4826 results about "Manufacturing line" patented technology

The invention provides a puree production line microbial pollution risk real-time monitoring and early warning method, which comprises the following steps: deploying multiple types of sensors, an industrial camera and an operation log interface, collecting environmental temperature and humidity, pH value, dissolved oxygen, image and operation behavior multi-modal data in a fermentation tank in real time, and carrying out edge calculation and standardized preprocessing to obtain the microbial pollution risk of a puree production line. Data missing, abnormity and time sequence difference are eliminated; multi-modal features and a dynamic knowledge graph constructed based on a production process and a pollution event are fused, and a deep learning model and a graph neural network are utilized to realize microbial pollution risk probability intelligent prediction; risk grading and a dynamic weighting algorithm are introduced, grading early warning signals and disposal suggestions are automatically generated, and a production control system is linked to execute response measures; according to the subsequent production state, continuous feedback is carried out, the model and the early warning threshold are adjusted in a self-adaptive mode, and the accuracy, response efficiency and production safety of pollution early warning are remarkably improved.

A full-automatic packaging scheduling method based on a multi-objective optimization algorithm, which method relates to the technical field of intelligent production scheduling in packaging workshops. The method comprises: on the basis of work orders to be packaged of a production line, extracting production data of the production line, and initializing parameters; constructing a multi-objective optimization model of the packaging production line; to address the problems of premature convergence and susceptibility to local optima in an INSGA-II algorithm, using the INSGA-II algorithm to improve an initialization method, crossover and mutation strategies and crossover and mutation factors, and performing solving on the basis of objective functions such as minimizing the maximum makespan, minimizing the maximum energy consumption and minimizing the total machine load, so as to generate an optimized scheduling scheme; and starting packaging operations on the basis of the generated optimized scheduling scheme. The method can increase the unit-time production capacity of a production line, and effectively solve the problems in packaging production scheduling, thereby reducing the production costs of enterprises and improving the packaging production efficiency.

The invention relates to the technical field of production process intelligent regulation and control, in particular to an intelligent regulation and control method and system for an instant gelatin production process, and the method specifically comprises the following steps: firstly, collecting process parameters such as reaction kettle temperature, acid-base concentration and the like in a production line sensor and control system to form production batch data; then, an optimization model and an intelligent regulation and control strategy are constructed, a dynamic incidence matrix is constructed through time-varying mutual information entropy, multiple types of features are fused to generate a process state descriptor, a constraint dominating relation and a population initialization strategy are optimized, and a self-adaptive genetic manipulation adjustment mechanism and a state-guided search and constraint processing method are designed; iteratively executing an improved NSGA-II algorithm, obtaining an optimized Pareto solution set, and evaluating diversity; and finally, selecting an optimal solution through a multi-criterion decision and a dynamic regulation and control strategy, and converting the optimal solution into an executable process parameter set value. According to the method, the accuracy and the high efficiency of the production process can be improved by realizing intelligent optimization regulation and control of the process parameters.

The invention relates to the technical field of intelligent manufacturing, and discloses an industrial processing production line adaptive control method and system based on reinforcement learning. The method comprises the following steps: collecting a processing monitoring signal and environment state data to construct a comprehensive processing state vector; deducing the comprehensive processing state vector by using a preset depth time sequence learning model to obtain an error evolution trend vector; determining an initial strategy parameter and a strategy matching state according to the error evolution trend vector; according to the strategy matching state and the initial strategy parameter, parameter iteration optimization based on a preset comprehensive error prediction model is carried out, and a target compensation control quantity is obtained; machine tool dynamics constraint verification is carried out according to the target compensation control quantity, and a final compensation instruction set is obtained; and performing instruction mapping and execution according to the final compensation instruction set to obtain processing feedback data, and performing reinforcement learning updating on the model based on the processing feedback data. According to the invention, high-precision self-adaptive compensation under complex dynamic working conditions can be realized.

The invention relates to the technical field of power battery manufacturing, in particular to a reconfigurable island type production line system and process of a side face liquid cooling battery module. The system comprises a battery cell grouping module, a module splicing module, a liquid cooling plate processing module and a Block stacking module which operate independently, and a guide carrying system flexibly connected with the modules, and the number and position layout of the modules can be reconstructed according to production requirements. The corresponding production process comprises the following steps: in a reconfigurable island type production line system, flexibly transferring materials through a guide carrying system, and sequentially executing battery cell grouping, module splicing, side surface liquid cooling plate processing and Block stacking. The technical problems that a traditional linear production line is large in occupied area and poor in flexibility, the overall situation is affected by local faults, and the reconstruction cost is high are solved, space is saved, and cooperative improvement of production flexibility and system reliability is achieved.

The invention discloses an automatic copper pipe tooth rolling production line, and belongs to the technical field of copper pipe tooth rolling machining. The gear rolling production line comprises a plurality of gear rolling machines, a feeding line and a feeding assembly. The feeding assembly comprises a feeding conveying mechanism, a jacking distributing mechanism, a feeding buffering mechanism and a longitudinal conveying mechanism. The feeding conveying mechanism comprises a feeding frame and a plurality of feeding conveying units, the feeding temporary storage mechanism comprises a feeding temporary storage frame, and a temporary storage part of the feeding temporary storage frame and a bearing surface of a discharging guide part are obliquely arranged; the jacking and distributing mechanism comprises a jacking and distributing unit, and the jacking and distributing unit comprises a first-stage jacking frame and a second-stage jacking frame. The position layout of the tooth rolling machines is reasonable, the space of a plant workshop is fully utilized, the feeding requirements of multiple tooth rolling machines can be met by arranging the feeding assembly on one layer and one feeding line in the workshop, the production rhythm of the multiple tooth rolling machines can be met, copper pipes are stable, reliable and high in conveying efficiency in the conveying process, the falling risk is avoided, and the production efficiency is improved. And the safety of equipment and personnel is ensured.

The utility model provides a powder assembly production line for eye shadow production, which comprises a support, a conveying mechanism arranged on the support, a plurality of eye shadow boxes arranged on the conveying mechanism in a linear array, and a plurality of powder assembly work stations arranged along one side of the conveying mechanism in a linear array. The powder assembling work station comprises a cabinet, a mechanical arm mechanism arranged in the cabinet, a conveying assembly arranged close to the mechanical arm mechanism and an upper computer arranged on the cabinet, and eye shadow powder blocks are arranged on the conveying assembly; the conveying mechanism is controlled through a speed regulator, the conveying assembly is controlled through a PLC, the mechanical arm mechanisms are all connected to the upper computers through doMot ion controllers, and the conveying mechanism and the upper computers are all connected to the main computer. According to the powder combining production line for eye shadow production, eye shadow powder blocks of different colors and types can be automatically placed in the eye shadow box, time and labor are saved, the production efficiency is greatly improved, and the production cost is reduced.

The invention relates to the technical field of production line control, and discloses a drinking water production line water quality real-time monitoring and intelligent adjusting system based on the Internet of Things. Registration equipment is unified with units, and NTP time synchronization is performed to eliminate deviation; the shift / CIP calibrates the zero range, and the EWMA monitors the drift weight drop; constructing state vectors containing IDs / batches, and performing auditing and warehousing; median / bilateral filtering denoising is carried out, and grid interpolation is unified; sliding window multi-scale aggregation is carried out, and self-adaptive sampling is carried out according to curvature and events; normalizing and interpolating a near-time training window; estimating indexes difficult to measure by a soft sensor, and depicting residual chlorine by a mechanism model; and carrying out uncertainty weighted fusion, and outputting estimation and intervals and auditing. According to the method, the whole process of real-time monitoring, quick response and traceable compliance is realized, the risk caused by chemical shortage or ratio error and environmental protection constraint is remarkably reduced, the capacity loss caused by cleaning and plan conflict is reduced, and the factory water quality stability and the treatment transparency are improved.

The invention relates to a full-automatic insert injection molding production line and a working method thereof. The full-automatic insert injection molding production line comprises a feeding mechanism, a transfer positioning mechanism is arranged beside the feeding mechanism, and an injection molding machine is arranged beside the transfer positioning mechanism; a first transfer mechanism is arranged between the feeding mechanism and the transfer positioning mechanism, a second transfer mechanism is arranged between the transfer positioning mechanism and the injection molding machine, a discharging conveying belt is arranged beside the second transfer mechanism, and a discharging detection mechanism is arranged at the feeding end of the discharging conveying belt. By arranging the first transfer mechanism, the second transfer mechanism, the feeding mechanism and the transfer positioning mechanism, multiple types of inserts can be supplied to the injection molding machine at the same time, so that full-automatic and high-precision production is achieved, the production efficiency is effectively improved, the production takt is compact, and the production period is shortened; and meanwhile, the transfer positioning mechanism and the second transfer mechanism can accurately position the insert, so that the insert is prevented from deviating when being embedded into the mold, and the yield is improved.

The utility model discloses an electromagnetic induction heating module, heating equipment and a slab production line, and relates to the technical field of induction heating. The heating module comprises a shell, a coil, and a first magnetic conductive assembly, a second magnetic conductive assembly and a third magnetic conductive assembly which are sequentially arranged along the length direction of the coil, the first magnetic conductive assembly and the third magnetic conductive assembly respectively correspond to two side parts of a to-be-heated object, and the second magnetic conductive assembly corresponds to the middle part of the to-be-heated object; the first magnetic conductive assembly and the third magnetic conductive assembly each comprise a first magnetizer which is matched with the coil and can move up and down, and the second magnetic conductive assembly comprises a second magnetizer which is matched with the coil and is fixed. By adjusting the gap between the first magnetizer and the to-be-heated object, the heating temperature of the area corresponding to the to-be-heated object is adjusted, and the temperature uniformity of the to-be-heated object in the width direction is achieved.

The invention relates to the technical field of industrial control, and discloses a soda water production line intelligent control method and system based on the Internet of Things, and the method comprises the steps: obtaining a backpressure valve reference pressure value according to the obtained real-time dynamic data through employing a pre-constructed carbonation digital twin model, and calculating the backpressure valve reference pressure value according to the backpressure valve reference pressure value; the carbonation digital twinning model is set to obtain a theoretical pressure value by adopting the Henry law according to the target gas content and the real-time data of the mixed liquid temperature, and obtain a predicted pressure value by adopting the pre-constructed pressure prediction model according to the target gas content, the real-time data of the raw material liquid injection flow and the real-time data of the carbon dioxide injection flow; further obtaining a reference pressure value of the back pressure valve; according to a first difference value between the target gas content and the real-time gas content of the mixed liquid, a first feedback correction algorithm is adopted to correct the reference pressure value of the back pressure valve, and a dynamic pressure control instruction of the back pressure valve is obtained; and controlling the back pressure valve to execute the back pressure valve dynamic pressure control instruction. According to the method, the taste stability of the soda water is effectively improved.

The invention discloses an intelligent information acquisition method and system for a steel pipe galvanizing production line, and relates to the technical field of information acquisition optimization. An intelligent information acquisition system for a steel pipe galvanizing production line comprises a steel pipe galvanizing production information acquisition module and a steel pipe galvanizing production optimization module. According to the method, the metal vapor concentration, the iron-zinc alloy layer frequency spectrum signal and the zinc liquid temperature data are collected at high frequency, and the dynamic zinc explosion risk coefficient is quantitatively analyzed, so that real-time early warning of a potential zinc explosion event is realized; compared with a traditional method depending on macroscopic temperature or concentration observation, the zinc explosion risk can be recognized in advance, the occurrence probability of zinc explosion accidents is remarkably reduced, and production safety is improved; according to the system, galvanizing production parameters, galvanizing liquid components, production microscopic signals and steel pipe state data can be collected at the same time, full-process and multi-dimensional data coverage is achieved, information integrity is guaranteed, and a reliable basis is provided for production monitoring and analysis.

The invention discloses a nondestructive flaw detection method in a seamless steel tube production process, which comprises the following steps of: initially acquiring a seamless steel tube image, quickly judging whether a crack exists or not, quickly detecting and judging a seamless steel tube product, positioning the crack position of the seamless steel tube according to the initially acquired seamless steel tube image, and spraying dry magnetic powder. Therefore, the seamless steel tube image data after spraying of the dry magnetic powder is collected for the second time, and the edge contour information of clearer magnetic traces at crack positions in the seamless steel tube image data is extracted. And finally, classifying the cracks and generating accurate pixel-level masks of the cracks by adopting a crack classification model. Inputting the cracks into a preset crack generation fault table in a classified manner to inquire fault generation steps and equipment in the seamless steel pipe production process; and the staff can quickly check faults and complete repair according to the inquired and output fault generation steps and equipment in the seamless steel pipe production process, so that the production recovery of the seamless steel pipe production line is accelerated.

The invention provides an intelligent cutting production line control method and system, and the method comprises the steps: firstly obtaining cutting original data, constructing a cutting defect digital diagram based on the cutting original data, then calling a preset cutting technology knowledge base, constructing a cutting negative resource pool through employing a negative resource construction strategy, and then obtaining cutting production plan data, meanwhile, asynchronous clipping task data is obtained based on the clipping negative resource pool, a dynamic interpenetration agent is trained and constructed, the asynchronous clipping task data is imported into the dynamic interpenetration agent, an asynchronous task scheduling scheme is obtained, and finally a real-time clipping path is obtained based on the asynchronous task scheduling scheme and clipping process data. The cutting action is executed based on the real-time cutting path, the cutting method which can utilize negative resources and can perform cutting task on the small parts in the flaw area and the main parts of the main task in an asynchronous and interpenetrating mode is provided, and the overall production efficiency and the material utilization rate are improved.

The invention provides a vehicle controller flashing and calibration system and method based on order data intelligent matching, and belongs to the technical field of vehicle production and manufacturing, and the system comprises an order docking interface which is used for obtaining and analyzing production order information; the electric control software configuration interface is used for inquiring a configuration information table of the vehicle electric control module; the program data management interface is used for downloading program files; the diagnostic communication adapter interface is used for connecting a diagnostic instrument on a production line, adapting a diagnostic protocol and establishing a diagnostic session; the flashing execution module is used for performing flashing operation on the same kind of controllers in sequence by diagnosing session matching and calling program files; the calibration execution module is used for reading a current parameter value of the controller through the diagnosis session according to the to-be-calibrated parameter identifier, matching and verifying the current parameter value with a target parameter value, and executing parameter writing; and the data tracing and reporting module is used for collecting flashing operation result data. According to the invention, automation of flashing and calibration of the vehicle controller is realized, and the efficiency and the production quality are improved.

The invention discloses a food safety risk assessment method and system based on model analysis, and relates to the technical field of food safety production monitoring, a production line virtual model synchronized with a physical production line in real time is constructed based on the structural parameters of a target production line, and the deviation of production line data is corrected through a model fusion algorithm, so that the risk assessment accuracy is improved. And identifying abnormal physical parameter inflection points strongly related to microbial pollution. According to the method, a production line virtual model is constructed through digital twinning, a model fusion algorithm is introduced, time-domain and frequency-domain multi-dimensional comparison and correction are performed on real-time misalignment data and historical steady-state data, instantaneous interference and systematic drifting are effectively filtered out, the corrected production line data are output, and the real-time misalignment data and the historical steady-state data are compared and corrected. And on the basis, abnormal physical parameter inflection points strongly related to microbial pollution are accurately identified, so that weak abnormal signals which are difficult to perceive by a traditional method can be captured in the initial stage of continuous production, and the early pollution risk perception capability for pathogenic bacteria is remarkably improved.

The invention discloses a self-adaptive production parameter optimization system and method. The method comprises the steps that physical data and environmental data of equipment operation are collected in real time through a plurality of sensors installed on a production line; the method comprises the following steps: preprocessing data acquired in real time, and removing noise in the data; constructing an adaptive optimization model based on a deep neural network, predicting and analyzing the preprocessed data through the model, and outputting optimal parameters; and the operation parameters of the production equipment are automatically adjusted according to the optimal parameters output by the self-adaptive optimization module. Parameters can be adjusted in real time to adapt to different production environments, the use efficiency of equipment is improved, the consistency of product quality is improved through accurate parameter control, bottlenecks in the production process are reduced through optimal parameter setting, the operation efficiency of a production line is improved, the effect in the aspect of reducing resource waste is remarkable, and the production cost is reduced.

The utility model provides a paint spraying tool which comprises a fixing support and at least three vertical supporting pieces, the vertical supporting pieces extend in the vertical direction, the bottom ends of the vertical supporting pieces are movably connected with the fixing support, and the horizontal positions of the vertical supporting pieces relative to the fixing support are adjustable. According to the paint spraying tool, the bottom ends of the vertical supporting pieces are movably connected with the fixing support, and the horizontal positions of the vertical supporting pieces relative to the fixing support are adjustable, so that the positions of the top ends of the vertical supporting pieces can be changed relatively, and the vertical supporting pieces can be flexibly adjusted according to the positions of supporting points at the bottom of a workpiece; according to the paint spraying tool, the positions of the multiple insertion connection structures of the paint spraying tool can meet the positioning requirements of different workpieces, the universality of the paint spraying tool is improved, the number of tools needing to be configured by a production line is reduced, and then the manpower, material and storage cost needed by the paint spraying tool is reduced.

The invention discloses a pole column welding control method and device, a welding system, a battery production line, a medium and a product, and relates to the technical field of battery production. First welding is carried out based on actual coordinates of target battery core pole columns, and the target battery core pole columns are part of battery core pole columns in a battery pack; all battery cell poles on the battery pack do not need to be subjected to low-power welding, so that the first-time welding time is shortened; and according to the deviation value between the actual coordinate of the target battery cell pole and the corresponding first welding coordinate after welding, the actual coordinates of other battery cell poles of the battery pack are compensated automatically, the second welding coordinate of the battery cell pole of the battery pack is obtained, and the deviation correction time consumption is reduced. And finally, based on the compensated second welding coordinate, carrying out second welding on the battery cell pole of the battery pack. And the welding quality is guaranteed, meanwhile, the adjusting time after product model changing is greatly shortened, and the production efficiency and adaptability are improved.

The invention discloses an automatic saddle welding device based on visual processing and a using method thereof, and relates to the field of saddle welding.The device comprises a conveyor, a clamping mechanism, a lifting mechanism, an electric rotating disc, a welding mechanical arm and a visual positioning device.A supporting table at the top of a base can move along a guide rail D and is matched with production line procedure arrangement; the clamping mechanism drives clamping plates to move oppositely through a screw A, flexible clamping is achieved in combination with a spring, workpiece damage is avoided, and the posture stability is guaranteed. The core logic of clamping first and then positioning is adopted, the visual positioning device accurately extracts welding seam feature point coordinates through a two-dimensional image acquisition module, a three-dimensional contour acquisition module and a light supplementing adjusting module, and the welding seam feature point coordinates are accurately obtained; positioning data are provided for a welding mechanical arm, the problem of welding deviation caused by workpiece transfer displacement, clamping posture adjustment and coordinate system difference in the prior art is effectively solved, and the defects of weld joint deviation, incomplete penetration and the like are overcome.

The invention relates to the technical field of industrial automation control, and discloses a distributed collaborative and intelligent master control system for a candy production line, which comprises a master control unit and a plurality of resource nodes which are connected with each other through an industrial field bus supporting a precise clock synchronization protocol, the general control unit generates and broadcasts a virtual main shaft position signal to define a system time sequence progress; each resource node corresponds to a physical execution unit with different physical response hysteresis characteristics, an electronic cam curve table for defining a mapping relation between a control set value and a virtual spindle position signal is stored, the resource nodes are configured with phase lead compensation logic, and a phase offset is superposed before the curve table is inquired, so that the phase lead compensation is realized. According to the method, phase pre-compensation is introduced in a logic instruction level, so that the problem of cooperative mismatch caused by response inertia difference of heterogeneous physical execution units is solved, and accurate alignment and seamless flexible remodeling of multi-section actions in a time domain are realized.

The invention provides a leather production line energy consumption optimization control method and system, and relates to the technical field of industrial control, and the method comprises the steps: obtaining a multi-source data set of a leather production line; dividing the multi-source data set into a plurality of working condition fragment data; performing feature extraction on each working condition fragment data to obtain a feature vector sequence; according to the feature vector sequence, through a semi-parameter generalized additive model, carrying out load prediction on the leather production line; based on the load prediction result, constructing a multi-target energy consumption optimization model of the leather production line; solving the multi-target energy consumption optimization model to obtain an optimal production strategy; according to the optimal production strategy, constructing a normalized performance index; performing performance robustness judgment on the optimal production strategy according to the normalized performance index; according to the normalized performance index, the process parameters of the optimal production strategy are adjusted in real time; and executing the adjusted optimal production strategy to complete the energy consumption optimization control of the leather production line.

The invention discloses an intelligent laser welding workstation for a high-voltage cable. The intelligent laser welding workstation comprises an operation table, a mechanical arm, a welding head, a camera assembly, a power assembly, a clamping assembly and a control piece. A control part is installed on the outer side of the operation table and comprises an image acquisition module, an image processing module, a track planning module, a motion control module, a quality detection module, a parameter adjustment module and a synchronous control module. Visual guidance and real-time monitoring in the welding process are achieved through the camera assembly, a welding center point is accurately positioned through an image processing algorithm, the track planning module generates a smooth welding path, and the parameter adjusting module dynamically adjusts welding parameters according to a molten pool image. The synchronous control module receives a production line signal to realize full-process automation of automatic clamping, welding and loosening; efficient and stable laser welding seamlessly integrated with the cable manufacturing process is achieved, and the automation degree, welding precision and product reliability of a production line are improved.

The invention relates to the technical field of production management, and particularly discloses a bearing processing productivity optimization and production scheduling system, which comprises a process state sensing module, a dynamic bottleneck identification module, a self-adaptive buffer control module, a collaborative scheduling decision module and a productivity optimization execution module. Through cooperative work of the dynamic bottleneck identification module and the adaptive buffer control module, a static management mode of a traditional fixed buffer area is thoroughly changed. The system can sense the change of a production flow in real time, accurately identify a bottleneck, and dynamically adjust the target inventory level of a buffer area based on a fuzzy control algorithm, so that a buffer strategy can adaptively match a current production state. The problem that upstream and downstream processes wait for each other due to improper arrangement of a buffer area is solved, and passive blockage and material interruption between the processes are converted into active and flexible rhythm cooperation, so that systematic equipment waiting time is remarkably shortened, hidden capacity is released, and the equipment comprehensive utilization rate and output efficiency of a whole production line are improved.

The invention discloses a secondary spraying turntable production line and a spraying processing method thereof, and belongs to the technical field of assembly line equipment. The production line comprises a circulating turntable mechanism, and a feeding mechanism, a secondary spraying mechanism, a drying room, a workpiece turnover mechanism and a discharging mechanism which are arranged in a surrounding manner. Wherein the circulating turntable mechanism carries a workpiece to be sprayed through a rotatable product spraying tool to realize process circulation, and the workpiece turnover mechanism comprises a primary spraying turnover device and a secondary spraying turnover device to realize automatic turnover of the workpiece. And the secondary spraying mechanism adopts a tool rotation driving belt to link a plurality of spraying tools to synchronously rotate, and the front and back surfaces of the workpiece are respectively sprayed in the primary spraying process and the secondary spraying process. Full-automatic operation of spraying different colors on the two faces of the automobile part is achieved, and the spraying efficiency and the quality stability are greatly improved through rotating disc type continuous production.

The invention discloses a detection device and a detection method thereof, and relates to the technical field of battery processing, the detection device is used for processing and circulating among a plurality of stations of a battery production line, the detection device comprises a battery cell profiling main body and a detection assembly, the battery cell profiling main body comprises a profiling shell and a profiling tab, the profiling tab comprises a plurality of tab profiling parts, and the plurality of tab profiling parts are distributed at intervals along the first size direction of the profiling shell; the detection assembly is arranged on the profiling shell, and the detection assembly is configured to at least detect stress parameters and motion parameters of the profiling tabs in the process that the battery cell profiling main body is processed and circulated at the multiple stations. According to the technical scheme provided by the invention, the detection assembly can detect the stress parameter and the motion parameter of the profiling tab, so that the processing state of the production equipment of each station is judged in time, and the problems of long period and low efficiency existing in the mode of manual spot inspection of the production equipment at present are solved.

The invention provides a circuit breaker fault arc pre-detection control method and system, and relates to the technical field of circuit breaker fault arc pre-detection control. Circuit breaker main loop current signals, frequency conversion equipment operation state information, production line load level information and production scheduling plan information are collected; a noise risk index and a potential fault risk index are introduced, an electrical signal alarm threshold value is dynamically adjusted according to the two risk indexes, high-frequency characteristics of a circuit breaker main loop current signal are extracted and compared with the dynamically-adjusted threshold value, and a total risk assessment and grading early warning mechanism is combined. According to the method, early warning information with different risk levels is generated, corresponding control intervention measures are taken, the defect that in the prior art, it is difficult to accurately recognize the early fault arc of the circuit breaker in a complex industrial environment is overcome, and the accuracy and reliability of fault pre-detection are remarkably improved.

The invention relates to the technical field of glue pudding quality detection, and discloses a glue pudding quality intelligent detection system and method based on multi-sensor fusion. The system comprises a data acquisition module, a stability analysis module, a detection parameter search module, a quality detection execution module and a quality judgment module. The data acquisition module acquires a surface image, weight and texture elasticity data set of the glue pudding sample through a multi-sensor array; the stability analysis module evaluates the acquired data to obtain image, weight and texture elastic stability factors; the detection parameter search module takes each stability factor as a joint index, and determines an optimal detection parameter group through density clustering search in a detection parameter space; the quality detection execution module detects the target glue pudding of the production line according to the optimal parameter group to obtain multi-source detection data; and the quality judgment module inputs the multi-source data into a classifier to obtain a quality grade judgment result. The system realizes comprehensive detection of the glue pudding quality, and improves the detection intelligence and automation level.

The invention discloses a steel production line dynamic scheduling system based on data integration analysis, which belongs to the technical field of steel production line plan scheduling, and is characterized in that a dynamic knowledge graph driven by integrated multiple models is constructed, and scattered and heterogeneous data acquired in real time is converged into a data stream; an initial scheduling scheme is produced based on original order data and production line resource data in the'data stream ', simulation deduction is performed based on digital twin driving, an optimized planned production list is obtained, production is executed with the planned production list as a reference, real-time production data and change order data in the real-time'data stream' are received, and the real-time production data and the change order data in the real-time'data stream 'are obtained. Process deviation and order change in the actual production process are dynamically responded, correlation root cause analysis and scheduling decision making are carried out on multiple disturbance factors based on model and map cooperation, dynamic scheduling based on priority and resource constraint is achieved, it is ensured that the actual production condition is consistent with a production plan after scheduling optimization, and production flexibility and adaptability are improved.

The invention provides a production line parameter real-time scheduling method based on reinforcement learning, and belongs to the technical field of production lines, and the method comprises the steps: collecting sensor data to form an original state vector, reducing the dimension of the original state vector into a low-dimensional feature state vector through a state compression encoder, inputting the low-dimensional feature state vector corresponding to a microcosmic scheduling unit into a parameter decision model, and carrying out the real-time scheduling of the microcosmic scheduling unit; the attention weight coefficient of the model is determined by the product of the historical scheduling success rate, the element value of the difference degree matrix and the maximum characteristic value of the inter-stage sensitivity matrix, after a scheduling instruction is output, simulation evaluation is performed in a digital twin platform, and after the scheduling instruction passes, a real production line is issued for execution and multi-time-scale deviation indexes are collected; and calculating a comprehensive reward value according to the indexes, and storing an experience sample to an experience playback buffer area for model online update training, thereby solving the technical problem that production line parameter scheduling is difficult to consider multi-level state feature recognition and dynamic decision weight optimization at the same time.