1471 results about "Strain sensor" patented technology

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 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 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 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 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 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.

The invention discloses a multi-mode-based flexible wearable heart function monitoring method, system and device, and relates to the technical field of health monitoring, and the method comprises the steps: outputting a multi-mode signal through a flexible patch sensing module when the flexible patch sensing module is arranged in a detection area; wherein the flexible patch sensing module comprises an electrocardio sensor, a pulse wave sensor, a heart sound sensor, a strain sensor and a microfluidic biosensor. According to the invention, high-precision synchronous signal acquisition is realized through flexible patch integrated multi-modal sensing, heart multi-source signal features are adaptively extracted in combination with a multi-scale convolutional neural model, and timing sequence relevance is captured by using a bidirectional long-short-term memory network, so that the accuracy and reliability of heart disease diagnosis are remarkably improved, and the accuracy and reliability of heart disease diagnosis are improved. Meanwhile, non-invasive and continuous monitoring and early pathological screening are supported.

The invention relates to the field of bridge construction, and discloses a hexagonal-section large-cut-angle and dip-angle combined tower steel-concrete combined section construction method which comprises the following steps: arranging a strain sensor group and a laser calibration target spot on the outer wall of a steel shell, establishing a pose parameter mapping model through multi-source data fusion, and resolving the six-degree-of-freedom pose of the steel shell in real time; constructing a load matrix based on the strain data, and solving a target stress value of the support rod by adopting a maximum flow-minimum cut algorithm; by adjusting the telescopic amount of the telescopic supporting rod in stages and combining servo motor driving and a closed-loop feedback correction mechanism, balanced distribution of supporting force is achieved; the weight coefficient of the multi-objective optimization algorithm is dynamically adjusted, and the pose precision and the structure safety requirement are balanced; and finally, after the pose convergence and stress balance conditions are met, the driving device is dismantled, and the supporting rod body is reserved to serve as a permanent reinforcing structure. The technical problem that in a traditional method, the steel shell pose precision and the stress balance of a supporting system are difficult to consider at the same time is solved.

The invention relates to the field of rehabilitation management, and discloses an orthopedic patient rehabilitation process tracking system comprising a patient data acquisition module used for acquiring joint motion parameters and three-dimensional skeleton point cloud data of an orthopedic patient; the muscle model building module is used for building a skeleton model of the orthopedic patient, defining skeleton degree-of-freedom motion of the skeleton model and building a specific skeleton-muscle model; the monitoring network construction module is used for determining a distributed flexible strain sensor and an epidermal electrode of the orthopedic patient, defining an ad hoc network protocol of the distributed flexible strain sensor and the epidermal electrode, and constructing a flexible electronic monitoring network; the monitoring network data acquisition module is used for monitoring local muscle electrical activity and mechanical stress distribution of the orthopedic patient; and the rehabilitation process tracking module is used for executing rehabilitation process tracking of the orthopedic patient after analyzing a function recovery curve of the orthopedic patient by using the joint contact force and the ligament strain energy. The evaluation accuracy of the rehabilitation process of the patient can be improved.

The invention relates to the technical field of friction plate detection, in particular to a friction plate damage identification and life prediction method and system based on multi-modal fusion, and the method comprises the following steps: synchronously obtaining a temperature field, a strain field and an acoustic signal of a friction plate through an infrared thermal imager, a strain sensor and an acoustic sensor; analyzing a damage identification result and a service life prediction result of the friction plate based on the synchronous temperature field and strain field through a thermodynamic two-way coupling model; and establishing synchronous mapping association between the sound signals and the damage identification result and the life prediction result by using a neural network, and forming a multi-modal fusion model for carrying out damage identification and life prediction on the friction plate according to the sound signals. According to the method, a friction plate detection process formed by a large-data-volume and complex thermodynamic coupling calculation process is migrated to a small-data-volume and easy-to-monitor acoustic signal by using the neural network, so that a high-precision detection result can be quickly obtained by using the acoustic signal, and the lightweight of friction plate detection is achieved.

The invention discloses a biaxial flexible strain sensor based on laser induced graphene. Comprising a flexible substrate, a flexible sensitive layer, a flexible spacing layer and a flexible upper cover, both the flexible substrate and the flexible upper cover are made of a polydimethylsiloxane (PDMS) material, the flexible sensitive layer is generated on a mixed substrate of polyimide (PI) and multi-walled carbon nanotubes (MWCNTs) through a laser ablation process, and then the sensitive layer is transferred to the PDMS substrate through a substrate transfer process. The flexible sensitive layer adopts an I-shaped structure, and two ends of the flexible sensitive layer are coated with electrode layer external wires. The method can be applied to the fields of wearable electronic equipment and medical monitoring. By means of the I-shaped structure, the sensitive layers in different directions can detect the strain in the main direction, and meanwhile interference caused by the strain in different directions is avoided. The preparation method is simple and efficient, a special gas environment is not needed, patterning preparation of graphene can be completed only through one step, and the production cost is reduced.

The invention discloses a catastrophe early warning system for a tunnel passing through an active fault zone, and relates to the technical field of tunnel early warning, the system comprises a multi-mode sensing layer used for obtaining micro-seismic wave, vibration, inclination and strain data along the tunnel fault zone, the multi-mode sensing layer comprises an optical fiber distributed sound wave sensing unit DAS, a data processing unit DAS and a data processing unit DAS, the optical fibers are arranged along the inner side of the tunnel lining at intervals of 10m and are used for monitoring tiny sound wave signals of a fracture surface in real time; the MEMS accelerometer arrays are arranged at four corners of each section and are used for detecting high-frequency vibration; gNSS and INS combined inclination strain sensors which are installed on the top plate and the side wall of the tunnel and are used for obtaining millimeter-level inclination and strain changes; and the acoustic cavity sensor is arranged in a near area of the sliding surface of the fault zone and is used for capturing an air sound wave precursor signal. According to the invention, through multi-mode fusion sensing, the function of capturing fracture micro-shock, vibration and deformation in all directions is realized, the problem of missed alarm and false alarm of single sensing is solved, the sensitivity order of magnitude is improved, and the false alarm rate is reduced.

The invention provides a parameter monitoring method and system in a composite material preparation process based on optical fiber sensing, computer equipment and a medium, and belongs to the field of composite material detection. Center wavelength signals in the resin diversion stage are continuously collected through optical fiber strain sensors in a distributed optical fiber grating sensing array, and the distributed optical fiber grating sensing array is embedded into the multiple layers of dry carbon fiber cloth in the fiber preform in advance; the flowing position and time of the resin in the fiber prefabricated body are determined according to the minimum value of the central wavelength signal offset in the resin flow guide stage, and the flowing front edge of the composite material is constructed according to the flowing positions and time of the resin corresponding to the optical fiber strain sensors in the fiber prefabricated body. According to the method, the change of the structure state of the composite material in the resin diversion process can be continuously monitored in a complex variable-temperature environment, and the accurate monitoring of the three-dimensional resin flowing front edge in the composite material liquid forming process is realized.

The invention provides a low-temperature-resistant performance evaluation method for a composite material, and belongs to the technical field of material performance determination.The method comprises the steps that firstly, in a low-temperature environment box, a temperature sensor is used for accurately controlling the temperature of a sample, and meanwhile, a high-frequency strain sensor is used for collecting data; then, wavelet transform is adopted to carry out denoising and decomposition on the strain signals, and key low-temperature characteristic parameters are extracted; and constructing a performance evaluation model based on deep learning, inputting the temperature field, the strain characteristics and the material basic parameters into the model, and predicting the low-temperature strength. A prediction result is verified through a standard test, a prediction error is calculated, an error distribution matrix is established, and an improved back propagation algorithm is adopted to optimize the model. Finally, a fuzzy comprehensive evaluation method is applied, indexes such as strength retention rate and strain stability are comprehensively considered, and the low temperature resistance grade of the composite material is scientifically evaluated. The method solves the problem that the actual mechanical properties of the composite material in the low-temperature environment are difficult to comprehensively reflect in the prior art.

The invention relates to the technical field of steel wire preparation, in particular to a high-strength prestressed steel wire preparation device and a use method thereof.The device comprises a pay-off mechanism, a pretreatment unit, a drawing mechanism, a heat treatment furnace, a cooling device, a stress applying mechanism, an online stress detector and a take-up mechanism; the stress applying mechanism comprises a tension roller set driven by a servo motor, a hydraulic damper and an on-line stress detector, and the on-line stress detector monitors the strain of a steel wire in real time through an optical fiber strain sensor and is linked with the PLC control system; dynamic prestress control and a multi-stage heat treatment process work cooperatively, so that the tensile strength of the steel wire stably reaches 1570 MPa or above, a closed-loop control tension system and an online detection technology are adopted, the prestress applying precision is high, automatic optimization of process parameters is achieved through an intelligent PLC system, the product percent of pass is greatly increased, and the production cost is reduced. And the energy consumption and the rejection rate are greatly reduced.

The invention relates to the technical field of actuation mechanism fatigue life evaluation, and discloses a data coupling actuation mechanism fatigue life evaluation method and system, and the system comprises a plurality of strain sensors and an evaluation server. According to the method, a sensor collects strain and load data to generate a fatigue state data set, a server constructs a regional stress distribution matrix, fatigue damage distribution characteristics are determined, and a maintenance strategy is dynamically adjusted. And when the distribution characteristics are determined, the matrix is processed to extract the characteristics, modeling is performed to divide regions, stress intensity gradient is calculated to predict damage distribution, spatial-temporal distribution characteristics are output through the coupling evaluation model, and a data set is updated. Maintenance strategy adjustment comprises mapping area identification, control load adjustment and other actions, and maintenance resources are dynamically distributed. According to the method, multi-dimensional data coupling analysis is realized, the fatigue life is accurately evaluated, the maintenance strategy is dynamically optimized, and the evaluation accuracy and the maintenance efficiency are improved.

The invention relates to a flexible strain sensor for ship cable safety monitoring and a preparation method and application thereof. The flexible strain sensor comprises a flexible substrate layer, a composite conductive sensitive layer and an environment protection packaging layer. The flexible substrate layer is an elastomer grid with a three-dimensional microstructure pattern and is used for bearing and transmitting mechanical stress. The composite conductive sensitive layer is attached to the surface of the three-dimensional microstructure of the flexible substrate layer and used for forming a conductive path changing along with deformation and converting mechanical deformation into a resistance signal. And the environment protection packaging layer coats the flexible substrate layer and the composite conductive sensitive layer, adopts an elastomer component which is homogeneous with the flexible substrate layer, and is used for forming an integrated sealing structure. Compared with the prior art, the problem that a traditional laminated structure is prone to stripping and losing efficacy in seawater is solved, and accurate and lag-free monitoring of the ship mooring rope tension force is achieved through modulus matching.

The invention provides a temperature and pressure optical fiber sensor, and belongs to the technical field of optical fibers. The sensor is packaged in a shell and comprises a pressure sensitive structure composed of a pressure film, a supporting beam and a fixing base, a fiber bragg grating strain sensor and a fiber bragg grating temperature sensor, the fiber bragg grating strain sensor and the fiber bragg grating temperature sensor share one fiber structure, and the fiber bragg grating strain sensor and the fiber bragg grating temperature sensor are installed on a shaft which is subjected to maximum deformation after the pressure sensitive structure is pressed. The fiber bragg grating strain sensor is located at a first segment of the fiber. The fiber bragg grating temperature sensor is located at a second segment of the fiber. The first section has prestress, and the second section is kept in a relaxed state. Pressure is sensed through the pressure sensitive structure, maximum deformation occurs in the axial direction where the fiber bragg grating strain sensor and the fiber bragg grating temperature sensor are installed, the magnitude of the pressure is reflected through wavelength drift of the fiber bragg grating, temperature is sensed independently through the fiber bragg grating temperature sensor, and temperature compensation is carried out, so that an accurate strain value is obtained. According to the invention, temperature and pressure dual-parameter sensing can be realized.