling efficiency is improved. Therefore, the resource utilization efficiency is greatly improved.

A method for performing an automated additive manufacturing (AM) process comprises obtaining a current state that includes a set of first AM factors and a set of first characteristics, which are geometric or physical properties of a first AM output are generated using the first AM factors. The current state is provided to a reinforcement learning (RL) framework designed to adjust welding parameters in real-time based on the current state and reward feedback. The RL framework includes an actor network and a critic network, and safety limits are enforced through a custom loss function. The first AM factors are updated based on the critic network's evaluation, resulting in second AM factors used to perform the second AM process and generate a second AM output.

The invention relates to the technical field of industrial automation control, and discloses a high-precision and quick-response industrial thermostat temperature regulation system which comprises a temperature acquisition module, a fluctuation analysis module, a dynamic calibration module, a gradient optimization module, a threshold management module and a regulation execution module. The temperature acquisition module acquires real-time temperature and environment thermal inertia parameters, and sets a temperature adjustment interval; the fluctuation analysis module divides temperature nodes and generates fluctuation feature vectors; the dynamic calibration module extracts a temperature deviation index and establishes an adjustment compensation rule; the gradient optimization module identifies a temperature change grade and calculates a fluctuation density difference value; the threshold value management module deduces an optimal adjustment threshold value and generates a threshold value deviation sequence; and the adjustment execution module is integrated to form a temperature adjustment execution scheme. The system realizes deep analysis and dynamic compensation of temperature fluctuation through cooperation of multiple modules, improves the adjustment precision and the response speed, and is suitable for high-precision temperature control industrial scenes such as chemical engineering and mechanical manufacturing.

The invention discloses an intelligent scheduling and optimization method for an industrial electrical automation system, and the method comprises the following steps: deploying a distributed sensor network to collect electrical parameters, an equipment vibration spectrum and production work order data in real time, and constructing a unified feature vector based on time-space alignment and confidence weighting; a production system-energy management-external power grid three-layer interaction model is established, and a dynamic carbon emission calculation engine and process deadlock detection module is embedded; an improved NSGA-III algorithm is adopted to solve a multi-target Pareto leading edge, and energy consumption, productivity and carbon emission target priorities are adjusted in real time in combination with a dynamic weight mechanism; distributed optimization is executed through an edge-cloud federated architecture, cross-system instruction synchronization is achieved, and closed-loop dynamic feedback is formed. According to the method, the limitation of traditional single system optimization is broken through, the energy consumption is reduced by 15%-30%, the carbon emission intensity is reduced by 12%-18%, the abnormal response speed is increased to 3 seconds, and intelligent decision making and green transformation in a complex industrial scene are supported.

The invention provides a method and system for obtaining optimal parameters of a machine tool based on an industrial mechanism AI data model, belongs to the technical field of industrial machine tool application, and solves the problems that an existing machine tool cannot obtain the optimal parameters in real time, autonomous intelligent control of the machine tool cannot be achieved and the like. Comprising the following steps: multi-dimensional high-frequency data fusion acquisition and monitoring; performing data synchronization and storage management; analyzing and mining data; mixing intelligent decision engines; constructing an industrial knowledge base and a model base; training an industrial knowledge base and a model base; intelligent decision making and optimization; equipment is automatically controlled; user visual interaction is carried out; carrying out on-line adaptive parameter optimization; continuously optimizing and iterating; and the production business system works cooperatively. According to the method, online self-adaptive parameter optimization can be realized, a closed-loop control system with rapid sensing, intelligent decision-making and accurate execution capabilities is constructed, the method develops in the direction of sensing-decision-execution full-link autonomous optimization, and finally autonomous intelligent control of the machine tool is realized.

The invention relates to the technical field of data analysis, provides an equipment state data management method and system applied to an automatic radix astragali seu hedysari refined production line, and is used for realizing equipment health precise prediction and active maintenance through whole-process data monitoring management. The method comprises the steps that a production line equipment state data set is acquired, a production line equipment state feature set is extracted from the production line equipment state data set, and the production line equipment state feature set comprises multi-dimensional time sequence features reflecting the equipment operation trend and topological features of the incidence relation between the equipment; training an astragalus membranaceus essence production line state prediction model based on the production line equipment state feature set, wherein the astragalus membranaceus essence production line state prediction model is used for predicting the equipment abnormal probability and the performance degradation trend in a selected time period according to the current equipment state feature; and generating an equipment management strategy set according to an output result of the radix astragali seu hedysari refined production line state prediction model, wherein the equipment management strategy set comprises production parameter adjustment instructions and maintenance node planning schemes for different equipment maintenance priorities.

The invention discloses a modular reconfigurable production line control system integration method, which relates to the technical field of industrial automation, and comprises the following steps: establishing virtual mapping based on physical attribute parameters to form a production line digital twin basic model framework; collecting data in real time based on an on-site sensor, and establishing a digital twinborn dynamic mapping mechanism synchronous with a physical production line state; a reconstruction scheme is imported into a virtual environment, and key performance indexes are analyzed through a production line digital twin model rehearsal module combination process. A virtual production line model is constructed through a digital twin technology, a rehearsal and verification reconstruction scheme in a virtual environment is supported, trial and error time and cost required by traditional physical debugging are remarkably reduced, a reconstruction strategy is further optimized through a cloud AI algorithm, closed-loop optimization from virtual verification to physical execution is achieved in combination with edge end real-time control, and the real-time performance of the virtual production line is improved. The production line can quickly complete local or overall reconstruction according to production requirements, and the response speed and flexibility of the production line are greatly improved.

The invention provides an intelligent fault diagnosis method, system and device and a storage medium, and relates to the technical field of industrial automation, and the method comprises the steps: obtaining the operation parameters of a plurality of devices in an industrial automation production line, and enabling the devices to be sequentially connected according to a production technology process; inputting the operation parameters of each device into a preset fault detection model to generate a first diagnosis result of each device; obtaining a topological relation between the devices, establishing a device association diagram in combination with a production process flow and the topological relation, and generating association parameters between the devices according to the device association diagram; generating a second diagnosis result of each piece of equipment according to the associated parameters; and combining the first diagnosis result and the second diagnosis result to generate a final fault diagnosis result of each device. The technical effect of the application is that accurate and comprehensive fault diagnosis of the equipment is realized.

The invention discloses a CAD automatic generation system and method based on intelligent model selection and application, and aims at achieving automatic modeling under the multi-modal design requirement. The system comprises a user interaction module for receiving multi-modal input such as natural language, sketch and voice; the intelligent demand analysis module is used for combining an industrial large language model and a product knowledge graph, combining semantic analysis and generating a structured demand; the intelligent model selection calculation module is used for matching the optimal parameter combination and the component list based on a multi-objective optimization algorithm; the CAD automatic generation module calls a parametric modeling engine to generate an editable three-dimensional model; the constraint solving module is used for processing hard constraints and soft constraints in real time and dynamically adjusting model parameters; and the model output and interaction module feeds back a design state and supports user iteration. The system realizes full-process automation from the design intention to the CAD model, improves the design efficiency and accuracy, and is suitable for the fields of mechanical design, intelligent manufacturing and the like.

The invention relates to the technical field of cable production automation control, and discloses a control method, system and device of cable extrusion equipment and the extrusion equipment, the method comprises the steps that sensor parameters are acquired, and the sensor parameters comprise the cable extrusion temperature and the extrusion speed; performing time delay correction on the sensor parameter to obtain a corrected parameter value; inputting the correction parameter value into a pre-trained extrusion speed and extrusion temperature dynamic compensation model to generate an adjustment instruction; and sending the adjustment instruction to equipment to adjust the heating temperature and the extrusion speed of the cable. Compared with the prior art, the method has the following effects that the heating temperature and the extrusion speed of the cable can be accurately adjusted in real time through combined use of time delay correction and an extrusion speed and extrusion temperature dynamic compensation model, and the waiting time and the adjustment time in the production process are reduced, so that the control precision and the production efficiency of cable extrusion equipment are improved.

The invention provides an aluminum alloy surface oxidation spot defect identification method and device based on machine vision, and relates to the field of intelligent manufacturing and industrial automation, and the method comprises the steps: obtaining an aluminum alloy surface color image, and carrying out the preprocessing of the image, so as to extract a brightness component image; self-adaptive threshold segmentation of local contrast enhancement is carried out on the brightness component image, a defect area binary mask is generated, morphological connected domains are extracted according to the mask, and three basic feature indexes of the area pixel value, the contour Fourier descriptor complexity and the area gray scale standard deviation contrast of each connected domain are calculated; and extracting and marking a connected domain boundary, verifying a boundary closed topological structure, and dynamically generating a curvature-driven self-adaptive sampling point through multi-scale B-spline curvature extreme value detection. Through optical-algorithm-process three-level collaborative innovation, the curved surface reflection false alarm rate is reduced, the pinhole detection rate is increased, and the boundary precision is + / -0.2 pixel.

The invention discloses an intelligent film-drawing and unwinding control method and system based on real-time tension, and relates to the field of automatic control and intelligent manufacturing, and the method comprises the following steps: ensuring normal starting of equipment and system states through equipment self-inspection and technological parameter input; tension and speed data are collected in real time and preprocessed, and support is provided for fuzzy PID control; the parameters of the fuzzy PID controller are dynamically adjusted by calculating the tension error and the change rate of the tension error so as to adapt to different membrane material characteristics and unwinding working conditions; an improved genetic algorithm is used for optimizing parameters of a PID controller, overshoot is minimized, the adjusting time is shortened, and the robustness and the anti-interference capability of the system are improved; a closed-loop control system is constructed, and the stability of unwinding tension is ensured through real-time feedback signals; and the system state is monitored in real time in combination with an anomaly detection algorithm, and a control strategy is automatically adjusted or fault protection is performed. The control method disclosed by the invention can be widely applied to the fields of film unwinding and automatic production lines, and has a relatively high intelligent level.

The invention belongs to the technical field of industrial automation control, and particularly relates to a dynamic optimization control method and system for multi-component batching of a paste making machine, and the method comprises the steps: generating a characteristic waveform spectrum corresponding to a residue molecular structure; dynamically generating electric lubricating medium parameters containing the electroosmotic liquid concentration and the release rate; triggering a low-frequency vibration module of the material layer to generate vibration waves, and enabling the solution and the vibration waves to form a molecular-level synergistic stripping effect; calculating a nanoparticle dispersity index, and generating an inverse gradient mixing instruction when the index exceeds a threshold value; activating a hot vortex ring in a spiral flow guide convex pattern area and establishing a directional heat energy migration channel; and verifying the dynamic equilibrium state of the temperature of the paste core area and the viscoelasticity of the target formula, and triggering a phase lock control signal to terminate the parameter adjustment chain when the energy consumption rate reaches a stable interval. According to the invention, the problems that the transition period during formula switching is prolonged and the phase differences among the execution mechanisms are difficult to match in real time due to a traditional staged regulation and control mode are solved.

The invention relates to the technical field of tin smelting, and discloses an AI intelligent tin smelting control system and method, and the system comprises a data collection and preprocessing module, an intelligent decision and control module, and a real-time monitoring and early warning module. The method comprises the following steps: collecting process data in a tin smelting production process in real time, and cleaning the collected process data; extracting and selecting characteristics of which the influence on the smelting quality and efficiency exceeds a preset threshold value, and carrying out standardization processing on the data; receiving the standardized process data, training the model, processing the data monitored in real time, and outputting the adjusted process parameters; various key parameters and data in the production process are displayed in real time, the real-time data are analyzed, abnormal conditions are detected, early warning is carried out on faults, and response measures are automatically triggered. Intelligent and automatic tin smelting is achieved, and the efficiency, quality and safety of the smelting process can be effectively improved.

The invention discloses automatic control equipment for pipe cutting and deburring, and relates to the technical field of pipe fitting machining, the automatic control equipment comprises a workbench, a moving mechanism is arranged on one side of the workbench, and a first rotating mechanism, a first limiting guide mechanism, a cutting mechanism, a second limiting guide mechanism and a second rotating mechanism are sequentially arranged at the top of the workbench; wherein the first rotating mechanism is close to one side of the moving mechanism, and a polishing mechanism is further arranged on the workbench and corresponds to the cutting mechanism. Through cooperation of the moving mechanism, the first rotating mechanism, the first limiting and guiding mechanism, the cutting mechanism, the second limiting and guiding mechanism, the second rotating mechanism and the polishing mechanism, automatic cutting and deburring machining are conducted on pipe fittings with different pipe diameters. And the polishing mechanism is in inclined butt joint with the outer edge and the inner edge of the notch of the pipe fitting, so that extending or hidden burrs on the outer edge and the inner edge are removed.

The invention discloses a furniture processing control system based on artificial intelligence, and relates to the technical field of intelligent manufacturing and industrial automation. According to the system, cutter vibration frequency spectrum, plate texture features and environment temperature and humidity data are collected in real time through a distributed sensor array, multi-source data are fused through a space-time attention mechanism, and a joint feature matrix is generated. And based on the joint feature matrix, utilizing a depth map neural network to deconstruct a topology constraint relation of non-standard customization requirements, and generating an initial processing parameter set. And driving the digital twin model to perform virtual processing according to the initial processing parameter set, and predicting a processing node deformation error and generating a compensation vector in combination with the three-dimensional laser point cloud and infrared thermal imaging data. The compensation vector and real-time working condition data are received, a path cost function is evaluated through Monte Carlo tree search and a time sequence convolutional network, the cutter feeding speed and the cutting depth are corrected, and dynamic regulation and control of cutter path parameters are achieved.

The invention relates to the technical field of automatic control, in particular to an automatic cutting fluid proportioning control system which comprises a flow control module, a delay compensation module, a dynamic adjusting module and a dynamic flow adjusting module. According to the invention, the flow and proportion of the cutting fluid are accurately adjusted, so that the requirements of different production stages are met, the use efficiency of the cutting fluid and the processing quality stability are improved, parameters such as the flow and the temperature are monitored in real time, the flow and the proportion are dynamically adjusted, instability caused by environmental fluctuation or equipment response lag is reduced, accurate supply of the cutting fluid is ensured, and the production efficiency is improved. Aiming at variable production requirements, environment changes are rapidly responded, a matching strategy is automatically optimized, machining errors and equipment abrasion caused by matching imbalance are effectively avoided, the improved control strategy improves the cutting fluid utilization rate, it is guaranteed that the machining quality and the equipment service life are synchronously prolonged, resource waste is reduced, meanwhile, the manual intervention requirement and the operation complexity are reduced, and the production efficiency is improved. And the overall production efficiency and the system stability are improved.

The invention relates to the technical field of industrial automation and intelligent manufacturing, in particular to a rubber tube production line monitoring method and system.The method comprises the steps that multi-mode production data of a production line is collected and preprocessed, a virtual production line model is generated through a dynamic twin model and compared with actual data, and virtual-real difference data is generated; performing anomaly detection on the virtual-real difference data based on a classification algorithm and a rule matching structure, and optimizing parameters of an anomaly detection model; generating abnormal trend prediction data by using the optimized model, and proposing a system correction suggestion according to the prediction data; and dynamically adjusting the operation parameters of the equipment through a closed-loop feedback mechanism. According to the invention, through innovative dynamic twin modeling, deep learning optimization and closed-loop control technologies, real-time monitoring and intelligent adjustment of the production line are realized, and the production efficiency and the product quality are effectively improved.

The invention relates to the technical field of industrial automation control, and discloses a change gear assembly processing technological parameter intelligent optimization system, which comprises a multi-dimensional data synchronous acquisition unit, an angular domain static map construction unit, a phase lead constraint control unit and an angle trigger execution unit, according to the method, a static angle-load topological graph with a main shaft angle as an index is established, a phase lead angle is calculated by combining inherent physical response lag time of a servo system, and a parameter adjustment instruction sequence is directly injected into a servo current loop at a preset angle window before physical load impact occurs; according to the feed-forward control mechanism based on angular domain reconstruction and phase lead constraint, the problem of physical lag of traditional time domain feedback control in response to high-frequency intermittent cutting is avoided, and zero time difference of periodic load impact and even pre-judgment torque compensation are achieved.

In various embodiments, an automated platform may autonomously move around a site, loading dock, dock leveler, and / or trailer to transport goods. In one or more embodiments, the automated platform may be loaded with goods and, once loaded, navigate a path from a loading area onto a trailer autonomously. In some embodiments, once the automated platform is loaded onto the trailer, the automated platform may utilize hydraulics to extend legs to the ground with enough force so that the automated platform wheels are not touching the ground in the trailer, which stabilizes the automated platform while it is being hauled to a different site. In at least one embodiment, once the trailer is at the different site, the automated platform may autonomously unload itself out of the trailer and navigate over a dock leveler, through the loading dock, and to an unloading area.

The invention discloses an MES work order dynamic scheduling and resource self-adaption system based on real-time event triggering, and relates to the technical field of intelligent manufacturing and industrial automation, equipment states and material events are monitored in real time through interfaces such as a PLC and an OPCUA, event types and validity are analyzed through hardware filtering, feature code recognition and double verification, and the real-time event triggering is realized. Event data, MES batch information and equipment load data are integrated to generate standardized scheduling input, a preset strategy is matched, an optimal dispatching scheme is generated through a priority algorithm, material, equipment and logistics resource allocation and dispatching operation are completed, synchronization and MES interaction ensure that page logic is consistent, full-process automation of dynamic work order scheduling is achieved, and work order scheduling efficiency is improved. The equipment state and the material event are responded in real time, the production cooperation efficiency is improved by more than 30%, the resource adaptation accuracy rate reaches 95% or above, manual intervention is reduced, and the consistency of data and MES is guaranteed.

The invention relates to the field of heat energy engineering and automatic control, and discloses a multi-mode optimization system for combustion efficiency of a thermal power boiler. The system comprises a multi-modal data perception and space-time alignment module, a tensor manifold modeling and physical constraint feature extraction module, a space-time coupling dynamic prediction and uncertainty quantification module, a quantum optimization decision and DCS cooperative control module and a combustion state derivative early warning and optimization feedback module. Through multi-modal data space-time alignment, five-order tensor physical constraint modeling, PDE deep network prediction, quantum optimization decision and a closed-loop feedback mechanism, space-time unified fusion and physical feature extraction of combustion data are realized, the reliability of combustion state prediction is improved, an optimal control instruction is efficiently solved, system parameters are dynamically corrected, and the reliability of combustion state prediction is improved. The problems that in the prior art, data fusion is difficult, modeling physical constraints are lacked, optimization real-time performance is poor, and adaptivity is weak are solved, and the combustion efficiency and the intelligent control level of the thermal power boiler are remarkably improved.

Embodied within a realm of transformative innovation, a proactive AI-powered system emerges, seamlessly and autonomously managing the addition of chemical admixtures to control and adjust the composition of concrete during production and transport. The system comprises a concrete mixer tank, reservoirs for the chemical admixtures, a mechanism to dispense them as needed, sensors, and proactive AI-based control system, embodying the pinnacle of the proactive intelligent automation. Sensors track the concrete's properties and environmental conditions, and the proactive AI-based control system analyses the sensor data in real time to discern the precise type, quantity, and timing of chemical admixtures required to maintain the optimal properties of the concrete within the mixer tank, ensuring unwavering fidelity to the desired specifications. This autonomous AI-based system aims to produce concrete with minimal water addition, ensuring consistent concrete quality and reducing reliance on manual intervention.

The invention provides an aluminum film sealing defect real-time detection method and system based on multi-algorithm fusion, and relates to the technical field of intelligent manufacturing, and the method comprises the steps: triggering an industrial camera at a detection station to collect an original image of a pesticide aluminum film sealing on a conveyor belt; performing adaptive equalization operation on the original image through pixel brightness distribution data, eliminating light fluctuation and surface reflection interference, and outputting a standardized image; three types of defect detection are synchronously executed based on the standardized image, a dynamic threshold segmentation algorithm is combined with local area brightness analysis to detect edge damage, a contour extraction algorithm is adopted to calculate bottleneck center offset to recognize seal offset, wrinkle defects are recognized based on a surface texture feature analysis algorithm, and a primary detection result is output. The aluminum film sealing defect detection method is based on multi-algorithm fusion, has strong anti-interference capability, real-time detection performance and data traceability, and provides an efficient and reliable automatic solution for aluminum film sealing quality management and control.

The invention belongs to the technical field of glue filling control, and particularly relates to a glue filling amount control method and system. According to the method, the glue filling deviation can be controlled within an extremely small range through multi-time iteration control of a stage theoretical value and real-time feedback, the filling consistency and the bonding strength are remarkably improved, initial compensation is conducted in combination with various working condition parameters such as temperature, pressure and materials, the pressure and the diffusion dynamic state are monitored in real time, and the working efficiency is improved. The system can automatically adapt to environmental fluctuation and glue performance change, accurately control the filling amount, respond to overflow and viscosity abnormity in time, reduce glue waste, avoid product reworking caused by excess or insufficient glue, combine closed-loop feedback with maintenance strategies, automatically identify and process abnormal states, improve the automation degree and operation stability of a production line, and improve the production efficiency. Manual intervention is reduced, rapid configuration and expansion can be carried out according to different workpiece shapes, materials and production working conditions, and the device is suitable for various glue filling scenes.

The invention relates to a part burr removing technology, in particular to an intelligent control system for removing burrs of machined parts, which is used for solving the problems that manual burr removing operation is poor in stability, parts are easy to damage, part of hidden burrs are difficult to perceive and remove, and the burr removing efficiency is low. Automation of the burr removing process is achieved through a plurality of modules arranged in the program controller, manual intervention is reduced, the production efficiency is improved, the path planning module conducts optimal planning on a burr treatment path according to a burr detection result, the efficiency and effect of burr removing work are improved, and the labor intensity of workers is lowered. The real-time monitoring module can monitor the working state of the deburring tool in real time, it is ensured that the deburring tool operates according to the optimal path, whether the deburring tool can continue to execute deburring work or not can be judged in real time, the whole control system is intelligently controlled through a program, dependence on manual deburring operation is reduced, the production cost is reduced, and the working efficiency is improved. And the safety risk possibly existing when the burrs are manually removed is avoided.

The invention provides a big data bamboo tableware manufacturing system based on production process monitoring, and relates to the field of intelligent detection systems.The big data bamboo tableware manufacturing system comprises an acquisition module, a prediction module, a preliminary distribution module, a disturbance response module, an optimization decision module and an abnormity self-inspection module. A dynamic stability index is calculated in combination with a dynamic image sequence under disturbance response, a rejection target is judged through an optimization decision-making module after scores are fused, a Bayesian method is introduced in the optimization process to dynamically adjust multiple decision-making parameters, a rejection action instruction is generated in real time, and an abnormity self-checking module monitors the rejection rate and score stability. Software and hardware signals are combined to realize multi-level fault diagnosis and system protection control; according to the invention, high-precision identification and dynamic elimination of bamboo tableware defects are realized, and the automation level and the production reliability are improved.

The invention discloses a terminal product surface defect intelligent judgment quality control system and method, and relates to the technical field of industrial automatic detection and intelligent quality control, and the system comprises the following steps: a data acquisition module generates an original detection signal containing environmental interference compensation; the dynamic detection module receives an original detection signal, performs illumination invariance processing through a self-optimization feature extraction network, and outputs a defect feature vector with confidence rating; the quality association module receives the defect feature vector and constructs a three-dimensional association map with real-time equipment state data, and generates a tracing analysis signal containing root cause probability distribution; and the feedback control module analyzes the key process parameter offset in the tracing analysis signal, generates an equipment adjusting instruction and feeds back the equipment adjusting instruction to the production line. The terminal product surface defect intelligent judgment quality control system and method can solve the problems of insufficient surface defect detection precision, difficulty in quality tracing and lack of process closed-loop control in industrial production.