4919 results about "Sensor array" patented technology

The invention discloses a multi-modal AI-based power equipment anomaly detection method and system, and the method comprises the steps: synchronously collecting electrical, mechanical and thermal modal data of power equipment through an edge computing node, carrying out the load adaptive dynamic preprocessing, and uploading the data to a cloud end; the cloud constructs a multi-modal feature extraction network based on a structural causal model, analyzes a causal path between modals through a Bayesian network and performs weighted fusion on feature vectors; capturing device state mutation by using a gating attention mechanism, and updating the feature vector; executing time-space consistency verification of the equipment group to identify regional group abnormality and suppress single-point misinformation; generating an interpretable report containing an abnormal root cause analysis and priority ranking maintenance strategy; and establishing a closed-loop feedback mechanism to correct the cause and effect probability distribution of the Bayesian network model. The system comprises a multi-modal sensor array, an edge computing node and a cloud analysis platform, wherein the cloud analysis platform is integrated with a causal reasoning engine, a space-time consistency verification module and the like. According to the invention, by analyzing the multi-modal deep causal association, the method adapts to the dynamic change of the equipment, reduces the false alarm rate, generates an interpretable report, supports closed-loop self-optimization, and improves the anomaly detection accuracy and operation and maintenance decision efficiency of the power equipment.

The invention discloses a trajectory optimization method based on a multi-source heterogeneous positioning data fusion algorithm, and relates to the technical field of intelligent navigation and high-precision positioning, multi-modal data are acquired through a multi-modal sensor array, positioning redundancy of scenes such as tunnels and indoor scenes is enhanced, a weight distribution strategy is dynamically adjusted through an Actor-Critic network architecture, and the positioning accuracy is improved. The state space input comprises an environment semantic tag, a historical error sequence and a real-time noise variance, the output action space is continuous weight distribution of each data source, a multi-target reward function optimization strategy is combined, scene adaptability is realized, a local SLAM map, inertial navigation error parameters and a weight distribution strategy are shared in real time based on a V2X protocol, and the real-time performance of the system is improved. According to the method, a single device accumulative error is compensated by using adjacent vehicle data, a terminal locally trains an error compensation model, parameters are uploaded to a cloud end through differential privacy encryption, the cloud end adopts a FedAvg algorithm to aggregate a global model and issue the global model, the error difference between devices is inhibited, and dynamic road network updating and scene differentiation model distribution are supported at the same time.

The invention provides a power station equipment state real-time monitoring and diagnosing method and system based on cloud edge collaboration, and the method comprises the steps: adjusting a data collection period dynamically determined based on an adaptive sampling frequency adjustment algorithm, and collecting a vibration signal, a temperature signal and a current signal through a multi-source heterogeneous sensor array disposed in a power station equipment body; carrying out preprocessing by utilizing the edge computing node, generating a compressed feature vector, and uploading the compressed feature vector to a cloud end through an MQTT protocol; a multi-modal data fusion analysis module is started through a cloud, a three-dimensional evaluation matrix of the equipment health state is constructed in combination with historical operation data and environmental parameters of the equipment, and a calculation task distribution strategy between an edge calculation node and the cloud is adjusted in real time according to an evaluation result of the three-dimensional evaluation matrix. Abnormal mode recognition based on a deep residual network and fault source tracing double-channel analysis based on a physical model are executed, fault types and fault reasons are diagnosed, and the accuracy and timeliness of fault diagnosis are guaranteed.

The invention discloses a distributed real-time monitoring and early warning system for a temperature field of a smelting furnace, and relates to the technical field of industrial process intelligent monitoring. The problems of accumulated measurement errors and non-stationary hotspot escape reconstruction hysteresis caused by static emissivity setting in an existing system are solved. Collecting multiband radiation intensity and voltage signals through time domain alignment of the multispectral sensor array and the thermocouple array; iterating emissivity parameters in real time by adopting a dynamic ash body spectrum ratio algorithm in combination with flue gas absorption characteristics; fusing non-contact and contact temperature measurement data based on weighted Kalman filtering and complementary filtering; constructing a space-time variable covariance function to carry out non-stationary Kriging interpolation; dynamically optimizing the local grid resolution by combining an adaptive grid module; the processing flow is accelerated through the parallel computing module; early warning is triggered based on abnormal probability judgment and is fed back to emissivity correction and grid optimization; according to the invention, the monitoring precision and real-time performance of the temperature field are obviously improved, and the risks of false alarm, missing alarm and equipment melting loss are effectively inhibited.

The invention discloses a mixed tower structure full-life damage intelligent sensing array early warning system, which relates to the field of mixed tower structure detection and comprises a multi-modal data collection module, an array topology optimization module, a self-adaptive signal processing module, a digital twin life prediction module, a grading early warning module and a visualization system. The multi-modal data collection module comprises a multi-modal sensor array, a self-powered module and a wireless transmission module. According to the invention, a full-scale sensing network is constructed, full-dimension damage perception from distributed monitoring to sudden damage capture and structural modal analysis is realized, wavelet transform and blind source separation are combined to eliminate environmental noise interference, a damage characteristic ultrasonic attenuation coefficient, an acoustic emission energy spectrum peak value, optical fiber strain gradient anomaly and vibration modal frequency deviation are extracted, and the detection accuracy is improved. And classification and positioning of damage types and intelligent diagnosis of severity levels are realized through a convolutional neural network and long and short memory neural network hybrid model, and a closed-loop processing flow from data acquisition to feature analysis is formed.

The invention belongs to the technical field of rail transit intelligent monitoring, and particularly relates to an intelligent monitoring method and device for a rail transit air conditioning system. According to the method, the sensor array with the self-adaptive sampling frequency is used for collecting the multi-modal operation data, then the collected multi-modal operation data is preprocessed, the feature degradation track atlas is established, powerful data support is provided for subsequent fault early warning and diagnosis, and the fault diagnosis accuracy is improved. Historical abnormal events are introduced to dynamically correct the health state evaluation base line, the timeliness and accuracy of the evaluation base line are ensured, in comparative analysis of real-time operation data and the dynamic evaluation base line, a health degree scoring and dynamic threshold mechanism is adopted, quantitative evaluation of the health state of the air conditioner system is achieved, and the evaluation accuracy of the health state of the air conditioner system is improved. According to the method, a decision graph containing fault diagnosis and predictive maintenance suggestions is generated by analyzing the propagation path and time sequence relevance of abnormal parameters in the air conditioning system and combining an equipment topological relation graph, so that the troubleshooting and repairing efficiency is improved.

The invention relates to the technical field of data processing, in particular to a distributed multi-source heterogeneous sensor data processing method and system. The method comprises the following steps: collecting environmental parameters of a leakage area in real time through a distributed sensor array; performing coordinate system unification and timestamp alignment on the environmental parameters of the leakage area to generate a standardized leakage situation data set; extracting gas diffusion characteristics and thermal imaging characteristics of the standardized leakage situation data set to calculate a gas cloud cluster diffusion path probability; performing risk decision instruction generation on the gas cloud cluster diffusion path probability based on a preset leakage level classification neural network to obtain a risk decision instruction; calling a matched emergency plan based on the risk decision instruction; and analyzing the implementation steps of the emergency plan and performing instruction conversion to generate an emergency plan instruction. According to the invention, through real-time data acquisition, intelligent risk assessment and automatic emergency response, the timeliness responsiveness of data processing is improved.

The invention discloses a bolt looseness online monitoring method and system, and the method comprises the steps: collecting an original data set containing a vibration signal of bolt connection, temperature gradient data and structural stress distribution through a multi-mode sensor array, and carrying out the time-space alignment and frequency domain decomposition processing of the original data set, obtaining a multi-dimensional feature matrix of the bolt nodes; based on the multi-dimensional feature matrix, a graph neural network model is adopted to carry out bolt looseness probability calculation, and a real-time looseness probability value of each bolt is output; generating a risk level map based on time evolution according to the real-time loosening probability value; and the risk level map is mapped in real time through a three-dimensional visual interface, and when it is detected that the bolt loosening risk level exceeds a preset threshold value, an early warning message is generated and uploaded to an operation and maintenance platform, and closed-loop monitoring response is completed. According to the embodiment of the invention, accurate assessment, dynamic prediction and visual early warning of the loosening risk can be realized, and the intelligent level of structure safety monitoring is improved.

The invention discloses an intelligent water quality regulation and control system and method based on multi-parameter real-time monitoring, and belongs to the technical field of water quality monitoring. According to the intelligent water quality regulation and control system, the water quality data of the water body is obtained in real time through the multi-parameter sensor array, and the monitoring regulation and control server can quickly generate an abnormal report and a water quality regulation and control scheme. The data processing module performs feature extraction on the water quality data to obtain target features; the water quality evaluation module is used for accurately evaluating the water quality by using a pre-trained deep neural network model; the abnormity identification module can timely judge whether the water quality has a pollution risk and generate an abnormity report; and the regulation and control module generates a water quality regulation and control scheme by adopting a multi-objective optimization algorithm. The system realizes real-time performance, accuracy and intelligence of water quality monitoring, can quickly respond to water quality changes, effectively reduces pollution risks, and improves the efficiency and effect of water quality regulation and control.

The invention relates to the technical field of structural health monitoring and intelligent diagnosis, in particular to a structural fatigue damage identification method based on acoustic emission and deep learning, and the method comprises the steps: collecting a structural response signal under a fatigue load through an acoustic emission sensor array, inputting the structural response signal to a CNN-BiLSTM-Attention mixed deep learning model, and carrying out the recognition of the structural fatigue damage through the CNN-BiLSTM-Attention mixed deep learning model; the model extracts local time domain features through a dynamic adaptive convolution kernel, captures long time sequence dependence by using a bidirectional long-short-term memory network, focuses key damage features through a bimodal space-time attention mechanism, divides damage stages based on a nonlinear dynamic threshold algorithm of fracture opening amount, constructs a training data set of physical-data fusion, and performs dynamic time domain feature extraction. The learning rate is optimized by adopting a gradient sensitive cosine annealing algorithm, and the robustness of the model is improved in combination with an anti-noise and anti-loss function. The method integrates physical characteristics and an intelligent algorithm, and has the advantages of adaptive noise suppression, strong cross-domain generalization ability, high real-time performance and the like.

The invention relates to the technical field of radar signal processing, in particular to a radar target analytical calculation method based on multi-dimensional data fusion and a radar device. Comprising the following steps: deploying a multi-band radar sensor array comprising an X band, a C band and a Ku band in a radar monitoring area; performing pulse compression and Doppler processing on the time domain echo signal, and extracting a time domain feature; spectral analysis is carried out on the frequency domain signals, and frequency domain features are extracted; performing angle estimation on the spatial signals, and extracting spatial features; a dynamic weight distribution model is constructed, a fusion weight is calculated through an adaptive algorithm based on three-dimensional quality indexes of a real-time signal-to-noise ratio (SNR), feature stability (SI) and data integrity (CI), and a joint representation vector containing time domain, frequency domain and space multi-dimensional information is generated. According to the invention, by deploying the multi-band radar sensor array, the recognition capability of the subtle feature difference of the target is improved.

The invention discloses a guardrail collision warning method and system, and the method comprises the steps: carrying out the nonlinear phase alignment of a multi-source heterogeneous signal through an adaptive variational mode decomposition algorithm according to the vibration acceleration, strain tensor and acoustic emission signals collected in real time through a multi-mode sensor array disposed at a guardrail key node, generating a three-dimensional dynamic strain field distribution vector; inputting the three-dimensional dynamic strain field distribution vector into a nonlinear dynamics reconstruction module, and extracting a chaotic feature fingerprint spectrum of the collision event; performing collision intensity grading processing on the chaos feature fingerprint spectrum, and outputting a quantitative evaluation matrix including a collision grade, a damage radius and a residual intensity prediction value; and triggering a multi-mode alarm protocol according to the quantitative evaluation matrix, and synchronously transmitting the multi-mode alarm protocol to a traffic management center and an adjacent vehicle OBU terminal. According to the embodiment of the invention, the state of the guardrail can be monitored in real time, and a collision event can be accurately evaluated and warned.

The invention discloses a power transformer partial discharge positioning method based on multi-sensor array fusion, and the method comprises the following steps: S1, selecting a sensor installation point, and laying a multi-sensor array structure; s2, partial discharge signals of the three types of sensors are collected, and primary signal processing is carried out; s3, calculating propagation time differences between the reference channel and other channels by adopting a generalized cross-correlation weighting algorithm, and generating a time difference matrix; s4, constructing a TDOA model in combination with the layout coordinates and the time difference matrix, and solving three-dimensional initial coordinates of a power supply; s5, establishing a structure correction model, compensating the path deviation, and outputting corrected positioning coordinates; s6, calculating an error and generating a confidence score; s7, mapping a positioning result to the three-dimensional model and generating an image; and S8, writing the positioning information into a database for filing management. According to the method, the multi-frequency sensor and the path correction model are fused, and high-precision three-dimensional positioning of partial discharge of the transformer is realized.

The invention provides a multi-source data real-time fusion processing method and system for a mobile intelligent device, and relates to the technical field of data processing.The method comprises the steps that 1, multi-dimensional original data streams are collected in real time through a heterogeneous sensor array integrated by the mobile intelligent device, data streams of different sensors are aligned by applying a space-time synchronization mechanism, and the data streams of different sensors are obtained; generating an original data set with consistent time and space; 2, dynamic interpolation compensation operation is executed on the original data set, and a dynamic calibration framework is constructed based on the internal topological relation of the data flow to form a dynamic sensing domain; and generating an evolution sequence according to the data unit evolution behavior of the domain boundary, generating a space correction value through the evolution sequence and the offset feature of the preset reference, and generating preprocessed data fused with the space correction value in combination with real-time data correlation analysis. According to the method, dynamic adjustment is triggered through anomaly detection, the fusion parameters are updated through the sliding window, and real-time efficient fusion processing of multi-source data of the mobile intelligent device is achieved.

The invention provides a method and system for monitoring the running state of a photovoltaic power station in real time, and relates to the technical field of photovoltaic power station monitoring, and the method comprises the steps: integrating a multi-mode sensor array in a photovoltaic module junction box, and collecting the sensor data of the photovoltaic power station in real time; performing localization preprocessing on the sensor data through an edge computing node, and transmitting different priority data to a cloud based on a dynamic hybrid communication protocol; sensor data are fused at the cloud, a four-dimensional digital twinborn model is constructed, and time-space continuous meteorological prediction, component aging and dust retention evolution data are fused; recognizing the fault mode of the photovoltaic power station, and dynamically optimizing the string topological structure of the photovoltaic power station in combination with the real-time operation state of the photovoltaic power station, the equipment health data and the fault mode recognition result; by integrating the distributed sensor, the edge calculation module and the intelligent alarm mechanism, the problems of delay, insufficient intelligent analysis and response lag in the prior art can be effectively solved.

The invention relates to the technical field of welding, in particular to a corrosion steel welding cooperative control method and system. Comprising the following steps that welding seam geometric parameters, molten pool dynamic characteristic parameters and welding heat input parameters in the corrosion steel welding process are collected in real time through a multi-dimensional sensor array; constructing a corroded steel welding seam feature space model based on the welding seam geometric parameters, and determining material corrosion grade distribution and mechanical property parameters of a welding seam area in combination with a preset corroded steel material database; a molten pool form evolution prediction model is established through an adaptive Kalman filtering algorithm by utilizing the dynamic characteristic parameters of the molten pool and the welding heat input parameters, and the solidification behavior and the welding seam forming trend of the molten pool are predicted in real time; according to the material corrosion grade distribution, the mechanical property parameters and the molten pool forming trend, a dynamic adjustment strategy of the welding process parameters is generated through a multi-objective optimization algorithm; the reliability and safety of the corrosion steel welding joint can be improved.

The invention discloses a defect repair method based on digital twinning and friction stir welding technologies, and relates to the technical field of intelligent manufacturing and digital twinning, and the defect repair method comprises the following steps: synchronously capturing full-dimensional data of a welding area through a multi-mode sensor array integrated by an actuator; secondly, segmenting defect boundaries by adopting a deep learning algorithm, constructing a dynamic twin model in combination with thermal-force field coupling simulation, and accurately mapping defect three-dimensional features; generating a repair track according to the twinborn model, converting the repair track into a robot joint instruction through a curved surface parameterization mapping algorithm, and implanting real-time anti-collision constraint; in the repairing process, based on reinforcement learning control of the material rheological resistance and the temperature gradient, the rotating speed, the advancing speed and the down force of the tool are dynamically adjusted; and after repairing, micro-focus CT scanning is started immediately, actually measured data is compared with twinborn prediction, and when the deviation exceeds a threshold value, a re-repairing process is triggered automatically. The method solves the problems that a traditional method depends on manual intervention and the precision of a sensor is easily interfered by the environment.

The invention relates to the technical field of intelligent navigation and control of ships. The invention provides a complex channel ship intelligent navigation and emergency decision-making method and system. The method comprises the following steps: acquiring environment data through a multi-source heterogeneous sensor array, and establishing a channel three-dimensional dynamic environment model; establishing a multi-objective optimization function, and performing dynamic path planning by adopting an improved model prediction control algorithm; synchronizing motion state parameters of an actual ship and a virtual ship model in real time, constructing an emergency decision tree in combination with an expert knowledge base, and verifying the feasibility of an emergency decision through Monte Carlo simulation; carrying out local route optimization by adopting edge computing nodes, carrying out multi-ship trajectory prediction through a federated learning mechanism, and generating a corresponding collaborative collision avoidance strategy; and establishing a dynamic priority scheduling mechanism, implementing hierarchical response, and confirming a global avoidance scheme through a distributed consensus algorithm. The problems that an existing inland ship intelligent system is limited in perception, rigid in decision and weak in collaboration in a complex scene are solved.

The invention relates to the technical field of machining precision control, in particular to a laser carving precision control method and system.The laser carving precision control system comprises a feature collecting unit, a model building and analyzing unit, a dynamic threshold value adjusting unit and an online incremental learning unit, and the feature collecting unit collects vibration, current and temperature data through a multi-source sensor array; the model construction analysis unit realizes dynamic prediction of processing parameters by combining a bidirectional long-short-term memory network with an attention mechanism, and the dynamic threshold adjustment unit dynamically updates parameters of a numerical control system based on a material hardness real-time detection and thermal coupling model. The online incremental learning unit automatically generates training samples through error data, continuously optimizes model parameters and constructs a'data acquisition-intelligent modeling-dynamic compensation-model evolution 'closed loop, so that accurate prediction and adaptive adjustment of machining parameters are realized, and the adaptability of the manufacturing process to multi-variety and small-batch working conditions is remarkably improved.

The invention relates to the technical field of elevator equipment state monitoring and fault diagnosis, and discloses an elevator traction machine running state real-time diagnosis method and system based on high-frequency sampling. According to the method, vibration (larger than or equal to 20 kHz), current (larger than or equal to 10 kHz), sound / sound emission, temperature and rotating speed signals of a traction machine are synchronously collected through a high-frequency multi-mode sensor array; capturing early weak fault transient characteristics; the edge computing unit completes data preprocessing, time synchronization, feature extraction and anomaly detection, and uploads key data to a cloud end through cloud-edge collaboration; the cloud end adopts a working condition self-adaptive strategy and a multi-modal fusion model to carry out deep diagnosis, and outputs fault types, positions and grades; and combining incremental learning and a degradation model to realize health quantification and residual life prediction. Through fusion of high-frequency data capture and an intelligent algorithm, the early fault detection capability, variable working condition adaptability and diagnosis real-time performance of the traction machine are improved, and a solution is provided for predictive maintenance of an elevator.

The invention provides a forest land tree height measurement and determination method and system based on laser radar point cloud data. Stress wave signals are collected based on a trunk base acoustic emission sensor array to generate an acoustic characteristic parameter set, the digital twin model is driven to complete forest stand structure topological optimization, and a three-dimensional growth vector model reflecting the internal mechanical state of a trunk is formed. And synchronously fusing high-precision slope point cloud data returned by the unmanned aerial vehicle laser radar in real time, correcting a terrain distortion error through a dynamic splicing algorithm in combination with stress distribution characteristics, and generating a crown segmentation boundary constrained by physical characteristics. And finally outputting a tree height parameter corrected by the abrupt slope topography through model iterative optimization and space vector analysis. According to the technical scheme, synchronous sensing of the three-dimensional shape and the mechanical state of the tree in the complex terrain environment is achieved, and the tree height measurement error is reduced.

The invention relates to the technical field of unmanned aerial vehicle positioning, in particular to an unmanned aerial vehicle flight path positioning method and system based on multi-source information fusion. The method comprises the following steps: acquiring an original observation data stream of an unmanned aerial vehicle in real time through a sensor array, and preprocessing to output a multi-source data sample set; calculating relative motion increment between adjacent key frames through an IMU pre-integration model, and extracting local pose estimation through a visual geometric calculation method; evaluating the confidence of each sensor in real time to dynamically generate an adaptive weight coefficient; and constructing a tight coupling fusion filter taking position, speed and attitude errors as core state quantities, and outputting a global optimal trajectory of the unmanned aerial vehicle. According to the invention, the weight is dynamically distributed through the real-time confidence of the sensor, the multi-source data association is fully mined, and the flight path positioning precision and environmental adaptability of the unmanned aerial vehicle are improved.

The invention discloses an article recognition system based on computer vision. The article recognition system comprises a multi-modal data acquisition module, a multi-modal data processing module and a computer vision processing module, wherein the multi-modal data acquisition module is used for acquiring multi-modal data through a multi-modal sensor array; the data preprocessing module is used for standardizing a multi-modal data format and generating a time-space aligned multi-modal tensor; the feature extraction module is used for respectively extracting modal specific features from texture, spectrum and geometric dimensions by adopting ResNet50, 3D-CNN and PointNet + +; the multi-modal fusion module is used for constructing cross-modal joint representation; the adaptive sensing module is used for modeling illumination invariance and scene dynamics based on self-supervised comparative learning and a 3D-STMN space-time memory network, predicting a shielded target trajectory by using Kalman filtering in combination with the shielding sensing propagation module, and generating an environment sensing parameter set; and the recognition engine module is used for integrating YOLOv8 detection, Mask R-CNN segmentation and multi-modal decision tree classification, outputting a target bounding box, a category and confidence in combination with the depth data, and generating three-dimensional space coordinates combined with the depth data.

The invention relates to the field of ring main units, in particular to an event-driven intelligent ring main unit and a control method, and the event-driven intelligent ring main unit comprises a cabinet body, a cabinet body built-in circuit breaker, an isolation grounding switch and a fusion sensing array. An array synchronously acquires mechanical and electrical quantities, an edge decision-making system takes a voltage zero crossing point as an absolute time mark nanosecond alignment event, a closed-loop control core generates a protection and topology reconstruction instruction in situ according to the event, and primary equipment is driven in a millisecond level; the intelligent communication interface dynamically schedules bandwidth according to the data priority, and key instructions are ensured to be issued in real time. According to the invention, the problems of fault misjudgment and local control delay caused by lack of a unified time sequence reference in a mechanical state and an electrical transient state of a traditional ring main unit are solved.

The invention relates to the technical field of instrument multi-task optimization, in particular to an intelligent instrument multi-task real-time optimization method and system based on dynamic resource scheduling. The optimization method comprises the following steps: acquiring a target item of each task in real time through a sensor array, constructing a multi-dimensional feature vector, dividing each task into task categories by using a fuzzy clustering algorithm, and presetting an initial priority for the task categories for multi-task feature parameter acquisition and classification modeling. According to the method, the multi-dimensional feature vectors including the task urgency degree, the calculation complexity and the data interaction frequency are constructed, and the fuzzy clustering algorithm of the task dependency constraint is introduced, so that the task categories are accurately divided, the cross-category interaction overhead of the dependency task is effectively reduced, and the compatibility of a scheduling strategy is improved from the source.

The invention relates to the technical field of cooperative control, in particular to a multi-machine cooperative control method and system for a bridge cable hoisting device, and the method comprises the following steps: collecting tension change characteristics through a distributed sensor array, extracting indexes through a sliding window algorithm to generate a response time window, and generating a cooperative time window through time sequence overlapping and speed trend adjustment. Calculating a compensation value by combining inertial parameters and acceleration to generate a trigger window, detecting tension and displacement difference, inputting the tension and displacement difference into a dynamic threshold function for screening and fusion, outputting a synchronous trigger condition, and generating a synchronous control instruction by encoding a device number and a tension change rate after timestamp alignment. According to the method, multi-node tension synchronous acquisition and normalization processing, dynamic response generation through a sliding window, boundary matching action triggering adjustment through an overlapping algorithm, response lag correction through inertia compensation, tension and displacement difference real-time detection, dynamic threshold screening and triggering condition fusion and timestamp alignment packaging instruction consistency are carried out; and the cooperation precision and the system robustness are improved.

The invention discloses a coastal protection dam settlement monitoring method, and belongs to the technical field of hydraulic engineering safety monitoring. The method comprises the steps that a longitudinal monitoring section is arranged on the slope surface of the back sea side of a dam, a three-measuring-line fiber grating sensor array is arranged, and vertical displacement and horizontal dip angle data are periodically collected through a synchronous triggering unit; establishing a vertical displacement-horizontal dip angle joint analysis model by using a multi-source data fusion module, and eliminating tide level interference through a Kalman filtering algorithm to generate a settlement distribution curve; and triggering third-level to first-level early warning signals based on the grading early warning rule, and transmitting the signals to the terminal equipment. The problems that a traditional monitoring method cannot effectively separate tidal interference, the real-time performance is poor, and multi-dimensional data collaborative analysis is insufficient are solved, high-precision settlement monitoring, complex environment anti-interference and rapid emergency response are achieved through multi-measuring-line sensor deployment, dynamic filtering optimization and a graded early warning mechanism, and the method is suitable for large-scale popularization and application. And the reliability and timeliness of dam safety monitoring are obviously improved.

The invention relates to the technical field of electromagnetic environment detection, and discloses a multi-mode electromagnetic environment detection system and method. The system acquires multi-modal data such as electromagnetic field intensity and spectrum distribution through a multi-modal sensor array to generate a data set, and realizes interference source identification and dynamic adjustment of detection parameters through nonlinear signal processing, depth feature extraction based on a convolutional neural network, topology analysis of a graph neural network and adaptive optimization of reinforcement learning. And an electromagnetic field simulation module is also arranged to simulate electromagnetic wave propagation and early warn abnormal radiation, and an abnormal mode library is constructed to identify potential interference types. The system can comprehensively and accurately detect an electromagnetic environment, efficiently position an interference source, adaptively adjust detection parameters, improve detection accuracy, real-time performance and system stability, and effectively meet complex electromagnetic environment monitoring requirements.

The invention discloses a dynamic leakage prediction method and system for an old oil and gas pipeline, and the method comprises the steps: collecting pressure, flow and temperature parameters in real time through a multi-source sensor, and recognizing abnormal fluctuation through the combination of time sequence analysis and frequency domain feature extraction; calculating a pipeline state evaluation result based on the material degradation model; establishing a leakage prediction model fusing a wall thickness degradation kinetic equation and an LSTM neural network, calculating a leakage probability by adopting a Monte Carlo method, and generating a diffusion velocity and a concentration gradient through CFD numerical simulation; when the diffusion prediction exceeds a safety threshold value, a control strategy is optimized through fuzzy logic and a genetic algorithm; the verification model is fed back after real-time adjustment, and online learning is carried out through Bayesian optimization; and finally, calibrating the model by using experimental data, and deploying and generating risk early warning. The system comprises a multi-source sensor array, a data processing platform and other modules, and full-chain closed-loop control from sensing to early warning is achieved.

The invention relates to the technical field of vacuum electric furnace monitoring, and discloses a state monitoring system suitable for a vacuum electric furnace. A multi-source sensor array of the system collects multi-dimensional physical signals such as temperature distribution, pressure change and vacuum degree fluctuation in a furnace in real time; a furnace cavity feature reconstruction module extracts sampling point feature parameters and correlates coordinates to construct a three-dimensional dynamic feature field; the process anomaly analysis module calculates a process deviation degree in combination with a preset reference parameter, and marks an anomaly coordinate area; the state transition evaluation module analyzes an abnormal trend according to historical records and predicts a state transition path and rate; the collaborative regulation and control decision-making module generates a multi-stage vacuum maintenance compensation strategy and a heating power regulation gradient scheme according to the multi-stage vacuum maintenance compensation strategy; and the running log feedback module records a strategy execution process, and associates the three-dimensional feature field data to generate a state tracing log. The system can realize comprehensive monitoring of the state of the vacuum electric furnace, accurate abnormity identification, trend prediction, cooperative regulation and control and state tracing, and helps to improve the operation management level of the vacuum electric furnace.