1832 results about "Strain sensor" patented technology

The invention discloses a wind power bolt state detection method and system, and belongs to the technical field of mechanical structure testing and health monitoring, and the method comprises the steps: obtaining the real-time strain data of each monitoring point; acquiring operation condition data of the unit in real time; dynamically generating a finite element model of the tower drum structure according to the operation condition data; the finite element model and the real-time strain data are combined to generate a stress distribution map of the whole tower cylinder bolts; inputting the stress distribution atlas into a preset fatigue damage model, calculating a bolt life attenuation rate and positioning an external load abnormal source; and when it is detected that the life attenuation rate exceeds a preset threshold value, generating a grading early warning signal and outputting a maintenance strategy according to a positioning result. According to the method, a wireless strain sensor network, dynamic finite element model correction and multi-dimensional fatigue damage analysis technology are adopted, a whole tower barrel stress distribution map is generated in real time, external working condition data are associated, the bolt service life attenuation rate can be accurately evaluated, a load abnormal source can be positioned, and a graded maintenance strategy can be output.

The invention belongs to the technical field of water conservancy and hydropower engineering, and discloses a water conservancy and hydropower engineering construction safety supervision system based on multi-source data fusion. The system comprises a multi-source sensing acquisition module, a heterogeneous data fusion processing module, a risk identification and early warning module, a safety behavior evaluation and feedback module, and a command scheduling and visualization module. According to the invention, by fusing multi-dimensional data such as image monitoring, environment sensing, personnel positioning, equipment state and the like, a space-air-ground three-dimensional sensing network is constructed, and in a high slope area, the distributed optical fiber strain sensors are linked with thermal imaging data of the unmanned aerial vehicle, so that millimeter-level deformation and temperature field abnormity can be captured in real time; a video stream is analyzed in real time by means of a YOLOv8 algorithm, illegal operation behaviors of personnel can be accurately identified, a cross-modal fusion model of a Transform architecture is combined, the system can dynamically capture potential correlation among data, and millisecond-level response to risks such as side slope landslide, equipment faults and personnel dangerous operation is achieved.

The invention relates to the technical field of bridge structure safety monitoring and maintenance, and discloses a road bridge real-time monitoring and maintenance system based on intelligent perception, which comprises a data acquisition module used for acquiring multi-dimensional data of a bridge operation state through a deployed multi-source sensor, the multi-source sensor comprises a vibration sensor, a strain sensor, a temperature and humidity sensor and image acquisition equipment; the data preprocessing module is used for carrying out cleaning, synchronization and feature extraction on the collected multi-dimensional data to obtain dynamic features, environment features and visual features; and a data fusion module. According to the method, the comprehensiveness and accuracy of bridge monitoring are improved through multi-source data fusion, the reliability of an evaluation result is ensured in combination with physical constraint optimization, health trend prediction and early warning functions are realized based on a time sequence model, scientific decision support is provided for bridge maintenance, and the monitoring and management efficiency is improved.

The invention relates to the technical field of intelligent detection in the electronic manufacturing industry, in particular to a PCB warping degree intelligent evaluation system integrating visual positioning and multi-modal sensing, which comprises a multi-modal sensing unit, a visual positioning unit, an intelligent evaluation engine and a closed-loop control unit, the multi-modal sensing unit integrates laser displacement, infrared thermal imaging and strain sensors to acquire three-dimensional deformation, temperature and stress data; the visual positioning unit realizes sub-pixel-level positioning by using a high-resolution industrial camera and a feature point matching algorithm, and compensates vibration errors; the intelligent evaluation engine fuses data based on a time-space synchronization protocol, predicts a thermal deformation trend through an improved multi-modal convolutional neural network, and dynamically adjusts a qualified threshold value; and the closed-loop control unit executes sorting and rechecking according to an evaluation result, and optimizes warping and leveling parameters. According to the system, multi-dimensional accurate detection and intelligent control are realized, the PCB warping degree detection accuracy is effectively improved, the process can be dynamically optimized according to the production working condition, and the equipment fault risk is reduced.

The invention provides an underground cavern surrounding rock parameter collaborative sensing method and system based on multi-source data fusion, and the method comprises the steps: firstly deploying a sensor group comprising an optical fiber strain sensor, a micro-seismic monitoring array, a three-dimensional laser scanner and an acoustic emission sensor, the optical fiber strain sensor is arranged along the axis of a cavern, the micro-seismic monitoring array is deployed according to a triangular network, and the three-dimensional laser scanner is arranged along the axis of the cavern; performing time synchronization on the sensor group through a synchronization pulse signal, establishing a local coordinate system at a central point of a cavern entrance, uniformly collecting data, generating a time-aligned data stream and a space-unified data set, and triggering associated sensors to enter a hierarchical response collection mode according to a strain rate and a vibration signal; and generating a multi-modal monitoring data set, finally carrying out dynamic weight distribution and conflict resolution processing on the multi-modal monitoring data set, extracting surrounding rock deformation characteristics and fracture event density, and outputting a surrounding rock stability evaluation result, thereby realizing underground cavern surrounding rock parameter collaborative perception and stability evaluation.

The invention provides a multi-source heterogeneous data fusion side slope digital twin modeling method, which comprises the following steps of: acquiring side slope multi-dimensional monitoring data by arranging a GNSS (Global Navigation Satellite System) sensor, a multi-point displacement meter, a distributed optical fiber strain sensor, an accelerometer, an osmometer, a monocular camera and satellite remote sensing image equipment; the collected data is converted into a unified format through time alignment, space registration and standardization processing and serves as modeling input; the method comprises the following steps: constructing an initial digital twinborn model reflecting the real form and physical characteristics of a slope by utilizing a three-dimensional modeling and finite element simulation technology; in combination with real-time sensing data, model evolution is dynamically driven based on a space-time fusion algorithm, boundary conditions and material parameters are automatically corrected through actual measurement deviation feedback, and continuous twin iteration updating of the model is achieved; and finally, extracting a landslide risk index to realize real-time early warning of the side slope. According to the invention, multi-source sensing and digital twinborn fusion is realized, and the accuracy, real-time performance and intelligent level of slope monitoring are improved.

The invention relates to the technical field of gate control, and discloses a gate control method and system based on dynamic cooperative scheduling and a storage medium. The method comprises the following steps: carrying out fatigue quantification on gate torque change and adjustment times through a strain sensor to obtain a comprehensive health index; establishing an adjustment frequency and equipment degradation correlation model according to the health index to obtain degradation prediction data; coupling and fusing the water level flow data and the degradation prediction data to obtain a water regimen-equipment coupling prediction result; a coupling prediction result is optimized through an adaptive weight algorithm, and a differentiated scheduling strategy of the healthy gate and the aged gate is obtained; and performing corresponding control processing on the healthy gate and the aging gate to generate a cooperative control instruction sequence. The technical problem that in an existing gate control technology, water regimen changes and equipment state evolution are independently processed, and differentiated collaborative scheduling based on equipment health constraints cannot be achieved is solved.

A robotic surgical system includes one or more robotic actuators configured to interact with biological tissue during a surgical procedure. A plurality of sensors include at least one of fiber Bragg grating sensors, piezoelectric strain sensors, or magnetostrictive sensors to capture real-time mechanical, elasticity, or deformation data from biological tissues. deep learning engine trained on a dataset comprising tissue mechanical responses across multiple tissue types, pathological states, and patient demographics. Pre-contact predictive adjustment profiles are generated for anticipated tissue interactions using preoperative imaging data registered to intraoperative coordinates. Intraoperative deviations are detected from predicted mechanical behavior and autonomously recalibrate actuator forces. Upcoming surgical maneuvers are anticipated based on prior task sequences and adjust actuator stiffness or damping properties in preparation for anticipated contact. An emergency override of actuator forces is provided via an anomaly detection module when real-time sensor data deviates beyond a threshold from the predicted safe mechanical response range. A feedback loop iteratively refines the deep learning engine during the procedure using supervised learning updates, anomaly detection, and reinforcement learning strategies. The reinforcement learning model is optionally shared across procedures to optimize distributed actuator force patterns for minimizing localized and cumulative tissue stress.

The invention provides a tunnel rock mass dynamic disaster monitoring system based on digital twinning, and relates to the technical field of tunnel rock mass dynamic disaster monitoring based on digital twinning. By integrating a multi-mode sensing network, the system comprises a self-adaptive infrared structured light scanner, a distributed micro-seismic sensing array and a fiber grating strain sensor; comprehensive and real-time monitoring of the rock mass state is realized; meanwhile, an intelligent analysis algorithm based on deep learning is introduced, abnormal conditions are automatically recognized, a rockburst risk comprehensive index is generated, in addition, a physical entity and a virtual model are combined through a dynamic digital twinborn modeling technology, and visual analysis of rock mass state changes is achieved; and finally, an edge-cloud cooperative communication technology is adopted to ensure low delay and reliability of data transmission and ensure that the monitoring system can quickly respond and timely take emergency measures.

The invention relates to the technical field of medical instrument control, in particular to a lung biopsy sampling needle stabilizing method and system based on dynamic respiration compensation. The stabilizing method comprises the following steps: acquiring a respiratory movement signal of a patient in real time through a multi-source biosensor, wherein the multi-source biosensor comprises an optical fiber strain sensor array, a diaphragm myoelectricity sensor and a respiratory airflow sensor which are deployed at the chest and abdomen. According to the method, millimeter-microsecond-level accurate and stable control over the lung biopsy sampling process is achieved by establishing a cooperation mechanism of a breathing phase-displacement nonlinear mapping model and a macro-micro composite driving framework. The biological rationality of displacement prediction is improved through deep coupling of multi-mode breathing signals, and the chest wall non-uniform deformation gradient is analyzed in space based on cross topology deployment of an optical fiber strain sensor in an intercostal space area in combination with cross-scale feature fusion of myoelectricity and airflow sensing.

The invention discloses a method for determining the lower limit of plugging thickness of a silt rock stratum based on temperature-stress field simulation, which comprises the following steps: judging that the fracture pressure of a shale reservoir is less than the minimum starting pressure of an overlying silt rock stratum, and carrying out the following steps: wrapping the side surface of a roof rock sample with a heat insulation material; arranging heating and stress devices and temperature and strain sensors on the bottom surface of the rock sample, and arranging a plurality of strain sensors on the side surface of the rock sample at set intervals; heating the rock sample by using a heating device until the bottom temperature is the set temperature, applying stress to the bottom of the rock sample from small to large step by step by using a stress device until the set stress, stabilizing the temperature and the stress until the measured values of the sensors are stable, and determining the lowest position where the strain of the rock sample is not higher than the set strain according to the measured values of the sensors; determining the lower limit of top plate plugging thickness according to the distance from the position to the bottom surface; and similarly, determining the bottom plate plugging thickness lower limit. The method can reasonably quantify the lower limit of the plugging thickness of the top and bottom plates of the shale reservoir through joint simulation of the temperature field and the stress field.

The invention discloses a dynamic compensation intelligent formwork real-time regulation and control method and system, and relates to the technical field of building intelligent control. According to the method, pulse signal sensing and cooperative compensation between nodes are realized by constructing a distributed neural unit network, and a three-level compensation mechanism is triggered when displacement or stress is detected to exceed a threshold value. A generative adversarial network is innovatively adopted to construct a virtual scene library to simulate extreme working conditions, a cross-scene migratable strategy library is generated through feature alignment, and intelligent strategy matching of a new construction site environment is realized. A full-structure rigidity field is dynamically generated based on an elastic mechanical model, a supporting arm rigidity switching path is optimized in combination with an ant colony algorithm, and directional dissipation of wind vibration energy is achieved through triangular rigid network construction. The system adopts a biological heuristic self-organizing algorithm to realize redundant network reconstruction when nodes fail, a supporting arm adopts a shape memory alloy and carbon fiber composite structure, and a micro strain sensor is integrated to realize directional release of corrosion stress. According to the method, the adaptive capacity of the formwork system under the complex working condition is effectively improved, and the structural failure rate is reduced through multi-modal compensation strategy fusion.

The invention relates to a shipbuilding safety monitoring system and a monitoring method thereof, and belongs to the technical field of shipbuilding monitoring, and the system comprises a multi-modal data collection module, an edge computing node, a digital twin platform, a digital twin platform, an analysis module, a block chain traceability module, and a feedback control module. The multi-modal data acquisition module comprises a visual sensor, an acoustic sensor, a thermal imager, a strain sensor, a laser displacement sensor and an environment sensor. The visual sensor, the acoustic sensor, the thermal imager, the strain sensor and the environment sensor are installed in a shipbuilding workshop. According to the invention, by fusing visual, acoustics, thermodynamics and other multi-sensor data and combining edge calculation real-time processing and digital twinning dynamic simulation, process quality prediction, defect detection and carbon emission tracking are realized, and the problems of insufficient real-time performance, difficulty in cross-system collaboration and lack of green manufacturing monitoring in the prior art are solved.

The invention discloses a dynamic geological survey system and a construction simulation method. The dynamic geological survey system comprises an intelligent sensing and data acquisition module and a multi-source data fusion processing module, the intelligent sensing and data acquisition module comprises a Raman scattering optical fiber strain sensor array, a three-component seismometer monitoring network and a distributed optical fiber pressure sensing subsystem; the Raman scattering optical fiber strain sensor array, the three-component seismometer monitoring network and the distributed optical fiber pressure sensing subsystem form a multi-dimensional sensing network for shield construction geological monitoring. By means of the integrated intelligent sensing and data acquisition module, geological changes in the shield construction process can be monitored. The Raman scattering optical fiber strain sensor array, the three-component seismometer monitoring network and the distributed optical fiber pressure sensing subsystem jointly form a multi-dimensional sensing network, geological strain, microseismic events, pore water pressure and other key information can be captured, and comprehensive geological data support is provided for construction.

The invention discloses a power transformer arc discharge multi-parameter detection simulation platform and a fault diagnosis method, and relates to the technical field of power system equipment state monitoring and fault diagnosis. The platform comprises a transformer body, a replaceable discharge module, a multi-parameter sensing unit and a signal processing and diagnosis module, and can truly reproduce various typical arc discharge faults of a needle plate, an air gap, a creeping surface, turn-to-turn and the like. The sensing unit is integrated with ultrahigh frequency and ultrasonic sensing probes, high-frequency current and voltage sensors, optical fiber temperature / pressure / strain sensors and the like, so that synchronous acquisition of multi-physical field signals is realized. According to the diagnosis method, through wavelet denoising and multi-dimensional feature extraction, a feature vector of multi-state parameter fusion in the process from partial discharge to arcing is constructed, and accurate classification of fault types is realized by using a support vector machine (SVM) model. The diagnosis method has high accuracy and early warning capability, effectively overcomes the limitation of single parameter diagnosis, and provides reliable technical support for transformer fault research and intelligent operation and maintenance.

The invention relates to the technical field of rock mechanics and petroleum and natural gas engineering, and particularly discloses a hydraulic fracture fracturing monitoring system, method and device based on optical fiber monitoring, and the system comprises a hydraulic fracturing simulation platform which is used for applying three-dimensional stress to a hydraulic fracturing test piece so as to simulate a stratum stress environment; the fracturing fluid injection device is used for injecting pre-prepared fracturing fluid into the test piece, so that a crack in the test piece expands; a plurality of strain sensor groups embedded in the test piece in the preparation process of the test piece; the optical fiber sensing demodulation device is used for collecting strain data detected by the plurality of strain sensor groups; and the data processing device is in communication connection with the optical fiber sensing demodulation device and is used for acquiring the strain data and processing the strain data so as to determine real-time state data of crack expansion in the test piece. According to the scheme, the fracture strain can be monitored in real time, and the accuracy and controllability of the hydraulic fracturing process are improved.

The invention discloses a method for determining the plugging thickness of a complex lithologic layer based on simplified lithologic combination, and the method comprises the steps: determining the vertical superposition relation of a silty rock layer and a shale layer and the total thickness of the silty rock layer and the shale layer according to the lithologic longitudinal distribution characteristics of an overlying stratum; integrally forming and preparing a roof rock sample by combining the properties of siltstone and shale; arranging a plurality of groups of temperature sensors, pressure sensors and strain sensors on the side surface of the rock sample at set intervals; heating the rock sample until the bottom temperature is the set temperature, pressurizing until the bottom pressure is the set pressure, applying stress to the bottom of the rock sample step by step from small to large until the set stress, and stabilizing at the temperature, the pressure and the stress until the measured value of the sensor is stable; determining the lower limit of the roof plugging thickness according to the measured value of each sensor and the up-and-down superposition relationship; and similarly, determining the bottom plate plugging thickness lower limit. According to the method, through simplified lithology and combined simulation of a temperature field, a pressure field and a stress field, the lower limit of the plugging thickness of the shale reservoir complex lithology roof and floor is reasonably quantified.

The invention discloses a siltstone adjacent layer plugging thickness lower limit determination method of a shale reservoir, which comprises the following steps: judging that the fracture pressure of the shale reservoir is smaller than the minimum starting pressure of an overlying siltstone rock layer, and executing the following steps: arranging heating, pressurizing and stress devices and temperature, pressure and strain sensors on the bottom surface of a roof rock sample; multiple groups of pressure and strain sensors are arranged on the side surface at set intervals; a heating device is used for heating the bottom of a rock sample to a set temperature, a pressurizing device is used for pressurizing the bottom to a first set pressure, a stress device is used for applying stress to the bottom of the rock sample from small to large step by step to a first set stress, and the stress is stable until measured values of all sensors are stable; determining the lowest position where the rock sample pressure is not higher than the second set pressure and the strain is not higher than the set strain according to the measured value of the sensor; determining a roof plugging thickness lower limit; and similarly, determining the bottom plate plugging thickness lower limit. The method can reasonably quantify the lower limit of the top and bottom plate plugging thickness through temperature, pressure and stress field joint simulation.

The invention discloses an energy storage battery state monitoring analysis system and method based on flexible strain sensors, and relates to the technical field of energy storage equipment monitoring, the energy storage battery state monitoring analysis system based on the flexible strain sensors comprises a plurality of flexible strain sensors, a measurement module and an analysis module; the plurality of flexible strain sensors are correspondingly embedded among the plurality of battery cells; the flexible strain sensor is used for generating impedance change by sensing the pressure change of the side surface of the battery cell; the plurality of flexible strain sensors are connected with the measurement module; the measurement module is used for collecting impedance data of the plurality of flexible strain sensors; the analysis module is connected with the measurement module; and the analysis module is used for acquiring impedance data based on the plurality of flexible strain sensors for analysis and calculation to obtain an energy storage battery state monitoring analysis result. The hysteresis of energy storage battery state monitoring analysis is reduced, the accuracy of the monitoring result is improved, and the application range of energy storage battery state monitoring analysis is expanded.

The invention discloses a structural health monitoring device for civil engineering. According to the technical scheme, the structural health monitoring device is characterized by comprising a multi-parameter sensing module used for collecting strain, acceleration, displacement, temperature, humidity, vibration frequency and structural surface crack image parameters of a civil engineering structure; the multi-parameter sensing module comprises a strain sensor group, an acceleration sensor group, a displacement sensor group, a temperature and humidity sensor, a vibration sensor and an image acquisition unit; the strain sensor group is composed of a plurality of fiber Bragg grating strain sensors which are installed at key stress parts of the structure in a distributed arrangement mode. According to the structural health monitoring device for civil engineering provided by the invention, by virtue of the unique multi-parameter sensing module, the advanced data processing module and the flexible and reliable data transmission module, various excellent effects are shown in the field of civil engineering structural health monitoring.

The invention relates to the technical field of artificial intelligence and data processing, in particular to a batholith water gate structure health state recognition method, which specifically comprises the following steps: arranging strain sensors at key positions on the surface of a water gate to acquire strain data to form a training set, and labeling the health state; performing normalization processing on the strain data in the training set by adopting a score normalization strategy in a sliding time window; constructing a health state recognition model of the batholith upper water gate structure, and inputting the normalized strain data into the model to recognize the health state of the batholith upper water gate structure; training the model, and terminating the training until a preset condition is met to obtain a trained model; and inputting newly collected strain data into the trained model to perform health state identification of the batholith upper water gate structure. By capturing and analyzing weak change signals of the water gate structure, potential safety hazards existing in the water gate structure are found in time, and safe and stable operation of the water gate is ensured.

The utility model relates to the technical field of sensors, and provides a fiber bragg grating strain sensor with temperature compensation, which comprises a fiber bragg grating, a sliding block and a prismatic base body, the prismatic base body is of a hollow structure, the sliding block is welded at one end of a diagonal line of the prismatic base body, the fiber bragg grating strain sensor further comprises a deformation plate, and the material of the deformation plate is consistent with that of a measured component. The two ends of the other diagonal line of the prismatic base body are welded with the deformation plate; the fiber bragg grating is welded to the sliding block and the other end, welded to the diagonal line of the sliding block, of the prismatic base body, and the fiber bragg grating is lengthened between the two welding points. Compared with the prior art, a compensation block which is inconsistent with the prismatic base body in material is abandoned, parameters influencing compensation are reduced, and the compensation effect can be better guaranteed.

The invention discloses a high-sensitivity metal micro-strain sensor label and a detection method thereof, which are applied to the field of nondestructive testing and the field of structural health monitoring. The problems that an existing tag antenna is low in strain detection sensitivity and difficult in micro-strain detection are solved. The high-sensitivity metal micro-strain RFID sensing tag is attached to the surface of a component, when the tag deforms, the impedance change of the tag is Zsened, and the radar cross section changes accordingly; the reader can remotely monitor antenna parameters according to the change of the RCS0, the RCS1 and the intensity while communicating with the tag, so that the change of the intensity RSSI of an echo signal is deduced, and the deformation of the tag is indirectly known; when the impedance of the tag antenna changes, the resonant frequency of the tag changes accordingly, and the deformation information of the tag can be indirectly obtained by analyzing the deviation of the resonant frequency.

The invention discloses an electric field fracturing coal seam anti-reflection gas extraction method, and belongs to the field of coal seam gas extraction. S2, generating a three-dimensional geologic model; s3, performing electric field fracturing simulation until an optimal fracturing path is obtained, and taking an electric field parameter corresponding to the optimal fracturing path as an electric field initial parameter; s4, based on the generated three-dimensional coal seam image, a distributed stress sensor, a strain sensor and a displacement sensor are arranged in the fracturing area so as to monitor stress concentration factors, strain and crack length and width changes in the coal seam; s5, applying an electric field initial parameter to the coal seam, and dynamically adjusting the electric field parameter to ensure that an optimal fracturing path is achieved; and S6, gas extraction. By the adoption of the electric field fracturing coal seam anti-reflection gas extraction method, the electric field parameters are dynamically adjusted based on real-time monitoring data, it is ensured that fracturing is conducted along the optimal path all the time, and unnecessary resource waste and potential safety hazards are avoided.

The invention relates to an energy accumulator durability test method and system, and the system comprises an extreme working condition simulation module which can achieve high-voltage, wide-temperature-range and high-frequency dynamic loading; the multi-sensor monitoring network integrates a fiber bragg grating strain sensor, a three-axis acceleration sensor and an acoustic emission sensor to realize collaborative monitoring of strain, vibration and microcracks; the intelligent control and energy recovery module is combined with a fuzzy PID and reinforcement learning algorithm, a magnetorheological fluid generator and a super capacitor bank, so that the control precision and the energy utilization rate are improved; and the data analysis and life evaluation platform accurately predicts the residual life by using an improved Bayesian network model and a real-time health index calculation module. According to the test method, a loading mode is dynamically adjusted through a real-time health index, an energy recovery strategy is optimized, and life early warning is triggered. The invention breaks through the limitation of the traditional test and provides an integrated solution.

The invention relates to the technical field of intelligent medical treatment, and discloses an intelligent knee joint rehabilitation system based on Internet of Things, comprising: a flexible multi-parameter sensing module for collecting a multi-modal biological signal of a knee joint; the edge calculation module is used for processing and analyzing sensing data in real time; the dynamic impedance adjusting module is used for dynamically adjusting impedance parameters of rehabilitation training according to the real-time data; the cloud-side collaborative decision module is used for realizing efficient transmission and collaborative decision of data; and the self-adaptive rehabilitation strategy engine is used for optimizing rehabilitation training parameters and comprises a real-time intensity adjustment module based on an improved TD3 reinforcement learning algorithm. According to the system, the flexible strain sensor array unit, the 9-axis inertial measurement unit and the surface myoelectricity sensing unit are integrated through the flexible multi-parameter sensing module, and multi-dimensional real-time monitoring of knee joint strain distribution, three-dimensional acceleration, angular velocity, magnetic field data and myoelectricity signals is achieved.

The invention relates to an intelligent soft rock tunnel surrounding rock deformation automatic monitoring and early warning system, and belongs to the field of tunnel construction safety monitoring. According to the system, multi-source data of surrounding rocks are synchronously acquired through a micron-sized laser displacement sensor, a fiber grating strain sensor, a pore water pressure sensor and a three-dimensional micro-seismic sensor array which are distributed; the edge calculation node fuses the data in real time and calculates the displacement rate, the strain gradient and the osmotic pressure coupling coefficient, and local early warning is triggered in combination with a dynamic threshold value; the cloud early warning platform predicts a deformation trend based on an LSTM-Transform fusion model, generates a multi-stage early warning signal through a risk grading engine, and is linked with the supporting equipment to regulate and control pressure; and meanwhile, a self-calibration device, an explosion-proof shell and a piezoelectric-thermal energy self-powered system are integrated. According to the method, closed-loop control from millimeter-level deformation sensing to active supporting can be achieved, and tunnel construction safety is remarkably improved.

The invention discloses a respiration monitoring system based on a flexible strain sensor and a monitoring analysis method, and belongs to the technical field of sensors and respiration monitoring equipment. The respiration monitoring system comprises a flexible strain sensor, a connecting line, a data acquisition card and an upper computer; the flexible strain sensor is a band-aid type flexible strain sensor and can be directly adhered to the skin surface of a human body, and the deformation of muscles related to respiration is monitored and converted into the change of a resistance signal. And the flexible strain sensor is connected with the data acquisition card through a connecting line. And the data acquisition card is connected with the upper computer and is used for transmitting the acquired resistance signals to the upper computer. The quantitative breathing mode judgment standard is provided through dynamic measurement of the respiratory muscle group, and the defect that judgment is mainly conducted through naked eyes of doctors for a long time can be overcome; the relation between the respiratory muscle group deformation and the lung function parameters is associated, and the problem that the actual respiration condition of part of patients does not conform to the detection condition in conventional lung function detection can be solved.

The invention relates to the technical field of intelligent traffic systems, in particular to a linear intelligent monitoring system for a bridge fabrication machine, which comprises a multi-source sensing module, a data processing module, a data processing module, a data processing module, a data processing module, a data processing module and a data processing module, and is characterized in that the multi-source sensing module comprises strain sensors, inclinometers, GPS positioning units and machine vision units; the edge computing node is internally provided with a self-adaptive data fusion algorithm and is used for denoising sensor data in real time; the cloud digital twin platform is used for synchronizing the BIM design model and generating a millimeter-level linear deviation early warning signal; the PLC control interface is used for receiving the early warning signal and automatically adjusting parameters of the template system and the walking mechanism; according to the method, environmental noise interference is effectively eliminated through multi-source sensor fusion and a self-adaptive data filtering technology, the monitoring precision is improved to a millimeter level, real-time comparison of construction data and a design model is realized based on a BIM and digital twinning dynamic interaction system, and the timeliness and accuracy of linear control are remarkably improved.

The invention belongs to the technical field of civil engineering monitoring, and discloses an optical fiber strain sensor performance verification method and system for highway infrastructure monitoring, and the method comprises the steps: analyzing the influence rule of the parameter change of a coupling layer on the strain transmission efficiency of a measured sensor based on a layered material coupling mechanical model; based on the three-dimensional entity numerical model, analyzing an influence rule of layout process parameters of the tested sensor on the strain transmissibility; verifying the performance of the sensor in a laboratory environment by using a reduced scale model; on a real highway infrastructure or a constructed full-scale model, the performance of the sensor on an actual structure is verified through monitoring data. According to the invention, by constructing an integrity performance verification system from a theoretical model to numerical simulation and then to a reduced scale model test and entity structure verification, multi-dimensional and progressive performance verification of the performance of the optical fiber strain sensor for highway infrastructure monitoring is realized.