736 results about "Sensor node" patented technology

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 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 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 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 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 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 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.

A dynamic learning server-based logistics system includes a node-based logistics receptacle operative to receive a delivery item as part of a logistics transaction and also includes a backend server maintaining a management profile related to operation of the node-based logistics receptacle. The node-based logistics receptacle includes a plurality of monitored receptacle components, a wireless accessory sensor node having a plurality of sensors, and a bridge node operative to retrieve event information from the wireless accessory sensor node. The backend server receives the retrieved event information, compares the retrieved event information with the management profile, identifies a threshold change condition from the comparison, dynamically revises the management profile when the threshold change condition is identified, and transmits an adjustment message to the bridge node. The adjustment message is based upon the revised management profile, and the adjustment message initiates a timing change to operation of the bridge node.

The invention relates to a hydrogen storage station safety monitoring method and system based on a wireless passive three-signal sensor, and belongs to the technical field of safety monitoring. Wireless passive three-signal sensor nodes are deployed in a hydrogen storage station to form a three-dimensional monitoring network; the temperature, strain and hydrogen detection units of the sensor are simultaneously excited through the multi-frequency-band signal transceiver, so that parallel acquisition of three-parameter signals is realized; performing signal decoupling, temperature compensation and primary risk assessment by using an edge computing gateway; deep data mining and risk assessment are carried out through a cloud early warning platform in combination with a digital twinborn model; wherein multi-parameter signal decoupling is realized by adopting a frequency division multiplexing and wavelet denoising algorithm, and safety early warning is carried out in combination with a three-level early warning decision tree. The hydrogen storage station electric spark risk can be eliminated, high-precision synchronous monitoring of temperature, strain and hydrogen concentration is achieved, and the hydrogen leakage early warning time is shortened.

Systems, methods, and computer-readable media for calibrating air quality sensors are provided. A sensor node includes a sensor node printed circuit board, a sensor module, and a communication module. The sensor node printed circuit board manages power of the sensor node circuitry, the sensor module, and the communication module such that power is provided from a primary power supply supplemented by a secondary power supply. The sensor module includes a plurality of air quality sensors to measure the concentration of air pollutants. The sensor module may be replaceable. The communication module may communicate air quality measurements to and receive configurations from a data management platform, which may perform processes to improve the accuracy of the air quality measurements.

The invention relates to the technical field of laser cutting equipment, and discloses a laser cutting machine tool real-time design method based on digital twinning. The method comprises the following steps: acquiring real-time operation data from distributed sensor nodes, wherein the real-time operation data comprises design change historical records and laser cutting track information; in a digital twin fusion design mode, performing compensation check on a design change historical record to obtain a design attribute index, and determining a trajectory interaction period of a design object on a cutting path according to the design attribute index; processing path information is obtained, risk characteristics are analyzed, cutting node risk data are obtained, and the risk situation level is determined in combination with laser cutting track information; and according to the track interaction period and the risk situation level, a dynamic design blind area in the laser cutting design process is optimized and adjusted. According to the method, the real-time data technology and the digital twinning technology are fused, the real-time performance and accuracy of design can be improved, and dynamic changes of a complex processing environment can be coped with.

The invention discloses a sensor-based agricultural information data acquisition system and method, and belongs to the technical field of agricultural information acquisition. Multi-type sensor nodes are arranged in an agricultural target area in a heterogeneous manner; the method comprises the following steps: constructing a multi-dimensional influence factor model according to crop growth stages and environmental historical fluctuation data, dividing initial sensing sub-regions, and configuring a sensor cluster; dynamically updating the sensing boundary based on the historical change rate and the spatial gradient information; fusing the heterogeneous data by adopting a multi-channel time synchronization mechanism to generate a standardized environment vector set W; calculating a sampling priority matrix according to a parameter change trend in the W, and adaptively adjusting a node state; integrating an energy consumption estimation model, and executing low-power-consumption scheduling; when any parameter exceeds the threshold, high-density sampling and remote early warning are triggered; according to the method, high-precision, low-power-consumption and dynamic-response data acquisition and intelligent early warning can be realized, and the efficiency and reliability of an agricultural sensing system are improved.

Systems and methods for detecting and diagnosing machine faults are discussed. An exemplary system includes at least one sensor node to sense a signal indicative of an operation status of a machine part, and a machine health analyzer circuit to generate a computational machine fault model comprising an autoencoder (AE) network and an associative module. The AE network encodes the sensed signal into signal features in a latent feature space, and decodes the signal features to produce a reconstructed signal. The associative module transforms the encoded signal features into an associative output using a dynamically updatable codebook. The machine health analyzer circuit detects a presence or absence of fault in the machine part based on reconstruction losses determined respectively from the reconstructed signal and the associative output. The detected fault can be presented to a user or to a process such as fault diagnosis or fault correction.

The invention relates to the technical field of animal monitoring, and provides a wild animal epidemic disease monitoring, prevention and control method and system based on artificial intelligence, and the system collects the video stream, the shell temperature, the air pathogen concentration, the sound characteristics, the VOCs spectrogram and other data of wild animals in real time through arranging multi-mode sensor nodes. Real-time reasoning is carried out through a wildness degree AI model in the edge calculation unit, the health state and epidemic disease risk of animals are evaluated, a multi-source risk knowledge graph and a graph neural network are combined, an epidemic situation occurrence probability threshold value is dynamically adjusted by the system, and accurate prevention and control instructions such as risk area division, isolation early warning and material putting schemes are generated; after the epidemic disease risk is confirmed, the system sends early warning information to a prevention and control center through various communication links, and continuously optimizes a prevention and control strategy through reinforcement learning. According to the method, intelligence and precision of epidemic disease prevention and control of wild animals are achieved, complex ecological environment changes can be coped with in real time, and prevention and control efficiency and accuracy are remarkably improved.

The invention relates to an intelligent port cargo scheduling method based on the Internet of Things, and the method comprises the steps: collecting cargo state information, equipment position coordinates, operation progress data and environment parameters in real time through Internet of Things sensor nodes disposed on a quay crane, a container truck, a storage yard sling and a cargo carrier, and fusing a port GIS map and a berth plan based on the real-time collected data, and establishing a multi-objective optimization model with maximization of the quay crane-container truck-storage yard cooperative efficiency as an objective and minimization of equipment conflict avoidance and path overlap as constraint conditions, and dynamically activating or sleeping storage yard operation partitions and adjusting the number of container truck marshalling according to the obtained storage yard congestion prediction index and the parking space vacancy rate.

The mold injection control method comprises the steps that three-dimensional point clouds of a current injection molding part and an adjacent previous injection molding part are obtained, the global difference degree between the current injection molding part and the adjacent previous injection molding part is calculated through overall registration, if the global difference degree exceeds a threshold value, a difference mask is generated and covers a real-time image of a cavity, a difference image is formed, and the difference image is subjected to injection molding. And performing hybrid clustering on the difference image and a known defect type database to obtain candidate defect types and membership degrees, calculating posterior probability contribution degrees of the sensing nodes to the candidate defect types by taking the membership degrees as input and combining a Bayesian reasoning algorithm, if the contribution degree of any sensing node exceeds a threshold value, outputting a defect root cause report, and if the contribution degree of any sensing node exceeds the threshold value, outputting a defect root cause report. And a machine table or a mold execution mechanism is driven to carry out correction. According to the invention, the problems of low detection efficiency, large subjective deviation and lagging root cause judgment due to the fact that automobile part defect detection and root cause analysis depend on manual visual detection or single-dimensional automatic detection are solved.

The invention relates to an underwater wireless sensor network path sensing routing method based on deep reinforcement learning, which comprises the following steps that: firstly, a node constructs and periodically updates a transmission preference model based on local and neighbor node interaction information; secondly, deploying a deep reinforcement learning model at each underwater sensor node to perform distributed routing strategy learning; and finally, generating a global guide vector by the sink node according to the routing path information of the received data packet, reversely spreading the global guide vector to the source node, fusing the global guide vector with a local transmission preference vector of the node to generate a guide reward, optimizing the deep reinforcement learning model, and updating a routing strategy. According to the method, the problems of difference and complexity of underwater transmission tasks can be solved, and the network data transmission efficiency and the overall service quality are improved in combination with local preference and global guidance while the node online learning is kept to adapt to the dynamic underwater environment.

The invention discloses a temperature field real-time monitoring method and system based on special ceramic sintering, and relates to the field of temperature monitoring of special ceramic sintering, and the method specifically comprises the following steps: S1, processing a sensor array; s2, construction of multi-parameter data acquisition; s3, a temperature field reconstruction and prediction model based on multi-physics field coupling; and S4, temperature field real-time monitoring and abnormity early warning are carried out. According to the temperature field real-time monitoring method and system based on special ceramic sintering, the temperature sensor is preset in the green body, a three-dimensional temperature field reconstruction function is combined, discrete sensor node real-time temperature data can be converted into a continuous three-dimensional temperature field cloud picture, temperature distribution of the surface of the green body can be clearly presented, and the temperature field can be monitored in real time. And the temperature gradient change from the surface layer to the core can be more accurately restored, so that an operator can intuitively master the temperature state of the whole green body, the situation that the whole process is misjudged due to local data is avoided, and the comprehensiveness and accuracy of temperature field monitoring are fundamentally improved.

The invention discloses a water supply pipeline leakage detection method and system, and relates to the field of artificial intelligence, and the method comprises the steps: collecting a multi-source signal through a plurality of sensor nodes disposed on a pipeline; preprocessing and space-time alignment are carried out on the collected multi-source signal at an edge computing node to obtain a standardized signal; analyzing the standardized signal by using a preset lightweight one-dimensional convolutional neural network model at the edge computing node to obtain a preliminary leakage detection result and a corresponding confidence coefficient; executing a hierarchical response strategy according to the confidence coefficient; and in the cloud analysis module, multi-model fusion analysis is performed in combination with the feature vectors, historical time sequence data and a hydraulic model simulation result, so that leakage points are accurately positioned and verified, and a leakage result is determined. According to the invention, a real-time, accurate and reliable complete solution can be provided for pipeline leakage monitoring.

The invention relates to a tartary buckwheat pest and disease damage dynamic monitoring method, system and device and a medium, and belongs to the technical field of agricultural intelligent monitoring, the dynamic monitoring method comprises the following steps: periodically collecting environmental parameter data through fixed sensor nodes deployed in a farmland, and obtaining leaf vibration signals and multispectral image data at the same time; performing space-time alignment on the blade vibration signal and the multispectral image data, correcting radiation distortion in the multispectral image data, and outputting a registration data set; according to the registration data set, fusing to generate a multi-modal feature vector, inputting the multi-modal feature vector into a pre-trained space-time analysis model, and outputting a risk level distribution diagram with a geographic coordinate mark; generating a control instruction set according to the risk level distribution map, and triggering execution equipment to execute pest control operation; and optimizing weight parameters of the space-time analysis model through the generative adversarial network based on the execution log of the control instruction set and the historical multi-modal feature vector. The scientificity and timeliness of pest control can be improved.

The invention relates to the technical field of digital twinborn management, and discloses a full-life-cycle digital twinborn management system and a modeling method. A model driving unit; a decision optimization unit; and a credible evidence storage unit. The modeling method comprises the following steps: S1, analyzing a product design bill of materials; s2, establishing a topological mapping relation between the sensor nodes and the virtual model area; s3, running Monte Carlo simulation to predict the defect probability; and S4, binding and uploading the new virtual model version and the physical product serial number. Aiming at the problem of data dispersion in the prior art, the system automatically converges three types of heterogeneous data of geometric dimension design, working condition manufacturing and maintenance monitoring into a shared cache pool through a main data table of a unified metadata structure, so that cross-stage semantic alignment and time sequence synchronization are realized, data format difference is eliminated, and data consistency is remarkably improved.

The invention relates to the technical field of electrical digital data processing, and discloses a data analysis system for dynamic monitoring and intelligent prediction of physical parameters and an implementation method, and the method comprises the steps: setting a sensor node comprising a measurement oscillation circuit and a reference oscillation circuit, so as to obtain a differential frequency signal which is mapped by a physical agent and is subjected to temperature compensation; and the central processing unit calculates the drift rate of the differential frequency signal to predict chronic degradation, and triggers the sensor node to switch to a multi-principle electrical detection mode when the rate exceeds a threshold value so as to diagnose the cause of an acute event. The inherent contradiction between the high sensitivity and the wide temperature range operation universality of the existing monitoring technology is avoided, meanwhile, the system is endowed with the capability of performing cause discrimination on the acute sudden risk, and the monitoring precision and the decision effectiveness are remarkably improved.

The invention discloses a multi-modal sensor abstract description and packaging method and system based on software definition. Firstly, a unified sensor abstract description model based on software definition is introduced, so that hardware independence is realized, and the differences of a bottom-layer multi-mode sensor in the aspects of interfaces, protocols and data formats can be effectively shielded; secondly, abstract packaging of data is achieved through an autonomous information processing module, and high-bandwidth and high-redundancy original data is converted into low-bandwidth and high-value structured information; in addition, by means of automatic registration, discovery, scheduling and control mechanisms of the sensor management control module on sensor resources, the system realizes a real plug-and-play function. According to the invention, by deploying the autonomous information processing modules at various sensor nodes, the original sensing data is preprocessed, and standardized data packaging with semantic consistency is output, so that the operation load of the central controller is reduced, and the information processing efficiency and the system response capability are also improved.

The invention relates to the technical field of sensor networks and Internet of Things, in particular to a data center air quality intelligent early warning method and system based on a sensor network. The method comprises the following steps: constructing a self-organizing cooperative network by deploying multiple types of sensor nodes in a key area of a data center, and collecting and correcting multi-dimensional air data in real time; the data reliability is improved through inter-node dynamic weight fusion and local anomaly recognition; establishing an air parameter and machine room structure correlation model by utilizing space mapping and time sequence analysis, and identifying a micro-scale diffusion trend; a self-adaptive dynamic threshold mechanism is constructed, and threshold rolling optimization is realized in combination with historical statistics and real-time feedback; and generating a graded alarm strategy based on multi-stage early warning judgment and triggering conditions, and continuously self-optimizing early warning precision and response efficiency through closed-loop feedback. According to the invention, all-around, intelligent and high-reliability early warning and regulation and control of the air quality of the data center are realized.

The invention relates to the technical field of coal mine automation control, and discloses a coal flow windscreen wiper multi-device cooperative control method and system. The method comprises the following steps: obtaining an original environment data set of each sensor node in an underground environment, processing the original environment data set to obtain a communication quality index, calculating bandwidth demand change of each node in combination with historical transmission records of the nodes, and predicting a resource allocation proportion; when the current available network resources cannot meet the requirements, sorting the key data packets to obtain a priority transmission sequence; extracting high-priority data from the sequence, temporarily storing the high-priority data to a local cache unit, and determining the state of a buffer area; dynamically adjusting bandwidth allocation by adopting an intelligent optimization model, and updating communication resource configuration; and judging whether the resource configuration meets a load balance requirement, if so, sending a data packet through a wireless channel, determining a transmission performance index, and generating a resource management scheme of a next period. The cooperative work efficiency of multiple devices of the coal flow windscreen wiper in the high-dust and high-humidity environment is improved.

The invention discloses a mass concrete temperature measurement early warning method and system, and belongs to the technical field of building engineering construction, and the method comprises the following steps: arranging a plurality of temperature sensor nodes in concrete through three-dimensional distributed real-time temperature collection, and collecting temperature data; performing temperature field reconstruction by using an edge computing gateway to generate a three-dimensional temperature cloud picture in the concrete; using an LSTM neural network to predict a future temperature change curve; calculating the temperature difference and the temperature rise rate of each node and the surface layer in real time, and dynamically determining a threshold value; and triggering multi-stage early warning, automatically starting a cooling system and pushing alarm information. According to the system, high-precision reconstruction of a three-dimensional temperature field in concrete and accurate prediction of a temperature change trend can be realized, generation of temperature cracks is effectively prevented through dynamic threshold determination and a multi-stage early warning mechanism, and the safety and durability of a mass concrete structure are ensured.

The invention relates to the technical field of intelligent operation and maintenance of photovoltaic power station equipment, and discloses a photovoltaic power station equipment operation data analysis method and system based on the Internet of Things. The operation data analysis method is applied to data analysis equipment and specifically comprises the following steps that S101, a data analysis request is received, and the data analysis request collects parameter data of current, voltage, temperature, irradiance and environment humidity in real time through Internet of Things sensor nodes deployed on a photovoltaic module, a combiner box, an inverter and a meteorological station; according to the method, the problem of space-time misalignment of multi-source heterogeneous data is solved, the outlier recognition accuracy is improved, the data availability rate is greatly optimized, the limitation of traditional single-dimensional analysis is broken through by innovative four-dimensional feature engineering, the ripple spectrum entropy in the electrical features and the response delay time in the environment coupling features are effectively improved, and the reliability of the multi-source heterogeneous data is improved. And by combining a thermal-electric propagation model constructed by GCN, the detection rate of early faults such as microscopic subfissure is increased from 60% to 95%.