13079 results about "Production line" patented technology

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 discloses a welded pipe conveying abnormity prediction method and system based on large model reasoning, and aims to solve the problems that multi-source data is difficult to align, cross-station false correlation is caused, prediction lacks executable positioning and time sequence, and linkage control reliability is insufficient. Event alignment is carried out by taking a controller edge signal and an encoder zero position as time anchor points, a production line topology semantic graph containing time delay, capacity and interlocking attributes is constructed, and topology reachability and physical time delay constraints are applied in a self-attention long sequence model to carry out multi-step rolling prediction. And outputting a risk probability, refining the risk probability to spatial positioning of a roller way section or a shaft and the minimum executable intervention time, and generating a risk interval in combination with uncertainty estimation and calibration so as to drive an upstream beat self-adaptive speed reduction, shunting or stopping strategy. The technical effects of improving accuracy and interpretability, reducing false alarm and missing alarm, ensuring that linkage can be executed in advance and meeting edge time delay budget are achieved.

The invention discloses a method and device for automatically grabbing detonators. A device for grabbing the detonator is arranged on a middle blocking production line or an opening closing production line of the detonator; a group of expansion heads are arranged at the bottom of the device; the opening of the detonator is expanded and clamped by the expansion heads through the lifting of a cylinder control device and the deformation of the expansion heads to realize the purpose that the detonator is automatically taken out from a detonator filling mould. In the invention, the expansion heads controlled by machinery are adopted, and the opening of the detonator is expanded and clamped after the expansion heads are deformed, so that the detonator is automatically taken out from the detonator filling mould. A plurality of expansion heads are arranged at the same device, thus a plurality of detonators can be taken out by one device at one time, thereby greatly improving the production efficiency. The method provided by the invention is simple; and the adopted device is easy to manufacture, and has a good using effect.

The invention belongs to the technical field of multi-stage centrifugal pump production, and particularly relates to an automatic production line for assembling a pump cover of a multi-stage centrifugal pump, which mainly comprises a lifting type turnover assembly, a first extrusion assembly, a second extrusion assembly, a strake pushing manipulator and an inner supporting piece feeding assembly, for a reinforced pump cover composed of a pump cover piece, an inner supporting piece and an outer hoop piece, a pump cover blank is adsorbed through a lifting type overturning assembly and is driven to overturn around the axis of the pump cover blank, and a first extrusion assembly and a second extrusion assembly are jointly used for sequentially extruding, forming and machining the pump cover blank into a pump cover semi-finished product and the pump cover piece; the second extrusion assembly is matched with the strake pushing mechanical arm to additionally install multiple circles of outer strake pieces on the outer side of the pump cover piece, the inner supporting piece feeding assembly is used for additionally installing an inner supporting piece on the inner side of the pump cover piece which moves to the second station, in this way, automatic assembling of the reinforced pump cover can be achieved, the reinforced pump cover keeps the advantage of light weight, and meanwhile the assembling efficiency is improved. The compressive strength and the overall rigidity are qualitatively improved.

The invention discloses an automobile production line gluing system based on visual inspection, and relates to the technical field of industrial automation and intelligent control, and the automobile production line gluing system comprises a gluing data acquisition module, a gluing feature extraction module, a compensation correction module, a gluing decision module and a closed-loop management and control module; the gluing data acquisition module preprocesses assembly line gluing track images and glue gun nozzle three-dimensional coordinate data, the gluing feature extraction module constructs a gluing feature sequence table, the compensation correction module outputs glue outlet opening degree compensation amount and nozzle track correction amount, and the gluing decision-making module outputs glue gun pressure adjusting amount and nozzle movement speed. And the closed-loop management and control module detects pressure abnormity and speed stability in real time, optimizes control parameters and generates a process quality map. Through real-time visual detection and dynamic closed-loop control, the glue line width precision is improved, the track compensation response is shortened, the glue gun speed fluctuation coefficient and glue waste are reduced, the gluing qualification rate is improved, the production cost is reduced, and the automobile manufacturing and production requirements are met.

The invention provides a high-speed lamination production line product quality detection system based on visual identification, and belongs to the technical field of visual detection. Comprising a multispectral image acquisition module (visible light, near infrared and ultraviolet band synchronous imaging), a motion compensation module (encoder synchronous correction displacement deviation), a preprocessing module (denoising, enhancement and ROI coarse positioning), a multi-scale feature fusion module (geometric, texture and spectral feature fusion) and a multi-task deep learning model. And a production line is linked to realize hierarchical control (sudden stop, early warning and parameter adjustment). The system eliminates image dislocation and blurring under high-speed motion through a multispectral information complementation and motion compensation technology; the recognition capability of tiny and complex defects is improved by utilizing multi-scale feature fusion; detection and decision-making integration is realized through a multi-task model, a high-precision and low-delay quality detection effect in a high-speed scene is finally achieved, and the quality control level of a lamination production line is effectively improved.

The invention relates to the technical field of automatic production control and industrial data processing, in particular to a PLC-based gypsum-based flame-retardant plate production line control system and method, and the system comprises a data collection module which collects sensor data in real time; the ideal state construction module is used for constructing a pure ideal state vector; the disturbance simulation module is used for generating a theoretical damaged state vector corresponding to a specific fault mode; the difference calculation module is used for generating real and theoretical deviation vectors; the coupling verification control module is used for calculating a similarity value between the actual deviation vector and the theoretical deviation vector; if the similarity value is larger than a preset judgment threshold value, it is judged that a real physical fault exists at present, and a PLC precise compensation instruction is generated and sent to a production line execution mechanism; otherwise, judging that non-physical noise exists at present, keeping the control parameters of the production line unchanged, and starting a dynamic filtering program; according to the method, the problem that physical faults and sensor clutters are difficult to distinguish in a high-noise environment is effectively solved, and missing report is avoided.

The invention provides a production energy efficiency optimization method and system based on industrial big data, and the method comprises the steps: generating an industrial production data set and creating an industrial knowledge graph according to multi-dimensional operation parameters, energy consumption state data and production line constraint information generated by a target factory, mining a causal association relationship among the multi-dimensional operation parameters through ontology reasoning analysis to generate a causal association path; executing a sequential association rule mining operation on the energy consumption state data to obtain association rule mining information of energy consumption fluctuation and operation parameter change, and matching and fusing the association rule mining information and a causal association path to generate a candidate root cause set of energy efficiency abnormality; inputting the candidate root cause set into a bidirectional long short-term memory network, positioning root cause information of energy efficiency abnormity through time dimension relevance modeling and spatial dimension feature reinforcement, and finally generating energy efficiency optimization guidance containing parameter adjustment priority and process optimization suggestions. The accuracy of energy efficiency anomaly root cause positioning and the pertinence of optimization measures are improved.

The invention relates to the technical field of smart factory decision-making, and discloses a digital twin-driven smart factory AI intelligent decision-making system, which comprises a data acquisition and preprocessing module used for acquiring and preprocessing real-time and historical data of a production line; the twinborn modeling identification module is used for constructing a twinborn body and carrying out parameter identification and uncertainty quantification; the alignment evaluation credible module is used for comparing twin prediction data with real data and generating availability marks and trust scores; the strategy simulation optimization module is used for generating candidate strategies and risk evidences based on the constraints and the key performance indicators; the grayscale online publishing module is used for screening and grayscale publishing a strategy according to the trust score and the performance result; the operation evaluation auditing module is used for collecting operation data and generating an auditing packet; and the drifting detection updating module is used for detecting distribution drifting and recalibrating and updating the twinborn body and the strategy library. According to the invention, the reliability, robustness and continuous optimization of the production decision of the smart factory are realized.

The invention discloses a quality detection method and system for vehicle safety belt production and a storage medium, and relates to the field of manufacturing quality control of automobile passive safety parts. The method aims at solving the problems that an existing detection means is weak in tiny defect recognition capacity, insufficient in defect evolution modeling and lack of intelligent feedback regulation and control. According to the method, multiple detection means such as medium-wave infrared thermal imaging, laser confocal microscopy, structured light three-dimensional reconstruction and capacitance sensing are fused, and multi-mode defect sensing of key parts such as a braid, an insertion area and an adhesive layer is achieved; deducing a defect evolution process and generating a high-credibility pseudo label by constructing a thermal-stress-rheology coupling finite element model; the graph neural network and the AI model are combined to complete defect type identification, risk quantification and grading, and automatic optimization of process parameters and production line control response are realized based on the quality grade, so that the detection precision, the response efficiency and the product yield are remarkably improved.

The invention relates to the technical field of control or regulation systems, and discloses a cooperative control method for a photovoltaic intelligent manufacturing equipment production line, and the method comprises the steps: a control unit collects the stock and change rate of materials in a physical cache region in real time; establishing and mapping the physical cache region into a virtual viscoelastic dynamic model with non-Newtonian fluid characteristics; calculating a virtual elastic restoring force enabling the stock to return to a balance point and a virtual viscous damping force preventing the stock state from changing based on the model; wherein an asymmetric anisotropic damping generation strategy is executed, and a damping coefficient is dynamically split according to a material flowing trend; and finally, superposing the virtual adjustment correction after vector synthesis to the basic transmission speed to generate a dynamic speed instruction, and by constructing a virtual dynamic field with rheological characteristics and an asymmetric damping mechanism, the problem of nonlinear cascade oscillation in discrete logistics transmission is solved, and differential self-adaptive suppression of accumulation and evacuation risks is realized.

The invention discloses a lens production defect detection method and equipment thereof, and relates to the technical field of visual inspection.The equipment comprises an information acquisition module, a central processing unit, a process regulation and control module and an interactive communication module.The information acquisition module acquires multi-source sensing information at regular time, so that both comprehensiveness and accuracy are improved; high accuracy of visual detection is guaranteed; through an information processing model, quantification is accurate and traceable, defect degree visualization and defect accurate classification are realized, then process defects are positioned, the interference degree of the environment on a detection result is determined, and misjudgment of the defects due to environmental factors is avoided; through linkage of defect data, process precision and environmental interference, a process optimization scheme is generated in a targeted manner, closed-loop response from defect generation to process adjustment is realized, and the production line response speed is improved; and finally, the lens production yield is remarkably improved, and the production cost of long-term detection is reduced.

The invention discloses a material weighing and conveying control method and system, and relates to the technical field of self-adaptive control systems. The method comprises the following steps: setting a visual positioning identifier in an unloading area, and configuring an identification code for a material; establishing an information management database, associating the identification codes of the materials with material attributes, and generating a material association attribute table; collecting an identification code of the material, and calling a material association attribute table based on the identification code; and based on the information management database, obtaining a production line state table of each production line and a warehouse state table of the current warehouse, generating a candidate transportation list, determining a comprehensive priority score of each candidate transportation target point, and selecting the candidate transportation target point with the highest score as a final transportation target point. Compared with the prior art in which fixed rules are used for scheduling, the material transportation targets can be dynamically distributed according to real-time production requirements, manual intervention is avoided, the logistics efficiency is improved, high-requirement materials are preferentially distributed, and production interruption caused by material shortage is prevented.

The invention relates to the technical field of industrial process automatic control, and discloses an intelligent manufacturing flexible production line cooperative beat control method. The method is used for solving the technical problem that dynamic adjustment of the real-time rhythm of the flexible production line is difficult to achieve under the extreme uncertainty condition in a traditional method. The method comprises the following steps: firstly, collecting operation data and external uncertainty signals of each station of the flexible production line, and carrying out preliminary integration through a central control module; classifying the data, and distinguishing internal deviation and external interference according to priorities; constructing a dynamic mapping relation based on a classification result, and associating uncertain factors to beat parameters to form a temporary adjustment framework; correcting station beat parameters step by step by using a frame, and transmitting an instruction from upstream to downstream; synchronously monitoring a response state during correction, and returning information to update a mapping relation; the updated mapping is applied to acquisition and processing of the next period to form closed-loop iteration; according to the method, the problem of real-time rhythm dynamic adjustment under the extreme uncertainty condition is solved.

The invention provides a production line equipment fault prediction method and system, and relates to the technical field of fault prediction.The method comprises the steps that multi-source time sequence signals are synchronously collected, a multi-dimensional health feature sequence is extracted, the comprehensive sensing capacity covering multiple states of equipment is established, and further, the fault prediction efficiency is improved. By selecting a key health feature sequence for each device and performing linear fitting to quantify the performance degradation severity, accurate description and early recognition of the performance degradation trend of the device are realized, and in addition, the causal influence intensity between the devices is quantified by adopting a transfer entropy algorithm, so that the accuracy of the performance degradation trend is improved. Scientific modeling and visual analysis of a fault propagation path and a linkage effect in a production line are realized, and a comprehensive risk index is generated through weighted fusion of performance degradation severity and a global impact factor, so that a comprehensive risk assessment index is formed. Based on the cumulative failure probability function, the comprehensive risk index is converted into a fault probability curve changing along with time, a visual mathematical expression of the risk trend is formed, and a scientific basis is provided for decision making.

The invention relates to a PCB production line process parameter intelligent matching method based on feature space mapping, and the method comprises the steps: collecting and fusing the material attributes, structure parameters, sizes and historical process records of a plurality of batches of PCB products, carrying out the normalization preprocessing, removing abnormal data, and constructing a high-quality feature matrix; after multi-dimensional feature expression is realized by utilizing a multi-scale embedded network, a mapping relation between features and process parameters is dynamically learned on the basis of an adaptive space mapping network in combination with a soft constraint multi-objective loss function, and gradient cutting, step length adjustment and a disturbance elasticity pool mechanism are introduced in a parameter recommendation process to guarantee convergence and stability. After the parameters are implemented, feedback data are collected in real time, periodic iteration distillation optimization and loss function recalibration are carried out, and finally a fine-tuning parameter recommendation scheme marked with conflict indexes, confidence intervals and weight suggestions is output for an engineer. According to the scheme, the intelligence, the traceability and the field adaptation capability of parameter recommendation are improved.

The invention provides a production line scheduling method and equipment fusing edge computing and machine learning, and belongs to the technical field of production line scheduling, and the method comprises the steps: deploying edge nodes in an industrial production line, and carrying out the life cycle management and control of state monitoring, task issuing, resource configuration and version management on the edge nodes by a cloud; the edge nodes collect production line data in real time; constructing a scheduling model at the cloud, and issuing the scheduling model to the edge node; outputting execution task priority judgment and path and resource optimization in combination with the scheduling model, generating a scheduling instruction, and issuing the scheduling instruction to a field control system to execute task scheduling; recording all scheduling processes and state changes, and synchronizing to the cloud after the network is recovered; according to the invention, the analysis rule and the model parameters can be adjusted according to the actual working condition and the monitoring scene of the equipment, accurate description and abnormal early warning of the operation state of the equipment are realized, and the efficiency, the accuracy and the adaptability of equipment monitoring in the industrial Internet of Things environment are effectively improved.

The invention discloses a refined disassembly and recovery production line for retired lithium batteries. Comprising an automatic battery feeding station, a battery pretreatment station, a battery discharging station, an electrolyte collecting station, a battery shell disassembling station, a roll core film removing station, a pole piece separating station, a pole piece conveying station, a crushing feeding station, a crushing station, a crushing conveying station, a first-stage separating station, a high-temperature drying station, a drying discharging station and a second-stage separating station. The automatic battery feeding station is connected with the battery pretreatment station, and the battery pretreatment station is connected with the battery discharging station. The refined disassembly and recovery production line for the retired lithium battery is provided with deep discharge equipment, the safety problem of electrified crushing or manual disassembly is avoided, full-process automation is achieved, the recovery efficiency is high, refined disassembly and recovery are achieved, an aluminum shell, a positive plate, a negative plate and a diaphragm are separated, the recovery rate and purity of valuable metal are improved, the production line process is short, and the production line cost is low. And no secondary pollution is generated.

The invention relates to the technical field of quality detection, in particular to a gas atomization powder sphericity degree multi-dimensional online analysis system and a method thereof.The gas atomization powder sphericity degree multi-dimensional online analysis system comprises a dynamic dual-mode imaging module, a transverse wind field auxiliary detection unit and a multi-dimensional feature fusion analysis module; the transverse wind field auxiliary detection unit is used for applying controllable transverse wind power through an airflow nozzle orthogonal to the powder falling direction and measuring the deflection track of gas atomization powder ball particles in combination with a laser displacement sensor. The online calibration module is used for spraying standardized spherical particles to the powder flow according to a preset period; the traditional density detection depends on destructive sampling and off-line measurement, the efficiency is low, and the whole production line particles cannot be covered; through an orthogonal wind field trajectory inversion technology, a density value and an internal defect mark are synchronously output through non-contact dynamic measurement, 100% lossless online full inspection of a production line is realized, and sampling limitation and aging bottleneck of a traditional means are broken through.

The invention relates to the technical field of state monitoring, in particular to an SMT production line analysis method and system based on real-time data acquisition, and the method comprises the following steps: calling a multi-device operation log to judge a device connection relation, analyzing a track gauge and pressure change to mark a structure turning point, recognizing a path code of rate offset temperature control, and generating a direction sequence; the method comprises the following steps: establishing a device connection relation based on a time sequence and intervals of a running log, dynamically determining a process link, analyzing an abnormal signal sequence to generate a path identifier, matching a migration direction according to a state score change to predict a fault trend, and generating a fault trend prediction result. Marking a structure turning point based on direction combination of track gauge and pressure and packaging a structure section, judging a dominant path behavior based on direction combination of rate offset temperature control and a repetition condition, positioning a main attribution chain starting point based on abnormal signal starting and a coverage section, and outputting fault evolution prediction based on scoring sequence and migration direction matching.

The invention discloses an ECU assembling and detecting production line for an automobile and a process of the ECU assembling and detecting production line. The ECU assembling and detecting production line comprises a circuit board feeding mechanism, an implanting mechanism in butt joint with the circuit board feeding mechanism, a detecting mechanism in butt joint with the implanting mechanism and a classifying and packaging mechanism in butt joint with the detecting mechanism. By means of the mode, circuit board feeding, injection molding embedding, injection molding material taking, detecting and classified packaging are integrated, the automation degree is high, secondary positioning and accurate positioning of the circuit board can be achieved, the horizontal position and angle of the circuit board can be automatically adjusted, and the injection mold can be aligned in a self-adaptive mode; each performance of a product can be synchronously detected, and code printing and identification can be carried out without repeatedly carrying and positioning the product and classifying and collecting defective products and non-defective products in different mold cavities, so that subsequent searching for reasons of the defective products is facilitated, and later maintenance of equipment is facilitated.

The invention relates to an agricultural microbial preparation production line multi-device collaborative control method and system, and the method comprises the steps: calculating a device health score based on operation state information, combining the device health score with a biological risk index to generate a task release score, and generating a production task instruction when the task release score meets a preset release threshold value, constructing a cooperative interlocking matrix between the multiple types of process equipment and the material handling equipment based on the production task instruction, distributing a control execution instruction to the multiple types of process equipment and the material handling equipment, respectively obtaining equipment feedback state information and material circulation information, executing consistency verification on the equipment feedback state information and the material circulation information, and sending a verification result to the material handling equipment; a consistency verification result is obtained, during manual modification, a revision audit chain corresponding to the control event information is generated, and when the running state information or the control event information triggers a preset abnormal threshold value, a disposal control instruction is generated. The method has the effect of improving the production safety.

The invention discloses a seamless steel tube production line real-time monitoring system, which relates to the technical field of real-time monitoring, and comprises the following steps: collecting multi-source heterogeneous state data of a multi-channel sensor, synchronously caching the multi-source heterogeneous state data in an edge monitoring node, and generating a standardized state data sequence; key energy feature dimensions are extracted according to the standardized state data sequence, energy mapping indexes are constructed, and a dynamic soft threshold boundary of monitoring parameters is generated based on historical production samples; and comparing the dynamic soft threshold boundary with a real-time observation value at a time point corresponding to the dynamic soft threshold boundary in the standardized state data sequence, extracting a preliminary anomaly mark, and screening out misjudgment anomalies by using a pseudo anomaly shielding mechanism to generate a credible anomaly layer. According to the method, a prediction driving method is adopted, and an optimized production parameter adjustment instruction is generated according to a dynamic risk grade grading interface and a standardized state data sequence, so that parameter adjustment strategy reasoning taking a risk evolution trend as a core drive is realized.

The invention discloses an industrial equipment defect detection method based on multi-modal feature fusion and dynamic optimization, and belongs to the technical field of computer vision, and the method comprises the steps: 1, multi-modal industrial data collection: deploying multiple sensors in a production line, collecting data in multiple periods, constructing a defect-free and multi-type defect sample library, and carrying out multi-modal industrial data collection; a time-space aligned multi-modal label is marked; step 2, data enhancement and defect synthesis; step 3, multi-modal hybrid model training: constructing a hybrid network, and performing pre-training and fine tuning by using a dynamic loss function; step 4, edge end dynamic optimization and deployment: edge end reasoning is realized through dynamic knowledge distillation, and model fine tuning is automatically triggered when false detection and missing detection are found; and step 5, intelligent labeling and result visualization: a front-end interface displays a detection result in real time. The problem that a current target detection framework is not high in small target recognition accuracy and low in efficiency is solved, and the reliability of industrial equipment defect detection is improved.

The invention relates to a copper-clad plate production line collaborative regulation and control method based on digital twinning, and the method comprises the steps: constructing a multi-level data set of dynamic virtual-real mapping through full-process digital twinning modeling, process data normalization and multi-level feature tagging, achieving the high consistency of a physical production line working condition and a simulation system, and achieving the collaborative regulation and control of a copper-clad plate production line in combination with an autonomous scheduling intelligent agent. Real-time perception and joint feature mining of multi-dimensional data such as equipment load, health degree and energy consumption are realized, the real-time adaptive scheduling capability of a production line to states such as sudden load and equipment aging is improved, scheduling target weights are periodically and adaptively generated, optimal instant balance of multi-target income is realized, and the optimal real-time scheduling capability of the production line is realized by adopting an evolutionary game and swarm intelligent optimization. A cross-device optimal resource scheduling scheme under the multi-target constraint is obtained, an evolution model is continuously fed back through real-time production data, a scheduling-execution-calibration-rescheduling closed-loop mechanism is formed, and the self-learning and long-period stability capabilities of a scheduling system are remarkably improved.

The invention discloses a glass fiber particle assembly line-based magnetic suction quick-release vibration disc system and a detection method, and relates to the technical field of production and processing, the system comprises a data acquisition module for acquiring data of a vibration light-emitting chassis, a magnetic suction column, an assembly line and particles by means of a distributed sensor network, and transmitting the data to an intelligent self-adaptive control module after signal processing; the intelligent self-adaptive control module realizes intelligent dynamic regulation and control of the parameters of the magnetic suction quick-release vibration disc, optimizes the production process of the glass fiber particles, and automatically generates an accurate control instruction based on a data analysis result by integrating multi-source information and performing deep fusion and analysis, so that the production efficiency of the glass fiber particles is improved. Parameters such as the vibration frequency and amplitude of the vibration light-emitting chassis and the magnetic attraction strength of the magnetic attraction column are adjusted to meet the requirements in different production states, so that the system can quickly respond to changes in the production process, defective particles are reduced, and the production efficiency is improved.

The invention provides a multi-AGV bidirectional channel dynamic avoidance method and system based on conflict prediction, and relates to the field of industrial logistics automation control, when a vehicle-mounted laser radar of a first AGV detects an opposite second AGV in a preset fan-shaped early warning interval, double-AGV priority quantification is carried out, avoidance role distribution is carried out according to a quantification priority comparison result, and an avoidance AGV is positioned; and the way-giving AGV receives the standard driving track of the non-way-giving AGV, calculates a smooth way-giving track, and carries out two-way traffic conflict dynamic resolution. The technical problem that the logistics efficiency of a high-beat precise assembly production line is seriously restricted due to the fact that the accuracy of collision conflict prediction and avoidance processing and the response timeliness are insufficient due to the fact that the autonomous collaboration of multi-AGV passing control is weak in a narrow industrial channel in the prior art is solved. Full-autonomous cooperative passage control of multiple AGV clusters in a narrow industrial channel is realized, and zero-interruption execution of high-value logistics tasks is ensured.

The invention discloses an industrial PCB cooperative control method based on open-source and Feiteng CPUs, particularly relates to the technical field of industrial automatic production line control, and is used for solving the problem that a quality control instruction and a board card physical flow in an existing PCB production line are difficult to accurately synchronize, so that an adjustment instruction is lagged or mistakenly acted. A unique identifier is generated for each printed circuit board, the position of each printed circuit board is tracked in real time, and when a board card enters a synchronous control area, a Feiteng CPU obtains the state of each execution device; selecting a data query strategy based on state consistency, and collecting node state feedback and quality data; according to the method, the consensus convergence rate and the phase-frequency characteristic distortion index of the system are calculated, the triggering time point of a control instruction is dynamically adjusted when the index is abnormal, and a correction instruction is sent to execution equipment through an open source gap distributed soft bus, so that high-precision cooperative control of the instruction and board card flow is realized, and the real-time performance and reliability of closed-loop quality control are improved.

The invention relates to the technical field of surface defect detection, in particular to a hardware surface defect online detection method based on multi-modal sensing fusion, which comprises the following steps: step 1, arranging multi-modal sensor groups above and on the side of a conveyor belt of a hardware production line; 2, preprocessing the multi-modal sensor data acquired in the step 1; 3, extracting features from the multi-modal sensor data preprocessed in the step 2; 4, performing reliability evaluation on the multi-modal features extracted in the step 3; 5, generating a fusion feature vector, wherein adaptive weighted fusion adopts a weighted feature splicing method; and step 6, outputting defect types and position information to a production line control system. The combined use of the multi-modal sensor can comprehensively detect the surface of the hardware from multiple dimensions, and the accuracy and stability of the system are further improved through reliability evaluation and weighted fusion.

The invention relates to a circuit board on-line defect detection method and system, and the method comprises the steps: carrying out the synchronous collection and structural integration of multi-source technological parameters such as production line environment temperature and humidity, equipment operation states, material batches and the like, and defect detection images, and achieving the construction of large-sample original data in a production process; through standardization and de-noising preprocessing, multi-modal features are fused, and a distribution mapping model of process and defect features is established by using algorithms such as mutual information analysis and principal component analysis. Based on a feature distribution model and real-time data, a dynamic anomaly detection threshold is adaptively generated, Bayesian inference and evidence reasoning are combined, multi-level confidence levels and risk response suggestions are output, and self-learning evolution of the model and the threshold is realized through a closed-loop feedback mechanism. According to the scheme, the accuracy of anomaly detection, the response timeliness and the risk disposal intelligent level are improved, the method adapts to complex working conditions and batch changes, and the closed-loop optimization and safety control capability of the production process is remarkably enhanced.