14391 results about "Force sensor" patented technology

The invention provides a pressure sensor data precision compensation method and system for industrial control, and the method comprises the steps: firstly obtaining an original pressure signal sequence outputted by a target pressure sensor in a preset collection period, the original pressure signal sequence containing a plurality of pressure signals arranged according to a timestamp; meanwhile, a real-time environment parameter set, covering temperature, humidity and mechanical vibration parameters in an acquisition period are detected, then the real-time environment parameter set is input into a pre-trained compensation parameter generation model, a corresponding dynamic compensation parameter set is generated, then each signal of an original pressure signal sequence is adjusted step by step according to the dynamic compensation parameter set, and a dynamic compensation parameter set is generated; and finally, the compensated pressure signal sequence is output to a signal processing module of an industrial control system to drive an execution mechanism, so that the data precision of the pressure sensor can be dynamically and accurately compensated by integrating multiple environmental factors, and the accuracy and stability of industrial control are improved.

The invention relates to the technical field of intelligent control, provides a flow regulating valve servo force control method and system based on a force sensor, and aims to solve the technical problems of response delay, weak overshoot suppression capability and poor long-term operation stability. The method comprises the following steps: acquiring a servo driving current data set and a valve displacement track data set of a target regulating valve; performing pressure feature mapping processing on the servo driving current data set to generate a pressure fluctuation feature set corresponding to the driving current waveform data; the pressure fluctuation characteristic set and the valve displacement track data set are input into a preset force control decision model for dynamic matching processing, and a servo control instruction set is generated; executing multi-stage dynamic adjustment operation on a servo driving unit of the target adjusting valve according to the servo control instruction set, and generating real-time pressure balance state data; and iteratively updating the dynamic matching processing parameters of the force control decision model based on the deviation value of the real-time pressure balance state data and the preset pressure reference value.

The invention discloses a method for correcting data of abnormal reading of a pressure sensor, and relates to the technical field of sensor equipment. Comprising the following steps: acquiring sensor readings, constructing a multi-sensor cross validation mechanism, establishing an anomaly detection threshold model, performing anomaly detection based on a time sequence mode, constructing a physical constraint feedback mechanism, performing dynamic drift compensation, processing the acquired readings, and performing self-correction feedback optimization. According to the method for correcting the abnormal reading data of the pressure sensor, through a physical constraint feedback mechanism, the output of the sensor is compared with a theoretical physical model for verification, and the recognition capability of nonlinear abnormality is enhanced; through dynamic drift compensation, linear and exponential drifts generated in long-term use of the sensor are separated and corrected, and accurate correction of the drifts is realized. According to the method, the reliability and the stability of pressure sensor data can be remarkably improved, a more accurate measurement basis is provided for a precise measurement scene, and meanwhile, the risk of system misinformation and failure is reduced.

Disclosed in the present invention are a multi-modal shared teleoperation system and method for a three-arm space robot. The system at least comprises a master-side teleoperation system, a communication module and a slave-side robot system, wherein the master-side teleoperation system at least comprises two force feedback hand controllers, a microphone array and upper computer software, and the slave-side robot system comprises two operating arms each equipped with a gripper at the end, an observation arm having a binocular camera mounted at the end, a vision unit, a force sensor and lower computer software. The method comprises: an operator controlling two operating arms of an extravehicular robot to execute a task, and controlling an observation arm to acquire a better local field of view. In the method, a multi-modal teleoperation method comprising pose control, voice control and force control is fused with autonomous control of a robot by means of a shared control algorithm, and thus, human-robot collaborative control over the position, orientation and contact force of a robotic arm can be realized on the basis of the requirements of the operator, and the robot autonomously executes other relatively simple tasks, thereby reducing the operation burden of operators, and improving the control efficiency.

The invention provides a magnetic core intelligent cutting parameter self-adaptive optimization system based on multi-mode perception, and relates to the technical field of data processing.The method comprises the steps that a multi-mode sensor module is integrated on magnetic core cutting equipment, and the module comprises a force sensor, a visual sensor and a temperature sensor; the acquisition units are respectively used for acquiring cutting force dynamic signals, cutting track image sequences and cutter temperature time sequence data in real time; magnetic core surface texture features and three-dimensional contour data are captured through a visual sensor, and an initial cutting parameter set is generated in combination with a magnetic core material type recognition result, associated parameters in a historical process database and preset process constraint conditions; and first workpiece trial cutting is executed based on the initial cutting parameter set, multi-modal data fusion collection is synchronously started, cutting force frequency domain feature vectors, a tool temperature change rate curve and cutting surface defect image features are obtained, and multi-modal data are obtained. According to the invention, multi-objective collaborative optimization of processing efficiency and energy consumption is realized.

A multimodal shared telerobotic system and method for a three-arm space robot, the system at least includes a local-site system, a communication module, and a remote-site system, where the local-site system includes two force-feedback haptic devices for left and right hands, a microphone array, and upper computer software; the remote-site system includes two robotic arms provided with end-effectors, an observation arm with a stereo camera installed at an end thereof, two force sensors, a vision unit and lower computer software; an operator can control the two robotic arms of the robot outside a cabin for performing operations, and control the observation arm to obtain a better local view; and a multimodal telerobotic control method of pose control, voice control, and force control is integrated with the robot's autonomous control through a shared control algorithm.

The invention discloses a grinding and polishing control method and system based on sliding mode control, and relates to the field of robot industrial control. The method comprises the steps that a force signal of a force sensor at the tail end of a mechanical arm is collected and decomposed into a normal force component perpendicular to the surface of a workpiece; determining a deviation value between the normal force component and preset target pressure, and constructing a sliding mode surface expression of sliding mode control; determining a force control component according to the force control gain coefficient; position deviation and speed deviation are determined according to the position signals of the joint encoders and a preset polishing track; determining a position control component according to the position deviation, the speed deviation, the position gain coefficient and the speed gain coefficient; constructing a selection matrix, and performing orthogonal decomposition and superposition on the force control component and the position control component to obtain a comprehensive control instruction; and the driving signals are converted into driving signals of the joint motors, and the driving signals are transmitted to the mechanical arm for grinding and polishing. By implementing the method, force control of robot grinding and polishing can be optimized, and response delay is reduced.

The invention relates to the technical field of grinding, in particular to a self-adaptive force control grinding system and method for a complex curved surface machining robot. Three-dimensional point cloud data of a workpiece are obtained through machine vision, gridding processing is carried out, and normal vector and curvature characteristic parameters are calculated; performing operation area division and operation path planning based on curvature characteristics; calculating operation parameters according to the normal vector and curvature characteristics; a self-adaptive impedance control algorithm is adopted, the polishing force is monitored in real time through a force sensor, and the rigidity and damping parameters of the robot are dynamically adjusted; and performing defect identification on the surface image of the processed workpiece by using the deep learning model, and when defects are identified, re-planning the operation path. According to the method, self-adaptive grinding of the complex curved surface workpiece is achieved, and the grinding efficiency and quality of the complex curved surface can be improved.

A peritoneal dialysis (“PD”) system having an ultrafilter is disclosed herein. In one example, the PD system includes a housing and a PD fluid pump. The PD system also includes a filter comprising an outer chamber, a central portion, a membrane, an inlet connected to the outer chamber, an outlet, a first venting port connected to the outer chamber, and a second venting port connected to the central portion. The first venting port has a valve preventing air flow into the filter via the first venting port. The PD system further includes a pressure sensor and a control unit configured to control the PD fluid pump. The control unit is further configured to determine a pressure inside the filter based on an output from the pressure sensor and determine an integrity status of the membrane based on the pressure inside the filter.

The invention provides an intelligent pressure sensor temperature compensation system, and relates to the technical field of pressure sensors, and the system comprises a hardware sensing module which comprises an elastic sensitive element and is used for reducing the temperature drift magnitude of a pressure sensor; the environment data acquisition module comprises a flexible thermoelectric sensor array and is used for acquiring temperature and humidity data; the humidity correction module is used for integrating a dynamic humidity correction algorithm and establishing a cross-sensitive matrix model to correct the original signal to generate a first pressure correction signal; the temperature compensation module is used for carrying an incremental neural compensation network and constructing a compensation model to perform temperature compensation on the first pressure correction signal to generate a second pressure compensation signal; and the fault compensation module is used for triggering a digital twinborn compensation model to perform fault compensation on the second pressure compensation signal to generate a target pressure output signal when a fault of the single sensor is monitored, so that the pressure measurement error in the whole temperature range can be accurately controlled, and the environmental adaptability and the intelligent level of the system are improved.

The invention discloses a seeding monomer seeding depth and suppression control method and a control device thereof.The method comprises the steps that control parameter data are obtained, and the control parameter data comprise a seeding mode and / or suppression / pressing parameters; determining an operation mode based on the control parameter data; when the operation mode is a downward pressing mode and a pressing mode, a pressing hydraulic cylinder and a downward pressing hydraulic cylinder are adjusted to stretch and retract in real time based on data collected by a pressing force sensor and data collected by a downward pressing force sensor, so that target pressing force and target downward pressing force are kept; and when the operation mode is a pressing / pressing cooperation mode, data collected by the pressing force sensor and the pressing force sensor are obtained at the same time, and the pressing hydraulic cylinder and the pressing hydraulic cylinder stretch out and draw back at the same time, so that the target pressing force and the target pressing force are kept. According to the scheme, on-demand switching of the pressing mode, the pressing mode and the pressing / pressing cooperation mode can be achieved, and compared with a fixed mode in the prior art, higher adaptability and flexibility are achieved.

The invention discloses a coal mine pressure risk assessment method based on complex system dynamics, belongs to the technical field of mine pressure risk assessment, is used for mine pressure risk assessment, and constructs a complex system dynamics model, combines various sensor data, and carries out real-time monitoring and risk assessment of mine pressure changes. The method comprises the steps of geological data acquisition, sensor data real-time monitoring, data preprocessing, mine pressure prediction model construction, model training and optimization, real-time risk assessment and the like, data of a stress sensor, a displacement sensor and an acoustic emission sensor are comprehensively utilized, and data preprocessing is performed by adopting a Kalman filtering algorithm; and optimizing the mine pressure prediction model by using a graph convolutional network and an attention mechanism, and performing real-time risk assessment through an adaptive graph neural network. According to the method, the adaptive graph neural network is adopted for real-time risk assessment, potential risks in mine pressure changes can be found in time, risk scores are generated, and scientific decision support is provided for coal mine managers.

The invention discloses a track generation method and device for casting blank deburring and a medium, and relates to the technical field of grinding process.The track generation method mainly comprises the steps that according to FPFH feature descriptors of respective outer contours of an actual point cloud model and a standard point cloud model, key points matched with the standard point cloud model are searched through a KD tree, and the FPFH feature descriptors of the outer contours of the actual point cloud model and the standard point cloud model are obtained; performing multi-contour segmentation division of the actual point cloud model according to the distance between the key points; receiving polishing parameters input by the user based on the segmentation division results of the contours, and generating an initial track according to the polishing parameters; smoothing discrete trajectory points of the initial trajectory through B-spline interpolation, and generating a local obstacle avoidance path in combination with an industrial robot kinematics model and a fast expansion random tree star algorithm; outputting the final polishing track to a control terminal, and adjusting the track depth in real time according to the feedback of the force sensor. According to the method, regional differentiation track generation is supported, and the process adaptability and the personalized demand response capability of a complex contour are improved.

According to the technical scheme, the automatic feeding system comprises a feeding bin, a pneumatic conveying pipeline, an air source device and a discharging bin, and further comprises a distributed sensor set used for monitoring the flux, pressure, temperature and vibration frequency spectrum of a preset point of the pneumatic conveying pipeline; comprising a flow sensor, a pressure sensor, a temperature sensor and a vibration acceleration sensor which are arranged at a plurality of preset points of a conveying pipeline, and a digital twin model building module which is used for building three-dimensional modeling of the pneumatic conveying pipeline and mapping state parameters of all positions of a pipeline distribution sensor group in real time; the blockage analysis module is used for marking a blockage position to three-dimensional modeling in real time based on data acquired by the distributed sensor group, extracting a multi-parameter change curve in a time period T before blockage, constructing a blockage feature library, and generating a blockage risk level through a weight change rate difference value of a feeding bin and a discharging bin; and the graded dredging execution module is configured with a differentiated dredging strategy, and the differentiated dredging strategy is started according to the blockage risk grade.

The invention provides a three-dimensional micro-nano spherical multi-parameter synchronous mapping method based on a longitude and latitude type scanning track, and belongs to the technical field of micro-nano device and cell surface physical property characterization methods. In combination with an eccentric distance real-time compensation control algorithm, latitude scanning path parameters are established, online compensation of eccentric displacement is realized in the process of rotating along the latitude, and a normal bending deformation signal of a probe micro-cantilever is used as feedback through an upper computer, so that displacement control of a scanning positioning platform is realized. And thus, synchronous scanning imaging of the surface morphology and the nano-mechanical property of the sample is realized. A probe integrated with a protuberant tip structure is used as a micro-force sensor, a novel controllable vector angle single-point servo detection method is combined, a normal bending signal of the probe is used as feedback, and synchronous measurement of the three-dimensional surface topography and the three-dimensional surface mechanical property of a micro-nano sphere sample is realized along a longitude and latitude scanning path.

The invention discloses a force and position monitoring and analyzing system applied to a press machine, and relates to the technical field of force and position monitoring of the press machine, the force and position monitoring and analyzing system comprises a data acquisition module which is provided with a force sensor and a displacement sensor, and force and displacement data of the press machine in the punching process are monitored and acquired in real time through the force sensor and the displacement sensor. According to the force and position monitoring and analyzing system applied to the press machine, force and displacement data are accurately collected in real time through the data collecting module, the optimal action parameters of the press machine can be calculated, and the punching action of the press machine is accurately controlled; in this way, the key parameters in the stamping process can accurately reach the preset target, the product quality problem caused by parameter deviation is effectively avoided, and the quality stability and consistency of products are greatly improved; the system can quickly adjust action parameters of the press machine according to real-time monitoring and analysis results, so that the stamping process is always kept in an optimal state, time waste caused by links such as debugging and reworking is reduced, and the production efficiency is remarkably improved.

The invention discloses a stress analysis method for bridge incremental launching construction, and particularly relates to the field of stress analysis. According to the method, a mechanical state matrix is generated by combining wavelet noise reduction and Kalman filtering through distributed optical fibers, a piezoelectric force sensor, a vibrating wire stress meter and other equipment and real-time data; a pushing system-temporary support-beam body coupling dynamic mechanical model is established, and a generalized alpha method is adopted for solving and Bayesian optimization calibration parameters are adopted; key parameters are identified on line through a three-level progressive framework, boundary conditions are dynamically updated in combination with a total station and a laser tracker, and supporting rigidity self-adaptive adjustment is achieved through fuzzy PID control. Model confidence evaluation adopts a residual norm-modal confidence criterion-Bayesian update three-level mechanism, finally, stress distribution, support reaction and displacement deviation are calculated based on a correction model, and a safety index, threshold alarm and risk analysis report is generated through a fuzzy comprehensive evaluation method and fed back to a construction control center in real time.

Embodiments of the invention provide a pen-stylus that comprises a first antenna having a hemispherical shape at a proximal end of the pen-stylus where the pen-stylus engages with a display of a tablet device. The pen-stylus also includes a second antenna, wherein the first antenna and the second antenna communicate with the tablet device to enable a pen-stylus user to draw on the display of the tablet device. The pen-stylus additionally includes an insulating material between the first antenna and the second antenna that prevents interference between the first antenna and the second antenna. In some embodiments, the first antenna and the insulating material reside in a marker tip attached to the pen-stylus, the marker tip transmits axial forces received by engagement of the pen-stylus with the display on the tablet to a force sensor. The pen-stylus further includes a force sensor that converts axial forces received from the marker tip into an electrical signal and transmits the electrical signal to the tablet by at least one of the first antenna and the second antenna.

According to the drilling, sealing and injecting integrated anchor rod drilling intelligent monitoring system and method, a rod body is sequentially provided with a head-section wave-shaped threaded section, a middle-section large threaded section and a tail-section wave-shaped threaded section from the head end to the tail end, and a grouting channel is formed in the axis of the rod body; the drill bit and the round nut are respectively mounted at the head and tail ends of the rod body; the anti-shearing tray, the constant-torque pressing sheet and the hexagon nut are arranged on the outer side of the tail-section wave-shaped thread section in a sleeving manner; the built-in mixer is inserted into the grouting channel; the drilling machine is connected with the drilling, sealing and injecting anchor rod through a monitoring sensor arranged outside the hexagon nut in a sleeving mode. A strain type force sensor, a rotating speed sensor, a high-frequency vibration sensor, a well deviation instrument and a load sensor are arranged in the monitoring sensor. The method comprises the steps that a drilling machine rotates forwards to drive a drill bit to conduct drilling operation, deslagging operation is conducted synchronously, and in the drilling process, various sensors are used for collecting various parameters in real time; pre-tightening is conducted through reverse rotation of the drilling machine; a built-in mixer is used for grouting operation; and blocking treatment. According to the system and the method, the operation process can be intelligently monitored.

The invention provides an industrial robot multi-station intelligent sorting and stacking method based on visual guidance, which relates to the technical field of robot control, and comprises the following steps: determining a target station by acquiring multi-station real-time state information, calculating an optimal grabbing time window by using a graph neural network state predictor, and obtaining a target grabbing time window; and the placement strategy is dynamically adjusted based on the visual image and the force sensing data in the article stacking process. According to the multi-station intelligent stacking system, the sorting efficiency in a multi-station scene can be improved, the grabbing precision of dynamic objects is enhanced, and stable and reliable intelligent stacking operation is achieved.

The invention discloses a lower limb exoskeleton gait track prediction method based on an LSTM-KAN fusion model, and the method comprises the steps: collecting human motion data through a sensor assembly, and carrying out the filtering, missing value processing and normalization of the human motion data; then constructing an overall architecture of a prediction model based on an LSTM-KAN network, optimizing parameters of the prediction model by using a particle swarm optimization (PSO) algorithm, extracting key features in the preprocessed data as a training data set, and inputting the training data set into the prediction model for training; and finally, collecting current human body motion data, pre-processing the current human body motion data, inputting the pre-processed current human body motion data into the trained prediction model, and outputting future human body gaits and tracks by the prediction model. And the controller takes a future gait track generated by the prediction model as a reference track to generate a driving signal and sends the driving signal to the actuator so as to realize accurate control of the actuator. By adopting the gyroscope sensor, the acceleration sensor and the pressure sensor for gait estimation, exoskeleton motion control can be effectively improved, and man-machine interaction experience is effectively improved.

The invention relates to an AI server cooling water pump vibration quantity self-adaptive calibration test method and system, an intelligent test platform integrates a six-dimensional force sensor and a temperature compensation vibration exciter, the platform rigidity is automatically calibrated, a water pump-pipeline system transfer function is obtained, and a rotating speed-lift-modal frequency three-dimensional mathematical model is established; a three-axis MEMS accelerometer array is arranged at sensitive parts such as a water pump bearing seat and a motor shell, and vibration, current and pressure signals are synchronously collected; carrying out time-frequency domain signal processing by adopting variational mode decomposition in combination with self-adaptive S transformation, and extracting a 128-dimensional full-frequency domain feature vector containing a modulation side frequency band; effective values of vibration acceleration, speed and displacement are calculated through a frequency domain integration algorithm, and current harmonic interference is corrected through an electromagnetic vibration compensation model; and finally, a vibration health degree evaluation model containing 18 characteristic parameters is established based on a support vector machine, the test system comprises an intelligent test platform unit, a multi-source sensing unit, an edge calculation unit and a data management unit, and microsecond-level synchronous acquisition and GB-level data throughput are realized through a time sensitive network. And online incremental learning and automatic generation of a test report are supported. The method has the effect of improving the water pump vibration quantity test precision.

The invention relates to the technical field of sickbed control, in particular to a control system of an intelligent bed and a control method thereof. The method comprises the following steps: acquiring structural data of the intelligent bed; carrying out human body fitting area gravity sensor deployment on the intelligent bed structure data to obtain sickbed sensor deployment data; performing gravity sensor data acquisition on the sickbed sensor deployment data to obtain an initial gravity sensor data set; carrying out data matrix processing on the gravity sensor data set to generate a multi-dimensional pressure sensing matrix; acquiring user pain part data and bed board motor initial position data; and performing pressure distribution mode recognition on the multi-dimensional pressure sensing matrix to obtain a uniform distribution mode and a concentrated distribution mode. According to the invention, through the personalized support regulation, global attitude optimization and real-time feedback mechanism of the intelligent bed, the control accuracy and intelligence of the intelligent bed are improved.

The invention relates to the technical field of unmanned aerial vehicle cooperative hoisting, and provides a multi-unmanned aerial vehicle cooperative hoisting system and an unmanned aerial vehicle module, the unmanned aerial vehicle module is composed of two or more unmanned aerial vehicles, and each unmanned aerial vehicle is equipped with an airborne computer, a flight control assembly, a power assembly, a space positioning sensor, a tension sensor and a communication unit; the spatial positioning sensor is used for acquiring spatial position data of the unmanned aerial vehicle; the tension sensor is used for acquiring tension data of goods hoisted by the unmanned aerial vehicles, and the airborne computer is in data connection with a communication unit of the unmanned aerial vehicles and is used for calculating real-time lift force and attitude adjustment amount of the unmanned aerial vehicles based on the tension data and spatial data acquired by the unmanned aerial vehicles and controlling flight parameters of the multiple unmanned aerial vehicles according to the real-time lift force and attitude adjustment amount; according to the invention, through a multi-source fusion positioning and sliding mode adaptive hybrid control algorithm, cooperative positioning and dynamic load balancing of the unmanned aerial vehicle group are realized, and the overall stability and anti-interference capability of the hoisting system under complex disturbance are ensured.

The invention discloses a quadruped robot autonomous decision control system based on multi-modal perception, and belongs to the technical field of robot motion, and the system specifically comprises the steps: collecting visual, force and sound information through multiple sensors of a camera, a force sensor and a microphone array, and carrying out the preprocessing; according to the processed information, a diagonal gait, leg track and motion path planning scheme is generated by adopting a motion planning strategy; generating a specific control instruction to drive the robot to move, and monitoring and adjusting in real time; after the task is completed, the motion performance of the robot is evaluated, and a motion planning and control method is optimized according to an evaluation result; the autonomous motion ability and adaptability of the quadruped robot in a complex environment are improved.

The invention provides a dexterous hand multi-object stable grabbing method and system based on vision and touch perception. The dexterous hand multi-object stable grabbing method comprises the steps that RGB images and depth maps of target objects are collected through a monocular RGB-D camera, and dense point cloud data are generated; geometric features are extracted by using a three-dimensional point cloud neural network, and composite object distribution features are analyzed in combination with visual information; an initial grasping strategy is generated based on the features, actions are executed, and the joint state and contact force data are synchronously obtained in real time through body sensing and a fingertip force sensor; in the carrying process, multi-dimensional force perception and the joint state are fused to analyze object stress distribution and center-of-gravity shift; vision and force sensing data are used as input. Vision, body sensing and force sensing data are fused, the problems of three-dimensional structure recognition and dynamic stress regulation and control in multi-object composite grabbing are solved, compared with a single sensing scheme, the grabbing planning precision and the self-adaptive capacity in a complex scene are remarkably improved, and the method is particularly suitable for stable carrying of combinations of loose objects such as dinner plates carrying tableware.

The invention discloses a novel textile fabric tensile strength detection device, and relates to the technical field of cloth detection equipment. The device comprises a base, a pressing block is arranged at the top of the base, push plates are arranged on the two sides of the pressing block, and first pressure sensors are installed in the push plates; an adjusting assembly is arranged on the top of the base. A rotating shaft, a gear and a toothed plate of the adjusting assembly are matched with a hydraulic rod and a clamping plate of the clamping assembly, accurate clamping and fan-shaped stretching of the side edge of cloth are achieved, and the defect that a traditional device can only conduct horizontal tension detection is overcome; an adjusting plate, an adjusting groove and a moving shaft of the linkage assembly are driven by a screw rod and a moving sleeve of the driving assembly, so that the cloth generates fan-shaped deformation under the action of lateral tension, the tensile strength of the side edge of the cloth can be evaluated more comprehensively in combination with dual monitoring of a first pressure sensor and a second pressure sensor, the accuracy of detection data is remarkably improved, and the detection efficiency is improved. The device is especially suitable for testing high-strength fabrics with easy-to-tear side edges.

The invention relates to the technical field of automatic manufacturing, and discloses a metal pipe part welding path intelligent planning and monitoring system which comprises a path planning module used for generating an optimal welding path according to the geometrical shape of a metal pipe part, welding process requirements and sensor feedback information; the sensor module monitors key parameters in the welding process in real time through a visual sensor, a thermal imaging sensor and a force sensor; the data processing and analyzing module is used for processing and analyzing the real-time data acquired by the sensor; the welding control module is used for controlling operation of welding equipment according to path planning and sensor feedback data; and the quality monitoring and feedback module is used for monitoring the quality in the welding process in real time. According to the system, the welding path is generated through the intelligent path planning module, and the path is dynamically optimized in combination with real-time sensor data, so that the accuracy of the welding process is improved, the system can adapt to changes in a complex welding environment, and the welding efficiency and quality are improved.

The invention discloses an intelligent health management system and method based on edge calculation and reinforcement learning. The system comprises an old people data acquisition module, an edge calculation module, a multi-modal fusion module, a reinforcement learning intervention module and a block chain evidence storage module. Physiological and environmental data are collected through a wearable device, a millimeter-wave radar, a pressure sensor and the like, data noise reduction and anomaly detection are realized at an edge end, a cloud end adopts a CNN-LSTM neural network to fuse multi-modal features, reinforcement learning is combined to dynamically generate personalized intervention actions, intervention behaviors and results are stored through a block chain technology, and the intervention effect is improved. And safe and credible strategy optimization is realized. According to the invention, continuous monitoring and intelligent intervention of the health state of the old people can be realized, and intelligence and credibility of community health management are improved.

The invention relates to an implantable vascular support system (10), comprising:—a fluid channel (13) passing through the support system (10) and through which fluid can flow; —a first pressure sensor (18a, b) arranged and configured to determine at least a static pressure or a total pressure in the region of the support system (10); —a second pressure sensor (17) arranged and configured to determine at least a static pressure or a total pressure in the region of the fluid channel (13).