1512 results about "Failure rate" patented technology

The invention relates to the technical field of industrial equipment scheduling, in particular to a HarmonyOS industrial equipment intelligent scheduling and real-time cooperative control method, which comprises the following steps: S1, industrial equipment autonomous behavior design and dynamic role switching based on bionic swarm intelligence; by introducing bionic swarm intelligence, multi-modal perception, transfer learning and block chain technologies, the scheduling efficiency and task execution capability of industrial equipment in a complex dynamic environment are remarkably improved, and in the actual use process, the equipment can dynamically adjust task priority and resource allocation according to the real-time operation state and scene change, so that the service life of the equipment is prolonged. And meanwhile, environment data is collected by utilizing a multi-mode sensor, prediction and reasoning are performed through a machine learning model, and the equipment can pre-judge a fault risk and optimize a task chain execution scheme, so that the fault rate and energy consumption in the production process are reduced, and the production efficiency and stability are remarkably improved.

The invention provides an AI-based assembly state intelligent monitoring method and system, and relates to the technical field of artificial intelligence, and the method comprises the steps: collecting a multi-angle real-time assembly process image of a target product, constructing an assembly digital twin model, inputting the image into a hybrid neural network in combination with an assembly knowledge graph, analyzing the assembly node coordinate and torque data deviation, and obtaining an assembly state of the target product. And obtaining an optimal assembly path and torque control parameters, and finally generating an assembly quality evaluation matrix. Through fault feature extraction and mode recognition, potential faults are predicted, and a dynamic risk coefficient is calculated. And when the fault occurrence probability exceeds a preset threshold value, the position and torque compensation parameters are calculated, an execution instruction sequence is generated to control the assembly execution mechanism to perform adjustment, closed-loop optimization control in the assembly process is achieved, the assembly precision and efficiency are improved, and the fault rate is reduced.

The invention relates to the technical field of motor temperature intelligent control, and particularly discloses a motor temperature monitoring and regulation method and system based on intelligent control and a storage medium, and the method comprises the steps: carrying out data collection circulation: judging whether a sampling period is reached, if yes, reading multi-mode sensing data, and outputting a calibrated temperature value T, and if not, outputting a calibrated temperature value T; if yes, vibration noise is eliminated, the environment temperature is compensated, multimode sensing data is added, and then the calibrated temperature value T is output; intelligent analysis is conducted on the calibrated temperature value T obtained through data collection circulation, if the temperature is normal, data analysis and prediction are conducted, and if the temperature is abnormal, the calibrated temperature value T is analyzed, fault self-diagnosis is conducted, abnormal reasons are judged, the system is controlled to conduct self-adjustment, and temperature early warning is conducted. The problems that in the prior art, monitoring is delayed, regulation and control are rigid, and maintenance is passive, so that energy efficiency is wasted, the failure rate is high, and operation and maintenance cost rises are solved; and a real-time temperature monitoring means and an intelligent control system are lacked.

Methods, systems, and. devices may assist in new radio (NR) sidelink (SL) operation in licensed or unlicensed spectrum. In an example, if listen-before-talk (LBT) failure rate is low, then implicit acknowledgement (ACK) and explicit non-acknowledgement (NACK) may be used. A first SL timer may start when a. SL transport block is transmitted. If no explicit NACK, is received before the SL timer expires, then ACK. may be assumed. If LBT failure rate is high, then explicit ACK and implicit NACK may be used. A second SL timer may start. If no explicit ACK is received before the SL timer expires, then NACK may be assumed.

The invention discloses an H-bridge key equipment service life and system reliability evaluation method and system for a cascade network construction type energy storage system, and belongs to the technical field of power system automation. The method comprises the following steps: firstly, extracting task profile parameters under multiple time scales, and constructing a time sequence feature model; secondly, estimating a hot spot temperature sequence of the IGBT device and the capacitor based on a multilayer feedforward neural network; then, in combination with a continuous extreme point paired temperature cycle extraction method and a Miner linear cumulative damage criterion, the damage factor and the residual life of the device are evaluated; then, task profile samples are expanded based on a generative adversarial network with gradient penalty, and life distribution and reliability indexes of key devices under different profiles are calculated; and finally, based on H-bridge series structure mapping device level information, constructing a system level reliability model, obtaining system failure rate, average fault-free operation time and a reliability function, and realizing health state perception and reliability quantitative evaluation of the energy storage system.

The invention relates to the technical field of data processing, and particularly discloses an improved inter-ERP (Enterprise Resource Planning) system data migration and synchronization method, which comprises the following steps of S1, metadata acquisition and preprocessing; s2, field intelligent matching; S3, machine learning recommendation; s4, mapping confirmation and feedback; s5, converting rule configuration; s6, carrying out total migration; s7, performing increment synchronization; and S8, monitoring and alarming: counting delay, throughput and failure rate of increment synchronization in real time, and automatically sending an alarm notification when an abnormal threshold is triggered. According to the method, a complete technical system which is infinitely infinitely and covers the whole migration and synchronous life cycle is constructed from metadata acquisition to intelligent mapping, rule driving and full / incremental ETL (Extract Transform Load), and then to monitoring alarm and rollback management; the method is suitable for efficient, reliable and extensible data migration and synchronization between heterogeneous ERP systems, and provides solid support for enterprise informatization upgrading, system splitting / merging and big data analysis enabling.

A cold rolling processing line-based digital steel coil real-time data tracking method and system. The method comprises: defining and configuring devices and positions on a cold rolling processing line, the configurations comprising position names and tracking modes, wherein the tracking modes include a first-stage tracking mode and a second-stage tracking mode; circularly traversing first-stage tracking message data and second-stage tracking message data, and matching each tracking position with material information; recording a material information change moment on each tracking position in real time to obtain the time of arrival and time of departure of a material at each tracking position; and meanwhile, on the basis of real-time production data of the material, in combination with the length by which and the moment at which the lead head of a steel coil passes the position, performing parameter calculation of the production data, including: length calculation, maximum / minimum average value calculation, multi-process standard hit rate calculation and alarm calculation. The method can accurately reflect actual production conditions, reduce the shutdown failure rate of the processing line, and greatly improve the product quality and the production efficiency.

The invention discloses an electric power material demand prediction analysis method and system based on big data analysis, and the method comprises the following steps: obtaining target electric power related data, and carrying out the preprocessing of the target electric power related data; based on the preprocessed target power related data, performing coupling analysis on the power grid equipment failure rate and the load fluctuation to obtain a candidate intervention variable set; performing screening operation on the candidate intervention variable set to obtain a final intervention variable set; and performing anomaly detection on the final intervention variable set, performing classification processing on anomalies according to the detected anomaly degree, dynamically adjusting a filling strategy through Bayesian optimization to minimize a data correction error, and finally outputting a cleaned training data set. According to the method, real causal variables are accurately screened through dynamic coupling analysis and triple causal verification, and the model prediction accuracy can be remarkably improved by combining a time domain-frequency domain joint analysis dynamic weight mechanism and an adaptive lagging order optimization technology.

The invention discloses a multidimensional monitoring system of an on-load tap-changer for a converter transformer and a fault prediction method, the system comprises a motor, a change-over switch core and other parts, and a data acquisition module is provided with a current, vibration and oil chromatography acquisition unit and a vacuum tube life monitoring unit. The current sensor collects motor current, the vibration sensor collects vibration signals of the change-over switch, the oil chromatography collection unit analyzes an oil sample, and the vacuum tube life monitoring unit evaluates the residual life of the vacuum tube. And a data transmission and processing analysis module. The fault prediction method comprises the steps of data acquisition, preprocessing, multi-dimensional data fusion, fault prediction model construction, feature extraction and selection, fault prediction and evaluation and the like. And screening characteristic parameters and fusion data by adopting a plurality of technical means, and constructing a machine learning model to predict faults. According to the invention, multi-source data acquisition and multiple analysis methods are integrated, the equipment state can be comprehensively reflected, the monitoring reliability is improved, faults can be predicted in advance, and the equipment fault rate is reduced.

The invention provides a flexible middle supporting structure of a vertical screw conveyor, which solves the problems of blanking buffering and the like of the screw conveyor, and comprises a conveying cylinder, the inner side of the conveying cylinder is movably connected with a connecting shaft through a screw blade, and a driving assembly and a flux adjusting assembly are arranged between the conveying cylinder and the screw blade. A flexible buffering assembly is arranged between the connecting shaft and the spiral blade. The device has the advantages of good structural stability, low failure rate and the like.

The invention belongs to the technical field of drilling, and provides an active vibration reduction method and system based on self-adaptive adjustment of a drill rod load in order to effectively reduce vibration of a drill rod. A real-time feeding oil pressure signal is converted into real-time feeding thrust, and a real-time rotating oil pressure signal is converted into real-time rotating torque; filtering the original vibration signal by adopting a singular value subspace decomposition method to obtain a real vibration signal; according to the preset initial value of the feeding thrust and the preset initial value of the rotating torque, a neural network self-adaptive PID control algorithm is used for conducting self-adaptive comparison calculation, the vibration adjustment amount is obtained, and active vibration reduction is conducted according to the vibration adjustment amount; the system comprises six closed-loop control modules including a preset parameter input module, a signal acquisition module, a signal filtering module, a control algorithm module, a control output module and an execution module, accurate vibration reduction control is achieved, the bounce phenomenon of a drill bit is eliminated, the deviation error of a drill hole is reduced, meanwhile, stick-slip vibration is restrained, and the clamping stagnation failure rate of a drill rod is greatly reduced.

The invention discloses a test data analysis method and device for a vehicle-mounted telescopic tent. The method comprises the steps that a performance index test data set and a corresponding test site set of the vehicle-mounted telescopic tent are obtained through measurement; the test site set comprises position information of test sites; the performance index test data set comprises a rainproof performance test data sequence, a snow load performance test data sequence, an expansion time test data sequence, a failure rate test data sequence, an energy consumption test data sequence and a storage time test data sequence; each piece of data in the test data sequence is obtained by testing at a test site in a test site set; data with the same serial number in all the test data sequences correspond to the same test site; preprocessing the performance index test data set to obtain a preprocessed data set; and performing evaluation calculation processing on the preprocessed data set to obtain a performance comprehensive evaluation result value of the field tent.

A predictive maintenance method based on an elevator operation and maintenance time sequence knowledge graph comprises the steps that firstly, the elevator operation and maintenance time sequence knowledge graph is constructed, predictive maintenance of electromechanical equipment parts is achieved based on elevator part maintenance period optimization, a theoretical distribution model based on Weibull distribution is constructed, and parameters of Weibull distribution are fitted through a least square method. Therefore, the model can accurately reflect the failure rule of the elevator parts; calculating an inference result of the elevator operation and maintenance time sequence knowledge graph through a graph convolution model and a Horkes process model, and carrying out recursive relation calculation of component fault rate functions in N maintenance cycles; an elevator component maintenance comprehensive cost simulation model is constructed, and the optimal maintenance cycle of elevator components is calculated through a continuous time Markov chain method; the fault prediction accuracy is improved.

The invention discloses an error correction method of a neural network processor and a related device, and belongs to the technical field of neural networks. The method comprises the steps that according to a fault PE table obtained through error detection, the fault PE table comprises a two-dimensional list of fault occurrence and position information, a newly detected fault PE is positioned according to the fault PE table, and a multi-stage fault-tolerant decision is executed: i, a fine-grained error correction stage; ii, a coarse granularity error correction stage; according to the current error correction mode, a pipeline submission mechanism or a submission mechanism is selected, and a calculation result is output. According to the method, a mixed granularity fault recovery scheme is adopted, and the error correction function can be achieved under the condition that any standby PE is not added. The fine-grained scheme provides more flexibility and does not waste PE, and the coarse-grained scheme ensures that the reasoning precision is not reduced along with the increase of the failure rate.

The invention discloses a magnetic core assembling device and an assembling method thereof, and relates to the technical field of magnetic core assembling equipment.The magnetic core assembling device comprises a case and a mounting table arranged on one side of the upper end face of the case, a synchronous conveying belt is arranged on the mounting table, and a plurality of positioning die holders used for containing magnetic core frameworks are evenly connected to the surface of the synchronous conveying belt at intervals; the machine box is provided with a feeding assembly used for placing a magnetic core framework into the positioning die holder, magnetic core feeding vibration discs are arranged on the two sides of the upper end face of the mounting table correspondingly, and arc-shaped feeding channels are formed in the magnetic core feeding vibration discs. According to the automatic gluing device, automatic gluing operation on the butt joint faces of the magnetic cores can be achieved while pressing is conducted through the arranged gluing assembly, meanwhile, the gluing action, the magnetic core inserting action and the magnetic core framework feeding action are driven by the linkage arm serving as a connecting piece, the corresponding actions are completed, control over the device is simpler, and the production efficiency is improved. And the failure rate of equipment is effectively reduced.

The invention belongs to the technical field of motors, and relates to a vertical magnetic suspension motor. On the basis of the magnetic bearing control module, the cooling control module and the control evaluation module, closed-loop control of the rotor stably suspending at the target position is achieved, oscillation and steady-state errors are reduced, and the control precision is effectively improved; by dynamically calculating the temperature gradient difference and the cooling power regulation and control value and combining the PID controller and the liquid cooling / air cooling cooperation strategy, on-demand cooling is achieved, the cooling energy consumption is reduced, the temperature balance is improved, and the problems of local overheating or too high energy consumption are avoided; by performing normalized weighted analysis on key parameters such as magnetic bearing energy efficiency and cooling efficiency, feeding back control evaluation coefficients in real time and guiding system parameter optimization, the stability and reliability of long-term operation of the motor are ensured, the failure rate of the motor is reduced, the functional integrity is ensured, and system-level optimization is facilitated.

The invention discloses a lifting platform for offshore pile top construction, and relates to the technical field of ocean engineering, the lifting platform comprises a main body mechanism, four lifting mechanisms are movably inserted in the inner surface wall of the main body mechanism, and two driving mechanisms are fixedly mounted on the outer surface walls of the four lifting mechanisms; the lifting mechanism comprises a fixing pile. According to the lifting platform, after the device is started, a motor can enable two half gears to synchronously and reversely rotate through a rotating shaft and a transmission gear, so that racks on the left side and the right side are sequentially driven to start to move, then the platform is driven to ascend and descend, and the platform is clamped by means of cooperation between two metal clamping blocks and positioning holes; compared with a traditional gear and rack clamping structure, the clamping tongue is matched with the fixing hole so that external interference can be better resisted and the stability of the lifting platform can be ensured on the sea where the environment is severe and stormy waves are large, meanwhile, the structure is relatively simple and reliable, the fault rate is low, and therefore the construction safety is improved.

The invention belongs to the field of equipment evaluation, particularly relates to an intelligent evaluation method and system for the operation quality of a flight simulator, and aims to solve the problems of single evaluation dimension, poor real-time performance, low intelligent degree and lack of quantitative standards in the prior art. The method comprises the steps that operation data such as fault time, duration and available time are collected in real time through a sensor network; calculating core indexes such as fault frequency per unit time, accumulated duration and malignant fault rate; dynamically weighting and fusing the indexes to generate a single-machine health index; a correlation model is constructed based on fault space-time correlation between devices, and cluster health scores are output in combination with single machine indexes; when the malignant fault exceeds a threshold value, driving causal inference by utilizing a knowledge graph containing entity causal relationships of fault types, equipment parts and the like, and outputting a root cause diagnosis and maintenance scheme; and acquiring intervened data to update atlas parameters and models to form closed-loop optimization. Multi-dimensional real-time monitoring and diagnosis closed loop of the operation state are realized, and the maintenance efficiency is improved.

The present application discloses a rigid pressure stabilizing device for an oil drilling pump, belonging to the field of oil drilling equipment. The key technical points of its technical solution include a housing, a separating ring, and a fixing column. Among them, the housing is provided with an inlet and outlet hole for fluid to enter and exit, which is communicated with the discharge pipe of the drilling pump; the separating ring divides the interior of the housing into a first space and a second space, and the two are communicated through a communication hole. A plurality of annular first fins are arranged in the first space, alternately arranged and provided with shunt holes, which helps to stabilize the fluid pressure; a plurality of annular second fins are arranged in the second space, also alternately arranged and provided with shunt holes. The inlet and outlet hole, the first space, the communication hole, and the second space form a circulation channel for fluid to circulate, achieving the effect of improving the working reliability of the drilling pump and effectively reducing the failure rate caused by pressure fluctuations.

The invention relates to the technical field of air compressor optimization, and discloses an intelligent air compressor optimization control method and system based on an IOE control system.The intelligent air compressor optimization control system comprises a collection unit, a preprocessing unit, an analysis unit and an optimization unit, rich operation data such as air inlet pressure, flow, exhaust pressure and temperature are comprehensively collected in real time, denoising is conducted through a moving average method, and data accuracy is ensured; normal, overload and abnormal fluctuation modes are clearly divided, accurate judgment is performed by means of a preset interval and reasonable calculation, and powerful support is provided for control decision making; in a normal mode, intelligent regulation is performed according to a parameter average value, and operation efficiency and stability are improved; the valve opening degree is adjusted according to the deviation degree in the overload mode, the load is effectively reduced, and equipment is protected; in the abnormal fluctuation mode, a PID control algorithm is used, the opening degree, the cooling amount and the like of the valve are flexibly adjusted according to deviation and the change rate, and parameters are rapidly recovered to be stable. The fault rate and the maintenance cost can be greatly reduced, and the working efficiency is remarkably improved.

The utility model discloses a follow-up floating assembling device which comprises a horizontal bottom frame, two sets of horizontal sliding rails are symmetrically installed on the horizontal bottom frame, a horizontal driving air cylinder is installed between the two sets of horizontal sliding rails and above the horizontal bottom frame, and a connecting piece is installed on the horizontal driving air cylinder. An upright frame is mounted on the two groups of horizontal sliding rails and the connecting piece; an up-down driving servo motor and a vertical sliding rail are installed on the upright frame, one end of the up-down driving servo motor is connected with a lead screw assembly, one end of the lead screw assembly is connected with a floating mechanism, and the floating mechanism is installed on the vertical sliding rail. According to the follow-up floating assembly device, the requirement for the workpiece positioning precision is reduced, the machining, assembling and debugging difficulty is lowered, the software control difficulty is further lowered, and the fault rate is lowered.

The invention discloses an electricity-heat-vibration multi-dimensional data fusion oil-immersed transformer abnormal state identification method, which comprises the following steps: analyzing multi-physical field coupling data on a transformer, extracting key characteristic parameters, and carrying out abnormal state identification by using an improved BES-LSSVM model. According to the method, a multi-source fusion data mining technology and a machine learning algorithm are combined, parameters are optimized by using a hybrid model of a BES optimization algorithm and a least square support vector machine, and classification and identification of abnormal working conditions are realized through a k-means clustering algorithm. According to the method, the accuracy and reliability of transformer abnormal state evaluation can be remarkably improved, and the fault occurrence rate and the maintenance cost are effectively reduced.

The utility model discloses a synchronous sample clamping device for a steel bar tensile test, which comprises a clamp seat, a left oil cylinder and a right oil cylinder are arranged on the clamp seat, and both the left oil cylinder and the right oil cylinder are connected with a hydraulic power system; the extending directions of a piston rod I of the left oil cylinder and a piston rod II of the right oil cylinder are parallel and opposite, the close ends of the piston rod I and the piston rod II are connected with a clamping plate for clamping a sample, and the other ends of the piston rod I and the piston rod II are fixedly provided with pistons which slide in the cylinder barrel in a matched manner. The synchronous shunt motor is arranged between the left oil cylinder and the hydraulic power system and between the right oil cylinder and the hydraulic power system, so that the left oil cylinder and the right oil cylinder are synchronized with high precision, the action time is shortened, and the system is simple in structure, low in failure rate and good in using effect. And the piston rod anti-rotation structures are arranged on the piston rod I and the piston rod II, so that the piston rods can be prevented from rotating, the clamping coaxiality and perpendicularity of the jaw are ensured, a sample is convenient to clamp, and the test precision is improved.

The invention provides a method and a system for evaluating the health degree of audio and video conference equipment, which comprehensively utilize a QFD thought and AHP and AI methods to construct an equipment fault model and a fault risk transfer model, solve the field problem of fault influence dependency analysis among an equipment principle, a real-time operation state and a historical operation state in equipment fault detection, and improve the reliability of the equipment fault detection. The evaluation method comprises the following steps: scanning a basic state in real time and detecting an environment in real time; verifying the integrity of the signal link; carrying out equipment performance standard test; the influence of the hardware loss on functionality is evaluated; performing actual use state testing, including limiting condition testing and fault injection testing; and performing health degree modeling and grading, and establishing a multi-dimensional comprehensive evaluation model. According to the method, a traditional regular maintenance mode is exceeded through multi-dimensional dynamic modeling and a predictive intervention mechanism, real-time environment sensing and hardware loss modeling are fused, a fault window can be pre-judged, the equipment operation fault rate is reduced, and preventive maintenance is achieved.

The invention discloses a maintenance and operation fault dynamic early warning system based on a high-speed railway communication network, and relates to the technical field of high-speed railway communication networks. The invention aims to solve the problems of inaccurate fault early warning, slow response speed and the like in the prior art. The system realizes real-time monitoring and intelligent analysis of a railway line through three-level elastic section classification, four layers of intelligent processing modules and a cross-domain cooperation module. The monitoring intensity is dynamically defined according to the annual average failure rate in the third-level elastic section classification; the four-layer intelligent processing module has the functions of multi-module sensing, edge calculation, digital twinning, decision control and the like; and the cross-domain collaboration module ensures the overall efficiency of the system. According to the invention, the accuracy and timeliness of fault early warning are improved, the maintenance cost is reduced, and a powerful guarantee is provided for safe operation of the high-speed railway. In the future, the system is continuously optimized, more application scenes are explored, and the system performance and the user experience are improved.

The invention discloses a fault prediction method and system for deviation rectification of a laser die cutting and winding all-in-one machine, and belongs to the technical field of intelligent manufacturing and industrial equipment fault prediction.The method comprises the steps that multi-dimensional sensor data of a deviation rectification mechanism is collected and preprocessed; a sequence variability coefficient is calculated through a sliding window, and a high-risk abnormal time period is screened in combination with a dynamic threshold value; an improved Transform prediction model is constructed to carry out offset trajectory prediction; fault judgment is carried out, and the model is deployed in edge computing equipment or a server to realize efficient prediction; according to the fault prediction method and system for deviation correction of the laser die cutting and winding all-in-one machine, prediction errors can be reduced, the early warning advance and accuracy can be improved, redundant calculation can be reduced, and the model efficiency can be improved to adapt to industrial deployment; potential fault recognition and early warning are achieved, the equipment fault rate and the shutdown risk are reduced, and remarkable economic and social benefits are achieved.

The invention discloses a novel power system energy storage optimization and load loss evaluation method in extreme weather, and relates to the technical field of power systems and energy storage optimization. In order to solve the technical defect of lack of a comprehensive extreme weather scene construction method in the existing power system and energy storage optimization technology in the prior art, the technical scheme provided by the invention comprises the following steps: establishing an extreme weather scene; establishing a power distribution network digital model, and simulating a power distribution network operation scene under an extreme meteorological condition; constructing a fault rate model of the electrical element, and generating a comprehensive time-varying fault probability model; obtaining operation parameters of the power distribution network, and analyzing response characteristics of the power distribution network under different operation conditions; calculating a load loss probability and a power shortage expected value, and comprehensively evaluating the load loss risk of the power grid; and the configuration and scheduling strategy of the energy storage equipment is optimized. The method can be applied to planning, operation scheduling and emergency management work of a power system under extreme weather conditions.

The invention provides a switch redundant resource scheduling method and device based on fault prediction, electronic equipment and a medium, and the method comprises the following steps: firstly, collecting multi-dimensional operation state data (such as port state, flow, environment parameters and the like) of a switch, and carrying out the standardization processing to generate a normalized feature matrix; calculating a fault rate estimation value and a health degree score based on the standardized data, and taking the values as prediction inputs; predicting a fault probability by using a deep learning model and combining health degree calibration to obtain an adjusted fault probability; based on the calibration probability and the health degree score, redundant resource priorities are calculated and sorted; finally, path selection is optimized according to the priority, pre-switching redundant resources are dynamically scheduled, rapid switching during faults is ensured, and fault extension is avoided. According to the invention, through reasonable scheduling, path optimization and load balancing, the fault response capability, resource utilization efficiency and stability of the switch system are effectively improved.

The invention relates to the field of suturing systems of medical instruments, and discloses a suturing system. The suturing system includes a control handle, a tube, a receiving cartridge, and a needle. The control handle comprises a shell, a driving mechanism and a rotation indicating piece, the rotation indicating piece is rotatably connected to the shell, and a plurality of position marks are arranged on the periphery of the shell. An actuating shaft in the driving mechanism is coupled to a first gear, an actuating pipe is coupled to a second gear, and the first gear and the second gear are arranged in the longitudinal direction and rotate synchronously. When the rack moves longitudinally, the conversion gear can be driven to rotate so as to drive the first gear to rotate, and the first gear and the second gear respectively drive the actuating shaft and the actuating tube to do longitudinal reciprocating motion, so that the needle is alternately combined with the actuating tube and the receiving bin. The driving mechanism in the sewing system is reliable in structure, and the failure rate can be effectively reduced. In addition, the rotation indicating piece is used in cooperation with a position mark arranged on the shell so that the positions of the shell and the driving mechanism after rotation can be indicated, and operation is convenient.

The invention discloses an automatic pipeline conveying control system driven by liquid level meter signal feedback, and particularly relates to the field of pipeline conveying control, which comprises a data acquisition module, a liquid level state analysis module, a hierarchical control module and a control execution feedback module, the data acquisition module is used for acquiring liquid level height data in the pipeline system and correcting a measurement error; and the real-time liquid level data is compared with a preset liquid level target value, a liquid level deviation value is calculated, and the deviation degree of the current liquid level and the target liquid level is determined. The liquid level meter and the collecting camera are combined, limitation of single equipment is reduced, more accurate and comprehensive data reflecting the liquid level condition can be obtained, the real-time state and the development situation of the liquid level can be accurately known, an accurate basis is provided for follow-up control, when the liquid level is abnormal, countermeasures can be taken in order according to a preset rule, and the accuracy of control is improved. And the failure rate of equipment is reduced, and the maintenance and repair times are reduced.