461 results about "Multi sensor" patented technology

The present application belongs to the field of desulfurization tower heat exchange performance monitoring and optimization control, and discloses a kind of power plant desulfurization tower heat exchanger heat exchange coefficient prediction method and system, the multivariate operating data of desulfurization tower heat exchanger system is collected by multiple sensors, and the graph structure data set containing the space relationship of measuring point is constructed after pretreatment;Design time decoupling module, decompose operating sequence into trend and periodic component, respectively extract long-term trend feature and periodic fluctuation feature and carry out fusion;Fusion operating condition mode, measuring point space attention and graph structure information, construct space-time feature matrix representing time correlation and multi-measuring point space correlation simultaneously;Through heat exchange coefficient prediction module, generate future multi-step heat exchange coefficient prediction sequence.The present application can effectively fuse time and space dependence and operating condition mode information in system operation, realize accurate, multi-step prediction of heat exchange coefficient, and provide reliable basis for desulfurization tower heat exchange performance evaluation and operation optimization control.

This invention relates to the field of industrial equipment fault diagnosis, and provides a self-supervised multi-sensor fault diagnosis method and system based on semantic embedding. The method preprocesses raw normal operating condition monitoring data, generates a fault window through single-channel fault injection using temporal semantic embedding, and generates a supervision signal for the fault window. Features are extracted from the training sample window composed of the pure normal operating condition window and the fault window using long-term Transformer branches and short-term CNN branches. The extracted long-term and short-term features are concatenated and mapped to a shared fault feature representation. A fault attribute matrix is ​​constructed, a semantic prototype library is generated, and semantic similarity is calculated. Based on the shared fault feature representation and semantic similarity, a multi-task prediction head is constructed. Based on the multi-task prediction head and the supervision signal, a multi-task loss function is designed to train an end-to-end fault diagnosis model. Post-processing optimization is combined to correct easily misjudged fault results. Online fault diagnosis results are output.

This invention relates to the field of factory environmental control technology, and discloses a distributed micro-orifice jet temperature control system and its control method for factory environments. The system includes a central unit, a main air supply duct, branch hoses, workstation-level electromagnetic regulating valves, female quick-connect fittings with leak-proof check valves, and terminal equipment with male quick-connect fittings. The terminal equipment is equipped with a hollow column composed of an outer and inner tube, a telescopic locking mechanism, and low-voltage cables with a helical spring-like extension structure. It also includes an AI controller, an ambient temperature sensor, a body surface temperature sensor, a personnel presence sensor, and a pulse width modulation generator. This invention uses leak-proof check valves to block airflow in vacant workstations to avoid energy loss, utilizes the helical spring-like extension structure to prevent breakage of low-voltage cables, and uses the AI ​​controller to adjust the flow cross-sectional area of ​​the workstation-level electromagnetic regulating valves based on multi-sensor parameters to achieve safe flow interruption and protect personnel at the workstations.

The specification discloses a multi-sensor-based combined navigation method and device, which can acquire sensor data collected by multiple sensors. Then, the sensor data corresponding to the sensors is input into a second-level filter according to a first combination mode to perform information fusion, so as to obtain a state estimation value of a local state quantity. Then, the output result of at least part of the second-level filter or the sensor data of several sensors is input into a third-level filter according to a second combination mode to perform information fusion, so as to obtain a state estimation value of a global state quantity. Finally, the state estimation values of each navigation state quantity from different data sources are input into a fourth-level filter, so as to obtain a final estimation value of each navigation state quantity. By combining different sensors, multiple combined navigation systems with independent navigation capabilities are constructed to redundantly estimate the navigation state quantity, so that the navigation state quantity remains independent of each other in the combined navigation process, failure coupling is avoided, and the reliability and fault tolerance of the navigation system are improved.

This invention relates to the field of intelligent technology for identifying hidden dangers on reservoir and river / lake embankments, specifically to a method for intelligent identification and risk assessment of embankment hidden dangers based on multimodal perception using a walking robot. The method involves a quadruped robot equipped with multiple sensor modules autonomously inspecting reservoir and river / lake embankment areas to collect multimodal data from the embankment surface. This multimodal data is then transmitted to a data processing center. Based on this data, the types and locations of hidden dangers on the embankment surface are identified. These hidden danger types include cracks, seepage or piping, settlement, and structural deformation. The types and locations of these hidden dangers are quantitatively analyzed to obtain quantitative results. Based on these results, a risk assessment report, a visual map, and an early warning are generated. This invention improves the spatial coverage accuracy and temporal synchronization of data acquisition, and possesses higher sensitivity and complementarity in identifying hidden danger types such as embankment cracks, seepage, and deformation, providing a perceptual foundation for subsequent analysis.

The application discloses a fire extinguishing system based on a water tank of a mine truck and a fire extinguishing control method. The system comprises a double-cavity water tank divided into a clean water cavity and a fire extinguishing agent cavity by a partition, a sensor group, a working condition data acquisition module, a proportional mixing module, a conveying pipeline, a water spraying component and a vehicle-mounted electronic control unit. The vehicle-mounted electronic control unit integrates a thermal accumulation pre-judgment algorithm, a temperature rise trend gradient algorithm and a multi-sensor confidence weighted fusion decision algorithm. The thermal accumulation risk is identified and a pre-warning or pre-cooling instruction is output before the temperature reaches a fire determination threshold. After the fire is confirmed, the proportional mixing module is controlled to dynamically adjust the mixing ratio of clean water and fire extinguishing agent according to the fire type, and the corresponding area nozzle is driven to implement zoned linkage and pulse jet fire extinguishing. The electric control valve and the mechanical emergency valve are arranged in parallel at the outlet of each cavity, and the fire extinguishing can be manually started when the electronic control fails.

The application provides a die cutting and hot stamping equipment intelligent state monitoring and fault diagnosis system and method, and relates to the field of intelligent monitoring.The method comprises the following steps: collecting working parameters of multiple key parts of the die cutting and hot stamping equipment, constructing a time sequence collection window, slidingly collecting the working parameters, and obtaining characteristic information reflecting the equipment state; based on the characteristic information, constructing an anomaly detection model, performing anomaly detection on the equipment state, obtaining an anomaly score, and judging whether the equipment state is abnormal according to the anomaly score; for the characteristic information judged as abnormal, constructing a fault diagnosis model based on the fault type to which the characteristic information belongs, performing fault diagnosis on the equipment state, and generating a diagnosis result; and generating a comprehensive diagnosis and operation and maintenance decision report according to the diagnosis result.The application realizes comprehensive perception of the internal state of a closed host through multi-sensor collaborative monitoring and dynamic time sequence collection, breaks through the limitations of traditional monitoring, and provides accurate data basis for early fault warning and predictive maintenance.

This specification provides a wafer cassette docking system that, through the systematic integration of local environmental control, multimodal sensing, and active compliant adjustment functions, achieves an overall performance improvement in the wafer cassette docking process. It can form a stable positive pressure laminar flow air curtain at the initial stage of docking, effectively isolating external particles and providing a continuous ultra-clean environment for the wafer. Employing a multi-sensor fusion scheme consisting of global vision, laser ranging, and feature recognition, it can quickly and comprehensively acquire the six-degree-of-freedom pose information of the wafer cassette, providing a high-precision data foundation for subsequent fine alignment. A micro-motion adjustment platform based on piezoelectric ceramic actuators, combined with real-time closed-loop feedback control, enables nanometer-level active compliant alignment, significantly improving alignment accuracy and reliability. An electromagnetic locking mechanism ensures the stable maintenance of the final pose. The collaborative work of each module improves docking accuracy and efficiency, and also enhances the system's adaptability and stability under different operating conditions.

This invention discloses a method for monitoring the cold chain environment of fruits and vegetables based on multi-sensor fusion, belonging to the field of intelligent sensing and agricultural product logistics technology. It includes: using a flexible multimodal sensor array attached to the surface of fruits and vegetables to acquire the temperature, humidity, and original level signals of the target gas in the surface microenvironment; performing a cross-drift compensation step based on local temperature and humidity physical feedback, using a multivariate nonlinear regression model to use temperature and humidity as dynamic compensation factors, calibrating the baseline drift of the gas sensor in real time, and obtaining high-confidence gas concentration values; calculating the rate of change of the gas concentration values ​​in real time, and automatically triggering an edge interruption when the rate of change exceeds a preset jump threshold, dynamically increasing the sampling frequency and adjusting the radio frequency duty cycle. This invention solves the technical problems of large measurement errors caused by the inability of traditional rigid sensors to conformally fit, severe sensor baseline drift in high humidity environments, and the mutual constraint between high-frequency monitoring and node energy consumption.

This invention discloses a multi-sensor fusion adaptive control method and system for a micro-agricultural box. The method simultaneously collects three types of data: environmental, cultivation medium, and crop images. It utilizes an improved unscented Kalman filter algorithm for multi-source data fusion and multi-parameter coupling calculation, combined with a BP neural network-optimized adaptive PID controller to achieve closed-loop control. The system also includes an image recognition dynamic correction unit and a fault diagnosis and tolerance unit. This invention addresses the pain points of existing micro-planting equipment, such as low control precision, large parameter coupling interference, inability to adapt to the entire growth cycle, and poor reliability for unattended operation. This invention improves temperature and humidity control precision to ±0.5℃ / ±3%RH, reduces pest and disease incidence by more than 95%, and achieves pesticide-free, unmanned adaptive planting, suitable for home and commercial micro-agricultural scenarios.

This application provides a multi-sensor-based target detection method, system, and vehicle terminal. The multi-sensor-based target detection method includes: acquiring initial sensor data from multiple sensors based on a primary time alignment rule; then processing the initial sensor data sequentially according to spatial alignment and secondary time alignment rules to obtain target sensor data; further acquiring input data based on the target sensor data and inputting the input data into a Transformer model to output fused data as the target detection result. Compared with existing rule-based post-fusion algorithms, this application exhibits greater stability and robustness under conditions of sensor characteristic changes and data anomalies. Combined with deep fusion of multi-sensor data, it enhances the perception and understanding of the environment.

The application discloses a kind of intelligent unmanned vehicle environment perception systems based on multi-sensor information fusion, it is related to unmanned vehicle technical field.The system includes: sensor module, for collecting multi-source heterogeneous data, including GPS / IMU, laser radar, camera and millimeter wave radar;Data acquisition and fusion module, through the Kalman filter to multi-source data space-time synchronous fusion;Environment perception module, based on deep learning identification road boundary, obstacle and traffic sign, constructs dynamic perception model;High-precision map construction module, using SLAM technology and semantic information constructs and updates semantic three-dimensional map;Positioning algorithm module, fusion visual odometry, fusion data and high-precision map, calculates vehicle real-time pose;And control module, according to positioning and perception result carries out path planning and navigation control, the application provides a kind of intelligent unmanned vehicle environment perception systems based on multi-sensor information fusion, can be autonomously navigated and stably driven under complex and severe environment.

The application relates to the technical field of industrial equipment health management and artificial intelligence, and discloses a cross-machine fault diagnosis method based on Laplace renormalization and manifold causal Mamba, which comprises the following steps: obtaining a one-dimensional time sequence signal, performing fast Fourier transform and data enhancement, and obtaining an enhanced frequency domain signal; after mask processing, multi-sensor fusion is carried out through high-order Laplace renormalization to obtain a fused frequency domain signal, and the fused frequency domain signal is input into a saliency hybrid extraction network to extract features, so that global dense features and un-pooled features are obtained; a few-sample class prototype is constructed based on the two features, a fault probability distribution is calculated by using manifold causal routing, and a reconstructed frequency domain signal is obtained by decoding; and a saliency hybrid extraction network optimized by using a combination loss function outputs a fault diagnosis result of a target machine. The application effectively overcomes the problems of serious domain deviation and multi-sensor pathological dependence in the cross-machine scene, and improves the cross-domain diagnosis precision under the condition of few samples.

A cardiopulmonary management system includes a multi-sensor device and a controller. The multi-sensor device includes an elongated body having a first end and a second end, a first electrode disposed at the first end of the body, a second electrode disposed at the second end of the body, an electrical circuitry disposed near a middle of the body, and a sound sensor. The multi-sensor device is configured to be disposed on a skin of a subject and to measure cardiopulmonary parameters from the subject. The controller is configured to derive a score of sound change, heart rate change, thoracic impedance change, respiratory rate change, QRS wavelength change, QT interval change, electromechanical activation time change, and left ventricular systolic time change based on the measured cardiopulmonary parameters. The controller is further configured to control an operation of the system when the derived score is greater than a threshold.

This invention belongs to the field of dehumidification equipment technology and discloses a precise temperature control system for a variable frequency dehumidifier with dual-mode temperature and humidity coordination. Through deep collaboration between the dual-mode decision module and the temperature and humidity calibration module, it dynamically switches between dehumidification-priority and temperature-priority modes based on a fuzzy PID fusion algorithm. Adjustment weights are allocated in real time according to temperature and humidity deviations. Combined with dew point temperature modeling and complementary correction technology, it achieves synchronous and precise control of both parameters. A multi-sensor layout covers key locations, coupled with an adaptive self-calibration mechanism and data preprocessing function, ensuring the accuracy and stability of the collected data and adapting to various extreme temperature and humidity conditions. During mode switching, collaborative commands link various execution modules, avoiding humidity rebound after cooling shutdown or dehumidification failure during temperature control. The variable frequency speed control module uses an adaptive fuzzy PID algorithm with multi-input fusion to dynamically optimize speed parameters. Combined with a dynamic frequency step size adjustment mechanism, it achieves rapid response to large deviations and precise fine-tuning for small deviations.

The application discloses a mechanical equipment safety interlock control method based on multi-sensor information fusion. The method collects the motion position, speed, acceleration, force and torque data of the mechanical equipment, and performs time alignment, amplitude normalization and short-time abnormality filtering, estimates the kinetic energy, potential energy and friction energy consumption of each energy node, and constructs an energy transmission matrix between nodes; fusion sensor data and matrix information extracts four types of abnormal characteristics of short-term energy surge, local energy reflection, friction energy abnormal fluctuation and energy accumulation accompanied by micro-vibration, and predicts dangerous energy states including single and complex dangerous energy states accordingly; based on the prediction of dangerous energy state and the current running state of the equipment, the safety interlock action is adaptively triggered, the driver torque, current amplitude limiting and active damping are dynamically adjusted, and the control of backflow energy and accumulated energy along the virtual dissipation path is realized. The method is suitable for high dynamic mechanical equipment and can realize fine safety interlock control of multiple nodes and multiple abnormal characteristics.

The application discloses a dynamic charging coordination method, system, equipment and medium under open sea grid monitoring, relates to the technical field of open sea monitoring and energy management, and comprises grid dynamic division and equipment priority allocation; the light receiving efficiency is optimized by adjusting the inclination angle of the photovoltaic film, a task adjustment and energy management balance mechanism is constructed, and abnormal detection and cooperative response are carried out based on a multi-sensor fusion technology. The method disclosed by the application carries out grid dynamic division and equipment priority allocation, optimizes the grid boundary by using a hierarchical quadtree algorithm and real-time data, improves the utilization rate and monitoring accuracy of monitoring resources, optimizes the inclination angle of the photovoltaic film and the task energy management balance mechanism, improves the task completion efficiency while ensuring long-time endurance of the equipment, and accurately identifies complex abnormal events and quickly allocates response tasks based on the abnormal detection and cooperative response of the multi-modal sensor fusion technology, thereby improving the efficiency and intelligent level of the monitoring system as a whole.

This invention relates to the technical field of water level information monitoring based on navigation aids, and discloses a method and device for water level information monitoring based on multi-intelligent sensor fusion of navigation aids. The method includes: using preprocessed multi-sensor data to perform a correction estimate of water depth based on a correction term to obtain the corrected water depth; performing position matching analysis between the location of the navigation aid vessel and its reference coordinates, and determining whether the location of the navigation aid vessel meets navigation standards based on the water depth; if the location of the navigation aid vessel does not meet navigation standards, generating an optimal adjustment path to drive the navigation aid vessel to the recommended location. This invention utilizes flow velocity, temperature, and attitude angle to construct a correction term, improving the accuracy of water depth estimation. It combines BeiDou position information with reference coordinates to determine whether the navigation aid vessel deviates from navigation standards. Once deviated, path planning drives the navigation aid vessel to adjust its position, achieving integrated water level monitoring and navigation aid management.

The application relates to the technical field of aviation ground equipment, and discloses an intelligent boarding system and a control method thereof. The system comprises an intelligent escalator and an intelligent boarding ladder. The intelligent escalator has full autonomous moving and scheduling capabilities. The intelligent boarding ladder is integrated with a double-layer telescopic "moving sidewalk" mechanism, a passenger flow sensing and regulating system, a high-precision self-adaptive docking execution system and a barrier-free power-assisted system. The control method comprises six steps of task receiving and scheduling, model identification and docking confirmation, multi-sensor cooperative positioning docking, intelligent passenger flow monitoring and regulation, barrier-free mode response and task end withdrawal, and realizes unmanned and intelligent operation of the whole boarding process. The application solves the problems of low efficiency of traditional boarding equipment passenger flow management, low precision of model adaptation, lack of barrier-free service and the like, has the advantages of high docking precision, safe and reliable operation, high space utilization and good humanized experience, and can significantly improve the airport operation efficiency and passenger boarding experience.

The application relates to a multi-sensor-based repositioning method, device, equipment and storage medium. The method comprises the following steps: acquiring environment images collected by respective sensors; performing global matching on the respective environment images and images in a prior environment image library to obtain respective robot poses before updating corresponding to the respective sensors; performing local matching on the respective environment images and respective surrounding environment images corresponding to the respective robot poses before updating in the prior environment image library to obtain score values of respective robot poses after updating; and determining a repositioning result of a current robot pose according to the score values of the respective robot poses after updating. By performing global matching and local matching on the environment images collected by different types of sensors and the images in the prior environment image library, the repositioning result of the current robot pose can be ensured to have accuracy and reliability.

The application discloses a machine tool spindle comprehensive performance testing device and testing method, which comprises: a ground iron, a three-dimensional section axis-determining multi-sensor adjusting module, an axial loading unit, a radial loading unit, a loading arm, a spindle fixing module, a static stiffness module, a working stiffness module, a detection module and a workbench. The device can detect multiple performances of the machine tool spindle through different tool handle modules and loading units, reduces the cumbersome work caused by replacing the test bench, and greatly improves the testing efficiency. The test projects that can be completed by the test bench include thermal elongation testing, thermal deviation testing, axial static stiffness testing, radial static stiffness testing, axial working stiffness testing and radial working stiffness testing of the machine tool spindle.

The application provides a ship multi-source perception data fusion in-port target clustering method, and belongs to the technical field of ship berthing perception and ship autonomous positioning. The method first acquires multi-source perception data of a ship-borne camera, a thermal imager and a laser radar and completes time synchronization, then realizes target detection and instance segmentation of a visible light video and target detection of an infrared video through YOLO11n-seg and YOLO11n algorithms respectively, projects laser radar point cloud data to a visible light image coordinate system and extracts effective point cloud indexes after verifying the effectiveness of cross-modal target detection results based on a calculation of an intersection over union, adopts a lightweight clustering algorithm based on local geometric features to construct a target point cloud set and generate a space bounding box, and finally calculates the distance and azimuth angle of the target relative to the ship, and fuses RTK positioning information of the ship to obtain the absolute position of the port target. The application solves the problems of under-segmentation of in-port dense targets, low multi-sensor data fusion efficiency and poor detection stability in poor visibility in the prior art.

The invention discloses an FPSO (Floating Production Storage and Offloading) intelligent directional spraying method based on multi-sensor fusion, which is suitable for fire prevention and control of an FPSO upper module. The method comprises the following steps: firstly, dividing protection sub-areas, arranging infrared, ultraviolet, visible light and attitude sensors, matching with mixed nozzles and networking; establishing a module coordinate system, and completing sensor calibration and multi-modal data registration; and a multi-task CNN fire identification model is constructed, and fire source positioning, type discrimination and spreading prediction are realized. When the system operates, image stabilization is performed through hull attitude compensation, fusion feature input models are extracted, and personalized fire extinguishing strategies are generated; and generating a spraying instruction through space calculation and the like, driving the nozzle to spray in a directional and constant-flow manner, and dynamically optimizing the strategy based on real-time feedback until the fire is extinguished. Point-to-point fire extinguishing is achieved, water stain loss and fire fighting water waste are reduced, and the accuracy and efficiency of fire fighting in the marine environment are improved.

The present application relates to the technical field of mechanical fault diagnosis and health monitoring, and particularly relates to a multi-sensor fault diagnosis method based on a graph neural network, the present application realizes a high accuracy of 95%-100% on multiple public data sets such as CWRU, PU, MFPT, SEU and the like through multi-relation fusion graph construction, node feature coding, topology-aware graph neural propagation and gate residual fusion, and is significantly superior to prior art methods in terms of weak fault recognition, the present application constructs an end-to-end framework of graph construction-feature coding-graph neural propagation-graph level reading-classification prediction through the synergistic effect of six steps, unifies the multi-stage processing mode in traditional fault diagnosis, breaks through the technical bottlenecks such as single structure modeling and one-sided feature expression in the prior art, and provides a new paradigm for intelligent fault diagnosis with high reliability and high robustness.

The application discloses a device and method capable of realizing all-around real-time monitoring of tunnel structure deformation. The device comprises multiple non-contact displacement sensors, a rotating support assembly, a three-dimensional space locator assembly, an inclination sensor and a control unit. The multiple non-contact displacement sensors are equidistantly arranged along tunnel walls on the same cross section of the tunnel and are fixed through the rotating support assembly; the rotating support assembly can realize scanning measurement along the cross section and the longitudinal direction of the tunnel. The three-dimensional space locator assembly is used for monitoring the position change of the base point of the sensor, the inclination sensor is used for accurately controlling the scanning angle of the first rotating assembly, and the control unit is responsible for coordinated control and data processing. The application integrates automatic scanning, multiple sensor data mutual verification, environmental compensation and remote monitoring functions, can realize real-time, continuous and accurate monitoring of various deformation parameters such as tunnel ellipticity, segment misalignment and cross section convergence, significantly improves the monitoring efficiency and precision, and guarantees the tunnel operation safety.