1325 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.

The invention discloses a remote monitoring and intelligent management and control system for fire-fighting equipment, and belongs to the technical field of fire-fighting early warning, and the system specifically comprises the steps: deploying a multi-parameter sensor array, and enabling each sensor node to form a redundant monitoring network through a honeycomb topological structure; carrying out real-time fusion analysis on the sensor network data through an edge computing gateway, and triggering an abnormal region positioning module when detecting that any monitoring parameter exceeds a preset dynamic threshold value; the abnormal region positioning module constructs a three-dimensional thermodynamic diagram model based on the parameter gradient change of adjacent sensor nodes, and dynamically calculates the diffusion direction and the potential risk level of an abnormal region; the mobile inspection robot cluster receives the coordinates and risk parameters of the abnormal area, a multi-stage response path is automatically generated based on a building digital twin model updated in real time, a primary path quickly approaches along a preset main road, and a secondary path is dynamically adjusted according to field obstacle distribution; and the tail end path adopts a contact type detection module to recheck the abnormal area.

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 relates to the technical field of the Internet of Things, in particular to an intelligent construction site management method and system based on the Internet of Things. The method comprises the following steps: acquiring construction site environment monitoring data in real time based on multiple Internet of Things sensor nodes; performing real-time environment state change mining on the construction site environment monitoring data, performing global environment perception modeling, and constructing a construction site digital environment perception map; obtaining a construction site omnibearing monitoring video and construction site construction equipment state monitoring data; carrying out target dynamic optical flow tracking on the omnibearing monitoring video of the construction site one by one, carrying out dynamic operation state evolution, and constructing a real-time behavior portrait model of each worker; and performing operation state equipment temperature change trend analysis and dynamic load change evolution according to the state monitoring data of the construction site construction equipment, and generating comprehensive state characteristics of each piece of equipment. The construction site management efficiency and the construction site construction safety are improved.

The invention relates to the field of environment monitoring, and discloses an efficient and accurate pollution source monitoring quality control system and method, and the system comprises a multi-modal data collection module, a high-dimensional data modeling module, a time-space correlation analysis module, a time sequence prediction module, a multi-task learning module and an anomaly detection feedback module. The method comprises the following steps: collecting data of pollutants such as air, water quality and noise, performing high-dimensional modeling and optimization on the data, and capturing space-time propagation paths of the pollutants among different sensor nodes; the system predicts the short-term change and the long-term trend of the pollutant concentration in real time; the multi-task learning model is used for predicting concentration changes of multiple pollutants at the same time, multi-scale decomposition is carried out on a time sequence through wavelet transform, abnormal changes in data are detected, and a feedback control strategy is generated. According to the invention, the precision and real-time performance of pollution source monitoring are improved, the dynamic change of multiple pollutants can be effectively handled, and the high efficiency and reliability of environment quality monitoring are ensured.

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 field of simulation environment monitoring, in particular to a remote monitoring and fault diagnosis method and system for an environment simulation system. The method comprises the following steps: identifying all distributed multi-dimensional sensor nodes, carrying out spatial topology distribution analysis, and constructing a plurality of node region models; carrying out omnibearing environment simulation operation state monitoring on the environment simulation sample box, and carrying out environment state nonlinear correlation mining to obtain nonlinear operation state correlation characteristics; performing dynamic state rendering on the plurality of node area models based on the nonlinear operation state association features, and constructing a plurality of environment simulation data twin models; and performing operation simulation monitoring processing on the plurality of environment simulation data twin models, and performing parameter difference analysis between the models to generate parameter difference characteristics of different models. According to the invention, efficient and accurate fault analysis and diagnosis of the environment simulation system are realized.

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 railway line monitoring alarm method and system, and relates to the technical field of computers, and the method comprises the steps: continuously collecting a dynamic monitoring data set comprising a track structure vibration waveform sequence, a track surface optical image sequence and an environment meteorological parameter sequence through a plurality of sensor nodes of a target railway line; carrying out multi-modal feature fusion processing on the dynamic monitoring data set, generating a comprehensive state evaluation matrix containing a vibration energy distribution vector and the like, carrying out risk alarm grade classification on the comprehensive state evaluation matrix by utilizing a preset track safety evaluation model, and outputting an abnormal event type code and an abnormal region position code; then, a multi-stage linkage control instruction set containing a train emergency braking triggering instruction and the like is generated based on the multi-stage linkage control instruction set, the multi-stage linkage control instruction set is synchronously transmitted to a central dispatching system, the central dispatching system is driven to execute rail blocking, train speed regulation and control and equipment maintenance operation according to instruction priorities, and comprehensive and intelligent monitoring and efficient alarm response to railway lines are achieved.

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.

An enhanced multi-compartment connected logistics receptacle system includes a storage receptacle, a bridge node, and wireless accessory sensor node with a plurality of sensors. A first subset of the sensors is disposed relative to a first monitored receptacle compartment and a second subset of the sensors is disposed relative to a second monitored receptacle compartment. The plurality of sensors monitor for a change in state of the storage receptacle. The wireless accessory sensor node detects the storage receptacle's state change based upon sensor data and broadcasts a signal with a flag set indicating there is event information available for upload. The bridge node detects the flag set, retrieves the event information available for upload from the wireless accessory sensor node, and transmits the retrieved event information to a backend server to cause the backend server to initiate a dispatched logistics operation related to the storage receptacle.

The invention discloses an industrial environment information wireless monitoring system based on the Internet of Things and a control method thereof, and relates to the technical field of industrial management, and the system comprises an intelligent edge calculation module, a data visualization module, a self-healing network management module, a precise energy consumption management module, an environment anomaly detection module and a wireless network optimization module. According to the industrial environment information wireless monitoring system based on the Internet of Things and the control method thereof, real-time analysis and decision are realized at a data source by using an intelligent edge computing module, data transmission delay is reduced, response can be quickly made in an emergency, potential faults are predicted through a time sequence abnormity self-learning network model, and the monitoring accuracy of the industrial environment information wireless monitoring system based on the Internet of Things is improved. And network self-healing is actively carried out before a problem occurs, so that the network downtime is effectively reduced. In addition, due to the introduction of a low-power-consumption ad hoc network protocol, the energy consumption optimization management of the sensor nodes is realized, and the service life of equipment is prolonged.

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 discloses an AFIS intelligent fire-fighting monitoring system based on the Internet of Things technology, and relates to the technical field of fire-fighting safety, and the AFIS intelligent fire-fighting monitoring system comprises a front-end module, a central monitoring module, a data analysis module, an early warning module and a fault diagnosis module. The front-end module is used for monitoring fire-related environment data and fire-fighting equipment states through a plurality of sensor nodes; installing and deploying various types of sensor nodes in the key area; the collected original data is subjected to primary processing, including data cleaning, denoising, standardization and the like, so that the accuracy and reliability of the data are ensured; according to the invention, through integration of various sensor nodes, real-time monitoring of environment parameters of a key area and the state of fire-fighting equipment is realized, so that the fire prevention and response efficiency is obviously improved, the system can wirelessly transmit data by using the Internet of Things technology, the complexity of installation and maintenance is reduced, and the monitoring range is expanded.

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.

A system for real-time data aggregation and analysis within an Internet of Things (IoT) network, consisting of: a multitude of IoT sensor nodes configured to collect data from multiple sources, each sensor node comprising a data acquisition unit for capturing data in real time and a local processing unit for preprocessing the captured data by performing data filtering, normalization and compression prior to transmission; a centralized aggregation server communicating with the multitude of IoT sensor nodes, the aggregation server comprising: a data aggregation module configured to receive, integrate, and synchronize pre-processed data streams from the multitude of sensor nodes, thereby enabling consolidated data storage; a timestamped metadata indexing unit coupled with the data aggregation module and configured to add metadata to each data entry, including a unique identifier, a geographic location, and a timestamp; an analysis engine integrated into the aggregation server, which includes the following: a data processing unit configured to perform real-time analysis of the aggregated data by applying pattern recognition and anomaly detection techniques and to generate processed results based on predefined parameters; a predictive analytics module that is connected to the data processing unit and configured to perform predictive modeling tasks by extrapolating trends from historical and real-time data; a communication interface connected to the aggregation server, configured to enable bidirectional data exchange with external monitoring devices, user interfaces, or remote management systems via secure communication protocols; and a dynamic load balancing unit embedded in the aggregation server, configured to dynamically distribute computing resources between the data processing unit and the predictive analytics module depending on data volume and complexity.

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.

The invention provides a saline-alkali soil improvement method and system based on Internet of Things big data analysis, and the method comprises the steps: collecting a target saline-alkali soil sample, and obtaining an improvement target and a preset improvement scheme through sample analysis and a knowledge base; randomly selecting a plurality of different types of areas, and setting sensor nodes for layered sampling; monitoring the change of the salt content and the PH value of the soil and the crop growth condition through a monitoring system; dynamically adjusting a sampling time interval; a comprehensive evaluation model is established, an improvement scheme is adjusted according to the soil salt content change value, the PH value change value and the change value of the evaluation model, and the system comprises a sampling node setting module, a sampling module and a model establishment and improvement scheme adjustment module; through the method and the system, the data volume is reduced as much as possible and the monitoring efficiency is improved on the premise of accurately setting the sampling nodes, accurately sampling and accurately and comprehensively monitoring the condition of the saline-alkali soil, and meanwhile, a monitoring model is provided to comprehensively evaluate the evaluation and improvement effect.

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 belongs to the field of remote monitoring, and discloses a remote monitoring system and method for coal mine production equipment, and the system comprises a multi-sensor integrated monitoring module which employs an integrated multi-sensor node, monitors the multi-dimensional parameters of the coal mine production equipment, collects data in real time through a wireless sensor network, and transmits the data to a central control platform; the cloud computing and big data analysis platform is used for uploading the real-time data to a cloud end, automatically analyzing the running state of the equipment by using a big data analysis and machine learning algorithm, and carrying out fault prediction and early warning; the remote control and maintenance module is used for checking the equipment state in real time and carrying out remote control and debugging; and the high-efficiency data transmission module is connected with the sensor nodes, the equipment and the monitoring platform by utilizing the Internet of Things technology and the 5G communication technology. The operation state of the coal mine production equipment can be accurately monitored in real time, the automation degree is improved, the environment adaptability is enhanced, the operation cost is reduced, and the data transmission efficiency is improved.