18 results about "Sensor grid" patented technology

The invention relates to the technical field of data processing, and discloses a building construction quality real-time monitoring method and system based on a sensor network. The method comprises the steps of constructing a sensor grid through hydration heat gradient mapping, recognizing welding defects based on acoustic emission spectrum texture and wavelet packet decomposition, obtaining a quality situation by adopting maintenance age weight time-varying fusion, performing multi-scale anomaly detection by applying a residual attention mechanism, and dynamically adjusting a threshold value to generate an intervention strategy in combination with a working condition switching trigger. And intelligent construction quality monitoring is realized. Through the multi-scale feature extraction and cross-modal data fusion technology, the accuracy and real-time performance of construction quality monitoring are remarkably improved.

A weight support device includes a sensor grid that measures pressure data while a user is on the weight support device. The weight support device is connected to a computer that analyzes the pressure data and generates a virtual figure to represent the user. Based on the pressure data, the computer determines how the user moves and adjusts relative positions of segments in the virtual figure that represent various body parts corresponding to the movements of the user. The relative positions of the segments may be determined based on a kinematic model. The virtual figure is presented on a display (e.g., in a video) to illustrate how the user moved.

The invention discloses a compressed air energy storage underground cavern temperature adjusting structure and a manufacturing method thereof, and belongs to the technical field of compressed air energy storage, the temperature adjusting structure is sequentially provided with a flexible sealing layer, a phase change temperature adjusting layer and an optical fiber sensing layer from inside to outside, the optical fiber sensing layer is connected with the inner wall of a concrete lining, and the phase change temperature adjusting layer is used for stabilizing temperature fluctuation in a cavern; the optical fiber sensing layer is used for monitoring temperature and a strain field in real time and is electrically connected with an external fiber grating sensor grid monitoring system through a sensor lead. According to the method, latent heat can be absorbed or released through the phase change process, temperature fluctuation in the cavern can be effectively stabilized, material fatigue and structural damage caused by thermal stress circulation are reduced, real-time monitoring of the temperature and strain of the sealing layer is achieved, and the sensing capacity and the early warning level of the operation state of the cavern are improved.

The present invention relates to a sensor system (20) for detecting structural deformation. The sensor system (20) comprises a sensor device (22). The sensor device (22) is designed to be mounted on a surface of a structure (10) and to detect structural deformation in this surface. The sensor device (22) comprises a sensor mesh (30) with at least one strain-measuring fiber (32, 34, 36). The sensor system (20) has a carrier substrate designed to attach the strain-measuring fiber (32, 34, 36) to the surface of the structure (10). The invention also relates to a sensor mesh (30).

The present application belongs to the technical field of liquid forming of composite materials, and particularly relates to a test method and system based on a carbon nanotube sensing fiber flow front. The present application forms a sensor grid by using carbon nanotube sensing fibers, and the carbon nanotube sensing fibers swell after absorbing resin, so that the contact resistance between the tubes is significantly improved, and thus the carbon nanotube sensing fibers have sensitive resistance response characteristics to resin infiltration. The present application uses carbon nanotube sensing fibers as sensors to monitor the liquid flow front in the liquid forming process of composite materials. The carbon nanotube sensing fibers serve as reinforcing fibers of the composite materials, and do not adversely affect the performance of the composite materials. Meanwhile, the monitoring sensitivity is high, and only the data of the resistance change rate of the carbon nanotube sensing fibers can be used to monitor the flow front in the horizontal direction and the thickness direction in real time, and the accuracy of calculating the liquid filling area at a certain time is also improved.

A system for monitoring a load distribution of an autonomous vehicle is provided. The system includes a processor in communication with a memory device. The processor is configured to receive weight data from a grid of weight sensors disposed on a cargo bed surface of the autonomous vehicle, generate a weight distribution map of the cargo bed surface based on the received weight data, the weight distribution map including an array of weight values, and control operation of the autonomous vehicle based in part on the generated weight distribution map.

The invention relates to the technical field of mushroom cultivation, in particular to a moderate intelligent control method for an edible mushroom and pleurotus eryngii cultivation workshop, which comprises a sensing grid unit, an execution unit, a control unit and a man-machine interaction unit, and further comprises the following steps: S1, partitioning and initializing; s2, data acquisition and monitoring; s3, performing partition decision and control; s4, performing stage conversion and linkage early warning; and S5, alarming and data recording. The workshop is divided into a plurality of independent control areas, and longitudinal and transverse sensor grids are deployed in each area, so that the defect that the whole workshop is regarded as a single and uniform environment in a traditional method is overcome, the system can sense and eliminate temperature and humidity gradients at different positions, independent and accurate regulation and control of the microenvironment of each area are realized, and the control accuracy of the whole workshop is improved. All mushrooms can grow under the optimal condition, and the uniformity, the commodity rate and the overall quality of the pleurotus eryngii are greatly improved.

The invention discloses a commercial freezer defrosting efficiency optimization method, and relates to the technical field of freezer defrosting, and the method comprises the steps: 1, key parameter collection and sensing grid layout, and 2, discrete thermal resistance network inversion. 3, multi-factor edge-level feature extraction and frosting index calculation; 4, identifying a frost region and generating a space boundary; 5, defrosting priority calculation and partition scheduling are carried out; step 6, performing iterative verification and judgment ending; according to the scheme, through an innovative link of thermal resistance network inversion, frost edge marking, space mapping and attribute defrosting, regular one-step extensive defrosting is thoroughly abandoned, on-demand, accurate and energy-saving defrosting based on the frosting degree and part importance is truly achieved, the two extreme problems of excessive defrosting and insufficient defrosting are fundamentally solved, and the defrosting efficiency is optimized.

A network, a particularly on board of an aircraft, such as a cabin network or a cargo bay network, comprises at least one central signaling backbone, a plurality of distributed sensor nodes connected to the central signaling backbone, and a plurality of network devices connected to the central signaling backbone. Each of the plurality of distributed sensor nodes includes one or more sensors configured to gather sensor measurement values and to route the gathered sensor measurement values over the central signaling backbone. Each of the plurality of network devices is configured to send a sensor control signal to one or more of the plurality of distributed sensor nodes, the sensor control signal triggering the receiving sensor node to send selected sensor measurement values over the central signaling backbone to the sending network devices based on the content of the sent sensor control signal.

Embodiments may relate to an air-permeable weight support system, which may include a microclimate fabric layer and an air-permeable capacitive sensor layer below the microclimate fabric layer. The air-permeable capacitive sensor layer may include two air-permeable substrates, each carrying a plurality of electrically conductive pathways that are impermeable to air. The two air-permeable substrates may securely position the electrically conductive pathways to define a sensor grid and position the electrically conductive pathways that are impermeable to air to be spaced apart to define a plurality of air pathways for the air-permeable capacitive sensor layer. The sensor data may be used to determine a sleep state of the person using the weight support system. A computer may identify the poses of the person based on the pressure sensor data. A machine learning model may rely on the poses, the heart rates, the respiration rates to determine the sleep state.

The present invention discloses a real-time monitoring method and system for port cargo hazard sources based on the Internet of Things, which is applied to the quality and safety monitoring of cold chain cargo storage areas in ports. The method comprises: configuring a temperature sensor grid, a humidity sensor grid and a thermal imaging device to collect the temperature distribution matrix, the humidity distribution matrix and the surface temperature image of the cold chain cargo area; using a recursive neural network to extract the time series variation characteristics of temperature and humidity, adopting a deep residual network to extract the spatial temperature gradient characteristics of the thermal imaging image, and performing feature fusion through an adaptive weighting mechanism; constructing the fused features into a time window sequence, and using a gradient boosting tree model to evaluate the abnormal risk level of the cargo status and locate the hazard source. The present invention can timely discover and accurately locate potential hazard sources, effectively improving the intelligence level and early warning capability of port cargo safety management.

A weight support device includes a sensor grid that measures pressure data while a user is on the weight support device. The weight support device is connected to a computer that analyzes the pressure data and generates a virtual figure to represent the user. Based on the pressure data, the computer determines how the user moves and adjusts relative positions of segments in the virtual figure that represent various body parts corresponding to the movements of the user. The relative positions of the segments may be determined based on a kinematic model. The virtual figure is presented on a display (e.g., in a video) to illustrate how the user moved.

The application discloses an underwater robot trajectory obstacle detection method, comprising the following steps: (1) generating a feasible trajectory set; (2) generating a footprint grid map set; (3) generating a probability footprint grid map set; (4) saving the probability footprint grid map set; (5) constructing a sensor grid map set; (6) integrating sensor data; and (7) collision detection. In step (2), the footprint shape of a robot moving along a trajectory in different shapes is modeled, so that the method is suitable for robots in various shapes. In step (3), the uncertainty of the robot moving in water is considered. In step (5), the uncertainty of sensor measurement is considered. In addition, steps (1)-(5) are offline calculation steps, only steps (6)-(7) need to consume online computing power, and step (6)-(7) is simple to calculate. Therefore, the method has the characteristics of high real-time performance, and can provide strong support for the safe operation of the underwater robot.

A gas turbine system includes a gas turbine engine and a gas treatment system for treating an exhaust gas, and a thermal control system to control temperatures of the exhaust gas such that the temperatures may be suitable for the gas treatment system. The thermal control system may include a tempering air control and a sensor grid control, which may cooperatively control a tempering air that may be injected into the exhaust gas. Specifically, the thermal control system may adjust the flow of tempering air by compensating for a failed sensor in a sensor grid system, using a worst case scenario assumption, to protect the gas treatment system from overheating and to allow for continuous operations of the gas turbine system without interruptions due to sensor failures.

Existing therapeutic methods lack interactivity and adaptability to individual needs. The present disclosure retrieves from a database, a hardware component design as an interactive table comprising a grid of sensor(s) to detect input(s) from a user. The detected one or more inputs from user are passed to a gaming engine connected to the hardware component. Reflex time of user is recorded, when user provides the inputs to the sensors. Reflex time is compared with predefined time Tn and at least one of following is performed based on comparison that includes incrementally changing position of object, resetting timer, increasing timer with a first delta value and moving position of object closer to initial position with a second delta value. An average response time, a progression metric and adjustments to timer are calculated. A cognitive function to assess the severity of cognitive decline of user is derived.

Embodiments may relate to an intelligent patient monitoring system, which may include a weight support device, a computer, and a display. The weight support device supports a patient and includes a sensor grid layer with a plurality of sensors to measure pressure data. The computer predicts a pressure injury outcome and / or a fall outcome based on the pressure data. The pressure injury outcome includes a prediction of risk of the patient developing a pressure injury. The fall outcome includes a prediction of risk of the patient experiencing a fall. The computer may utilize a machine learning model to determine either or both outcomes. The display presents a notification generated based on the pressure injury outcome or fall outcome. The notification indicates that an adjustment of a positioning of the patient is needed to aid in the prevention of the pressure injury or the fall.

There is provided a sensor arrangement (220) for an automation equipment (110), the sensor arrangement (220) comprising a first wireless sensor (222-1) configured to obtain first wireless sensor data (Dws-1) related to the automation equipment (110) and to perform short-range wireless communication; and a first relay node (221-1) configured to obtain the first wireless sensor data (Dws-1) from the first wireless sensor (222-1) via a short-range wireless communication and to provide first relay node data (Drn-1) indicative of the obtained first wireless sensor data (Dws-1) for a long-range communication.

The invention relates to the technical field of open-air slope landslide mass recognition and early warning, in particular to an open-air slope landslide mass recognition and early warning system and method, and the system comprises a data collection module, a data fusion module, a dynamic evaluation module, a grading response module and a communication transmission module. According to the method, multi-type sensor gridding layout is adopted, adaptive Kalman filtering and normalization processing are combined to reduce noise interference, a random forest model and an adaptive threshold algorithm are utilized to dynamically predict the landslide probability, and emergency response is triggered in a graded manner. The system supports 4G / 5G and Beidou short message communication switching, has a self-checking function, and ensures stability and safety. According to the method, the key area of the slope can be fully covered, the data consistency and the prediction accuracy are improved, and accurate risk response and reliable early warning are realized.