4137 results about "Sensor system" patented technology

The invention relates to the technical field of intelligent assessment and decision making, in particular to a robot dynamic risk assessment and decision making system and method based on multi-modal perception, and the system comprises a multi-modal sensor module which is used for collecting environment vision, acoustics, mechanics and position data in real time; the edge calculation unit is used for carrying out space-time alignment and feature fusion on the sensor data; the dynamic risk assessment model is used for integrating the environment uncertainty quantification module and the robot state prediction module based on a reinforcement learning framework; the decision execution interface is used for outputting a risk level and obstacle avoidance, speed reduction and shutdown instructions; by integrating visual, acoustic, mechanical and position multi-source sensor data and the like, the system can comprehensively capture various risk factors in a complex dynamic environment, so that the defect that a traditional single sensor system is insufficient in sensing dimension is overcome, and the system is particularly suitable for terrains and weather conditions with variable regions.

The invention relates to the technical field of program control systems, in particular to a pentahedron machining center precision calibration method and system based on multi-sensor fusion, and the method comprises the steps: a sensor system construction and calibration module is used for field calibration, drift correction and redundancy deployment to ensure data precision, and achieves the whole-course traceability of a calibration process through a block chain technology; the multi-source data preprocessing and fusion module is used for time-space synchronization of heterogeneous data and dynamic fusion of multi-source information; the intelligent modeling and state prediction module is used for performing real-time and multi-task prediction on key states such as tool wear and thermal deformation; based on the prediction result, the adaptive compensation and path optimization module is used for dynamically optimizing the tool path; meanwhile, through an online learning mechanism, the calibration model is continuously updated by utilizing a processing result; the distributed cooperative control module executes data processing and calibration algorithms locally and makes a cooperative decision with a numerical control system, and low-delay and intelligent response to machining abnormity is achieved.

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.

A sensor system for optical detection of multiple analytes is discussed. An example system is presented that includes a plurality of lenses configured to receive individual light signals from analyte-sensing chromophores disposed on an array when the analyte-sensing chromophores are illuminated by light while being exposed to different specific analytes within an interstitial fluid. The system further includes at least one volume phase grating (VPG) communicatively coupled to at least one of the plurality of lenses and a detector communicatively coupled to the at least one VPG so that light signals from the analyte-sensing chromophores are focused by the plurality of lenses to the at least one VPG and transmitted to the detector. The detector being operable to generate spectra with each spectrum corresponding to an individual light signal from a single analyte-sensing chromophore.

Systems and methods for monitoring glucose levels of a user are described. Data indicative of glucose levels of the subject is received. A count is assigned for each glucose episode based at least on an area under the curve of the each glucose episode in a dataset of time-correlated glucose data. A running sum of counts for each glucose episode in a time period is calculated. The running sum of the counts may be displayed in a numerator of a fraction and a target count goal in a denominator of the fraction. A progress indicator representative of the running sum of the counts relative to a target count goal for the time period may also be displayed. A length of a variable portion may be proportional to the running sum of the counts, and a total length of the progress indicator may be proportional to the target count goal.

Systems and methods for monitoring glucose variability in a subject are described. Data indicative of glucose levels of the subject is received from a sensor control device. A first glucose variability metric of the subject in a first time period is determined. The first glucose variability metric may be compared to a threshold. A first indicator is displayed if the first glucose variability metric does not exceed the threshold and a second indicator is displayed if the first glucose variability metric exceeds the threshold. Additional glucose variability metrics may be determined for subsequent time periods according to a rolling window, and the indicators may be displayed real time or in a report. The glucose variability metric may be a measure of variability compared to a baseline, a difference between a maximum and minimum glucose level, or time in or out of a target range.

The invention relates to the field of channel management, in particular to an intelligent channel management method and system based on multi-source data fusion. The invention discloses an intelligent navigation channel management system based on multi-source data fusion. The system comprises a combined ship identification module, a positioning frequency updating module, a collision risk assessment module and an intervention management module. According to the invention, the system can sense multi-dimensional environmental parameters such as illumination intensity, visibility, AIS signal quality, radar echo signal-to-noise ratio and the like in real time by constructing a combined ship identification model containing AIS, radar, vision and infrared, and dynamically adjust the fusion weight of each sensor to compensate for the deficiency of a single sensor; according to the method, the robustness of ship dynamic monitoring is improved, high-precision tracking can be kept in severe weather, the data redundancy contradiction of a traditional multi-sensor system is avoided, and therefore continuous and stable target state input is provided for follow-up risk analysis.

The invention discloses a TBM cutterhead blockage early warning system based on distributed temperature measurement, and belongs to the technical field of mechanical engineering, a multi-modal data fusion unit comprises an integrated multi-source sensor module and a data fusion analysis module, the integrated multi-source sensor module adopts a bionic cobweb topological structure to arrange a flexible fiber grating sensor, and the data fusion analysis module adopts a data fusion analysis module to analyze the flexible fiber grating sensor. The data fusion analysis module carries out fusion processing on multi-source data through a machine learning algorithm and constructs a multi-modal data input layer, and the data fusion analysis module also designs dynamic weight distribution through a process scheduling algorithm. A bionic cobweb topological structure is adopted to arrange the flexible fiber grating sensor, the strain self-compensation characteristic of the flexible fiber grating sensor is utilized to adapt to cutterhead deformation, a machine learning algorithm is utilized to fuse multi-source data, a multi-modal data input layer is constructed, dynamic weight distribution is achieved through a process scheduling algorithm, and the cutterhead deformation detection accuracy is improved. The problem of data islands of a traditional single sensor system is solved, and the monitoring precision and adaptability are improved.

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 biped robot reinforcement learning control method which comprises the following steps: after a model prediction controller receives a walking instruction, outputting expected angle data of each joint of a robot to a reinforcement learning neural network, and meanwhile, returning joint angle data of an actual strategy of the reinforcement learning neural network to the neural network by a sensing system at a bottom layer; a joint angle taking time as a sequence and planned by model prediction is compared with a joint angle actually generated by a neural network, and model prediction control is fused into a reinforcement learning training process by setting a reward function for punishment, so that the reinforcement learning training efficiency is improved, and a stable gait is more quickly achieved. Excellent gaits planned by the MPC are transplanted into reinforcement learning control, and control robustness can be improved under the condition that the excellent gaits of the MPC are reserved; and meanwhile, the joint angle data which is obtained by taking time as a sequence and is obtained by taking MPC as a planner can accelerate the reinforcement learning training process, so that the training speed and the control effect of reinforcement learning are greatly improved.

The invention relates to the technical field of gas monitoring, and discloses an air quality monitoring method based on an olfactory chip, which comprises the following steps: step 1, preparing a multi-modal sensor system which comprises an olfactory chip module, a surface enhanced Raman scattering module and a microfluidic time division multiplexing module, the sensor surface of the olfaction chip module is modified with a metal organic framework material, and the substrate of the surface enhanced Raman scattering module is made of a core-shell structure nano material; and 2, synchronously collecting resistance response data of the olfaction chip module, spectral data of the surface enhanced Raman scattering module and temperature, humidity and wind speed data of the environment sensor. According to the invention, a multi-modal sensor cooperative detection technical scheme is adopted, the high-sensitivity detection effect on low-concentration harmful gas in air is achieved, and compared with a technical scheme of a single sensor and a fixed fusion algorithm in the prior art, the defect that accurate recognition of trace gas cannot be realized under ppb-level concentration is overcome.

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.

The invention relates to microwave-free sensor technology with NV centers in diamond. In contrast to the prior art, the intensity of the pump radiation is not PWM-modulated, but monofrequency and sinusoidally modulated. Further measures, such as the use of semiconductor-based pump radiation sources and operation with an offset, prevent the formation of harmonics in the temporal intensity profile of the pump radiation irradiating the NV centers in the sensor element. This allows the sensor system to use the phase shift to determine measured values ​​of external physical parameters such as magnetic flux density, acceleration, temperature, etc., within a recognized and disclosed frequency range of the intensity modulation of the pump radiation, thereby minimizing the influence of noise from the pump radiation source. The galvanic isolation does not need to be compromised by cables from additional sensors.

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 relates to the technical field of sensors, and discloses a detection and management method and system for purification equipment. The method comprises the following steps: carrying out three-phase coupling dynamic modeling on a gas phase flow state, a particle phase movement track and a sensor phase interface characteristic in the purification equipment to obtain a gas mass transfer characteristic parameter; performing response characteristic analysis on a real-time detection signal of a sensor array in the purification equipment to obtain a sensor calibration compensation parameter; based on the sensor calibration compensation parameters and the gas mass transfer characteristic parameters, deposition kinetics analysis of pollutants on the surface of the sensor is carried out, and equipment operation state evaluation data are obtained; and performing spatial layout optimization on the sensor array according to the equipment operation state evaluation data and the gas flow consistency constraint condition to obtain an optimal layout scheme and generate a staged adaptive management control instruction. According to the invention, self-adaptive adjustment and intelligent switching of the sensor management strategy are realized, and the detection performance and reliability of the purification equipment sensor system are significantly improved.

Systems and methods are disclosed for tracking objects in a physical environment using visual sensors onboard an autonomous unmanned aerial vehicle (UAV). In certain embodiments, images of the physical environment captured by the onboard visual sensors are processed to extract semantic information about detected objects. Processing of the captured images may involve applying machine learning techniques such as a deep convolutional neural network to extract semantic cues regarding objects detected in the images. The object tracking can be utilized, for example, to facilitate autonomous navigation by the UAV or to generate and display augmentative information regarding tracked objects to users.

The invention relates to a commercial vehicle integrated brake control and sensor system. The system comprises a multi-source sensor module used for acquiring sensor signals of operation of the commercial vehicle brake control system. The feature extraction module carries out filtering processing on sensor signals and extracts key feature parameters to construct a feature parameter matrix. In the braking decision execution module, a front and rear axle control unit calculates a front and rear axle braking force distribution coefficient based on the characteristic parameter matrix and generates an intelligent braking instruction; and the braking execution unit dynamically adjusts single-wheel braking force distribution according to the intelligent braking instruction to obtain braking execution parameters. And the evaluation optimization module judges the stability of the braking system according to the braking execution parameters and the operation real-time feedback data, and adjusts the braking force distribution parameters of each wheel to generate an optimized braking control strategy. By adopting the system, the multi-source data fusion precision, the dynamic adaptability of braking force distribution and the communication stability under complex working conditions in the braking process of the commercial vehicle can be improved, and the safety and reliability of the braking system are enhanced.

The invention provides a distributed multi-sensor tracking method based on privacy protection and anomaly suppression. The distributed multi-sensor tracking method based on privacy protection and anomaly suppression comprises the following steps: acquiring original measurement information of local sensors; according to the original measurement information, adopting a pre-established robust Kalman filter to suppress random abnormal interference to obtain a local state estimation value; and encrypting the local state estimation value by adopting a dynamic mask mechanism, and fusing the encrypted estimation values transmitted between the adjacent sensor nodes by adopting an average consensus filter according to the topological structure of the network system, thereby obtaining global consistency state estimation of the multi-sensor system. According to the method, the local state estimation value is encrypted, privacy information is protected, and the method is applied to a multi-sensor scene through global consistency state estimation of a multi-sensor system, so that the technical problem that a distributed sensor tracking method is lacked in the prior art to avoid privacy leakage is solved.

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 a temperature control method and system based on a time sequence-residual module cascade algorithm, and belongs to the field of heating and ventilation design and automatic control design. The method comprises the following steps: acquiring environmental data in real time by using a sensor system, and generating a data set suitable for model training and prediction through data preprocessing and feature extraction; a deep learning model is awarded, wherein a time sequence-residual module cascade model is adopted for temperature prediction; the network structure captures the long-term trend of a time sequence by using the LSTM, the Res residual module extracts local fluctuation features by using a convolutional layer, and the local fluctuation features and the Res residual module realize cooperation of a time sequence dimension and a space dimension through feature parameter jump connection remodeling, so that the capability of predicting the future temperature change trend is remarkably improved, and the prediction efficiency is improved. The indoor temperature can be adjusted in real time to maintain comfortable environmental conditions. According to the intelligent indoor temperature prediction method, the comfort degree of the indoor environment is improved, the energy consumption is remarkably reduced, the remarkable energy-saving effect is achieved, and the positive promotion effect on environment protection is achieved.

The invention discloses a dual-polarization multi-layer optical current sensor and a multi-physical field disturbance resisting method thereof, and belongs to the technical field of current sensors of power systems. The sensor comprises a dual-polarization multi-layer sensor system and a dual-polarization multi-layer optical current sensor real-time signal processing system which are connected with each other, the output end of a double-layer diamagnetic photoelectric sensing signal processing unit and the output end of a temperature-resistant signal photoelectric sensing signal processing unit in the real-time signal processing system of the dual-polarization multi-layer optical current sensor are connected to the input end of a dual-polarization multi-layer signal processing unit; and the output end of the dual-polarization multi-layer signal processing unit is connected to the multi-physical field anti-interference signal output unit. According to the invention, the resistance of the straight-through optical path type MOCT to ambient temperature field fluctuation can be effectively improved, and the influence of external magnetic interference at any position and the influence of ambient temperature fluctuation interference can be resisted.

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 relates to the technical field of multi-sensor information fusion and intelligent manufacturing, and discloses a guide rail precision evaluation method and system based on multi-sensor fusion, and the method comprises the steps: constructing a multi-modal sensor value quantification model based on information entropy, calculating the contribution degree of sensor information and constructing a multi-sensor mutual information coupling model; constructing a self-optimization sensor network configuration system, abstracting a multi-modal sensor system into a graph structure, and generating optimal sensor combination configuration by applying a graph reinforcement learning algorithm; constructing a multi-time scale optimization framework and realizing adaptive data compression based on Huffman coding; constructing a dynamic Bayesian network model to fuse heterogeneous sensor data; constructing a self-evolution evaluation strategy system, and continuously improving an evaluation strategy through historical performance data; through information entropy-driven resource allocation, resources can be dynamically allocated according to the information value of the sensor.

The invention discloses a laser drilling method and system for a heterogeneous laminated material. The laser drilling method comprises the steps that a first acoustic emission sensor and a second acoustic emission sensor are arranged; in the blind hole forming process, the depth of the blind hole is obtained through a second acoustic emission sensor; determining the laying angle of the carbon fiber prepreg cloth according to the depth of the blind hole, calling a first acoustic emission sensor, and collecting an acoustic emission signal; judging whether the metal layer is exposed or not according to an acoustic emission signal acquired by a first acoustic emission sensor, and if the metal layer is considered to be exposed, acquiring a spectral signal in real time through a laser-induced breakdown spectroscopy detection system; and outputting the laser beam again, and controlling the laser beam to perform layer-by-layer scanning. The position of the metal layer in the blind hole machining process is preliminarily positioned based on the acoustic emission sensor system, then LIBS is used for accurately positioning distribution of the metal layer, laser process parameters are fed back and adjusted, and the spiral track during laser scanning is adjusted, so that the blind hole machining quality is improved.

The invention relates to an information fusion method and device of a multi-source navigation system, computer equipment and a computer program product. The method comprises the following steps: constructing a preset number of sub-models according to sensor systems in the multi-source navigation system, taking the product of the expected credibility and the real-time credibility of the performance of each sensor system as the comprehensive credibility of each sensor system, and determining a state transition probability matrix of the sub-models according to the comprehensive credibility; state prediction updating and measurement updating are carried out on each sub-model, and a system error state and a system state covariance are calculated; and calculating a predicted posterior probability of each sub-model, determining an interactive weight between the sub-models according to the predicted posterior probability and the state transition probability matrix, and correcting and updating the system error state and the system state covariance according to the interactive weight to obtain a final system error state and a final system state covariance. By adopting the method, the robustness of information fusion can be effectively improved.

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, apparatus, system, and computer program product for manufacturing an unconsolidated composite material. Sensor data is received from a sensor system for a composite material manufacturing system, wherein the sensor data is received during manufacturing of the unconsolidated composite material by the composite material manufacturing system. A set of predicted properties is determined for a number of portions of the unconsolidated composite material as completed from manufacturing by the composite material manufacturing system using the sensor data. A quality level for the number of portions of the unconsolidated composite material is identified based on the set of predicted properties for the number of portions of the unconsolidated composite material.