5656 results about "Production quality" patented technology

The invention provides a precision injection mold accessory production quality traceability management method and system, and relates to the technical field of intelligent manufacturing, and the method comprises the steps: collecting material, process, equipment and environment data in real time through a distributed sensor; fusing multi-source data based on dynamic material characteristic parameters and process stability indexes, and quantifying melt flow and process fluctuation characteristics; constructing a mixed kernel function anomaly detection model to identify quality deviation; establishing a cross-process association map to reveal the space-time relationship among the raw materials, the process and the finished product; generating a three-dimensional traceability identifier containing the material hash, the process compression code and the block chain address; a block chain enhanced database is adopted to realize tamper-proof storage; and generating a visual traceability report through reverse analysis. Through dynamic modeling, cross-process association and block chain technologies, the problems of data isolation, detection lag and low traceability credibility in a traditional method are solved, the quality traceability efficiency and precision are remarkably improved, and precise injection molding full life cycle management is supported.

The invention relates to the technical field of intelligent manufacturing, discloses a papermaking equipment fault tracing method and system based on a process knowledge graph, and discloses the papermaking equipment fault tracing method and system based on the process knowledge graph. The method and the system comprise data acquisition and preprocessing, papermaking process knowledge graph construction, fault event detection and matching, fault tracing and propagation path reasoning, and maintenance scheme recommendation and optimization. The method overcomes the limitation that the traditional method is difficult to capture the cross-equipment, cross-process and cross-time sequence deep causal association and fault propagation path of the papermaking equipment. By constructing a comprehensive papermaking process knowledge graph, equipment operation state data, process parameter data, production quality data, equipment structure principle, process flow knowledge, fault mode knowledge, maintenance experience and other heterogeneous knowledge are subjected to deep fusion and semantic association, so that the system can exceed the correlation of the data surface; and an internal mechanism and a propagation chain of the fault are deeply excavated.

The invention provides a new energy automobile control circuit board production process optimization control method and system, and relates to the technical field of production control, and the method comprises the steps: collecting production process parameters in real time, inputting the parameters into a process digital twin model, and obtaining process deviation data; and inputting the deviation data into a process parameter optimization model based on a deep reinforcement learning algorithm to generate an adjustment instruction, performing real-time parameter adjustment on the production line, and feeding back to a digital twinborn model to form closed-loop control. According to the invention, accurate monitoring and intelligent optimization of the production process can be realized, the production quality and the production efficiency of the circuit board are improved, and the rate of defective products is reduced.

The invention provides an intelligent factory process optimization method and system based on multi-modal data fusion, and the method comprises the steps: obtaining a multi-modal data set generated in the production process of an intelligent factory, carrying out the multi-modal fusion processing of the multi-modal data set, generating a fusion data set, and carrying out the process feature extraction operation based on the fusion data set. The method comprises the following steps: generating a process optimization feature set, carrying out dynamic optimization strategy matching processing according to the process optimization feature set, generating a process parameter optimization strategy, executing the process parameter optimization strategy, carrying out optimization adjustment on a process execution process of the intelligent factory, and monitoring optimized process execution state data in real time to trigger a strategy iteration updating operation. According to the method, the production quality stability and the process resource utilization efficiency can be synchronously improved.

The invention provides a production data monitoring method and system based on artificial intelligence, and belongs to the technical field of data processing, and the method comprises the steps: generating an initial monitoring strategy according to a quality influence factor evaluation model; based on the multi-modal data acquisition parameters, controlling multi-modal data acquisition equipment to acquire multi-source data in the charging pile assembling process; performing fusion processing on the multi-source data to generate a quality feature vector representing an assembly state; comparing the quality feature vector with a quality judgment threshold value, and outputting a state monitoring result; inputting the quality feature vector into a preset quality prediction model to obtain an assembly quality prediction result; determining a quality defect type and a risk level of the charging pile through a predefined quality defect mapping rule based on the state monitoring result and the assembly quality prediction result; according to the quality defect type and the risk level, at least one of the initial monitoring strategies is adjusted, and an updated monitoring strategy is generated; and the production quality and the production efficiency of the charging pile are improved.

The invention relates to an electronic tag production quality control method in the technical field of information, and the method comprises the steps: carrying out the noise reduction of an image through a multi-scale denoising algorithm in a second image for vibration interference and dust interference, and obtaining a third image; the online detection system judges the printing quality of the electronic tag according to the OCR recognition result, and if abnormality is detected, an abnormal signal is generated and transmitted to the process control system; the process control system receives the abnormal signal, adjusts operation parameters of printing, fitting and cutting processes, ensures smooth process connection, and avoids unbalance of the production rhythm; comparing the adjusted process parameters with a preset product consistency standard, and if the parameter deviation exceeds a preset range, starting an automatic calibration module to ensure the product consistency; the operation state of the whole production process is monitored in real time through a system monitoring module, if the stability of the system is reduced, an early warning mechanism is triggered, and a maintenance signal is generated and transmitted to a maintenance system.

The invention discloses an extruder control system for cable protection pipe production, and relates to the technical field of cable protection pipe processing. Comprising a sensor module used for collecting operation parameters of the extruder, the operation parameters comprise the charging barrel temperature, the machine head temperature, the extrusion pressure, the screw speed and the traction speed, and a control module connected with the sensor module and used for receiving the operation parameters and generating a control instruction based on preset control logic and algorithm. A fuzzy self-adaptive PID algorithm is adopted for temperature control, nonlinearity and time-varying properties of extrusion temperature control can be adapted, the temperature control precision is improved, and the influence of temperature fluctuation on material extrusion is reduced, so that the quality of a cable protection pipe is improved; interference factors such as material characteristic changes can be effectively handled, stable control over extrusion pressure is achieved, and the production quality and production efficiency of the cable protection pipe are guaranteed.

The invention relates to the technical field of industrial production intelligent process control, in particular to an AI-based electrolytic manganese process production quality intelligent prediction method and system, and the system comprises a drift calibration module, a structure matching module, a process parameter inversion module, a path correction module and a deviation verification module. According to the method, dynamic compensation of deviation between input parameter drift and output parameters is achieved through the response relation between input and output of all links, the actual influence degree of fluctuation of all the parameters on the target control parameters and product quality is effectively recognized according to correlation matching of the periodic change of the production process and the fluctuation frequency of the target control parameters, and the accuracy of the product quality is improved. Potential interference of key process parameters on stability of each process is locked in time, diffusion starting time and space of impurities are clearly traced through path judgment of residual liquid concentration trend and raw material adding position, and migration path parameters are further adjusted based on ion retention behaviors of boundary detection points.

The invention relates to the technical field of aircraft manufacturing, and discloses an aircraft part production quality optimization method based on data analysis. The method comprises the following steps: collecting multi-process real-time processing parameters and quality inspection data of a production line, and generating a dynamic quality weight matrix according to a process parameter coupling degree; extracting process path difference characteristics of qualified products and unqualified products in historical batches, and encoding the process path difference characteristics as a quality evolution chain matched with adjacent process parameter mutation relevance; optimizing process parameters at a quality evaluation node, driving a parameter combination to iterate to minimize quality fluctuation, generating an adjustment amount, updating an evolution chain constraint coefficient, and synchronously constructing a stability evaluation function of a correlation weight matrix and a defect propagation path; triggering process compensation according to an adjustment amount gradient, and verifying the cohesion through a tolerance rule by taking a reference parameter matched with the target evolution chain as compensation data; and generating a feedback matrix by using the quality fluctuation index and the compensation result, and correcting the mapping relation between the weight matrix and the evolution chain.

The invention discloses an intelligent management system based on metal powder production, and relates to the technical field of industrial automation intelligent management. Sensor arrays are deployed in an ultrasonic atomizer and a vacuum drying furnace, the dust concentration, the vibration frequency, the pressure, the humidity, the temperature and the oxygen concentration are measured in real time, and the current stage is obtained through a production plan; dynamically and finely adjusting the weight matrix according to the sensor parameters corresponding to different stages, generating a weighted comprehensive index, calculating theoretical control parameters based on a physical model, correcting the theoretical control parameters through an LSTM neural network in combination with real-time environmental data, outputting an equipment control instruction, and extracting the sphericity and the moisture residue of the metal powder after the production of the metal powder is completed, so as to obtain the spherical degree of the metal powder. And the production quality is judged, and different responses are adopted according to the judgment result. The multi-parameter cooperative control precision is further improved, and the system stability is enhanced.

The invention discloses a vehicle production quality risk multi-source data collaborative evaluation full-chain optimization method, and relates to the technical field of vehicle production quality risks, and the method comprises the following steps: dynamically adjusting a multi-source data fusion rule based on the data source feature change under the condition that a production line data source is determined to be a supplementary production line data source, and carrying out the optimization of the multi-source data fusion rule; standardizing the unit, precision, frequency and format of the supplementary production line data source through a preset data mapping rule, and synchronously adjusting a data fusion weight parameter; and after multi-source data fusion rule adjustment is completed, dynamically adjusting the quality risk assessment model based on the data source feature adaptation degree. According to the method, the problem of quality risk assessment failure caused by data isomerism of the supplementary production line and the main production line is solved, efficient and accurate quality risk assessment and production optimization are realized by dynamically adjusting the data fusion rule and the risk assessment model, and the quality control capability of the production process is improved.

The invention belongs to the technical field of production quality management, and provides a production quality online prediction method for a cotton carding process. The method comprises the following steps: constructing and operating a fiber movement mechanism model based on a physical law and a data driving model based on machine learning; the first quality prediction result and the second quality prediction result are input into a dynamic weight fusion mechanism, and the dynamic weight fusion mechanism executes the following operations to generate a high-precision digital twinborn model: deploying the digital twinborn model integrated with optimal weight configuration on an industrial operation and maintenance platform, and driving the digital twinborn model by utilizing cotton carding process production data acquired in real time, so as to realize online dynamic prediction of the production quality. According to the method, the physical interpretability of the fiber motion mechanism model is reserved, the advantage of high precision of the data driving model is fully played, and the prediction precision and robustness of the digital twin model under different working conditions are remarkably improved through adaptive weight adjustment.

The invention provides a thin film production quality traceability system, method and device and a storage medium, and relates to the technical field of quality traceability. Production data of each process section in the production process of a thin film product is collected in real time; acquiring historical production data, determining index association degrees among the process indexes in different process sections according to the historical production data, and screening out all key quality control indexes based on all the index association degrees; determining a quality control index domain corresponding to each key quality control index according to the historical production data; extracting a plurality of pieces of quality control production data, and determining all quality loss segment points through each piece of quality control production data and the corresponding quality control index domain; and determining a mass loss root cause by using all the mass loss segment points, and regulating and controlling the production process of the thin film product through the mass loss root cause, thereby realizing quality tracing in the production process of the thin film product. According to the method, the root cause of the quality problem can be evaluated in combination with historical data and production environment characteristics, so that the accuracy and reliability of thin film production quality tracing are improved.

The invention relates to the technical field of spliced motor stators, in particular to a splicing method of a spliced motor stator. Comprising the following steps: step 1, positioning the outer circle of a stator, and adjusting the position of a supporting block on a tool until the outer wall of the supporting block is completely matched with the outer wall of the required stator; step 2, positioning the inner circle of the stator, mounting the core mold in the middle of the tool, and enabling the outer wall of the core mold to be matched with and fixed to the inner wall of the required stator; step 3, mounting a single stator iron core unit, aligning one unit with one supporting block and inserting the unit into a mounting area formed between the core mold and the supporting block; and 4, splicing and forming the stator, and sequentially and adjacently splicing the plurality of stator iron core units in the radial direction. The splicing method of the spliced motor stator has the advantages of being high in working efficiency, high in precision, good in production quality, high in structural strength, simple in structure, convenient to operate and the like.

The invention discloses an intelligent production and quality monitoring system and method for premixed concrete, and particularly relates to the technical field of concrete production quality monitoring, and the method comprises the following steps: constructing an initial control parameter set; environment data is collected and acts on the initial control parameter set to generate an applicable control parameter set; performing mixing production based on the applicable control parameter set, collecting process behavior data, and inputting the process behavior data into a machine learning model to predict compressive strength and setting time; comparing the prediction result with a performance target, if the tolerance is exceeded, determining that the quality is low and correcting control parameters, and if the tolerance is not exceeded, determining that the quality is high and allowing discharging; according to the method, the initial control parameter set is constructed by using the historical weighing deviation and the strength actual measurement result, the applicable control parameter set is dynamically generated in combination with the real-time environment data, the performance result is predicted through the model and compared with the preset target, and a quality judgment and parameter correction closed loop is formed. And the parameter rationality, the environmental adaptability and the quality control intelligent level of concrete production are improved.

The invention discloses a system and a method for multi-parameter cooperative control in PVC steel wire hose production, and relates to the technical field of plastic hose production, the system comprises a parameter monitoring module, a central processing module, a parameter adjusting module and a quality detection module, the parameter monitoring module is a high-precision intelligent sensor and has a self-adaptive filtering function, and key parameters are measured and transmitted to the central processing module; the central processing module uses an improved multi-objective optimization algorithm to calculate an optimal cooperation value, a simulated annealing-genetic hybrid algorithm is used for iteration, the parameter adjustment module uses intelligent PID, adaptive variable structure control and model prediction control adjustment equipment, and the quality detection module uses multi-modal fusion deep learning and high-precision physical performance detection equipment. And if not, recalculating the adjustment parameters. The production quality and efficiency of the PVC steel wire hose are improved, the cost is reduced, parameters are precisely and cooperatively controlled, the hose is stable in physical performance and few in surface defect, the raw material and equipment maintenance and energy consumption cost is reduced, and the enterprise revenue and market competitiveness are improved.

The invention relates to the technical field of chemical production process control, and particularly discloses a production process export quality prediction method based on performance-driven knowledge distillation, which comprises the following steps: step 1, acquiring observable process data of an industrial production process and test data of export quality indexes, and forming a data set; 2, cleaning and standardizing the data set, and randomly dividing the data set into a training set, a verification set and a test set according to a certain proportion; 3, building a basic quality predictor, and embedding an LSTM module into the basic quality predictor through a twinning neural network framework; 4, training a plurality of basic twin LSTM quality predictors to obtain a teacher model; step 5, training student models with the same structure by taking performance as drive and combining knowledge distillation and supervised learning; and step 6, inputting process data of the test sample, and obtaining an output predicted value, so as to improve the accuracy of the soft measurement of the production quality index in the industrial process on the basis of ensuring the stability of the model.

The invention relates to the technical field of comprehensive factory control, in particular to a mold whole-process quality monitoring system and device and a storage medium, and the system comprises a data collection and synchronization module which is connected with a plurality of mold manufacturing machine tool connection networks, collects machining data and operation states of mold manufacturing machine tools, generates a real-time data flow, and carries out the real-time processing of the mold manufacturing machine tools based on the real-time data flow. And analyzing the working efficiency and state of the mold manufacturing machine tool to obtain an equipment state result. According to the method, the machining data and the operation state of the mold manufacturing machine tool are collected in real time, the real-time data flow is formed, the machine tool operation efficiency and the equipment state are dynamically analyzed, state monitoring and production quality prediction in the mold manufacturing process are achieved, and the transparency and the management and control capacity of the mold machining link are improved; and according to the quality prediction result obtained through analysis, the production line performance and the equipment efficiency are dynamically evaluated, the production tasks are scheduled in real time, the equipment load distribution is optimized, a dynamic production scheduling system is formed, and the production efficiency and the resource utilization rate are improved.

The invention discloses motor stator core laminating equipment, and relates to the technical field of motor production, a plurality of electricity inlet and outlet sliding rails are fixed at the bottom end of the inner side of a fixed operation frame at equal intervals, an operation alignment frame is mounted at the top ends of the electricity inlet and outlet sliding rails through sliding rail seats, and alignment motors are symmetrically mounted at the top end of the inner side of the operation alignment frame through motor seats; an alignment limiting plate is clamped to the bottom end of an output shaft of the alignment motor, a plurality of limiting electromagnets are installed at the bottom end of the inner side of the operation alignment frame at equal intervals, processing electric push rods are fixed to the tops of the two ends of the fixed operation frame, and a processing limiting plate is fixed to one ends of the processing electric push rods; the problem that in the prior art, the size of equipment cannot be adjusted according to the diameter of the stator iron core and the size of a groove in the iron core is effectively solved, iron cores of different sizes can be produced through single equipment, the production range is widened, meanwhile, the size is corrected, stable correction cooperation is achieved through top end and side end limitation, and the production efficiency is improved. And the production quality of products is ensured.

The invention relates to the technical field of injection molding quality monitoring, in particular to a real-time monitoring method and system for injection molding production quality. The method comprises the following steps: acquiring a standard pressure curve and a standard temperature curve of each production stage in each injection molding production cycle; calculating the local intensity of each sampling moment; obtaining an abnormal fluctuation coefficient of each sampling moment, and constructing a multi-dimensional coupling dynamic weight of each sampling moment; multiplying the abnormal fluctuation coefficient by the multi-dimensional coupling dynamic weight to obtain a weighted dynamic fluctuation curve; integrating the weighted dynamic fluctuation curve to obtain a form deviation index; and performing anomaly detection on the current production cycle based on the form deviation index. And the monitoring precision of injection molding production is improved.

The invention discloses a flexible circuit board production quality monitoring method and system based on data feedback, and the method comprises the steps: collecting the physical parameters of development etching, drilling and copper plating processes, such as line width, line distance, aperture, roundness and copper thickness, and workshop temperature and humidity and dust concentration data; generating a single-variable control chart for a single-process physical parameter, generating a multivariable T2 control chart for a multi-coupling parameter process, and calculating a process stability index and a comprehensive fluctuation index; inputting the abnormal data into a BP neural network, identifying an abnormal mode type and outputting characteristic parameters; generating a parameter correction instruction according to the abnormal mode and the characteristic parameters, and adjusting equipment parameters in real time; recalculating the process capability index based on the adjusted data, and triggering secondary feedback if the process capability index does not reach the standard; and dynamically adjusting the threshold value of the control chart according to the standard deviation and the mean value of the process capability indexes of the continuous batches. Production key parameters are comprehensively covered, abnormity is found in time, accurate and comprehensive detection is achieved, real-time adjustment is achieved, stability and controllability are ensured, and the product quality is improved.

The invention relates to the technical field of textile machinery, and discloses a covering yarn machine spindle speed and twist control system which comprises a data acquisition module, a global control module, an individual diagnosis module, a power execution module and a man-machine interaction module. The actual average speed of the whole machine is calculated by collecting the speed of each spindle in real time, and PID closed-loop control is carried out on the actual average speed and a set value, so that the systematic speed deviation of the whole machine is accurately corrected. Meanwhile, the individual diagnosis module calculates spindle difference signals of the speeds of the spindles and the average speed of the whole machine in parallel, time domain and frequency domain analysis is carried out on the signals, and hidden faults such as steady-state deviation, periodic fluctuation and transient impact existing in a single spindle are accurately recognized. And finally, the system carries out visual early warning through a human-computer interaction interface and provides a predictive maintenance instruction. According to the method, high-precision control of the twist and high consistency of products are achieved, an effective predictive maintenance mechanism is established, and the production quality and the intelligent level are remarkably improved.

The invention provides a filling method for syrup, and relates to the technical field of food processing.The filling method comprises the steps that before filling is started, the filling amount threshold value of a target container is preset; dynamically adjusting the filling speed based on a comparison result of the liquid level height, monitored in real time, in the target container and a preset threshold value; when it is detected that the liquid level is close to a preset threshold value, the filling speed is gradually decreased till stopping, so that it is ensured that the preset filling amount is accurately reached, and overflowing is avoided; in the filling process, a sensor is used for continuously monitoring liquid level changes, and the liquid level changes are fed back to a control system for real-time adjustment. By means of the scheme, the problem how to achieve accurate control over syrup in the filling process so as to avoid overflow and waste can be solved, meanwhile, the wall hanging situation in the filling process can be effectively prevented in the filling process, the production quality and stability can be improved, and the overall operation efficiency of a production line and the qualified rate of final products are remarkably improved.

The invention discloses a coating production line control method, device and equipment, a storage medium and a program product, and relates to the field of production scheduling, and the coating production line control method comprises the steps: monitoring production environment parameters and equipment operation parameters of coating production line equipment; parameter adjustment is carried out according to the production environment parameters and the equipment operation parameters through a pre-trained deep reinforcement learning model, the equipment parameter adjustment amount is obtained, and the pre-trained deep reinforcement learning model is obtained by carrying out model training on a preset initial deep reinforcement learning model based on historical coating production data obtained in advance; and adjusting the equipment operation parameters of the coating production line equipment according to the equipment parameter adjustment amount, and controlling the coating production line equipment subjected to parameter adjustment to perform coating production. The production line equipment parameters are dynamically adjusted through the deep reinforcement learning model, the problem that the film coating production quality is affected by film coating production line equipment parameter immobilization in the prior art is solved, and the film coating production quality is guaranteed.

The invention discloses a continuous processing technology for pet feed production and processing equipment thereof, and relates to the field of pet feed production.The processing equipment comprises a main box body and a motor, the motor is fixed to the lower end face of the main box body, a first rotating shaft is fixed to the output end of the motor, and the first rotating shaft is connected to the main box body through a bearing. According to the continuous processing technology for pet feed production and the processing equipment thereof, an elastic downward-pressing rolling mechanism is adopted, bonded feed particles can be effectively separated, and therefore the production quality is guaranteed, specifically, a third rotating shaft, a circular shaft, a fixing plate, a pressing plate and a rubber pad are made to rotate through the transmission effect between a gear and a convex tooth rod; the circular shaft, the fixing plate, the pressing plate and the rubber pad rotate synchronously under the cooperation of the sliding effect between the spiral groove and the shifting rod, and after the rubber pad makes contact with feed particles, through the downward pressing and rotating effect of the rubber pad, extrusion and rolling of feed are achieved, and separation of bonded materials is facilitated.

The invention relates to the technical field of on-line monitoring, in particular to an automobile industry part on-line monitoring method and system, and the method comprises the following steps: based on collected part production line data, extracting the assembly data flow of parts, carrying out the quality screening of the real-time assembly data flow, eliminating the noise and invalid data in the data flow, and carrying out the real-time quality screening of the real-time assembly data flow; and obtaining the screened assembly data. According to the method, early recognition and early warning of abnormal conditions in the production process are achieved by analyzing and predicting potential production deviation in real time, data points deviating from a standard process can be accurately recognized and rapidly adjusted by comprehensively evaluating assembly data and a production trend, production quality and improvement of efficiency are ensured, and the production efficiency is improved. Through continuous monitoring and analysis of key performance indexes, machine setting is optimized, the stability of the production process is ensured, meanwhile, resource waste is remarkably reduced, maximization of cost benefits is achieved, and control over a production line is enhanced.

The invention relates to the technical field of aluminum profile production equipment, and particularly discloses an aluminum profile automatic production line and a control method thereof.The aluminum profile automatic production line comprises a heating device used for heating an aluminum bar, an extrusion device used for conducting extrusion forming on the peeled aluminum bar, a traction device used for conducting traction cutting and a straightening device used for straightening and correcting the aluminum profile; the heating device is arranged close to the outer part of the extrusion device, the peeling device is arranged between the heating device and the extrusion device, the traction device is arranged close to a discharge port of the extrusion device, the traction device comprises N traction clamping heads and a guide rail group, and the straightening device is arranged close to the tail end of the guide rail group. And the framing device is arranged close to the outer part of the straightening device, so that the continuous production action of aluminum profiles can be effectively and automatically realized, and the production quality of the aluminum profiles can be ensured while the production efficiency is improved.

The invention relates to the technical field of computer vision, and discloses an engineering plastic production quality detection method and system. According to the method, an engineering plastic surface image sequence on a production line is collected in real time through an industrial camera, and injection molding machine technological parameters are synchronously obtained. And constructing a ViT-Adapter visual feature extraction network based on a multi-scale sliding window, and fusing surface texture features and process parameters. A defect classification model is trained by adopting a combined loss function of improved Focal Loss and comparative learning, zero sample abnormal segmentation is realized based on WinCLIP, and pixel-level positioning of surface bubbles and scratches is realized. And finally, outputting a defect thermodynamic diagram, and carrying out result visualization in linkage with production line parameter self-adjustment. According to the method, the defect detection accuracy and efficiency are improved, automatic adjustment is realized, manual intervention is reduced, and the production quality and the intelligent level are improved.

The invention relates to the technical field of optical communication, and discloses FA coupling equipment and a coupling method thereof.A feeding and discharging part of the coupling equipment comprises a feeding streamline module, a carrying mechanism, a cleaning module and a detection module, a coupling part of the coupling equipment comprises a jig module, a coupling module and a dispensing vision module, an optical module and FA are clamped on an outer support together, and the optical module and the FA are arranged on the outer support. An MPO plug used in the coupling process is integrally clamped on an MPO clamp, automatic feeding and discharging of an optical module and an FA and automatic cleaning, wiping, detection and plugging of the end face of an MPO optical fiber jumper wire can be achieved through coupling equipment, dependence on personnel is reduced, the feeding and discharging difficulty is lowered, and dependence on the skill of an operator is lowered; through the coupling method, the product coupling working efficiency can be improved, the production quality stability is improved, the yield is improved, and the production cost is reduced.