58 results about "Sensor cluster" patented technology

The application discloses a kind of sensor cluster management method, system and equipment based on industrial internet of things, it is related to industrial internet of things technical field, method includes: the physical space is divided into multiple subspaces and obtains internal perception node;Theoretical and actual coordinates and attitude angle of each node are extracted;Unit offset value is calculated in combination with sensitivity matrix, and then group offset value and anchoring drift rate representing mechanical stability are obtained;According to the time point of node reporting, a typical time difference is extracted, and a standard time difference is calculated in combination with bottom layer backoff and retransmission delay, to obtain network initial coordination value;According to the difference between actual and theoretical attitude angle, an attitude penalty value is obtained;Anchoring drift rate, attitude penalty value and initial coordination value are integrated to obtain the target coordination value of each subspace;Finally, the total coordination value is calculated in combination with space weight, and the bottom control instruction is triggered accordingly.The application has the advantages of precise quantification, multidimensional fusion and closed-loop intervention.

This invention relates to the field of data information processing technology, and discloses an intelligent operation and maintenance method and system based on data assets. The method includes: collecting device status data through a distributed IoT sensor cluster; standardizing the raw data and optimizing the data transmission path based on a dynamic topology network; dynamically adjusting the data model structure according to real-time data streams; performing multi-dimensional detection based on user-configured data quality rules to generate a quality detection report; cleaning abnormal data in real time based on the detection results to generate an interactive decision view; predicting operation and maintenance risk events using a multi-algorithm fusion model; and dynamically adjusting the workflow task execution path according to the risk level. This method optimizes the data acquisition and transmission process, improves the real-time performance and accuracy of data quality monitoring, and enhances the intelligence of abnormal data processing and operation and maintenance risk prediction, making operation and maintenance management more flexible and efficient.

The application provides a medicine cold chain logistics transportation whole-process tracking method and system, and relates to the technical field of logistics transportation monitoring. First, the original sensor tracking data stream output by the internal sensor cluster of a transportation carrier in a continuous collection time window is acquired. Then, the transportation carrier operation trajectory line is generated according to the geographic positioning parameters, and the multi-dimensional environment state marker set is generated by combining the physical parameters at the same time of each geographic position coordinate point. Meanwhile, the logistics transfer node topology connection network containing the logistics transfer node unit and the directed transfer path edge is constructed. The operation trajectory line, the multi-dimensional environment state marker set and the topology connection network are input into the pre-constructed space-time state feature fusion tracking model to generate the whole-process state tracking feature tensor. The abnormal state type identification and the space-time distribution feature are obtained by performing the abnormal state mode matching on the whole-process state tracking feature tensor, and the abnormal early warning instruction set is generated. The application can comprehensively and accurately track the whole process of medicine cold chain transportation, intelligently identify the abnormality and timely generate the early warning.

The invention discloses a cluster head selection method and device, electronic equipment, a storage medium and a program product, and the method comprises the steps: obtaining a network environment parameter of a target sensor network, and determining at least one candidate sensor cluster head in the target sensor network based on the network environment parameter; acquiring evaluation parameters of each candidate sensor cluster head under different preset evaluation dimensions; determining an environment adaptive value of each candidate sensor cluster head under each preset evaluation dimension based on the evaluation parameters; and determining at least one target sensor cluster head in each candidate sensor cluster head based on each environment adaptive value corresponding to each candidate sensor cluster head. According to the method, the candidate sensor cluster heads are comprehensively measured through the multi-dimensional evaluation parameters, the reliability of target sensor cluster head selection can be improved, and stable operation of the target sensor network is guaranteed.

This invention relates to the field of construction monitoring technology, specifically to a method and system for monitoring structural offsets using BIM technology. The method involves using a multi-source sensor cluster to collect real-time location information of multiple components within the construction structure, obtaining real-time monitoring data. Based on the real-time monitoring data and the preset coordinates of the components in the BIM model, a first offset evaluation index for the target component is calculated. The component importance is then obtained. The first offset evaluation index is corrected based on the component importance score to obtain a second offset evaluation index. This second offset evaluation index is compared with a preset offset threshold to determine monitoring and early warning information. This approach enables dynamic assessment of offset risks, establishes an early identification mechanism for potential problems, and optimizes the early warning system. It can dynamically capture offset trends and comprehensively consider them, achieving early risk identification and optimized early warning, thereby improving the accuracy and efficiency of construction structures.

This invention discloses a safety processing algorithm and system for dual-Hall collaborative verification position sensing of an electric wheelchair joystick, relating to the field of electromechanical control technology. The algorithm includes acquiring a first output signal from a triaxial Hall sensor and a second output signal from a linear Hall sensor, and performing dynamic baseline calibration to obtain two anti-drift position signals. Co-occurrence difference calculation and consistency determination are performed on the two anti-drift position signals to obtain a sensor cluster health status value. Signal fusion and fault reconstruction are performed based on the sensor cluster health status value and the two anti-drift position signals to obtain a fused position command. A confidence-based safety joystick command is generated based on the fused position command and the sensor cluster health status value, and simultaneously sent to the microcontroller module. This provides complete information, including reliability assessment, for subsequent control decisions, thereby constructing a full-link safety protection system from signal perception to control decision-making.

Systems and methods for monitoring apparatus(es) and equipment using a sensor cluster are described. The systems and methods can be used to automatically repair or otherwise address actual and predicted failure modes of the apparatus(es), which include electrical systems, power systems, energy storage systems, and other systems.

This invention discloses a 5GRedcap IoT control method and system for oilfield equipment based on big data. The technical solution acquires multi-source sensor data generated by the sensor cluster of oilfield equipment through 5GRedcap IoT; constructs a large database based on the attributes of the multi-source sensor data; extracts task event datasets of the oilfield equipment from the large database; predicts the operating status trend of the oilfield equipment based on the task event datasets; identifies abnormal events of the oilfield equipment based on the operating status trend; identifies abnormal equipment links in oilfield production based on the distribution of abnormal events; and isolates abnormal equipment links through IoT and adjusts the working status of these links. By constructing a multi-source sensor database for oilfield equipment and identifying abnormal events, abnormal equipment links in the production scenario can be identified, enabling multi-equipment associated anomaly monitoring and improving the overall operational reliability and fault isolation efficiency of the oilfield production system.

The invention relates to the technical field of electric energy metering, and provides an error detection method of a cluster voltmeter. Voltage signals from the same common standard voltage power supply are transmitted to each voltage sensor and a voltage standard appliance by utilizing a special voltage signal line of each voltage sensor of the voltage sensor cluster; the relative error between the voltage data measured by each voltage sensor and the voltage data measured by the voltage standard instrument is calculated by using the data acquisition and calculation system, and the detection of the measurement error of each voltage sensor of the voltage sensor cluster is realized. The invention provides a new technology which can conveniently detect or monitor the measurement error of the cluster voltage sensor.

The application relates to the technical field of offshore wind turbine inspection, and discloses an offshore wind turbine inspection and monitoring system based on digital twinning. A twin data synchronization module of the system collects wind turbine structure state data and environmental hydrological data in real time through a multi-source sensor cluster, and constructs an offshore wind turbine digital twin. An inspection path dynamic generation module analyzes a wind turbine fragile area and risk distribution based on the digital twin, generates a multi-unmanned aerial vehicle (UAV) cooperative inspection path in combination with real-time marine weather forecasts, and dispatches the UAV fleet to execute tasks according to the cooperative inspection path, and dynamically responds to real-time data updated by the twin data synchronization module. A real-time anomaly identification module fuses and analyzes UAV return data, identifies wind turbine defects, outputs abnormal information, calculates relative spatial deviation, generates attitude control instructions, and sends the attitude control instructions to the UAV cooperative control module to realize real-time adjustment of the UAV attitude.

1. The name of the design product: safety monitoring device (tunnel sensor cluster type protection and power supply integration). 2. The use of the design product: for monitoring the safety state inside the tunnel. 3. The design points of the design product: in shape. 4. The picture or photo that best indicates the design points: perspective view 1.

The present application relates to the technical field of ship testing and state monitoring, and proposes a ship speed dynamic determination method and device based on operation data, which comprises the following steps: automatically collecting the data of six-way draft, high-precision position and ground speed, main engine speed and water depth of the ship in the operation process through the sensor cluster installed on the ship; selecting the water area meeting the preset still water condition in the daily route to set the electronic fence area, controlling the ship to sail at different stable main engine speed points and collecting data; using the three-step iterative regression analysis algorithm to clean and model the data after cleaning, and finally fitting the relationship model between the ship still water speed and the main engine speed. The present application changes the speed determination from the expensive special voyage test to the low-cost and normalized operation process data utilization, realizes the dynamic, accurate and convenient determination of the ship speed, and provides the key data basis for the ship energy efficiency management, technical evaluation and intelligent operation and maintenance.

The embodiment of the invention discloses a method for processing equipment state information before starting of an extruder and a related device, the method is applied to a server in an extruder control system, the method comprises the steps that a production task list is acquired, target product information in the production task list is analyzed, and the target product information comprises a formula identifier; acquiring initial process parameters of the target product according to the formula identifier of the target product information; obtaining working parameters, detected by the sensor cluster, of the core part of the target extruder; determining a pre-starting state of a core component of the target extruder according to the working parameters; the initial process parameters are adaptively adjusted according to the pre-starting state of the core component; and controlling the target extruder to start up according to the adjusted initial process parameters. Therefore, systematized management and control of the startup process information of the extruder are realized, the information processing efficiency is improved, the comprehensiveness and accuracy of equipment state information acquisition before startup are improved, and the service life of vulnerable parts in the extruder is prolonged.

The application discloses a three-dimensional cutting dynamic parameter adjusting system and method based on deep learning, relates to the application technical field of three-dimensional five-axis optical fiber laser cutting machines in the automobile manufacturing industry, and comprises a multi-modal sensor cluster, an edge computing unit, a closed-loop control module and an AI model library. The multi-modal sensor cluster collects cutting data. The edge computing unit receives multi-modal sensor data and outputs dynamic adjusting parameters. The closed-loop control module transmits the dynamic parameters to a laser generator and a five-axis motion controller through a high-speed communication bus, forming a 'perception-decision-execution' closed loop. The application solves the problems of artificial experience dependence and dynamic response lag by designing multi-modal sensor cooperative decision-making, real-time perception of cutting state and risk, reducing the defective rate and improving the response speed.

The application discloses a kind of intelligent prosthesis control method and system with feedback mechanism, and the application relates to intelligent prosthesis control technical field, comprising the following steps: S1: acquisition step: through the sensor cluster of prosthesis foot bottom, ankle joint and knee joint deployment, real-time acquisition terrain feature, joint motion parameter and residual limb electromyogram signal.The intelligent prosthesis control method and system with feedback mechanism, through the double driving architecture of biomechanics cognitive model and reinforcement learning strategy model, in combination with the double verification mechanism of mutual inspection arbitration unit, realizes the adaptive accurate matching of feedback signal type, intensity and timing under complex dynamic environment.Biomechanics model ensures that feedback parameters meet clinical safety boundary, reinforcement learning model optimizes environmental adaptability in real time, mutual inspection arbitration resolves model conflict through physiological tolerance screening and control stability prediction, solves the problems of control misalignment, too high user cognitive load and walking confidence decline caused by traditional static rule feedback.

The invention relates to the technical field of power grid data monitoring, in particular to a networking SVG (static var generator) dynamic optimization compensation device based on an intelligent power grid, which comprises a mutual inductor cluster, a sensor cluster, a dynamic optimization compensation system, an IGBT (insulated gate bipolar transistor) inverter bridge, a driving circuit, a direct current side capacitor, a connecting reactor, a circuit breaker and a connecting line. According to the device, the dynamic optimization compensation system carries out current compensation on the power grid according to the compensation current instruction, and the compensation process is updated and optimized, so that the current compensation process of the power grid is adjusted in real time, and the accuracy of dynamic compensation is improved.

A smart switch system and method are disclosed. The system includes a smart switch device and a cover plate. Further, the smart switch device includes a capacitive touch surface enabling a user interaction without physical pressure application and a dimmable light source within a front panel of the smart switch device. Further, the smart switch includes a mesh networking capability for a seamless communication with a plurality of devices, a rear connector facilitating integration with a plurality of custom sensor clusters, and a control unit for adjusting brightness levels of a dimmable light source based on the user interaction with the capacitive touch surface. Additionally, the cover plate includes a plurality of sensor clusters interchangeably connectable to the smart switch device with a cable.

The invention relates to the technical field of operation and maintenance management, in particular to an intelligent operation and maintenance management method for a digital twin water quality automatic monitoring station. The method comprises the following steps: acquiring an equipment sensor signal set through a sensor cluster pre-deployed in an automatic monitoring station, and executing digital signal conversion; executing sensor signal collaboration according to a digital signal conversion result to obtain equipment sensing data; determining an abnormal sampling point according to the numerical value change amplitude of the equipment sensing data; performing drift correction on the abnormal sampling point, and replacing a drift correction result to a corresponding sampling value in the equipment sensing data to obtain sensing correction data; combining the sensing correction data with a preset equipment mechanism model to construct an equipment operation twin model; and executing equipment operation simulation by using the equipment operation twin model, and determining an operation and maintenance management strategy according to an operation simulation result. According to the invention, the overall operation and maintenance efficiency, reliability and fine management capability of the automatic water quality monitoring station can be improved.

The application discloses a kind of risk prediction methods in the process of scaffold, belong to scaffold technical field.It includes: in the key parts of scaffold deployment sensor cluster, real-time acquisition dynamic response and geometric deformation parameter;Physical information neural network is constructed, structural dynamics equation is embedded into loss function as physical constraint, network is trained and finite element model is corrected, obtain digital twin;By analyzing natural frequency variation, frequency band energy variation and modal curvature variation, overall stiffness degradation is judged, local damage is identified and quantified fastener loosening;Image is collected by camera, and construction conditions are identified and quantified by target detection algorithm;Damage information and working condition parameters are input into digital twin for stress evolution simulation, predict future stress response indicators and compare with safety threshold, generate graded warning information.Structure state and construction conditions are deeply integrated, with stress trend prediction capability, which improves risk prediction accuracy and timeliness.

The invention relates to the technical field of water quality analysis, in particular to a real-time dynamic water quality change trend analysis method based on multi-modal perception and deep learning. The method comprises the following steps: collecting a multi-modal sensing data set through a heterogeneous sensor cluster pre-deployed in a monitoring area; transmitting the multi-modal sensing data set to a preset edge computing node so as to execute dynamic Kalman filtering; identifying abnormal readings in the aligned dynamic Kalman filtering results, and performing self-calibration on the abnormal readings to obtain a sample data set; constructing a water quality change trend prediction model according to the sample data set; predicting the water quality change trend of the monitored area by using the water quality change trend prediction model; and matching and comparing the prediction result of the water quality change trend by using a preset grading early warning threshold value, and triggering an early warning mechanism in a grading manner according to the matching and comparison result. According to the invention, the scientificity and accuracy of identifying the water quality change in the complex water environment can be improved.

The invention discloses a flood early warning and forecasting method and device and a storage medium, and relates to the related field of intelligent flood early warning and forecasting, and the method comprises the steps: deploying a flood sensor cluster network in a to-be-monitored region, and configuring an edge node for each sensor layer in the flood sensor cluster network; collecting a multi-modal flood sensing data stream, and performing multi-scale feature extraction based on N sensor layer edge nodes to obtain N multi-scale sensing feature sets; a flood historical case database is collected, flood prediction generation confrontation training is carried out, and a flood early warning and forecasting channel is built; and carrying out fusion early warning analysis on the N multi-scale perception feature sets based on the flood early warning and forecasting channel, determining a flood early warning level of the target area, and carrying out flood early warning and forecasting through the flood early warning level of the target area. The technical problem of insufficient early warning accuracy and timeliness of the existing flood early warning forecast is solved, and the technical effect of improving the early warning accuracy and timeliness is achieved.

This invention discloses an intelligent inspection method and system based on the collaboration between a UAV and a large-scale model, belonging to the field of UAV technology. It includes: synchronously acquiring multimodal data of the inspection target through a cluster of onboard sensors on the UAV; inputting the synchronized multimodal data into a pre-trained multimodal alignment encoder to generate a unified high-dimensional semantic feature descriptor; inputting the semantic feature descriptor into a large-scale language model integrated with a thought chain technology, triggering the large-scale language model to perform hierarchical progressive causal reasoning and outputting a reasoning conclusion including the fault type, the reasoning logic basis, and a confidence score; when the confidence score is lower than a preset threshold, the large-scale language model autonomously generates flight compensation commands, driving the UAV to perform proactive fine-tuning sampling of the fault area, and iteratively executing the aforementioned steps until the confidence score reaches the target. This invention improves the autonomy, accuracy, and reliability of inspection.

The invention provides a BPNN-based bionic takifugu fin self-adaptive movement mechanism, and belongs to the technical field of bionic fish driving, the BPNN-based bionic takifugu fin self-adaptive movement mechanism comprises a base, the base comprises a main control cabin section and an energy cabin section, the main control cabin section comprises a control system, the control system comprises a main controller, a BPNN-based pressure monitoring system and a signal conditioning circuit, and the energy cabin section comprises an energy source module; the controller is connected with the bionic flexible fluctuation propulsion system, the signal conditioning circuit is connected with the sensor cluster system, and the sensor cluster system is installed on a fin ray of a bionic fin of the bionic flexible fluctuation propulsion system. According to the invention, fish form and joint linkage is accurately simulated, and flexible and smooth bionic fish fins are molded; the pressure monitor integrated at the fin can sensitively capture real-time pressure data generated by water flow to the fin, accurately sense subtle changes of the water flow, remarkably enhance the adaptive capacity to the environment, promote the energy efficiency, and explore a more accurate and energy-saving technical path for application scenes such as underwater robots and marine scientific investigation equipment.

The application discloses a kind of low-power consumption sensing cluster monitoring systems and methods suitable for electromagnetic environment of converter station.The system includes sensor node, wireless communication module, power module and control management module.Sensor node is used to collect at least two different nature sensing parameters.Control management module controls node to switch between deep sleep mode and real-time communication mode based on the comparison of sensing parameters and preset threshold.In deep sleep mode, node is periodically awakened to collect data;In real-time communication mode, wireless communication module evaluates channel quality before sending, and automatically switches to standby channel to send data if the quality is below the threshold.The application solves the technical problems of single sensor function, poor communication reliability and high power consumption in converter station monitoring through multi-parameter integration, threshold triggered dynamic power management and channel adaptive switching, and realizes long endurance, multi-dimensional and high reliable monitoring of the state of converter station.

The invention discloses an intelligent digital management method and system for equipment assets, and relates to the technical field of equipment management.The method comprises the steps that a sensor cluster is used for collecting a multi-modal time sequence data stream sent by physical equipment, and the multi-modal time sequence data stream is converted into a high-dimensional feature vector; deducing internal state variables of the equipment at the current moment by using the high-dimensional feature vector, and generating an equipment health state vector after combination; analyzing the equipment health state vector under the time sequence arrangement, and generating a health degree degradation track; inputting the health degree degradation track to a decision maker, and generating an optimal maintenance action sequence for the equipment; and executing the generated maintenance action sequence, and continuously optimizing the decision maker according to the executed equipment state. According to the method, a traditional equipment management information island is broken, non-planned shutdown can be reduced, the maintenance cost is reduced, the equipment utilization rate and the management efficiency are improved, and the transformation from passive maintenance to predictive management is realized.

The invention relates to the technical field of safety monitoring, in particular to a rotary kiln environment combustible gas concentration monitoring and emergency cut-off method and system. The method comprises the following steps: grouping combustible gas detectors into sensor clusters according to positions, and calculating a concentration representative value; calculating a cross-correlation function of the concentration representative value sequence of each sensor cluster in the sliding time window, and extracting correlation intensity and propagation time delay; constructing a statistical topological graph taking the sensor clusters as nodes; and when the concentration representative value exceeds an early warning threshold value, screening the association clusters based on a statistical topological graph, determining a troubleshooting time window by utilizing propagation time delay, counting the number of the association clusters of a concentration rising trend, and outputting an emergency cut-off instruction when the concentration representative value exceeds a voting threshold value. According to the method, propagation consistency verification is realized through sensor cluster cooperative sensing and cross-correlation analysis, real leakage and environmental interference are effectively distinguished, the false alarm rate is reduced, and the emergency response reliability is improved.

The invention discloses a communication iron tower inclination angle measurement method based on an inertial sensor, particularly relates to the technical field of communication iron tower structure safety monitoring, and is used for solving the problem that in an existing fusion measurement method, systematic errors are introduced due to potential displacement of an external radio reference, and the long-term measurement reliability is reduced. The method comprises the following steps: installing sensor clusters at different heights of an iron tower, establishing a historical health data interval of each sensor, generating an inertial data confidence coefficient representing the current data confidence coefficient, and fusing the data of the sensor clusters to obtain an inertial measurement inclination angle at each height position; and generating a characteristic index representing the stability of the overall causal network by analyzing the information transfer relationship of the inertial measurement inclination angles between different heights, and finally intelligently judging whether to trigger radio ranging correction according to the characteristic index. And when correction is needed, carrying out weighted fusion on the inertial measurement inclination angle and the radio distance measurement correction inclination angle according to the inertial data confidence, and outputting a final inclination angle.

A system and method for adaptive environmental monitoring with intelligent sampling and power management are disclosed. The system includes at least a sensing device configured to monitor an environmental condition associated with at least a structure, wherein the at least a sensing device includes at least a sensor cluster including one or more device sensors, and a controller communicatively connected to the at least a sensing device, wherein the controller is configured to receive one or more device datasets from the at least a sensing device, wherein the one or more device datasets includes a sensor measurement, determine at least a sensor usage pattern as a function of the one or more device datasets, generate a power managing command as a function of the at least a sensor usage pattern, and execute the power managing command in the at least a sensing device.

The present invention relates to a method for imbalance detection in a motor vehicle, in which a passive, semi-active, or active suspension is arranged in the motor vehicle, in which a sensor signal (311) is continuously transformed into an FT frequency profile (313) by means of a Fourier transform (312) during driving operation, in which the presence of a wheel imbalance is identified by the occurrence of a frequency anomaly in the FT frequency profile beyond a body natural frequency and / or a respective wheel natural frequency and proportional to a current driving speed (321), in which, in the case of identification (342) of the wheel imbalance, an error message is stored in the control unit if an anomaly threshold is exceeded and a critical danger threshold of the frequency anomaly is simultaneously undershot.However, if a critical hazard threshold is exceeded, the driver is prompted to visit a workshop, and in the case of semi-active suspension, the valve current is predictively adjusted according to an expected imbalance, or in the case of active suspension, the valve current is predictively and proactively adjusted according to the expected imbalance. Furthermore, a device is presented.

A system and method for adaptive environmental monitoring with intelligent sampling and power management are disclosed. The system includes at least a sensing device configured to monitor an environmental condition associated with at least a structure, wherein the at least a sensing device includes at least a sensor cluster including one or more device sensors, and a controller communicatively connected to the at least a sensing device, wherein the controller is configured to receive one or more device datasets from the at least a sensing device, wherein the one or more device datasets includes a sensor measurement, determine at least a sensor usage pattern as a function of the one or more device datasets, generate a power managing command as a function of the at least a sensor usage pattern, and execute the power managing command in the at least a sensing device.