774 results about "Spatiotemporal correlation" patented technology

The invention belongs to the technical field of bridge diagnosis, and discloses a bridge crack intelligent diagnosis system based on multi-modal data fusion. By fusing multi-source data such as visual images, sound wave detection and vibration signals, comprehensive perception and characterization of crack features are realized; constructing a bridge crack characteristic spectrum diagram by adopting a cross-modal feature extraction and heterogeneous feature coding technology; generating a crack evolution situation map based on space-time correlation analysis and knowledge graph construction; the robustness of the system in a complex environment is improved through environmental adaptability feature enhancement and multi-scale characterization; constructing a bridge safety risk hypergraph in combination with multi-dimensional risk analysis and multi-agent collaborative diagnosis; analyzing and revealing a crack evolution mechanism by applying a causal relationship; and finally, through dynamic fusion and uncertainty quantification, a crack intelligent diagnosis comprehensive report is generated. According to the system, the limitation of traditional single-mode diagnosis is broken through, and dynamic prediction and accurate risk assessment of fracture evolution are realized.

To significantly improve the prediction accuracy, response speed and management efficiency of large river bank slope disasters, an integrated bank slope monitoring method based on multi-parameter collaborative recognition is proposed. The solution includes step S1 of synchronously collecting data on bank slope displacement, pore water pressure, inclination angle, vibration frequency and environmental temperature and humidity to form an original monitoring dataset and construct a multi-parameter collaborative recognition network; step S2 of using a multi-modal data fusion algorithm to generate a fusion data matrix including spatiotemporal correlation features and perform spatiotemporal data alignment and outlier cleansing; step S3 of combining a geomechanical parameter library and a past disaster case library to output a risk level map and perform dynamic risk assessment model analysis; and step S4 of triggering a multi-level early warning mechanism and generating linked control commands including treatment suggestions to perform multi-level early warning and linked control.

The invention discloses an agent autonomous decision control method based on multi-modal data fusion. The method comprises the following steps: S1, synchronously collecting multi-source heterogeneous data; s2, dynamic weight adaptive fusion is carried out; s3, generating a task-driven decision; and S4, performing autonomous decision closed-loop optimization. According to the method, through dynamic weight distribution and space-time correlation modeling, the problems of heterogeneity and environment adaptation in multi-modal data fusion are solved; furthermore, a risk-sensitive reinforcement learning framework and a closed-loop feedback mechanism are combined, so that full-link cooperative control from data fusion, strategy generation to optimization execution is realized. In the mechanism level, the method breaks through the limitations of static fusion, single-target optimization and offline training, can adapt to a dynamic environment, ensures that the intelligent agent is in a complex scene such as noise interference, illumination abrupt change and task emergency switching, and meets the requirements of decision-making efficiency, safety and environment robustness at the same time.

The invention relates to the technical field of coal mine safety monitoring, and discloses a coal mine safety data comprehensive analysis and early warning system which comprises a data integration module, a three-dimensional visualization module, a risk assessment module, a linkage control module, a model training module and a central processing unit and can further comprise a decision support module, a storage cluster and a communication gateway. The data integration module constructs a multi-source heterogeneous data acquisition channel and performs dynamic topology modeling; the three-dimensional visualization module dynamically renders the monitoring data based on the space-time reference axis; the risk assessment module generates a danger situation map through space-time correlation analysis; the linkage control module establishes a multi-level response mechanism; the model training module optimizes the risk prediction model; and the central processing unit schedules each module to operate. According to the system, integrated analysis, dynamic visualization, risk prediction and cross-system linkage disposal of coal mine safety data are achieved, and the intelligent level and emergency capacity of coal mine safety monitoring are improved.

The invention provides a water and soil loss dynamic monitoring method and system based on a remote sensing image, and the method comprises the steps: firstly obtaining a multi-temporal remote sensing image data set of a target region, which comprises a plurality of time period remote sensing image subsets and land surface coverage information, and then carrying out the land surface feature extraction of the multi-temporal remote sensing image data set, after vegetation coverage, terrain gradient and soil exposure features are obtained, a water and soil loss prediction model based on time-space correlation is constructed, the features are input for prediction, a water and soil loss grade distribution map is generated, a target loss risk area is identified according to the water and soil loss grade distribution map, and a treatment priority sequence and a vegetation recovery strategy are generated. And finally, the information is fed back to a monitoring platform to trigger regional governance task allocation operation, so that dynamic monitoring of water and soil loss is realized, and governance tasks are effectively planned.

The invention discloses a road and bridge parameter anomaly detection method, and belongs to the technical field of civil engineering. Comprising the following steps: step 1, establishing space-time relevance and a data mapping relation between monitoring points; 2, self-adaptive updating of the judgment rule is achieved so as to adapt to state evolution of a bridge service stage; step 3, performing intelligent attribution analysis on abnormity in the monitoring data; step 4, carrying out joint identification and comprehensive evaluation on the sudden structural damage and the slow degeneration degradation process; and 5, carrying out quantitative analysis on the deviation degree of the bridge health state, and generating a standardized bridge health index. According to the method, the space-time correlation network and the environment-structure mapping function between the monitoring points are established, and a multi-dimensional feature extraction and dynamic weight distribution mechanism is combined, so that environment interference elimination, abnormal attribution intelligent analysis and joint identification of sudden damage and slow degeneration are realized, and finally, a standardized health index is quantified and generated.

The invention is suitable for the technical field of electrical equipment state monitoring, and provides an electrical equipment state monitoring method based on a non-contact leakage current sensor, and the method comprises the steps: collecting a leakage current signal of the surface of an insulating part of electrical equipment, and obtaining infrared thermal image data and an ultrasonic signal; variational mode decomposition is carried out on the leakage current signal to obtain a plurality of intrinsic mode functions; separating a leakage current effective component and an independent noise source based on a blind source separation algorithm; extracting a time-frequency characteristic of the effective component of the separated leakage current, extracting a local temperature gradient characteristic of the infrared thermal image data and a frequency spectrum energy characteristic of the ultrasonic signal, and generating a multi-modal characteristic vector; analyzing the space-time relevance of the multi-modal feature vector, dynamically distributing each modal weight coefficient for fusion, and generating a comprehensive fault feature; according to the method, the signal-to-noise ratio of the weak leakage current signal is improved, and the misjudgment rate is effectively reduced.

The invention relates to the technical field of intelligent monitoring, in particular to an intelligent diagnosis system and method for a comprehensive energy station, and the system comprises a dynamic trajectory capture module, a lag response analysis module, a mismatch behavior diagnosis module and a fault chain positioning module. According to the method, a dynamic time warping algorithm is adopted to process sliding window time sequence data and calculate track slope difference, capture precision of instantaneous disturbance characteristics is enhanced, a grey correlation model is utilized to analyze track matching degree and set a dynamic lag threshold value, and a continuous non-convergence condition trigger mechanism is combined to improve hidden fault identification sensitivity. Multi-parameter range calculation and direction consistency check integrate start-stop frequency and load rate change trend, reduce misjudgment risk caused by single parameter fluctuation, construct a time sequence alignment axis and calculate delay response joint probability distribution, realize time-space association of historical data and real-time monitoring, accurately locate a coupling relationship of a multi-source fault chain, and improve the reliability of the multi-source fault chain. The abnormal state early warning capability is enhanced, and the fault positioning efficiency is optimized.

The invention discloses a digital intelligent management method and system for hydraulic engineering construction, particularly relates to the technical field of hydraulic engineering construction management, and is used for solving the problems of regulation lag and insufficient environmental disturbance adaptability of an existing method under the action of multi-physics field coupling. According to the method, a concrete three-dimensional temperature field model is established, initial regulation and control parameters are generated, and a heat conductivity coefficient under the seepage influence is corrected based on space-time relevance of pore water pressure and temperature data; the constraint boundary is dynamically adjusted in combination with the construction progress and the material strength curve, a cooling water pipe coordinated regulation and control matrix is constructed, and target flow instructions of space differentiation are generated through the included angle between the distance and the water flow direction; setting a lag time threshold and optimizing an execution time sequence according to the thermal inertia parameter, splitting the threshold and allocating a weight coefficient when the environmental wind speed suddenly changes, and finally updating the model parameter and the regulation and control instruction; accurate temperature control response and abnormal heat conduction suppression under multi-field coupling are achieved, and the structural safety and construction efficiency under complex working conditions are improved.

The invention relates to the technical field of transformer monitoring, and discloses an explosion-proof intelligent monitoring and early warning device for a transformer. The device comprises a multi-source sensing module used for collecting multi-dimensional heterogeneous data of transformer operation; the feature extraction module is used for fusing data cross-domain features to generate various feature representations; the anomaly detection module is used for generating an abnormal signal space-time incidence matrix based on a dynamic causal network construction model; the risk early warning module outputs a risk level and an early warning instruction through a multi-task decision-making mechanism; and the self-adaptive regulation and control module is used for optimizing monitoring parameters and hardware resource allocation according to instructions. The device also can carry out critical state identification and emergency intervention, and constructs an insulation degradation prediction model to correct an early warning threshold value. According to the device, omnibearing monitoring, accurate early warning and intelligent regulation and control of the transformer are realized, the operation safety and reliability of the transformer are effectively improved, and the fault risk and loss are reduced.

The invention relates to the technical field of geological disaster monitoring, and discloses an intelligent early warning method and system for geological disasters in geotechnical engineering, and the system comprises a data collection module, a data processing module, a feature extraction module, an early warning model module, a response execution module and an optimization feedback module. Static geological parameters, dynamic environment parameters and historical disaster data are integrated, a standardized space-time correlation data set is constructed, the limitation of a single data source is broken through, multi-dimensional dynamic response characteristics of a rock-soil body are captured, a reliable data basis is provided for accurate early warning, the rigidity defect of a traditional fixed threshold value is avoided, and the early warning accuracy is improved. The method achieves the self-adaption of the risk early warning sensitivity, reduces the misjudgment and missing judgment caused by environment interference, intercepts a dynamic data stream in real time through a sliding window, calculates the risk mean value and variance, quickly responds to sudden environmental changes such as rainfall sudden change and vibration abnormality, and generates a graded early warning signal.

The invention relates to the technical field of power equipment fault diagnosis, in particular to a power equipment fault intelligent diagnosis method and system based on deep learning. The method comprises the following steps: automatically learning high-dimensional space-time correlation features in original time series data through a deep feature extraction network, and generating feature vectors representing potential abnormal modes of equipment; performing adaptive weight distribution on the high-dimensional space-time correlation features by using an attention enhancement mechanism, and marking a fault sensitive area to form enhanced fault features; inputting the enhanced fault features into a multi-level classifier for joint fault mode recognition and severity evaluation, and outputting a diagnosis result tensor containing a fault type and confidence; and an equipment maintenance decision signal is triggered based on the diagnosis result tensor, and the feature extraction network and classifier parameters are iteratively optimized according to feedback data, so that the intelligent level of operation and maintenance of the power equipment can be comprehensively improved.

The invention relates to the technical field of industrial Internet of Things, in particular to a machine equipment online state monitoring and fault diagnosis system, which comprises the following steps of: acquiring multi-source heterogeneous sensing data through an edge computing node deployed on an equipment body, performing adaptive noise filtering and feature dimension reduction processing on original data, and acquiring multi-source heterogeneous sensing data; outputting a standardized equipment state vector set; inputting the equipment state vector set into a dynamic knowledge graph engine, constructing a fault evolution network comprising space-time correlation characteristics based on an equipment operation entropy change quantification model, and generating a graph node connection relationship with a weight coefficient; and inputting the fault evolution network into a migration reinforcement learning module, and outputting a diagnosis decision set comprising a fault type, a severity degree and an evolution path through knowledge migration of a cross-device fault mode. According to the method, the problems of edge redundancy and single feature expression in traditional rule-based atlas construction are effectively avoided, and the structuring ability and physical traceability of fault recognition are improved.

The invention discloses a grassroots society digital governance method and system, and the method comprises the steps: collecting multi-source heterogeneous data in real time through an Internet of Things sensing network disposed in a grassroots community, and generating a standardized multi-mode sensing data flow; based on the multi-modal perception data stream, outputting a time-space associated cleaned data set; inputting the cleaned data set into a multi-scale space-time encoder, and generating a feature tensor containing regional hotspot distribution and a risk propagation path; based on the feature tensor, constructing a dynamic risk knowledge graph, and outputting a decision matrix including a risk level and an optimal intervention path; inputting the decision matrix into a strategy optimization engine to obtain a hierarchical governance instruction set; and on the basis of real-time governance feedback data after the hierarchical governance instruction set is executed, a conflict instruction in the strategy chain is autonomously corrected through a fuzzy reinforcement learning algorithm. By utilizing the embodiment of the invention, an efficient, intelligent and traceable digital governance scheme can be realized, so that the social governance efficiency and the service quality are improved.

The invention relates to the technical field of smart energy management, and discloses an energy consumption optimization method and system based on multi-dimensional data fusion, and the method comprises the steps: collecting energy consumption data, environment data and equipment operation state data of a target region in real time, carrying out the data preprocessing, and generating a sample data set; based on space-time correlation analysis, integrating the sample data set into an energy consumption feature matrix by adopting a multi-dimensional data fusion strategy combining feature-level fusion and decision-level fusion; inputting into an energy consumption prediction model constructed based on a deep learning algorithm, and outputting an energy consumption prediction result of the target area in a future time period; and according to an energy consumption prediction result, generating an optimal energy consumption control strategy through a dynamic programming algorithm. The dynamic optimal energy consumption control strategy is generated through linkage of the deep learning algorithm and the dynamic programming algorithm, and the problems that in the prior art, energy consumption optimization precision is insufficient, and adaptability is poor are solved.

The invention relates to the technical field of intelligent bid evaluation, and provides an intelligent multi-dimensional bid evaluation analysis and decision-making method based on big data, which comprises the following steps: acquiring original bid evaluation data from a multi-source heterogeneous data interface, and fusing through semantic role labeling and a timestamp alignment algorithm to generate a time-space association data set. And performing multi-level cleaning to generate a high-confidence bid evaluation data set. And extracting a multi-dimensional index based on the domain knowledge graph, generating a dynamic feature tensor, and dynamically allocating a weight by adopting a coupling attenuation weight model. And constructing a bidder association network, calculating a node influence score, detecting a potential bidding behavior and generating a risk correction coefficient. And injecting the real-time data stream into the dynamic feature tensor, updating the index weight, and generating a three-dimensional scoring vector through a multi-target aggregation decision algorithm. And performing Pareto optimization by using the asymmetric game equilibrium model, and outputting an optimal bid-winning party sequence and a risk early warning report. The bid evaluation efficiency and fairness can be improved, and the bid invitation risk is reduced.

The invention, which relates to the technical field of customer relationship management, discloses a multi-channel interactive customer relationship management system comprising a dynamic routing decision module, a multi-modal data fusion module and an intelligent feedback optimization module. The dynamic routing decision module evaluates channel load through a deep neural network, dynamically allocates client requests to an optimal node by utilizing reinforcement learning, and realizes load balancing and service continuity; the multi-modal data fusion module integrates text, voice and image data, constructs a space-time correlation graph, identifies a cross-channel behavior mode, and ensures data consistency through multi-dimensional verification; the intelligent feedback optimization module combines customer satisfaction evaluation and multi-modal sentiment analysis, optimizes a service strategy by using a genetic algorithm, and synchronizes the service strategy to a cross-channel knowledge graph to realize adaptive iteration; according to the method, the problems of unreasonable multi-channel load distribution, insufficient data fusion and consistency verification and inaccurate service strategy optimization are effectively solved, and the customer service quality and experience are improved.

The invention relates to the technical field of abnormal state prediction, provides a multi-dimensional time sequence equipment abnormal state prediction method and system, and solves the problems of high false alarm rate and risk prediction inaccuracy in the prior art. The method comprises the following steps: collecting a multi-node operation state data set and communication transmission layer time sequence offset information of an equipment distributed system; jitter and delay association processing is carried out on the data, and a communication stability feature set is generated by quantizing space-time correlation of a jitter extreme value and delay fluctuation; obtaining physical layer deformation and temperature drift data of the communication cable, analyzing and generating a physical layer disturbance feature sequence based on optical signal feature offset, and converting the physical layer disturbance feature sequence into channel abnormal strength features; and cross-level feature coupling is carried out on the communication stability feature and the channel abnormal strength feature, and a communication node failure or data transmission abnormal risk is predicted based on a coupling result. According to the invention, millisecond-level linkage prediction of equipment-level physical disturbance and system-level communication risks is realized, the fault false alarm rate is reduced, and the cascade failure risk is blocked.

The invention provides an online monitoring method and system based on a power transmission line, and relates to the technical field of power transmission line monitoring. According to the method, the heterogeneous sensing terminal, edge calculation, the graph neural network and Bayesian reasoning are combined, multi-source data acquisition, state identification and risk prediction are realized, the fault diagnosis accuracy and the risk early warning capability are improved, and the intelligent level and the safety guarantee capability of power transmission line operation are enhanced; the monitoring data is analyzed in real time through an edge calculation unit to generate a state label, a potential fault mode is recognized by combining graph neural network modeling space-time relevance, a risk factor library is further constructed, and a real-time fault probability graph is generated based on a Bayesian network. And dynamic identification and early warning of risk types such as wire strand breakage, icing overrun and mechanical fatigue can be realized.

The invention discloses a grape disease recognition and early warning method based on the Internet of Things, and relates to the technical field of plant disease recognition, image acquisition equipment and environment sensing nodes are arranged in a vineyard, and leaf images and corresponding temperature and humidity, illumination and soil moisture parameters are obtained; inputting the image into a neural network fusing dilated convolution and a residual attention mechanism, realizing extraction of a disease spot region and a disease spot variation feature, and generating a preliminary recognition result; constructing a multi-factor evolution sample set in combination with the recognition result and the environment state of the time node; constructing a space-time correlation graph model based on a graph neural network, estimating a disease propagation risk path and a diffusion probability, and performing early warning judgment at a gateway end through a multi-factor gating discrimination algorithm; the method disclosed by the invention is high in recognition precision and strong in response timeliness, has adaptive prediction and targeted treatment capabilities, and remarkably improves the intelligence and precision level of grape disease management.

The invention relates to the technical field of meteorological observation, and provides a ground hail identification method and system based on a hydrogel classification result, and the method comprises the steps: carrying out the time-space correlation of multi-source hail data through a time-space matching algorithm, and obtaining a hail event data set of time-space matching; through a dynamic membership function optimization algorithm, self-adaptive phase state identification is carried out on the dual-polarization radar data, and multi-elevation hail phase state characteristic parameters containing rain-ice mixture categories are obtained; based on the multi-elevation hail phase state characteristic parameters, performing integrated preprocessing on the multi-source meteorological data to obtain standardized multi-dimensional meteorological characteristic data fused with phase state characteristics; performing unsupervised pre-training and supervised fine-tuning training on the DCNN-DBN hybrid neural network through the standardized multi-dimensional meteorological feature data to obtain a ground hail recognition model; and outputting a hail falling area identification result through the ground hail identification model. According to the invention, the distinguishing capability of easily-confused phase states is improved, and the false alarm rate and the missing report rate of hail identification are reduced.

The invention discloses a method and system for managing expressway construction based on a BIM technology. The method comprises the following steps: acquiring multi-modal data in expressway construction; constructing an environment-load-response multi-modal data fusion risk matrix by using a space-time correlation model; constructing an AI prediction model by using bridge motion signals and humidity and CO2 concentration data in the multi-modal data; dynamic risk early warning and hierarchical response are realized; the bridge vibration signals and thermal infrared imager data are utilized to analyze and identify invisible faults in highway construction; constructing a fault and health management scheme based on dynamic risk early warning, hierarchical response and invisible faults; performing construction simulation, conflict elimination and extreme working condition deduction by using the digital twin environment of the BIM technology; and according to a deduction result, carrying out highway construction abnormal area management through VR visualization. According to the scheme of the invention, the safety early warning, fault prediction and health management levels of highway construction can be improved.

The invention discloses an intelligent reconstruction and prediction method and system for an ocean three-dimensional flow field, and relates to the technical field of data processing, and the method comprises the steps: obtaining the multi-source observation data of an ocean through a large language model and an edge monitoring device; preprocessing and fusing the multi-source observation data to obtain fused observation data; taking the target observation parameters in the fused observation data as nodes, and taking the space-time correlation and physical quantity coupling relationship among the target observation parameters as edges to construct a space-time correlation map; reconstructing and determining reconstruction data corresponding to the multi-source observation data according to the space-time correlation atlas; the three-dimensional flow field of the ocean is obtained through prediction according to the reconstruction data, and a visual three-dimensional flow field is output. According to the method, the multi-source observation data are fused and reconstructed, and the accuracy of predicting the ocean three-dimensional flow field can be improved.

The invention discloses a smart factory management system and method based on MES, and relates to the technical field of smart manufacturing, and the method comprises the steps: calculating a health degree score based on equipment historical maintenance records, and generating a time-space correlation multi-dimensional analysis data set containing an equipment topological structure and health degree parameters in combination with a digital twin network; performing spatial-temporal feature coupling and topological weight dynamic adjustment on the spatial-temporal correlation multi-dimensional analysis data set through a dynamic topological analysis algorithm, and generating a high-risk equipment list and an anomaly control instruction set; based on the high-risk equipment list, constructing a standardized multi-dimensional abnormal feature vector, and generating an aging weighted danger level signal through an entropy weight method; according to the method, space-time alignment of equipment operation state data and physical topology data is realized through a multi-rate Kalman filtering algorithm and an iterative nearest point algorithm, a high-fidelity digital twin network is constructed in combination with a dynamic graph convolutional network, and accurate anomaly detection and health degree evaluation are supported.

The invention discloses an urban water body pollution traceability system based on multi-source sensing data fusion, and the system comprises a data acquisition module which is used for deploying a multi-source water quality sensor to collect initial multi-source water body data, and carrying out the time-space unified alignment processing, and obtaining the time-space aligned multi-source time-space water body data; the pollution factor tracing module is used for constructing a pollution event deconstructor and a factor tracing reasoning engine based on a water network topological graph neural network on the basis of multi-source space-time water body data, and outputting pollution component vectors through pollution component decomposition driven by the pollution event deconstructor; inputting the pollution component vector into a tracing reason inference engine to carry out tracing reason space-time correlation to obtain a tracing reason pollution fusion map; and the traceability decision module is used for performing inversion through a reverse traceability algorithm based on the traceability pollution fusion map, calculating the probability that each upstream area is a pollution source, mapping the probability that each upstream area is the pollution source to a GIS platform, obtaining a pollution traceability confidence distribution map, and realizing accurate traceability of the urban water pollution source.

The invention discloses an intelligent water affair monitoring management method and system based on the Internet of Things, particularly relates to the technical field of water affair management, and is used for solving the problems of control instruction mismatching, redundant execution and equipment overload caused by the fact that a static topology model cannot sense the dynamic change of a pipe network in real time in the prior art. Through real-time collection of pipe network operation data and analysis of time-space correlation characteristics of water flow propagation delay parameters and pressure mutation, a pipe network topology change event is dynamically perceived; the flow direction sudden change reasonability is verified in combination with fluid mechanics conservation constraint, and a corrected topological mapping table is generated through reverse calculation; candidate paths are screened based on water flow inertial parameters and pressure gradient threshold values, a safety control instruction set is generated through a multi-stage verification rule, dynamic matching of a pipe network regulation and control instruction and a real topological structure is achieved, the matching degree of the control instruction and the physical state of a pipe network is effectively improved, the leakage risk and energy waste are reduced, the manual maintenance requirement is reduced, and the safety of the pipe network is improved. The service life of equipment is prolonged.

The invention belongs to the technical field of earthquake disaster scene construction, and discloses an earthquake disaster scene construction method based on big data, and the method comprises the following specific steps: 1, multi-source heterogeneous data collection and preprocessing ground structure data collection: employing a multi-platform remote sensing technology to obtain urban building group space distribution characteristics; a three-dimensional coupling model of an underground pipe network and a ground building is constructed by integrating multi-source data, so that the defect of isolated analysis of a ground system and an underground system in a traditional method is overcome, and synchronous simulation of a dynamic association process of pipe network fracture and building damage is realized; the additional influence of pipe network failure on the building foundation stability and the linkage effect of soil erosion caused by leakage fluid are accurately analyzed, meanwhile, a time-space association rule of pipe network fracture events and multiple types of secondary disasters such as building fire disasters and fire fighting failure is established, and the building group function paralysis risk and the disaster diffusion range under different damage degrees are dynamically predicted; and the disaster evolution path is displayed in real time through a visual platform.

The invention discloses a ternary internet-of-things battery management system, particularly relates to the technical field of lithium battery management, realizes lithium battery thermal safety closed-loop management through multi-sensor fusion, and comprises the following steps: deploying a digital sensor array to obtain time-space associated data; constructing a spatio-temporal feature vector including fusion of a local temperature gradient, a neighborhood temperature gradient and a stress coupling ratio; a density clustering algorithm is adopted to dynamically divide spontaneous heating, radiant heat and stress coupling clusters, and cluster boundaries are updated in real time; calculating a dual thermal coupling coefficient based on a clustering result, and eliminating the interference of temperature reading through a dual temperature compensation formula; a three-level alarm mechanism of a radiant heat proportion, a stress coupling degree and a compensation error is established, a side gateway is linked to realize parameter adaptive optimization, and the thermal runaway early warning precision and the structural failure detection rate are improved; strain monitoring resource optimization is realized through physically-driven key point screening and dynamic graph neural network prediction.

The invention belongs to the technical field of deep well engineering safety monitoring, and particularly provides a deep well multi-parameter integrated monitoring method, system and device and a medium, and the method mainly comprises the steps: obtaining deep well parameters, and fusing the deep well parameters to generate a space-time coupling tensor; performing three-dimensional decoupling on the space-time coupling tensor to generate a dynamic risk hologram containing an interference gradient; phase conjugate matching is carried out based on the dynamic risk hologram, and an annular closed co-evolution unit is identified; manifold expansion is carried out on the co-evolution unit along the energy path, and entropy ratio weighted space folding coordinates are generated; and based on the space folding coordinates, catastrophe coordinate early warning is triggered through the index relationship between the phase tearing index and the entropy ratio. According to the method, space-time correlation analysis and risk mode three-dimensional presentation of deep well parameters are realized, catastrophe coordinates can be accurately positioned and early warning can be triggered in advance through energy path manifold expansion and entropy ratio weighted calculation, and the timeliness and accuracy of safety monitoring of deep well engineering are improved.

The invention provides a microgrid power load prediction and dynamic control method based on CNN-LSTM-AM, and relates to the technical field of intelligent control of a power system. According to the method, multi-source data is collected, data preprocessing is carried out, a CNN-LSTM-AM hybrid prediction model is constructed, and a CNN layer comprises a double-branch multi-scale one-dimensional convolution kernel; the output of the input layer and the output of the LSTM layer are connected to the DSTCW module, the DSTCW module outputs weighted load characteristics and photovoltaic / wind power characteristics, the charging and discharging priority is optimized based on the energy storage SOC and the real-time electricity price, a multi-stage cooperative stability control strategy is triggered through a closed-loop control link, and dynamic control is achieved. Wide-area spatial features of distributed photovoltaic / wind power are extracted through a multi-scale one-dimensional convolution kernel, and the control response speed is increased by combining the spatial-temporal relevance between a dynamic attention mechanism focusing load and renewable energy sources; and the load power and the photovoltaic / wind power output are synchronously predicted by adopting dual-task output, so that the power grid stability and the control real-time performance in a high-proportion renewable energy scene are improved.