5453 results about "Sensor system" patented technology

The invention discloses a numerical control tool machining monitoring method. The method comprises the following steps that firstly, data collection and primary processing are conducted; 2, constructing a digital twinborn model; 3, adjusting a self-adaptive threshold value; 4, performing prediction maintenance and fault early warning; and 5, optimizing machining parameters in real time. Multi-dimensional data such as temperature, vibration, stress and cutting force are collected in real time through a multi-sensor system, high-frequency sampling and signal denoising technologies are combined, comprehensiveness and accuracy of monitoring data are ensured, and a foundation is laid for follow-up analysis; the virtual machining model is constructed through the digital twinning technology, real-time simulation and synchronization of the machining process are achieved, the current state of the tool can be comprehensively reflected, anomaly detection and pre-judgment are conducted in combination with historical data and real-time data, and the fault monitoring accuracy is improved.

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.

The invention relates to the technical field of refrigeration equipment control, and discloses an artificial intelligence-based refrigeration equipment operation energy-saving control method and equipment, and the method comprises the following steps: collecting multi-dimensional data in a refrigeration environment in real time through a multi-sensor system, the data including environment variable data and equipment operation data; preprocessing the collected multi-sensor data, and generating a comprehensive environment variable through credibility evaluation and data fusion; carrying out load prediction on the current environment variable and the equipment operation state based on a deep learning model, and predicting a future refrigeration load demand; and according to the load prediction result, an optimization objective function is constructed, and an optimal control strategy of the refrigeration equipment is generated. According to the invention, through a deep learning load dynamic prediction technical scheme, a technical effect of high-precision prediction of a future refrigeration load demand is achieved, so that the change trend of an environment variable and an equipment operation state can be accurately captured, and the reliability of load prediction is improved.

A system for analyzing a boiler fault and a diagnosis method, the system comprising: a sensor system, a data acquisition and analysis system, and an early warning system. The sensor system is used to monitor temperature, pressure, and water level indicators inside of a boiler and transmit data. The data acquisition and analysis system is used to acquire, count, and analyze the temperature, the pressure, and the water level inside of the boiler in real time. The data acquisition and analysis system comprises a data acquisition module and an intelligent algorithm module. The data acquisition module is used to acquire real-time temperature, pressure, and water level data inside of the boiler. The intelligent algorithm module is used to analyze and process data and to predict and diagnose an operation state of the boiler. The early warning system comprises an early warning module. The early warning module is used to receive a signal of a fault model and to send, on the basis of the signal, a first-stage early warning signal and second-stage early warning information. Thus, real-time monitoring and early warning of the operation state of the boiler is achieved, and problems are promptly discovered and measures can be taken, therefore normal operation of the boiler is guaranteed, and production safety is guaranteed.

A system may include a robotic control circuit configured to control one or more robotic functions of a robot. A system may include a plurality of sensors configured to collect data. A system may include a governance analysis circuit configured to analyze the data and select one or more governance frameworks based on the analyzed data. A system may include a governance model circuit configured to generate a model that applies the one or more governance frameworks to determine one or more governance actions, wherein the robotic control circuit is configured to control the one or more robotic functions in accordance with the one or more governance actions, wherein the robotic control circuit, the governance analysis circuit, and the governance model circuit are integrated on a single substrate.

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.

The invention relates to the field of intelligent titration control, and discloses a high-precision intelligent titration control method and system.The method comprises the steps of sample information input and preprocessing planning, sample automatic preprocessing, multi-sensor system calibration and preparation, intelligent titration parameter setting and scheme optimization, and multi-sensor data real-time collection and fusion in the titration process. Titration end point prediction and judgment based on machine learning, result calculation, verification and storage, system self-cleaning, maintenance and state monitoring. Through multi-module collaborative operation and a series of intelligent technical means, the accuracy, the reliability, the automation degree and the adaptability of high-precision titration in each link are comprehensively improved, and the high requirements on the titration precision and the titration efficiency in a complex and diversified chemical analysis scene are met.

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.

The invention relates to the technical field of smart bracelet positioning, and discloses a smart bracelet position tracking and navigation method, device and equipment and a storage medium, and the method comprises the steps: collecting multi-source positioning data through a built-in multi-sensor system of a smart bracelet, and carrying out the preprocessing, thereby obtaining the preprocessed multi-source data; performing feature extraction and space-time embedding processing to obtain an embedded multi-source feature sequence; inputting the embedded multi-source feature sequence into a multi-source information fusion model for processing to obtain an initial position estimation result and position uncertainty; constructing an adaptive pose map based on the initial position estimation result and the position uncertainty, and optimizing the adaptive pose map to obtain optimized position information; and a multi-level environment map is generated according to the optimized position information, and dynamic path planning is performed to obtain a navigation path and a navigation instruction, so that high-precision and low-power-consumption position tracking and navigation functions are realized, and smooth user experience is provided at the same time.

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 relates to the technical field of flow anomaly identification, and discloses a flow anomaly identification method for a numerical control machine tool protocol, which comprises the following steps of: 1, multi-source data acquisition: acquiring various types of data from a numerical control machine tool and a control system thereof by arranging a data acquisition module, the data sources include network protocol flow data, machine tool sensor data, operation log data and equipment environment data, and the data formats of the data sources are kept uniform; the collected original data is subjected to standardization processing, the problems of data dimension inconsistency, noise interference and missing values are solved, and alignment of the data on a time axis is guaranteed through a synchronization algorithm; through a multi-source data acquisition and high-precision sensor system, the real-time monitoring of the flow of each key fluid system of the numerical control machine tool is realized. And by combining a plurality of feature extraction methods, the traffic anomaly can be effectively identified.

The invention discloses a multi-source modal data fusion-oriented charging pile fault diagnosis method and system. The method comprises the following steps of: acquiring message stream data, audio data and infrared sensing data associated with a charging equipment fault through a sensor system; and based on the characteristics of each modal data, fault characterization and identification are carried out by adopting a random forest, a convolutional neural network and a YOLO-v8 target detection model. According to the marked fault data, a mapping relation among multi-source data is established, and a Dempster-Shafer multi-source fusion theory is utilized to aggregate a multi-modal fault judgment result, so that the equipment fault is accurately diagnosed. According to the method, the accuracy of fault diagnosis and diversified presentation of fault types are effectively improved, the repair rate is reduced, and the method has a good engineering application prospect.

A heater assembly for a haircare appliance comprising a hair contacting member 42, a heating element 40 to heat the contact member and a support member 54 in thermal contact with the heating element and contact member which has a thermal diffusivity of at least 1.0 mm2 / s. The support member 54 may be a polymer foam such as silicone foam. A heat spreading layer with a thermal resistance no greater than 22 Kelvin / Watt may be used. The heating elements may extend across 95% of the length of the hair-contacting surface. A sensor system may be provided to increase the temperature as the appliance moves from a root to a tip of a tress of hair. The heating may be based on a time relating to the length of tress. The support member may be flexible to allow deflection.

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.

The invention discloses an underwater robot dynamic environment sensing and navigation device and method, and relates to the technical field of underwater robos.The device comprises an underwater robot, a sensor system, a data processing system, a control system, a navigation system and a power system, and the sensor system, the data processing system, the control system, the navigation system and the power system are arranged on the underwater robot; the surrounding environment can be comprehensively sensed in real time, and the optimal path is calculated through the path planning module according to the real-time environment data; performing obstacle detection according to the environment image to obtain an obstacle detection result; when an obstacle is detected, the control system sends out an obstacle avoidance decision instruction, after the path planning module receives the obstacle avoidance decision instruction, an adaptive algorithm is used for adjusting the optimal path according to the information of the obstacle to be detected, the adjusted path is obtained, and autonomous navigation for a complex underwater environment and autonomous navigation are achieved. And the autonomous navigation and operation capabilities of the underwater robot are obviously 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 discloses an FPSO stand pipe supporting structure stability online identification method and system, and belongs to the field of ocean engineering.The identification method comprises the following specific steps that firstly, the state of an FPSO stand pipe supporting structure is monitored in real time through a sensor system, and collected original sensor data are preprocessed; iI, training a stable recognition model by using historical sensor data, and globally optimizing the trained stable recognition model; the method can adapt to the rapid change of the environment in real time, improves the response capability of the whole system, effectively reduces the possibility of instability, remarkably reduces the probability of instability accidents, improves the safety of platform operation, achieves the dual optimization of safety and economy, and improves the reliability of the system. The comprehensive operation benefit of the system and the overall efficiency and accuracy of the stability online identification method are improved; the security risk of centralized storage of data can be avoided, the calculation efficiency of the system is improved, the network bandwidth pressure is reduced, the robustness and accuracy of the model are enhanced, and the fault-tolerant capability and stability of the system are improved.

An absorbent structure, including a sensor system, can sense and measure the level of wetness contained within the absorbent structure as well as sense and measure environmental conditions. The sensor system may include passive RFID sensors or tags, RFID readers, antenna, a tuning module, a processing module, a memory module and a wireless communication module. The absorbent structure may further include two or more sensors, with a first sensor placed in a first area most likely to first be exposed to liquid and a second sensor spaced apart from the first sensor in an area likely to be exposed to liquid only after the absorbent structure has become more saturated.

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.

An arrangement for guiding an agricultural vehicle (10) across a field (58) and a corresponding procedure comprise: a visual sensor system (62) directed at the field (58), which is connected to an image processing system (36) via signal transmission, which image processing system (36) is configured to identify features of the field (58) in the image signal of the visual sensor system (62) and to calculate a first path to be traveled for the vehicle (10) based on the identified features, a navigation unit (48) for determining the position of a reference point on the vehicle (10), a memory (72) in which stored waypoints for a second route to be traveled by the vehicle (10) are stored, a path calculator (70) configured to convert the first and / or second path into a common coordinate system, and a fusion unit (74) configured to merge the first path and the second path and, based on the merged path obtained in this way, to supply a signal to a steering control unit (34) to steer the vehicle (10) along the merged path and / or to store waypoints calculated on the basis of the merged path for the positions traveled by the vehicle (10) in a memory (72). It is proposed that the first path and / or the second path includes or comprises more than two waypoints and / or that the path calculator (70) or the image processing system (36) is configured to select field features (58) used to determine the first path based on the second path.

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 discloses a method for automatically keeping a chassis of mobile intelligent equipment horizontal on a slope, and relates to the technical field of mobile intelligent equipment.The method comprises the following specific steps that terrain detection is conducted, specifically, the current terrain condition is detected in real time through a sensor system installed on the mobile intelligent equipment, and the terrain condition is determined; comprising slope angles, soil types and rock distribution information; by integrating the sensor system and the central processing unit, real-time monitoring and intelligent analysis of slope topography and road surface conditions are achieved, mobile intelligent equipment can rapidly sense and adapt to various complex topographies including soft soil slopes, rugged rock slopes and the like, and by automatically adjusting the level of the chassis, the slope topography and the road surface conditions can be monitored and analyzed in real time. And the stability and the operation performance of the equipment on different terrains are ensured, so that the terrain adaptability of the equipment is remarkably improved, the mobile intelligent equipment can play a role in a wider environment, and the application range and the practicability of the equipment are improved.

A locomotive sensor system includes a sensor configured to be in contact with lubricant within an engine of a locomotive. The sensor includes a sensing region circuit that is configured to generate stimuli at different times during an operational life of the engine. The system also includes one or more processors configured to receive signals from the sensor. The signals are representative of responses of the lubricant to the stimuli. The one or more processors are configured to analyze the responses and determine a characteristic of the lubricant that represents one or more of a total base number (TBN) or a total acid number (TAN) of the lubricant. The are configured to determine an unhealthy state of one or more of the engine or the lubricant based on the characteristic of the lubricant that is determined.