2860 results about "Sensor system" patented technology

A non-invasive physiological sensor system implemented as a smart watch or other wearable device includes an emitter for emitting light and a detector for collecting light after the light interacts with a tissue of a wearer. The system can measure light intensity at one or more radial distances to estimate attenuation parameter values that can be used to correct physiological parameter bias across subjects. In addition or alternatively, the system can determine whether a detector is positioned sufficiently proximate to an obstructing tissue (for example, an artery or vein) so as to cause inaccurate measurements by the detector.

A sensor system for monitoring patients is provided. The sensor system includes a wireless charging dock, one or more patient sensors, and a processing module. The patient sensor is configured to collect patient physiological data and send the data to the processing module. The processing module wirelessly transmits the patient physiological data to a patient monitor system. The wireless charging dock is wirelessly and removably coupled to the processing module to wirelessly provide power for the processing module. The wireless charging dock is magnetically coupled to the processing module.

Disclosed are a three-dimensional laser point cloud-based method and system for in-depth vegetation management in transmission corridors. The method comprises: collecting multi-source data by means of an unmanned aerial vehicle- and helicopter-mounted multi-sensor system, and preprocessing the multi-source data; using the processed multi-source data to create a digitalized power grid corridor and construct a vegetation management analysis model; on the basis of data analyzed by the vegetation management analysis model and geofencing technology, designing a hazard vegetation clearing-cost-compensation prediction model; and developing a mobile application for analyzing hazard vegetation data flows and the closed-loop management of vegetation management service flows. The described method achieves high-precision and high-coverage data collection, precisely identifies transmission line digital twins and hazard vegetation, standardizes and automates the management of hazard vegetation clearing and related compensation, and develops a mobile application for analyzing hazard vegetation data flows and the closed-loop management of vegetation management service flows, thereby achieving the end-to-end digital and mobile management of vegetation management. The organic combination of steps comprehensively improves the intelligence and efficiency of transmission line vegetation management.

The invention discloses a mountain flexible photovoltaic support service state evaluation method and device, and aims to improve the operation safety and operation and maintenance efficiency of a photovoltaic power station in a complex mountain environment. Comprising the steps of establishing a digital twinborn model of the flexible photovoltaic support, deploying a multi-source heterogeneous sensor system, and collecting environmental load, structural response, material state and geological environment data in real time. Collected data are subjected to preprocessing, feature extraction and operation condition recognition, the data are input into a digital twinborn model for multi-physics coupling simulation analysis, and model parameters are inversed and corrected based on real-time monitoring data. And finally, based on the optimized digital twinborn model and real-time data, carrying out comprehensive evaluation on structure health, material aging, anchoring stability, performance degradation and the like, and generating risk early warning and maintenance strategy suggestions according to evaluation results. According to the method, the limitation of a traditional evaluation method in application of the mountain flexible photovoltaic support is overcome, and comprehensiveness, real-time performance and intelligentization of evaluation are achieved.

An autonomous vehicle system includes a vehicle including a base assembly having a front surface, a rear surface opposite the front surface, and side surfaces extending between the front surface and the rear surface. A sensor system is coupled to the base assembly and configured to detect one or more objects located in an area near the vehicle. The sensor system includes a first sensor oriented parallel with at least one of the front surface, the rear surface, or the side surfaces, and a second sensor oriented non-parallel with the front surface, the rear surface, and the side surfaces. A control system is configured to receive a communication regarding the detection of the one or more objects from the sensor system and generate one or more controls for at least one of the base assembly or one or more tractive elements.

The invention discloses a power equipment inspection path planning method and device based on laser and vision, and relates to the technical field of intelligent robots and power inspection, and the method comprises the steps: constructing a multi-sensor system, carrying out the external parameter calibration and time synchronization of each sensor, and carrying out the environment modeling and positioning through employing an improved LIO-SAM algorithm; performing power equipment identification; generating an optimal inspection path based on the calculated coordinate set of the power equipment; according to the optimal inspection path, dynamic obstacles are detected in combination with semantic and geometric information, obstacle avoidance is achieved through local track adjustment and path re-planning, and a planned path is obtained; and driving the inspection robot to autonomously move according to the planned path, acquiring an image and infrared data at the target power equipment, and feeding back a result to the background system to complete closed-loop inspection. According to the invention, the autonomous inspection of the whole process of environment modeling, target equipment identification, path generation, dynamic obstacle avoidance and the like can be effectively realized.

The invention belongs to the technical field of sensor error correction, and discloses a data synchronization and error correction method based on multi-source gas detection, and the method comprises the steps: firstly achieving the time-space synchronization of a multi-source sensor through the combination of GPS / Beidou hardware time service and a gas diffusion transmission model; non-negative matrix factorization (NMF) is adopted to decouple sensor cross interference, and an LSTM neural network is utilized to predict a sensor drift amount caused by environmental temperature and humidity changes; and finally, carrying out dynamic weight fusion based on the real-time confidence of each sensor, and outputting a high-precision gas concentration value. The problem of data distortion caused by time sequence dislocation, cross sensitivity and environment drift in a multi-sensor system is effectively solved, the accuracy of detection data and the reliability of the system are remarkably improved, and the method is particularly suitable for multi-component gas detection scenes in the fields of chemical engineering, environment monitoring and the like.

The invention discloses an extrusion process multi-parameter dynamic cooperative control method and system based on deep learning, and belongs to the technical field of high polymer material processing control, the extrusion process multi-parameter dynamic cooperative control method and system based on deep learning comprises the following specific steps: 1, data acquisition and preprocessing, the method comprises the following steps: acquiring multi-source time sequence data in an extrusion production process in real time through a sensor system, and preprocessing the data to form a standardized data set; the multi-source time sequence data comprises equipment operation parameters, process physical parameters and environment parameters. According to the method, the control system is endowed with powerful nonlinear time sequence prediction and multi-parameter collaborative decision-making capabilities through deep fusion of deep learning and reinforcement learning technologies, the quality trend can be accurately predicted, a collaborative control instruction can be given prospectively, and the problems of parameter coupling and control lag are effectively solved.

The invention discloses a complex terrain-oriented quadruped robot self-adaptive gait generation system and method. The system comprises a sensor system used for collecting terrain data and robot state information; the terrain sensing module identifies terrain types and terrain feature parameters according to sensor data, and quantifies terrain features into parameters; the multi-modal gait library is used for storing a plurality of basic gait modes and corresponding gait parameters; the parameterized gait generation module is used for selecting a basic gait from the gait library and optimizing and generating an optimal gait adaptive to the current environment according to terrain parameters and task parameters; the gait smooth transition algorithm synthesizes all intermediate postures in the time and space interpolation generation transition process to obtain a smooth transition trajectory; the terrain adaptability adjusting module is used for finely adjusting the gait parameters according to real-time feedback; and the execution control module is used for converting the generated gait parameters into joint control instructions for controlling the robot so as to execute corresponding actions. The quadruped robot can adapt to more complex environments.

The invention relates to the technical field of power equipment monitoring, in particular to a multi-mode sensor power equipment state monitoring system based on AI smell. The data acquisition module is used for acquiring multi-dimensional sensing data around the electrical equipment; the AI olfaction processing module is used for converting original sensor data into standardized odor feature fingerprints by performing spatial-temporal feature extraction and multi-modal feature fusion on the multi-dimensional perception data; the overheating state analysis module is used for generating an overheating comprehensive index and an equipment state evaluation result based on the smell characteristic fingerprints; the multi-stage early warning and response module is used for carrying out early warning grade judgment based on the overheating comprehensive index and the equipment state evaluation result and generating graded early warning information; and the storage module is used for storing historical monitoring data and early warning event records. According to the invention, through system integration of the multi-mode sensor array of the bionic olfaction mechanism, AI feature fusion processing and intelligent analysis decision, active sensing and accurate identification of the overheating state of the power equipment are realized.

A method may include receiving an input signal, generating a baseline signal based on the input signal, generating a corrected input signal by subtracting the baseline signal from the input signal, determining a threshold level change of the input signal when the corrected input signal exceeds a level change threshold, and responsive to the threshold level change, updating the baseline signal to the level change threshold.

The invention discloses a water electrolysis hydrogen production electric pile performance evaluation method, and relates to the field of electric pile performance evaluation, and the method comprises the steps: S1, collecting operation parameters through a sensor system, carrying out preprocessing and time sequence alignment, and constructing a real-time data set; s2, according to the real-time data set and historical data, a stack health index is calculated through multi-layer feature hierarchical fusion and a machine learning model; s3, on the basis of the real-time data set, calculating a composite evaluation index parameter containing a safety margin factor and a performance degradation rate; s4, on the basis of the stack health index, analyzing in combination with the composite evaluation index parameters, and determining the stack performance state grade; and S5, extracting a maintenance suggestion in a preset level and maintenance strategy mapping relation based on the pile performance state level, and transmitting the maintenance suggestion to a visual interface for display through the cloud platform. Through multi-layer feature fusion and machine learning, the method achieves the precise evaluation of the health index of the galvanic pile, dynamically monitors the fault early warning, automatically generates an intelligent maintenance strategy, optimizes the performance, and prolongs the service life of equipment.

Techniques are discussed herein for detecting and resolving disparities between sensor data perceived by sensor-based systems operating in an environment and corresponding map data of the environment. Sensor data may be captured by a vehicle or other sensor system operating in an environment, including representations of objects at various locations in the environment. The object representations may be used to determine disparities between the sensor data and map data associated with the same locations. Such disparities may be caused by, for example, physical changes in the environment, map data changes, and / or localization errors of the sensor system. The techniques discussed herein further include analyzing the map data and sensor data to determine causes associated with the disparities, and resolving the disparities by updating the map data and / or transmitting updated sensor configuration data to sensor systems in the environment.

The invention relates to the technical field of model construction, in particular to a construction method and system of an electrolysis energy consumption model and a storage medium. The method comprises the following steps: collecting electrolysis process parameters by using a multi-mode sensor system, wherein the electrolysis process parameters comprise a current value, a voltage value, an electrolyte temperature and electrolysis time; constructing an electrolysis energy consumption initial model based on the electrolysis process parameters, and inputting the electrolysis process parameters into the electrolysis energy consumption initial model to output a unit product energy consumption value and a current-voltage efficiency curve; electrolytic bubble acoustic monitoring is conducted on the side wall of the electrolytic cell to obtain bubble breakage acoustic wave signals; and analyzing the sound intensity and the rupture frequency of the bubble rupture sound wave signal. According to the method, through the multi-modal sensor, multi-dimensional signal analysis and dynamic parameter correction, the defects of single data and static state of a traditional electrolysis energy consumption model are broken through, and high-precision dynamic modeling and optimization of energy consumption in the electrolysis process are achieved.

A method and an apparatus for constructing hierarchical semantic map based on deep-learning based perception, a server, a storage medium are provided. The method includes: obtaining 3D point cloud data, scene images and motion state data; projecting 3D point cloud data onto an image plane to obtain a depth image; performing a point cloud registration and a pose prediction on the 3D point cloud data and the motion state data to obtain an odometer pose; generating a point cloud map according to the 3D point cloud data and the odometer pose of multi-sensor system; based on the depth image and the odometer pose of the multi-sensor system, performing an external ellipse fitting process on each semantic region in the depth image to obtain an object-enveloped map for respective object images; fusing the coordinately aligned point cloud map and object-enveloped map into a semantic map.

The invention relates to a manipulator multi-task cooperative control method and system based on artificial intelligence. The method comprises the following steps: firstly, receiving a multi-task request group sent by an external system, and analyzing to obtain task intention description and execution constraint conditions of each task; then environment state data and real-time operation parameters of the manipulator are collected through a multi-sensor system, and an operation capability evaluation result is generated; inputting the information into a pre-trained multi-task collaborative planning model, and generating a multi-task collaborative control strategy through collaborative processing; according to the strategy, action sequence instructions of all joints of the manipulator are generated and sent to a driving control system; and finally, collecting feedback data in real time by utilizing multiple sensors, dynamically adjusting an action sequence instruction, and ensuring ordered execution of multiple tasks. According to the method, efficient cooperative control over multiple tasks of the manipulator is achieved, and the task execution efficiency and stability are improved.

The invention belongs to the technical field of tunnel catastrophe, and particularly relates to an intelligent feedback type tunnel catastrophe monitoring method, which comprises the following steps of: 1, deploying a tunnel monitoring system; a temperature sensor, a smoke sensor, an air velocity sensor and a gas concentration sensor are arranged in the tunnel; the temperature sensor is used for monitoring temperature changes in the tunnel. According to the intelligent feedback type tunnel catastrophe monitoring method, the safety and the emergency response capability of the tunnel are effectively improved through real-time data monitoring and analysis and an automatic control mechanism. Firstly, through temperature, smoke, gas concentration and air velocity sensors, the system can detect and identify potential catastrophe risks in a tunnel in time, such as fire, smoke diffusion and harmful gas leakage. Once an abnormality occurs, the system performs catastrophe prediction through an algorithm model, and performs accurate evaluation on a fire propagation path and smoke diffusion in combination with a simulation analysis result, thereby providing reliable data support for quick response.

A light detection and ranging (LIDAR) system for a vehicle can include: a light source configured to output a transmit beam at a first orientation; a reflective surface configured to redirect the transmit beam from the first orientation to a second orientation; and a lens interface configured to receive the transmit beam at the first orientation and focus the transmit beam onto the reflective surface; wherein the LIDAR system emits the transmit beam at the second orientation into an environment of the LIDAR system.

The invention provides a method for controlling the precision of an inner molded surface of a cast-in-place slab hung below a net rack, and belongs to the technical field of wind tunnel construction.The method comprises the steps that a C-shaped clamping piece and a sensor system are installed on a net rack lower chord, a monitoring network is established, fatigue characteristic data are analyzed through a minimum cut algorithm, and a load imbalance coefficient is calculated; a high-precision adjustable suspender and a prestress adjusting device are installed to construct a dynamic supporting system, a three-level precision compensation mechanism is established to achieve preventive compensation before construction, real-time monitoring compensation in the construction process and corrective compensation after pouring is completed, and multi-target collaborative optimization is achieved through a dynamic adjusting algorithm and a game optimization model. A complete technical system including fatigue point prediction, load redistribution and precision real-time control is formed, and the technical problem that in the construction process of hanging the cast-in-place slab under the net rack, the plane precision control lacks a systematic dynamic adjustment mechanism is solved.

The present disclosure relates to a method and system for monitoring the road condition by a machine learning system and to a method for training the machine learning system. The methods include: providing or acquiring data by a sensor system of a vehicle, wherein the sensor system captures the surroundings of the vehicle (2) as training input data X; providing or acquiring data which characterize the road condition by means of a reference sensor fitted in or on the vehicle as training target values, and training the machine learning system. Training data, which include training input data X and training target values corresponding to these training input data X, are provided. The training data are used to adjust parameters of the machine learning system in such a manner that the machine learning system generates output data similar to the training target values when the training input data are input.

Systems and methods for measuring soil moisture are provided. The system may include a neutron sensor configured to be disposed at a sampling location and including one or more neutron detectors configured to detect fast neutrons and thermal neutrons at the sampling location, and a computing device including at least one data processor and a memory storing instructions, the instructions are configured to cause the processor to perform operations including receiving a first electrical signal characterizing a fast neutron count at a sampling location and a second electrical signal characterizing a thermal neutron count at the sampling location from the neutron sensor, determining a ratio of fast neutrons to thermal neutrons present at the sampling location, a soil moisture measurement is determined based on the ratio, and the soil moisture measurement is provided.

An autonomous guided vehicle includes a frame, a drive section, a payload handler, a sensor system, and a supplemental sensor system. The sensor system has electro-magnetic sensors, each responsive to interaction or interface of a sensor emitted or generated electro-magnetic beam or field with a physical characteristic, the electro-magnetic beam or field being disturbed by interaction or interface with the physical characteristic, and which disturbance is detected by and effects sensing of the physical characteristic. The sensor system generates sensor data embodying at least one of a vehicle navigation pose or location information and payload pose or location information. The supplemental sensor system supplements the sensor system, and is, at least in part, a vision system with cameras disposed to capture image data informing the at least one of a vehicle navigation pose or location and payload pose or location supplement to the information of the sensor system.

The invention belongs to the technical field of mobile robots, and particularly relates to an amphibious hexapod robot which comprises a robot body, six walking feet, a protection box, propellers, a multi-sensor system and a central control unit. The six walking feet are arranged on the two sides of the robot body, and a waterproof driving joint is arranged at the tail end of each foot. The protection box is arranged at the root of the walking foot and internally integrated with propellers; the sensor system comprises a water depth sensor, an attitude sensor and a grounding detection module; the central control unit is used for collecting sensor data in real time, judging whether the current environment is a land or a water area, automatically switching a walking mode or a water propulsion mode, and meanwhile adaptively adjusting gaits and the rotating speed of propellers according to the water depth and the terrain. The robot has environment perception, mode decision, gait generation, grounding prevention protection and energy optimization strategies, full-autonomous and high-adaptability movement in the land and water environment is achieved, and the working capacity and safety of the robot in the complex environment are improved.

Systems, devices, and methods including: an air inlet configured to receive air; an air outlet configured to expel air; a duct connected between the air inlet and the air outlet; and an open-cavity optical sensor disposed in the duct, where an air flow stream within the duct passes through the open-cavity optical sensor.

A sensor including at least one sensor element configured for measuring an external parameter and outputting one or more results representative for the external parameter, an output buffer configured for receiving the one or more results or a processed version thereof and for applying an analog signal representative for the result or a time multiplexed sequence of analog signals representative for the results on a sensor terminal, and a control circuit configured for obtaining an ID of the sensor, and for receiving a trigger signal, and for determining whether the trigger signal matches the ID of the sensor, and for enabling the output buffer during a predefined period when the trigger signal matches the ID of the sensor.

Techniques for displaying an extended reality (XR) indication of a potential collision between a portion of an object and a portion of a computer-assisted system include the following. The computer-assisted system comprises a sensor system configured to capture sensor data of an environment, and a control system communicably coupled to the sensor system. The control system is configured to: determine a pose of a portion of an object in the environment based on the sensor data, determine a pose of a portion of the computer-assisted system, determine at least one characteristic associated with a potential collision between the portion of the object and the portion of the computer-assisted system, select the potential collision for display based on the at least one characteristic, and cause an XR indication of the potential collision to be displayed to an operator via a display system.

The present invention relates to a crane, in particular in the form of a rotating tower crane, having a crane jib, from which a hoist rope connected to a load handling means runs, a load position sensor system for sensing the position of the load handling means and / or of a load fastened thereto, a surroundings sensor system for collecting surroundings data to establish a surroundings model, and a crane controller for controlling hoist rope and / or crane movements on the basis of the sensed position of the load handling means and / or of the load fastened thereto and on the basis of the established surroundings model. According to the invention, the surroundings sensor system has an imaging sensor system for providing three-dimensional real-time surroundings data and a geo-referencing device for geo-referencing the 3D real-time surroundings data and establishing a geo-referenced 3D surroundings model.

A roof for a motor vehicle, in particular for a passenger car, having a roof skin which is disposed above a roof substructure and having a sensor system which has at least one sensor module having at least one environment sensor for detecting a vehicle environment. The roof has a cooling device for the sensor module, said cooling device having a compressed-air device by which compressed air is applied to the sensor module.

The invention belongs to the technical field of metal processing, and discloses an automatic welding method and system.The method comprises the steps that a functional gradient transition layer is arranged between a steel workpiece and a copper workpiece, components of the transition layer change in a gradient mode from a Ni-rich layer to a Ni-Cu solid solution layer in the thickness direction, and the transition layer is doped with nano reinforced particles or rare earth elements; the metallurgical compatibility is fundamentally improved; in the welding process, a composite energy source and an auxiliary physical field are adopted, and a sensor system is used for monitoring multi-modal data in real time; monitoring data are input into a predictive control model based on artificial intelligence, the model can predict the defect trend of a future moment, and welding parameters are actively adjusted in a feedforward mode. According to the method, the problems that in a traditional method, intermetallic compounds are difficult to inhibit, and process control lags are solved, and a welding joint which is free of cracks and pores, extremely thin in IMC layer and excellent in mechanical property can be obtained.