1689 results about "Sensor web" patented technology

The invention discloses a building ground real-time settlement monitoring device and a use method thereof, and belongs to the field of building structure safety monitoring. The monitoring device comprises a hierarchical sensor network which is used for carrying out multi-time-scale real-time data acquisition and comprehensively obtaining deformation data and related environmental parameters of a building structure; the data processing and analyzing module is used for performing real-time processing and intelligent analysis on the acquired data, and identifying and classifying abnormal deformation characteristics of the building structure in time; the deep learning prediction module is used for quantitatively predicting the probability state and the evolution trend of building settlement by constructing a multi-scale time sequence prediction model; the multi-factor analysis module is used for carrying out coupling modeling and comprehensive analysis on the environmental factors, the structural characteristics and the abnormal evolution process so as to identify key influence factors and action mechanisms thereof; and the risk assessment and early warning module is used for performing grading assessment on the building settlement risk based on the prediction and analysis result and generating corresponding early warning information and decision support schemes.

The invention discloses a mechanical equipment state monitoring method and system based on multiple sensors, and the method comprises the five core steps: multi-modal data collection and preprocessing, dynamic feature fusion, adaptive threshold diagnosis, digital twin fault tracing and predictive maintenance decision. All-domain coverage of equipment is realized through a three-layer sensor network architecture, the problems of data synchronization and interference resistance are solved by utilizing a temperature and vibration integrated sensor, deep fusion and anomaly detection of multi-source data are realized in combination with an attention mechanism, a Gaussian mixture model, a three-dimensional convolutional neural network and the like, and finally a precise maintenance strategy is generated through digital twinning and reinforcement learning. The multi-sensor-based mechanical equipment state monitoring system comprises a sensor network layer, an edge computing layer, a cloud platform layer and a man-machine interaction layer, supports federated learning to protect data privacy, improves real-time diagnosis capability through edge-cloud collaboration, and enhances a reality interface to realize intelligent operation and maintenance interaction.

A structural-health-monitoring system is disclosed for real-time detection and autonomous maintenance of physical structures. The system includes a sensor network comprising at least one strain gauge and one tri-axial accelerometer mounted on the structure to generate real-time sensor signals. A perception module filters and normalizes the signals and extracts numerical features such as peak amplitude and dominant frequency. A digital-twin module maintains a finite-element model updated in response to the extracted features. A data-driven surrogate model predicts sensor behavior and refines itself using machine-learning techniques. An anomaly-detection module computes an anomaly score from model residuals or classifier outputs. Upon exceeding a threshold, a maintenance module initiates a maintenance action, including generating an inspection schedule or issuing a control signal to an autonomous inspection or repair device. A learning module continuously improves system performance using reinforcement learning based on historical outcomes. The system supports predictive diagnostics, robotic repair, and automated optimization for long-term structural integrity.

The invention relates to the technical field of ships, in particular to a fault monitoring system and method for a ship power system, and the system comprises a multi-source data collection module which carries out the real-time collection of the operation parameters, environment parameters and equipment state parameters of the ship power system through a collection device. The fault early warning module is used for predicting the development trend of the fault after the fault diagnosis is completed; the early warning decision module generates early warning information of different levels according to the state evaluation result, the fault diagnosis result and the RUL prediction result, gives targeted operation and maintenance decision suggestions, and pushes the suggestions to a ship cockpit, a shore-based operation and maintenance center and an operation and maintenance personnel mobile terminal; 24-hour uninterrupted multi-parameter acquisition of the ship power system is realized through the multi-source sensor network, three types of parameters of operation, environment and state are covered, acquisition delay is reduced, and the problems of poor timeliness and incomplete parameter coverage of traditional manual inspection are solved.

The invention discloses a data center machine room AI energy-saving control method and system, a digital twin model of a machine room operation state is constructed through a holographic perception and heterogeneous data fusion technology, centimeter-level monitoring of an equipment state and environmental parameters is realized, and the system integrates a laser radar array, an acoustic sensor and a gas sensor network. The time-space alignment of multi-modal data is completed by combining edge computing nodes, holographic mapping including thermodynamic characteristics, vibration characteristics and gas leakage risks is formed, historical temperature control strategy characteristics are extracted by adopting a variational auto-encoder based on a dynamic strategy generation mechanism of generative artificial intelligence, and a load trend is predicted by combining a long-short-term memory network. Constructing a self-adaptive strategy pool; the multi-agent reinforcement learning framework enables temperature control, equipment scheduling and power grid response to form game optimization, the strategy robustness in a complex scene is improved, and the system innovatively fuses power grid real-time electricity price and carbon transaction data so as to establish a multi-target decision system.

The invention discloses an electrical equipment operation state monitoring method, system and terminal, full life cycle health management of equipment is realized through multi-dimensional perception and intelligent analysis, a composite sensor network can be constructed from the level of the method, and high-frequency current, ultrahigh frequency, fiber grating temperature vibration, multi-parameter gas and acoustic sensors are integrated. Electromagnetic characteristics, mechanical states, environmental parameters and voiceprint characteristics are covered; the adaptive signal processing technology performs classification and noise reduction on multi-source data, and the three-dimensional digital twin model realizes time-space fusion of a temperature field, a vibration field, an electric field and a sound field; a lightweight space-time convolutional network is deployed to fuse a time domain waveform, a spectrogram and spatial distribution characteristics for diagnosis, and a hidden semi-Markov model and a particle filter algorithm are combined to dynamically predict the residual service life of equipment. The system architecture comprises a self-organizing sensor network with edge computing capability, a time-sensitive industrial communication network and a containerized analysis engine, and supports mixed reality visual interaction.

The invention relates to the technical field of water conservancy project detection, and discloses a water conservancy project safety detection early warning method based on artificial intelligence. The method comprises the following steps: acquiring multi-modal monitoring data of a key part through a distributed sensor network, and extracting a dynamic feature sequence in a preset time period through space-time alignment and noise filtering; inputting the image into a deep neural network fused with an attention mechanism, constructing a multi-scale space-time correlation map through hierarchical feature learning, and generating a high-dimensional representation of an engineering structure state; historical accident case data is used as a supervision signal, a hybrid expert model is used for performing multi-task training on high-dimensional representation, and the contribution weight of each monitoring index to the safety risk is obtained; combining real-time environment parameters and structural response characteristics to construct a dynamic threshold adjustment model, adaptively updating an early warning threshold according to a risk probability, and screening out key risk factors of which the contribution weights are greater than the updated threshold; and on the basis of spatial and temporal distribution characteristics, through graph neural network node association reasoning, multi-source early warning information is fused to generate a graded early warning result.

The invention discloses an optical storage and charging integrated micro-grid energy management system, relates to the technical field of micro-grid energy management, and is used for solving the problems of low energy scheduling efficiency and insufficient stability in an optical storage and charging system. The system comprises a power generation monitoring module, an energy storage management module, a charging load control module and an energy coordination module. The photovoltaic power generation state and environmental parameters are monitored in real time through a multi-type sensor network, and the running state of the photovoltaic module is judged; based on battery monitoring data and photovoltaic output characteristics, a differential energy storage management strategy is formulated; the charging power is dynamically distributed in combination with the energy state to realize charging and discharging intelligent regulation and control; by converging multi-source information, source storage and load interaction is coordinated to cope with different power states; accurate monitoring, intelligent scheduling and efficient cooperation of the photovoltaic, storage and charging integrated micro-grid are realized, and the energy utilization efficiency and the operation stability are remarkably improved.

The invention relates to the technical field of aircraft structure detection, and discloses an aircraft structure crack intelligent identification method based on deep learning. The method comprises the following steps: acquiring original vibration response signals and electromagnetic field distribution data on the surface and inside of an aircraft structure in parallel through a multi-source sensor network; synchronously processing the data by using a multi-scale convolutional neural network, and extracting time-frequency domain abnormal fluctuation features and space magnetic field distortion features; constructing a cross-modal correlation model, analyzing a topological dependency relationship of the two types of features through a graph attention mechanism, and generating a fused damage sensitive feature vector; inputting the vector into a pre-trained deep belief network to obtain a probability distribution mapping relation for different crack types; and according to the mapping relation, carrying out adaptive weighted fusion on original multi-sensor data, inhibiting environmental noise and structural background interference, and separating and reconstructing an accurate three-dimensional morphology map of the target crack. According to the method, multi-source data information can be effectively fused to improve the accuracy of aircraft structure crack identification.

The invention provides a water conservancy project safety monitoring method and system based on data processing, and relates to the technical field of monitoring, and the method comprises the steps: obtaining and carrying out the multi-dimensional preprocessing of water conservancy project multi-source heterogeneous monitoring data through the deployment of a sensor network, and extracting multi-scale space-time fusion features from the data; performing structural state modeling, anomaly prediction, risk assessment and early warning by using a long-short-term memory neural network model integrated with a multi-head attention mechanism; and intelligent suggestions oriented to maintenance decisions are generated, so that comprehensive, accurate and prospective evaluation and early warning of the structural state of the water conservancy project are finally realized, the exception identification and risk prediction capabilities are effectively improved, the false alarm rate is reduced, refined and initiative intelligent maintenance decisions are provided, resource allocation is optimized, and the service life of the project is prolonged.

The invention discloses a method for intelligently regulating and controlling production parameters in a fruit concentrated juice production process, which comprises the following steps of: acquiring multi-dimensional process parameters such as temperature, pressure, flow, concentration, equipment state and the like in real time through a multi-channel sensor network, and forming a standardized data sequence after filtering, normalization and drift correction; extracting stage features by using technologies such as a sliding window and Fourier transform, and inputting the stage features into the lightweight classification model to realize production stage identification; in combination with an identification result, dynamically calling a corresponding multi-target optimization sub-model, and realizing nonlinear prediction and optimal solution selection of process parameter setting by adopting an LSTM and a multi-target genetic algorithm; on the basis of real-time feedback, the performance of the model is automatically evaluated, self-adaptive adjustment and optimization of the optimization algorithm are achieved through reinforcement learning and an incremental updating mechanism, multi-target collaborative optimization, self-adaptive adjustment and model switching in the production process can be achieved, and the consistency of production efficiency and product quality is improved.

The invention relates to the technical field of equipment monitoring, in particular to an equipment corrosion evaluation and life prediction method and application, and the method comprises the following steps: deploying a sensor network in an easily-corroded area of coal chemical equipment, and collecting multi-dimensional data; carrying out abnormal value elimination, data compression, time synchronization and space-time alignment preprocessing on the collected multi-source data; image features are extracted through a convolutional neural network, processed data are analyzed through an LSTM-attention model, and a fuzzy comprehensive evaluation matrix is established to evaluate the corrosion level; a physical model based on the Faraday electrolysis law and a data driving model based on the Transform network are constructed, and the residual life is predicted through Bayesian network fusion output and Monte Carlo simulation. Through fusion of multi-source data and an intelligent algorithm, accurate evaluation of the corrosion state of the equipment and accurate prediction of the residual life are realized, and safe and efficient operation of the coal chemical equipment is guaranteed.

The invention discloses an underground mine operation state analysis system and method based on video monitoring data, and the system comprises a data collection module which is used for collecting mine video and environment parameter data through a distributed sensor network, and generating a multi-dimensional data fusion set based on a space-time label technology; the edge analysis module is used for extracting feature parameters through a convolutional neural network algorithm based on the multi-dimensional data fusion set and generating a mine operation state recognition result; the fence construction module is used for constructing a three-dimensional digital model and a dynamic safety boundary based on the mine operation state recognition result to form a real-time monitoring reference framework; and the decision execution module is used for performing hierarchical risk assessment on the monitoring data in the security boundary based on the real-time monitoring reference framework, and generating a security early warning and disposal scheme with a tracing identifier. Each piece of early warning and disposal information is attached with a unique tracing identification code, so that follow-up event backtracking analysis is facilitated, and the risk management and control capability is continuously improved.

The invention discloses an earth and rockfill dam seepage-deformation early warning method and system based on time-space combined anomaly, and belongs to the field of dam body safety data research. The method comprises the following steps: constructing a spatio-temporal topological graph based on an engineering coordinate system, integrating multi-dimensional data by nodes, and constructing a dynamic adjacency matrix according to spatial distance and seepage relevance; extracting features by using a space-time diagram convolutional network, a self-loop mechanism and cross-layer attention; and executing dual-drive early warning through standard threshold preliminary screening, multi-scale LSTM prediction and a time decay evidence theory. The system comprises a sensor network and an intelligent computing module, and the intelligent computing module has adaptive modeling and visualization functions. According to the scheme, seepage-deformation space-time correlation quantitative analysis is achieved, the hysteresis effect is captured, the threshold value is dynamically corrected, multi-source evidences are fused, the early warning timeliness and accuracy are improved, and the risk of false alarm and missing alarm is reduced.

The invention discloses a valve vibration impact test method and system based on digital twinning, and belongs to the technical field of industrial equipment test and evaluation. The method comprises the following steps: firstly, deploying a multi-source sensor network, constructing a physical entity layer of a valve vibration impact test, collecting and preprocessing various physical monitoring data, and constructing a digital twinborn body layer; secondly, real-time data assimilation with a physical test bed is realized through parameter calibration and updating of a digital twinborn model based on various physical monitoring data and the digital twinborn layer; and finally, executing a virtual vibration impact test, realizing prediction and evaluation of global response and key part information of the physical entity according to a test result, and finally realizing interaction and performance prediction. And the valve vibration impact test is realized through the valve vibration impact test system. According to the method, the fault diagnosis capability, the early damage identification capability and the valve state prediction and evaluation capability can be remarkably improved, and the defect of insufficient field monitoring information is overcome.

The invention discloses an urban water supply management data trend analysis method based on space-time analysis, and relates to the field of data processing, and the method comprises the steps: collecting data in real time through an urban water supply pipe network sensor network, building a space-time unified coordinate system, building a space-time Kriging interpolation model based on pipe network topology, and achieving the space-time alignment of multi-source data; dividing an adaptive space-time grid by using a Voronoi diagram and a sliding window mechanism, and calculating multi-dimensional features; constructing a dynamic space-time diagram by taking a grid as a node, performing multi-step prediction in combination with a space-time diagram convolution circulation network, fusing a Kriging interpolation result, and evaluating an abnormal probability and a confidence interval through a Bayesian neural network; a monitoring layer, a prediction layer and a risk layer are overlaid in a three-dimensional GIS, a dynamic thermodynamic diagram is generated, an early warning path is optimized based on a Dijkstra algorithm, and a minimum risk topology path is output. The method has the advantages that through space-time analysis and accurate prediction, the intelligence, stability and emergency response efficiency of urban water supply management are remarkably improved, and powerful support is provided for smart city construction.

The invention relates to the technical field of building construction monitoring, and discloses a method for evaluating mechanical properties of a steel structure building construction whole process based on digital twinning. The method comprises the steps of establishing a digital twin model fusing multi-source information, and performing real-time linkage with a sensor network arranged on site. Collected data such as deformation, temperature and wind speed are processed through dynamic fusion and an anomaly recognition algorithm, model parameters are continuously corrected, and real-time dynamic high-fidelity mapping of the mechanical state in the construction process is achieved. And based on the updated model, the intelligent analysis module performs cooperative calculation, autonomously identifies construction abnormity, quantitatively predicts potential risks, generates a process optimization decision instruction and feeds back the process optimization decision instruction to a site. According to the method, a closed-loop regulation and control mechanism from data perception, model analysis to decision execution is constructed, and accurate online evaluation of construction mechanical properties and active prediction control of safety risks are realized.

The invention provides an intelligent monitoring system of a liquid cooling cabinet and processing equipment thereof, and relates to the technical field of data processing, and the system comprises a cold source self-control module which is used for dynamically adjusting the operation state of a cooling tower of primary side circulation and the flow of a circulating water pump according to cooling water parameters, and achieving the variable flow control of cooling water; the dynamic environment monitoring module is used for monitoring the secondary side pipeline state and the cabinet environment in real time through the cooling capacity distribution unit and the distributed sensor network; and the comprehensive monitoring sub-module is used for realizing leakage positioning through a built-in liquid leakage sensor, a temperature sensor and a pressure sensor, and automatically supplementing liquid by using a liquid storage tank. Through multi-mode sensor fusion, dynamic threshold value self-adaption and multi-parameter collaborative analysis, accurate leakage positioning and graded alarm are achieved, automatic liquid supplementing closed-loop control and variable flow adjustment are combined, the system reliability is improved, and energy consumption is reduced.

The invention discloses an intelligent construction site construction risk early warning system and method based on a BIM technology, and relates to the technical field of the Internet of Things. Multi-dimensional data of the field environment, structure, equipment and personnel are collected through a multi-source sensor network and are preprocessed; mapping the data to a BIM model, analyzing a spatial interaction relationship among personnel, machinery and environment by using a space-time diagram neural network, and identifying a periodic risk through an autocorrelation algorithm; calculating an index weight in combination with a dynamic weight distribution algorithm, and constructing a risk value calculation model; risk levels are analyzed and judged according to three conditions that personnel are at a mechanical operation position, under mechanical operation and no personnel are on site; a grading response strategy is adopted according to the risk grade; and optimizing an early warning threshold value through a threshold value judgment algorithm and an LSTM model by using a block chain evidence storage risk event, and iteratively updating a prediction algorithm.

The invention relates to the technical field of equipment collaborative operation of the Internet of Things, in particular to a transit hub equipment collaborative operation method and system based on the Internet of Things technology. According to the method, original attitude motion data of a target ship are collected in real time through an attitude sensor network of the Internet of Things, and a real-time attitude drift vector of the target ship relative to a geodetic coordinate system is calculated; constructing a dynamic coordinate transformation matrix according to the real-time attitude drift vector; acquiring an expected operation track control instruction preset by the quay crane control system according to the geodetic coordinate system; performing coordinate transformation operation on the expected operation track control instruction through a dynamic coordinate transformation matrix to obtain a compensated cooperative control instruction; the compensated cooperative control instruction is transmitted to a quay crane control system in real time through an Internet of Things communication link, and a quay crane lifting appliance is driven by the quay crane control system to conduct lifting operation according to the compensated track instruction; and the safety and efficiency of the hoisting operation between the ship and the quay crane can be effectively improved.

The invention provides an intelligent maintenance method and system for the wall of a squat silo, and the method comprises the steps: applying controllable environment parameters to a concrete test block group with the same proportion through an environment simulation device, and obtaining test block deformation data, moisture content data and stress data; performing time sequence correlation mapping on the test block deformation data, the moisture content data and the stress data and environmental parameters to obtain a maintenance decision data set; acquiring a real-time environment vector through a sensor network deployed on the site of the squat silo; inputting the real-time environment vector into a maintenance decision model, and generating a spraying control instruction by matching the maintenance decision data set; and driving a spraying system to execute operation according to the spraying control instruction, and dynamically correcting spraying parameters based on moisture monitoring data. According to the method, through data-driven intelligent maintenance decision and dynamic closed-loop control, the crack risk of the wall of the squat silo is remarkably reduced, meanwhile, water resource consumption is reduced, and double breakthrough of maintenance quality and resource efficiency is achieved.

The invention provides a split type direct current charging multi-split group charging and group control control system, and belongs to the technical field of split type direct current charging piles. The split type direct current charging system comprising a main control cabinet, a power distribution unit, a charging pile terminal and other hardware architectures is constructed; a voltage monitoring unit, a current sensor module, a harmonic filter and other multi-dimensional sensor networks are integrated to acquire power grid state data in real time, and a sliding time window method is used to carry out segmented analysis on the data to extract power grid characteristic parameters; establishing a multi-objective optimization model comprising a power balance equation and a power grid stability equation to calculate an optimal power distribution matrix, adopting a neural network to realize intelligent distribution and dynamic balance adjustment of the power of each charging pile terminal, and starting an emergency power redistribution mechanism to ensure stable operation of the system when the power grid is detected to be abnormal. The technical problem of unbalanced power distribution of the multi-pile charging system caused by power grid voltage fluctuation and harmonic pollution is solved.

The invention relates to the technical field of marine disaster monitoring and early warning, in particular to an AI-based marine disaster prevention monitoring and early warning system. Comprising a multi-source data acquisition module for acquiring multi-dimensional data through a sensor network, remote sensing data access and a meteorological data interface; the data preprocessing and fusion module is used for carrying out cleaning, space-time calibration and feature fusion on the collected data; the artificial intelligence analysis module is used for realizing accurate analysis and prediction of different levels of storm surge by using a multi-scale prediction model cluster unit; the risk assessment and early warning generation module is used for calculating risk indexes according to a related index system and dividing early warning grades; and the interaction and release module is used for realizing visual display and multi-channel release of the early warning information. According to the invention, advantages of multi-source data and artificial intelligence technology are integrated, and real-time and accurate monitoring and early warning can be carried out on storm surge and coast submerging conditions.

The invention discloses an equipment fault intelligent early warning system based on abnormal voiceprint AI analysis of energy equipment, and relates to the technical field of equipment health management, the equipment fault intelligent early warning system comprises an equipment fault early warning platform, and the equipment fault early warning platform is in communication connection with the following modules: a data sensing fusion module, a voiceprint AI analysis module and a voiceprint AI analysis module; the data acquisition module is used for acquiring high-frequency voiceprint signals, temperature field distribution and vibration data in real time during operation of energy equipment through a distributed sensor network to form a comprehensive data set; and the voiceprint AI analysis module is used for extracting voiceprint features from the comprehensive data set, and identifying whether the equipment emits abnormal voiceprints or not by using a pre-trained voiceprint AI model. According to the method, early abnormity is identified through the high-precision AI model, sudden equipment faults are effectively prevented, equipment physical field interaction is simulated in combination with the digital twin technology, and a fault evolution path is dynamically deduced, so that operation and maintenance personnel can take intervention measures at the initial stage of the faults, the stability and reliability of equipment operation are remarkably improved, and the non-planned downtime is shortened.

The invention discloses a dam safety monitoring agent system based on a multi-dimensional large model and digital twinning, and belongs to the technical field of safety monitoring of water conservancy and hydropower engineering. According to the method, a cognitive decision kernel is used as a core innovation module, multi-source data is collected by relying on a sky-ground hydraulic engineering integrated sensor network, cross-modal data fusion and anomaly recognition are achieved through a multi-dimensional large model cluster, and multi-physics coupling simulation is completed in combination with a digital twinborn body; the cognitive decision kernel generates an optimal combination scheme through case retrieval and measure unit adaptation degree calculation, a control instruction is output after digital twin rehearsal verification, and finally closed-loop control of dam safety real-time early warning, autonomous decision making and efficient disposal is achieved through an autonomous response mechanism. The problems of data island, passive response, expert dependence and the like of a traditional monitoring system are solved, the dam safety management intelligent level and the emergency response capability are remarkably improved, and the method has wide engineering application value.

The invention relates to a detection and analysis method and system for an energy storage system, and aims to improve the state monitoring precision, the fault early warning capability and the response intelligence level of the system. The method comprises the following steps: firstly, synchronously acquiring and transmitting multi-source data in real time through a sensor network deployed in a battery module, a power conversion unit and a cooling system; and then, key dynamic features are extracted from the multi-source data, and dynamic feature state information of the energy storage system is comprehensively formed. And inputting the state information into a pre-trained deep learning model, and outputting a health state score and a fault probability of the system. Based on the evaluation result and the fault traceability information, the system triggers a multi-level early warning mechanism and executes an active response strategy, and after response execution, the system re-evaluates the health state in a closed loop mode and verifies the response effect; and if the improvement does not reach the expectation, making a decision to start an artificial diagnosis protocol. According to the invention, a complete process from dynamic perception, intelligent diagnosis and accurate response to closed-loop verification is realized, and the safety and reliability of operation of the energy storage system are improved.

The invention discloses an industrial internet of things real-time monitoring and predictive maintenance system based on digital twinning, and relates to the technical field of industrial digital twinning operation and maintenance, the system comprises a multi-modal data acquisition module, a sensor network is deployed, and edge calculation preprocessing is carried out; the digital twinning construction module is used for constructing a high-precision model by fusing a physical law and deep learning; the real-time monitoring module is used for detecting abnormity by using a space-time diagram neural network; the predictive maintenance module is used for optimizing a maintenance strategy in combination with a probabilistic algorithm; and the man-machine interaction module supports AR / VR and brain-computer interface operation. In addition, the system integrates functions of block chain security, energy management and the like, and realizes full-life-cycle intelligent management of equipment. The operation and maintenance efficiency of the industrial equipment is greatly improved. The data acquisition precision reaches the nanoscale, and the early warning time is advanced to 72 hours; the maintenance cost is reduced, and the equipment availability is improved; the AR interaction enables the operation efficiency to be improved and the training period to be shortened. And meanwhile, energy consumption reduction is realized.

The invention discloses a fault diagnosis and remote monitoring system and method for a solar power supply system, and relates to the technical field of fault diagnosis of a solar system, and the system comprises a heterogeneous multi-mode sensing module which collects the multi-dimensional information of an assembly through a plurality of sensors; the memristor storage and calculation integrated unit is used for realizing data filtering and feature extraction; the multi-scale causal diagnosis engine is used for diagnosing faults by fusing deep learning and causal diagrams; a self-adaptive topology communication network ensures data transmission; a digital twinborn monitoring platform and visual operation and maintenance are adopted, and in addition, an intelligent evolution decision and self-reconfiguration sensor network module is further arranged, so that the intelligence and reliability of the system are improved. Through cooperation of multiple modules, accurate fault diagnosis and positioning are realized, the diagnosis time is shortened, stable data transmission is ensured, self-repairing and autonomous learning capabilities are provided, the operation and maintenance cost can be reduced, the power generation efficiency can be improved, and the reliability and economic benefits of a solar power supply system can be enhanced.

The invention relates to the technical field of lithium ore conveying, and discloses a lithium ore hopper energy-saving conveying system based on intelligent self-adaptive control. The system comprises a data acquisition module, a self-adaptive control module, an energy-saving optimization module and an execution adjustment module. The data acquisition module acquires multi-dimensional operation data of the lithium ore hopper through a distributed sensor network, and a hopper conveying state database is constructed after standardization processing; the adaptive control module performs dynamic feature extraction on the database, generates a real-time control parameter matrix, establishes an association rule graph of each parameter, and constructs an intelligent adjustment decision model based on the association rule graph; an energy-saving optimization module extracts a key energy consumption feature vector from the real-time control parameter matrix, calculates an energy consumption influence coefficient of each operation parameter, and screens out an optimal energy-saving control sequence in combination with an intelligent adjustment decision model; and the execution adjustment module drives an execution mechanism according to the optimal energy-saving control sequence, and self-adaptive adjustment of hopper conveying parameters is completed.

The invention relates to the technical field of electric vehicle power battery thermal management, and discloses a temperature change-based power battery thermal management system, which comprises a thermal simulation pre-judgment end, a sensor network end, a dynamic liquid cooling control end, a BMS decision end and a fault diagnosis module, aiming at the technical defects of out-of-control temperature difference, response hysteresis and leakage risk caused by static flow channel design of the existing liquid cooling system, breakthrough is realized through a three-dimensional technical architecture: a CFD (Computational Fluid Dynamics) prejudgment layer: automatically calibrating sensor distribution points based on temperature field standard deviation, and improving temperature difference identification precision; according to the dynamic valve control layer, an electromagnetic three-way reversing valve is adopted to execute a directional temperature control strategy, an edge battery cell is preferentially introduced under the low-temperature working condition, and heat dissipation is enhanced under the high-temperature working condition; according to the redundant safety layer, the valve body and the pipeline are integrally formed, the leakage rate is reduced, and flow maintenance under single-point failure is achieved through a double-water-inlet parallel pump source. The method has the effects of improving the convergence speed of temperature difference between modules, reducing the attenuation rate of battery life and reducing the risk probability of thermal runaway.