1081 results about "Sensor node" patented technology

The present disclosure generally relates to a two-stage system for detecting malicious nodes in Wireless Sensor Networks (WSNs), enhancing network security and resilience. The system employs a distributed approach, leveraging Cluster Heads (CHs) and a central server for efficient and accurate detection. Initially, sensor nodes are monitored for comprehensive node and network metrics, statistically ranked by significance in identifying malicious behavior. CHs perform a resource-aware first-stage detection based on their resource weight, filtering potential threats locally. Results are then aggregated at a server for a second-stage analysis using a hybrid Machine Learning (ML) and Deep Learning (DL) approach. This advanced analysis, combined with statistically relevant metrics, significantly improves detection accuracy. By integrating resource-conscious CH operation with powerful server-side ML / DL, this system offers a scalable, energy-efficient, and highly effective solution for securing WSNs against malicious node attacks, surpassing traditional detection methods in both speed and precision.

A system for AI-supported leak detection and localization in water distribution infrastructures, consisting of: a large number of distributed sensor nodes mounted along a water pipe, each sensor node comprising the following: a pressure sensor configured to measure local hydraulic pressure fluctuations within the pipeline with a resolution of at least 0.01 bar; a flow sensor configured to measure the volume flow within the pipeline with an accuracy of at least ±0.5% of the measured value; an acoustic sensor configured to detect vibration signatures caused by leaks in a frequency range between 50 Hz and 20 kHz; an embedded microcontroller with integrated analog-to-digital conversion circuitry for digitizing sensor outputs; a wireless communication module configured to transmit time-synchronized sensor data to a cloud-based processing platform; and a local energy subsystem with a rechargeable battery and an optional circuit for generating photovoltaic energy; the cloud-based processing platform includes an artificial intelligence engine that comprises the following: a data acquisition module configured to receive and decode the transmitted sensor data and to perform time alignment; a supervised learning module that is trained on historical data of flagged leaks and non-leaks to classify incoming sensor patterns; an unsupervised learning module configured to detect anomalies by modeling normal operating baselines of the pipeline; and a topology-aware localization module configured to determine leak coordinates using the topology of the pipeline network, modeling the propagation of hydraulic waves, and estimating the arrival time difference from multi-node acoustic detections; and wherein the system is configured to provide real-time leak alerts and georeferenced visualization via a remote monitoring interface.

The invention provides a comprehensive pipe gallery abnormal state early warning method and system based on the Internet of Things, and the method comprises the steps: collecting multi-source monitoring data including environment state parameters, equipment operation parameters and structure deformation parameters in real time through multiple types of sensor nodes disposed in a comprehensive pipe gallery; the multi-source monitoring data is preprocessed to generate a preprocessed monitoring data set, feature fusion processing is carried out on the preprocessed monitoring data set to generate a fusion feature vector set containing time and space related features, and then an abnormal state analysis model is called according to the fusion feature vector set to calculate and obtain real-time state evaluation parameters of the comprehensive pipe gallery. And finally, the real-time state evaluation parameters are compared with a preset early warning threshold interval, an early warning strategy is generated and fed back to a monitoring system to trigger early warning response operation, and timely early warning of the abnormal state of the comprehensive pipe gallery is achieved.

The invention discloses a slope geological disaster multi-mode early warning method and system, and relates to the technical field of slope monitoring, and the method comprises the steps: laying a distributed sensor network, and collecting slope multi-source monitoring data; the multi-source monitoring data comprises displacement data, stress data and vibration frequency data; feature extraction is performed on the multi-source monitoring data by using a graph neural network, and the feature extraction comprises capturing spatial correlation among sensor nodes and identifying an abnormal mode, identifying a potential instability area of the slope according to the abnormal mode, and outputting features of the potential instability area; and generating a slope stability risk grade assessment report based on the characteristics of the potential instability region and a disaster evolution graph constructed by combining historical disaster data. According to the invention, the comprehensive monitoring of the slope from the outside to the inside and from the static state to the dynamic state can be realized, the abnormal mode and the potential instability area can be accurately identified, and the accurate assessment and timely early warning of the slope risk can be realized based on the historical data.

The invention relates to the technical field of wind power generation, in particular to a long flexible blade monitoring method and system based on multi-source data fusion, which adopts a multi-source data fusion technology, collects optical fiber strain, vibration, environment and image data by arranging sensor nodes on a fan blade, and generates comprehensive feature data through preprocessing; and extracting potential feature vectors by a multi-modal auto-encoder, and mapping the potential feature vectors into predicted stress data by using a physical information neural network in combination with offline finite element simulation data and physical constraint conditions. Then, a control decision is generated through the multi-modal deep fusion network and hierarchical reinforcement learning, the blade state is adjusted in real time, and the model is optimized through a closed-loop feedback mechanism; according to the invention, precise monitoring and active regulation and control of special working conditions such as blade pollution and icing are realized.

The invention relates to the technical field of material physicochemical property monitoring of a multi-source sensing network, in particular to a multi-source sensing storage environment collaborative monitoring and early warning method, which comprises the following steps of: dividing grid units according to material storage types, binding sensor node coordinates, constructing a grid region reference model, and constructing a grid region reference model; integrating the volatilization characteristic parameters analyzed by the laboratory, historical monitoring data and an environment threshold value to generate a substance and environment relation matrix; the method comprises the following steps of: calling model parameters to invert a theoretical gas concentration value, and triggering finite element physical field simulation through residual analysis to generate a three-dimensional simulation field data set containing a temperature gradient and a diffusion path. And the dynamic correlation analysis precision and the early warning reliability of storage environment monitoring are improved.

The invention provides a particulate matter and ozone source monitoring method and system based on a distributed sensor, and relates to the technical field of pollution treatment. According to the invention, sensor nodes with geographic perception capability are deployed in a monitoring area in a high-density manner, pollutant concentration and meteorological parameters are collected in real time, and data are uploaded to a cloud platform for preprocessing and dynamic calibration; a machine learning model is constructed based on the combined features of the pollutants and the meteorological factors, a driving relation is mined, and pollutant influence factors are extracted; further performing joint modeling on the influence factors and regional emission source data, identifying the coupling strength between pollutants and emission sources by adopting a classification or clustering method, and judging the categories of main sources; backward trajectory simulation, source fingerprint analysis and multi-source regression decomposition are combined to realize pollution path inversion and source contribution rate quantification; and finally, constructing a geographic information visualization platform, displaying a pollution thermodynamic diagram, a contribution change diagram and an evolution path diagram, and providing support for multi-source pollution traceability and scientific management and control.

The invention relates to the technical field of tobacco logistics and intelligent control, and discloses an intelligent temperature and humidity regulation and control system and method for tobacco transportation, and the system comprises a multi-dimensional environment sensing module, a self-adaptive strategy generation module and a closed-loop efficiency verification module. The method comprises the following steps: capturing three-dimensional space parameters in a transportation carrier in real time through distributed sensing nodes, outputting a structured environment situation matrix, and providing a space-time reference for a regulation and control decision; after receiving the environmental perception data, executing dynamic strategy optimization, establishing a transportation stage-spatial position two-dimensional regulation and control strategy library, processing sensitivity differences of tobacco leaves in different regions by adopting an asymmetric weighting algorithm, and generating a regulation and control scheme containing a gradient regulation instruction; real-time evaluation and strategy iteration of the regulation and control effect are achieved, microenvironment response detection is deployed, tobacco physical characteristic change data are collected, and a triangular verification model is constructed. The method effectively controls moisture content distribution, inhibits browning and mildew risks, and maximally retains tobacco leaf quality characteristics.

The invention relates to the technical field of sensing data security, and discloses a sensing data chip-level dynamic key negotiation method, which comprises the following steps of: acquiring a unique hardware identifier and key parameters of a sensor node, and generating a dynamic key seed matrix; after acquisition is completed, randomly intercepting data segments, performing median filtering and normalization preprocessing, extracting local statistical features and global features to generate a data feature sequence, and splicing the data feature sequence to a seed matrix to obtain a dynamic key generation matrix; a dynamic negotiation key is generated through standardization and SM3 Hash algorithm encryption, and is stored in a cloud and node security unit; during verification, dual verification is realized through hash comparison and plaintext bit-by-bit matching; and setting an environment parameter exception triggering mechanism, and updating the key if accumulative exception exceeds the limit. According to the method, hardware and dynamic data features are fused, and the key security and adaptability are improved.

The invention discloses a plant disease and insect pest early warning method and system based on plant monitoring, and belongs to the field of plant disease and insect pest early warning, and the method comprises the steps: extracting the contour of a lesion region through an edge detection algorithm, carrying out the smoothing of the contour through the combination of morphological operation, and obtaining a more precise lesion region boundary; according to an image segmentation result and a disease type identification result, a plant health condition evaluation model is established, and the plant damage degree is quantified; environmental data and image data are acquired from a plurality of sensor nodes distributed in a field, and the data are gathered to a regional gateway through wireless transmission; carrying out preprocessing and feature extraction on the converged heterogeneous data in a regional gateway, removing noise data and redundant information, and extracting key features; and carrying out pest detection and counting on the preprocessed image by using a deep learning model, carrying out modeling on a pest number change trend, predicting population density change in a period of time in the future in combination with environmental factors, and generating a detection result.

The invention relates to the technical field of three-dimensional imaging, and discloses a high-precision underwater structured light three-dimensional point cloud imaging method and system, and the method comprises the following steps: deploying a plurality of sensor nodes, monitoring the temperature, salinity and turbidity key parameters of a water body in real time, selecting proper light source wavelength and power according to the collected data, and carrying out the three-dimensional point cloud imaging of the underwater structured light. It is ensured that the optimal signal-to-noise ratio is obtained under the current condition, and meanwhile the exposure time and gain setting of the camera are initialized. According to the method, the optimal signal-to-noise ratio is obtained by monitoring water body conditions in real time and dynamically adjusting light source characteristics, the optimal structured light pattern is predicted by using a machine learning model, scattering noise is removed, advanced registration and fusion algorithms and an accurate phase unwrapping method are applied, and a high-quality comprehensive structured light pattern is generated. Point cloud gaps are filled through a geometric correction algorithm and an interpolation / filtering algorithm, so that a reconstruction model is closer to the surface of a real object.

The invention relates to the technical field of RFID smart tags, and discloses an RFID smart tag positioning and tracking printing method and system, and the method comprises the steps: inputting an RFID tag radio frequency signal into a conditional variation automatic encoder for convolution processing and attention weighting, and obtaining an RFID tag global feature vector; establishing a layered sensor node network to perform multi-dimensional acquisition on environmental parameters to obtain an environmental spatio-temporal data set; inputting the global feature vector of the RFID tag and the environment spatio-temporal data set into a deep fusion network for feature fusion to obtain a multi-modal fusion feature; performing sequence modeling based on the multi-modal fusion features to obtain an article trajectory prediction model; the object track prediction model is applied to a target area grid coordinate system for density distribution calculation, a real-time object positioning heat map and a track database are obtained, an efficient object positioning heat map generation and track data management mechanism is established, and real-time position visualization and historical track query are facilitated for the system.

The invention relates to the technical field of video monitoring, and discloses a camera cooperative monitoring method and system based on multi-modal perception, and the method comprises the steps: detecting an abnormal event signal in an environment through a non-visual sensor node, triggering the activation of a camera, and carrying out the visual capture of a target region; the camera performs relay tracking based on target feature matching to generate a continuous motion track; performing multi-view collaborative shielding on privacy sensitive information in the tracking target, generating desensitized monitoring data, and uploading the desensitized monitoring data; and optimizing the energy consumption of the camera in a non-event triggering period, and updating the behavior recognition model based on the desensitization data. According to the method, the contradiction between privacy protection and monitoring efficiency is solved, cross-regional model evolution and energy consumption reduction are realized, and the problems of response lag, data redundancy, privacy disclosure and high energy consumption of traditional monitoring are systematically avoided.

An enhanced node-based connected logistics receptacle system includes a storage receptacle, a bridge node, and wireless accessory sensor node with at least one sensor monitoring for a change in state of the storage receptacle. The system's wireless accessory sensor node detects the storage receptacle's state change based upon sensor data, records timestamped information reflecting the detected change, and broadcasts a signal with a flag set indicating there is event information available for upload. The system's bridge node is operative to detect the flag set, retrieve the event information available for upload from the wireless accessory sensor node, and transmit a message related to the retrieved event information to a backend server to cause the backend server to initiate the dispatched logistics operation related to the storage receptacle.

The invention provides a forest fire prevention early warning method and system based on a multi-source sensor, and the method comprises the steps: firstly obtaining a multi-source sensing monitoring data set of a target forest region, including data subsets of temperature, humidity, smoke concentration, visible light images and the like, and collecting each subset by at least two heterogeneous sensor nodes; performing space-time alignment processing on the multi-source sensing monitoring data set to generate a space-time distribution characteristic set containing various characteristics such as temperature gradient, calling a dynamic anomaly detection model to compare fire danger indexes, generating a fire danger grade quantitative score and an anomaly fluctuation index set, and performing data processing on the fire danger grade quantitative score and the anomaly fluctuation index set; the method comprises the steps of obtaining a fire hazard grade quantitative score, matching a multi-level early warning strategy based on the fire hazard grade quantitative score and a preset early warning threshold interval, generating a dynamic fire hazard early warning instruction, triggering fire extinguishing resource scheduling according to the dynamic fire hazard early warning instruction, generating a forest region escape path optimization strategy in combination with an abnormal fluctuation index, and achieving efficient and accurate forest fire prevention early warning and coping.

An enhanced connected logistics receptacle system that selectively retrieves and reports event information to a backend server to initiate a dispatched logistics operation related to a delivery item. The system includes a storage receptacle for receiving the delivery item, a bridge node mounted to the storage receptacle, and a wireless accessory sensor node coupled to a plurality of sensors. The wireless accessory sensor node is operative to detect a change in state of the storage receptacle based upon sensor data, record timestamped information reflecting the detected change in state, and broadcast an updated advertising signal having a data available flag set within the updated advertising signal. The bridge node processor is operative to detect the data available flag, selectively retrieve event information from the wireless accessory sensor node, and transmit the retrieved event information to the backend server to cause the backend server to initiate the dispatched logistics operation.

The invention discloses a multi-point distributed high-precision industrial equipment operation monitoring method and system, and relates to the technical field related to industrial equipment monitoring, and the method comprises the steps: independently collecting the operation parameter data of equipment through a plurality of sensor nodes; transmitting the operation parameter data to a central control unit in real time for fusion processing, and generating an equipment operation state feature set; performing fault prediction and health assessment on the equipment operation state feature set to obtain an equipment health score; and dynamically triggering an alarm signal according to the equipment health score, determining a target sensor node, and generating a target maintenance instruction. The technical problems that in the prior art, the equipment operation monitoring efficiency is low, the overall condition of the equipment is difficult to master comprehensively, a flexible monitoring and early warning mechanism is lacked, the fault position of the equipment cannot be accurately positioned and quickly responded and processed, and industrial equipment cannot efficiently and stably operate are solved. The technical effect of improving the accuracy and stability of equipment fault positioning is achieved.

The invention relates to the technical field of photovoltaic power generation, in particular to a flexible photovoltaic intelligent monitoring and management system and method based on the Internet of Things, multi-source heterogeneous data are comprehensively collected through deployed multiple types of Internet of Things sensor nodes, the data are uploaded to a cloud platform after being cleaned and standardized through edge nodes, a big data processing architecture integrated with flow and batch is adopted, and the intelligent monitoring and management system and method based on the Internet of Things are established. The method comprises the following steps: performing real-time analysis and state judgment on a real-time data stream, performing deep batch processing and feature mining on historical data, extracting high-order features such as a performance attenuation trend and an abnormal mode, fusing real-time and historical features, and realizing comprehensive scoring of a health state of a component and accurate prediction of residual life by utilizing a machine learning model. And based on an evaluation result and a preset knowledge base, automatically generating a differentiated precise operation and maintenance instruction, and issuing and executing the differentiated precise operation and maintenance instruction to form closed-loop management. According to the invention, the monitoring depth and breadth of the flexible photovoltaic system are effectively improved, the conversion from passive alarm to active predictive maintenance is realized, and the operation reliability of the system is significantly enhanced.

The invention discloses a wiring harness dynamic load and electromagnetic interference coupling test method, and relates to the technical field of wiring harness testing, and the method comprises the steps: S1, defining a plurality of environment parameter groups, and constructing a test platform; s2, dynamic current pulse and multi-axis vibration excitation are synchronously loaded; s3, applying broadband electromagnetic interference signals and monitoring signal crosstalk amplitude and bit error rate changes; s4, the sensor nodes are dynamically configured according to the topological structure of the wire harness; s5, generating a feature vector; s6, outputting a wire harness performance index; s7, outputting a wire harness fault probability prediction value; s8, obtaining prediction data; s9, triggering a high-speed data recording mode, and storing original sensor data 60 seconds before the fault; and dynamically adjusting the gradient step length and the loading sequence in the environment parameter group. Performance evaluation, fault prediction and service life management can be carried out on the wire harness under complex working conditions, and the reliability and safety of an automobile electrical system are improved.

The invention relates to the technical field of power grid monitoring, and discloses a voltage sensor multi-parameter real-time monitoring method and device in an Internet of Things environment. According to the method, real-time voltage fluctuation data of a target area is acquired by deploying a multi-channel voltage sensor array and is transmitted to an edge computing node; dynamic feature extraction is executed in the edge nodes, and a multi-dimensional feature matrix containing voltage fluctuation ratio, harmonic distortion and phase deviation features is generated; identifying voltage sag, overvoltage and harmonic resonance event characteristics based on an anomaly detection model; spatial correlation modeling is carried out in combination with power grid topological structure parameters, the coupling strength of adjacent sensor node parameters is calculated, and a multi-parameter correlation map containing event propagation paths and influence ranges is generated; inputting the voltage into a diagnosis model to generate a power grid state diagnosis report, and dynamically adjusting the sampling frequency and filtering parameters of the voltage sensor according to the power grid state diagnosis report. According to the method, real-time monitoring and analysis of multiple parameters of the power grid can be realized, and comprehensive support is provided for power grid state evaluation.

The invention discloses a pressure pipeline-oriented crude oil storage and transportation safety state dynamic monitoring method, and belongs to the field of pressure pipeline engineering safety monitoring, and the method comprises the steps: obtaining multi-modal sensing data collected by a plurality of sensor nodes; performing signal space-time fusion processing on the multi-modal sensing data to generate environment interference compensation parameters; constructing a dynamic compensation model based on the environmental interference compensation parameters, and outputting a pipeline body damage feature vector and a medium state feature vector; carrying out migration enhancement analysis on the pipeline body damage feature vector and the medium state feature vector to generate a migration enhancement feature vector; and generating a corresponding safety early warning signal according to a comparison result of the migration enhancement feature vector and a preset dynamic threshold parameter. According to the invention, a multi-sensor space-time fusion and migration enhancement analysis technology is adopted, real damage signals can be accurately identified, and dynamic threshold early warning can be realized.

The invention discloses a pollutant anomaly detection method based on multi-pollutant collaboration and spatial-temporal feature fusion. The method comprises the following steps: S1, constructing a multi-pollutant sensor network data set; s2, cleaning and preprocessing the multi-pollutant sensor network data set in the research area; s3, learning a directed graph adjacency matrix of the single-pollutant sensor network for the multi-pollutant sensor network data set through Bayesian variation inference, and modeling an asymmetric causal relationship between sensors; s4, processing the sensor nodes of the predicted target location by using a single-pollutant spatial feature extraction module, and performing spatial feature fusion among multiple pollutants based on a hierarchical attention mechanism; s5, performing multi-pollutant data prediction on the multi-pollutant sensor network data of the predicted target location by fusing spatial features and a directed graph by using a spatial-temporal feature fusion module; and S6, performing anomaly detection based on an anomaly score and a dynamic update threshold value of the air pollutant monitoring value generated by cooperation of multiple pollutants.

The invention discloses a long-term vibration monitoring method and system based on multi-channel data acquisition, and the method comprises the following steps: installing a sensor node integrated with a multi-mode energy collection module on a monitored object, and providing continuous power supply for a multi-channel data acquisition and edge processing unit; according to the invention, through a dynamic adaptive sampling mode and edge end extraction key features, the data load in a distributed monitoring scene is significantly reduced; the multi-mode energy collection module is integrated to collect vibration and environmental mechanical energy, and the energy management unit is combined to dynamically distribute stored energy and supply power, so that long-term autonomous operation of the sensor node is realized; the abnormal state is locally inferred and identified through the edge processing unit, and only key feature data instead of full data is uploaded, so that the real-time response capability to the equipment abnormality is improved; multi-dimensional correlation features are extracted by collecting multi-channel sensing data and combining a deep learning model and a time sequence-space feature fusion algorithm, and the health state of the equipment is comprehensively reflected.

The invention provides a water environment monitoring system and method, and the method comprises the steps: preliminarily screening an abnormal region with an abnormal water environment from all local monitoring regions through the information difference degree of each piece of water spectral data, and extracting the water spectral features of each sensor node in the abnormal region; performing spectrum fusion on all the water body spectrum characteristics according to the topological incidence relation among the sensor nodes in the abnormal area, and further determining the fusion state quantity of water quality pollution in the abnormal area; interference characteristics of the spectrum sensor in the external environment in the abnormal area are obtained, correlation analysis is further carried out on the interference characteristics of the external environment and the fusion state quantity of the water quality pollution, and then the abnormal credibility of the water quality pollution in the abnormal area is determined according to an analysis result. And whether water quality abnormity exists in the water environment monitoring area is judged through the abnormity credibility. By adopting the scheme of the invention, multi-level anomaly analysis of the water environment monitoring area can be realized, so that the reliability of a monitoring result is improved.

The invention discloses a pipe network water leakage point detection and distance positioning method based on flow analysis. The method comprises the following steps: S1, constructing a flow balance model of a pipeline network; s2, monitoring the flow state of each node of the pipeline network in real time based on the flow balance model constructed in the step S1, and performing preliminary positioning on a leakage point when pipeline leakage is detected; s3, flow gradient analysis and reverse hydraulic fine positioning: in the suspected leakage area locked in the step S2, meter-scale precision positioning of a water leakage point is realized through a flow gradient analysis and reverse hydraulic iterative model; and S4, a plurality of sensor nodes are arranged on the water leakage pipeline determined in the step S3, detection data are collected, the position of a leakage point is determined according to a related positioning fusion algorithm, comprehensive decision making is carried out on the leakage point and the leakage point determined through flow gradient analysis and reverse hydraulic power in the step S3, and finally the accurate position of the pipeline leakage point is judged. The problems of low precision, weak interference resistance and the like of a traditional method can be solved, and accurate detection of leakage points is realized.

The invention relates to the technical field of big data monitoring, in particular to a highway environmental protection construction monitoring method and system based on big data, and the method comprises the following steps: calculating the spatial distance between sensors based on the geographic position information of a data collection point and the raised dust concentration distribution and particulate matter monitoring value of a road construction environment, and calculating the spatial distance between sensors; and connecting sensor nodes with a direct adjacency relation through difference calculation and normalization processing. According to the invention, through probability density calculation and time sequence analysis, abnormal edge screening, abnormal change detection precision improvement, misjudgment reduction, abnormal edge connection node position and space density calculation, high-density area and cluster center determination, accurate positioning of pollution points is realized, and the method is suitable for the real-time monitoring of the pollution points in combination with monitoring data change trend and diffusion path tracing. The method comprises the following steps: reconstructing a pollution diffusion chain, determining a pollution propagation range, optimizing pollution treatment, accurately identifying a pollution source through weight association degree and gradient analysis between path nodes, reducing monitoring blind areas, and providing comprehensive support for construction monitoring.

The invention relates to a flying dust monitoring data processing and classifying method based on multi-source sensing fusion, and the method specifically comprises the following steps: firstly, deploying multi-source flying dust monitoring sensor nodes in a target region to collect data, carrying out the marking, and generating a data set; performing continuous wavelet transform on the acquired data, extracting a wavelet energy spectrum and a Shannon entropy, and splicing to obtain an enhanced feature tensor; secondly, through a two-stage fusion and coding strategy, frequency band energy features are extracted through wavelet packet decomposition, multi-channel cross-correlation, statistical moment and ratio features are calculated to form time sequence mode coding features, and multi-source heterogeneous feature fusion is achieved in combination with a local time sequence feature matrix; then constructing a deep learning model containing a multi-scale time sequence feature extraction and dynamic fusion module, and inputting a fusion feature matrix for training; and finally, inputting the preprocessed new monitoring data into the trained model, and outputting a dust source and pollution level classification result. The dust monitoring data classification accuracy and the dust source identification precision can be effectively improved.

The invention discloses a geological disaster real-time monitoring method and system based on a ground sensing network, and belongs to the technical field of geophysical exploration, and the method comprises the steps: obtaining an acceleration parameter and a moisture content parameter of each sensor node; according to the change rate of the acceleration parameter and the moisture content parameter, identifying an environment sudden change event, generating an environment trigger signal to activate an emergency sampling mode of a target node and an adjacent node, and constructing a dynamic monitoring cluster; requesting a plurality of nodes in the dynamic monitoring cluster to synchronously measure similar parameters, and comparing measurement results to generate a compressed alarm packet; adjusting weight factors of different monitoring parameters by using the compressed alarm packet and pre-acquired real-time environment parameters, and generating an environment calibration risk index; and when the environment calibration risk index exceeds a preset risk threshold value, outputting a geological disaster early warning instruction. According to the method, multi-parameter cooperative triggering, dynamic cluster response, weight adaptive evaluation and compression transmission technologies are adopted, and the monitoring precision, timeliness and environmental adaptability can be comprehensively improved.

A system for self-executing enhanced interaction with a node-based logistics receptacle. The system includes a wireless accessory sensor node disposed on the node-based logistics receptacle to monitor storage receptacle components of the node-based logistics receptacle to generate sensor data. The system includes a bridge node disposed on the node-based logistics receptacle that uploads information related the sensor data, detects an external device separate from the node-based logistics receptacle, communicates with the external device to establish a smart contract based connection that provides an interaction privilege, interfaces with the external device according to the interaction privilege, and transmits an update message to the backend server that corresponds to at least a portion of the uploaded information related to the sensor data and information related to interfacing with the external device.

The invention relates to the technical field of power system operation or management, in particular to a partial discharge monitoring strategy optimization method and system based on dynamic resource allocation, and the method comprises the steps: constructing a three-stage monitoring system comprising a sensor node, a sink node and a cloud processing center, firstly initializing monitoring parameters, and then obtaining system state information periodically or in a triggering manner, calculating the risk level of each monitoring point in combination with a dynamic risk evaluation model; constructing an efficiency-maximized resource allocation optimization model based on risk levels and resource constraints, solving an optimal scheme by adopting an improved multi-target particle swarm algorithm, and issuing the optimal scheme to each node to adjust monitoring behaviors to form closed-loop optimization; and meanwhile, model parameters are dynamically updated through an online learning mechanism. According to the method, dynamic matching of risks and resources is realized, the monitoring accuracy and the resource utilization rate are improved, the adaptability of the system to the equipment state and the environment change is enhanced, and the method is suitable for partial discharge monitoring scenes of various power equipment.