809 results about "Risk probability" patented technology

The invention discloses a welded pipe conveying abnormity prediction method and system based on large model reasoning, and aims to solve the problems that multi-source data is difficult to align, cross-station false correlation is caused, prediction lacks executable positioning and time sequence, and linkage control reliability is insufficient. Event alignment is carried out by taking a controller edge signal and an encoder zero position as time anchor points, a production line topology semantic graph containing time delay, capacity and interlocking attributes is constructed, and topology reachability and physical time delay constraints are applied in a self-attention long sequence model to carry out multi-step rolling prediction. And outputting a risk probability, refining the risk probability to spatial positioning of a roller way section or a shaft and the minimum executable intervention time, and generating a risk interval in combination with uncertainty estimation and calibration so as to drive an upstream beat self-adaptive speed reduction, shunting or stopping strategy. The technical effects of improving accuracy and interpretability, reducing false alarm and missing alarm, ensuring that linkage can be executed in advance and meeting edge time delay budget are achieved.

The invention discloses a geological disaster monitoring and early warning method based on multi-factor dynamic coupling, belongs to the technical field of geological disaster monitoring and early warning, and aims to solve the problems that a traditional method cannot fuse multi-source factors in real time, is low in early warning precision, lags in response and the like. A geological environment static background factor is combined to construct a susceptibility evaluation model, a dynamic weight is analyzed and calculated by adopting a time sequence, a dynamic Bayesian network is utilized to carry out coupling analysis, and a geological disaster risk probability value is output in real time, so that a corresponding early warning level and an emergency response are triggered. The method is mainly used for real-time monitoring, accurate risk assessment and timely early warning of geological disasters.

The invention relates to the technical field of power distribution station inspection, and discloses a power distribution station automatic inspection method based on image recognition. According to the method, multi-spectral image data of multiple areas in a power distribution station are collected in real time, and a multi-channel feature tensor of an equipment state is generated through a feature extraction network; performing feature fusion of space and frequency spectrum dimensions on the multichannel feature tensor by using a multi-scale convolutional attention network, and outputting an enhanced device feature map; inputting the data into a cascade anomaly detection module, positioning an equipment surface defect region by adopting a region segmentation algorithm, and analyzing and generating a defect evolution trend vector in combination with time sequence characteristics; on the basis of the vector, probability distribution of equipment fault risks is predicted through a space-time propagation model, and a dynamic risk field is generated; and finally, constructing a self-adaptive early warning decision tree for the dynamic risk field, and generating an inspection maintenance instruction according to risk probability threshold grading. According to the method, automation and intelligentization of power distribution station inspection are realized, and support is provided for efficient maintenance of the power distribution station.

The invention relates to the technical field of financial risk control, and discloses a telecommunication fraud risk identification method based on a bank card transfer scene. The method comprises the following steps: a basic feature construction stage: acquiring multi-source data of historical victim users, extracting multi-dimensional features, and processing the multi-source data into standardized time sequence data through feature coding and a DTW algorithm; in the multi-modal fusion and adversarial learning stage, a core layer containing a multi-modal analysis engine, a risk reasoning model and an adversarial generation model is constructed, and multi-modal feature fusion, risk probability output under an RLHF framework and simulated fraud feature generation driven by WGAN-GP are achieved; in the strategy output stage, risk scores are mapped through a double-layer scoring system, and three-level interception is triggered; in the model management stage, model iteration is achieved by means of a monitoring instrument panel and a rolling time window, the online effect is guaranteed by combining gray release and A / B testing, the telecommunication fraud in the transfer scene can be accurately recognized, and the risk control efficiency is improved.

The invention relates to the technical field of computer vision, in particular to a high-rise facility operation risk monitoring method based on deep learning and point cloud detection, and the method comprises the steps: collecting a three-dimensional point cloud in real time, and extracting a target point cloud through dynamic threshold denoising and template registration; performing joint coding on space geometry and time sequence motion by using a pre-trained space-time diagram network in combination with an attention mechanism; high-reflectivity beacon points are identified, and the change rate of displacement and angular velocity is calculated; constructing a gating fusion model, dynamically weighting and coupling semantic features and measurement data, and generating risk probability distribution through mutual information consistency check; a fuzzy logic classifier with membership degree optimization is used for mapping to four-level early warning, and grading response is triggered; after early warning, a key frame incremental learning fine tuning model is extracted, and preprocessing parameters are reversely optimized to form a closed loop. According to the method, through multi-source heterogeneous data fusion, dynamic adaptive weighting and a self-evolution mechanism, the real-time performance, accuracy and robustness of risk monitoring in a complex construction environment are remarkably improved.

The invention relates to the technical field of transformer substation intelligent inspection, in particular to a meta-learning-based small-sample transformer substation equipment state adaptive inspection system, which comprises a state acquisition module for acquiring the current feature vector and environmental parameter data of a target node; the drift detection module is used for comparing environment parameters to judge data drift and dynamically adjusting a confidence coefficient threshold value; the risk assessment module inputs the feature data into a meta-learning model to output an initial risk probability, and generates an effective risk probability based on threshold filtering; the blind area measurement module is used for acquiring unobserved nodes and calculating system state blind area entropy; the scheduling decision-making module is used for comparing the blind area entropy with a threshold value and generating an entropy reduction bottom instruction or a self-adaptive routing inspection distribution instruction; the strategy updating module is used for extracting an actual inspection result and feeding back to the model for parameter updating; according to the invention, the scheduling difficulty when the resources are limited is solved, and the self-adaptive capability of the system under different environment interferences is improved.

The invention provides a mining area surrounding soil heavy metal spatial distribution inversion method based on hyperspectral data, and the method comprises the steps: processing multi-source monitoring data of mining area surrounding soil, and obtaining a consistent reflectivity data set; performing soil spectrum purification based on the consistent reflectivity data set to obtain a pure soil signal; spectrum key features are screened out from the pure soil signals; obtaining a modeling data set in combination with the spectrum key features and the heavy metal concentration labels so as to construct a multi-task inversion model, and outputting each metal prediction interval and an over-standard risk probability graph; and outputting a multi-layer package based on the model, performing global and local interpretation and mechanism verification, and generating an interpretation report and traceable evidence. According to the method, full-link unification and purification can be achieved, domain deviation and mixed pollution are remarkably reduced, the accuracy and interpretability of feature screening can be considered, and the model generalization ability, prediction accuracy and space credibility are improved.

The invention relates to the technical field of meteorological disaster early warning and emergency management, and discloses a meteorological disaster risk assessment and prevention method based on artificial intelligence, and the method comprises the steps: obtaining multi-source heterogeneous data, carrying out the cleaning, alignment and standardization processing of the multi-source heterogeneous data, and constructing a multi-dimensional feature data set; based on the multi-dimensional feature data set, outputting predicted meteorological elements of the target area in a future preset time period through a meteorological prediction model, extracting interaction features of the predicted meteorological elements and non-meteorological factors from the multi-dimensional feature data set, and inputting the predicted meteorological elements and the interaction features into a long and short term memory-convolutional neural network hybrid model to obtain a long and short term memory-convolutional neural network hybrid model; outputting the meteorological disaster risk probability and risk level of each grid unit in the target area; based on the meteorological disaster risk probability and the risk level, differential prevention instructions for different risk level areas are generated, and the technical problems that in an existing meteorological disaster risk assessment and prevention method, multi-source data integration is difficult, and meteorological prediction precision is insufficient are solved.

The invention discloses a VTE real-time monitoring and intelligent prevention and treatment system, and belongs to the technical field of intelligent prevention and treatment, and the system comprises a baseline construction module which is used for collecting multi-dimensional VTE parameters, calculating the normal fluctuation interval of each parameter to form an initial individualized baseline, and constructing a self-adaptive individualized baseline through threshold calibration; the trend identification module is used for dynamically setting a sliding window duration, calculating a VTE trend slope and a VTE product deviation, constructing a trend constraint and a product deviation constraint, and when the two constraints are not met at the same time and the continuous deviation duration is exceeded, judging that the deviation is continuous abnormal deviation; the time sequence risk prediction module is used for calculating deviation values of various parameters of the target patient, constructing a space-time fusion feature matrix, inputting a time sequence risk prediction model and outputting a VTE risk probability; and the early warning and intervention module is used for performing double judgment and intervention, setting three-level early warning and intervention measures, calculating an improvement rate to verify a prevention and control effect in real time, and realizing VTE early recognition and intelligent prevention and control.

The invention relates to the technical field of computer application, and particularly discloses a chemical industry park safety risk assessment method based on an agent model. The method comprises the following steps: constructing a multi-agent collaborative perception framework, and fusing multi-source heterogeneous data of equipment, environment, materials and personnel to form a park panoramic view under a unified space-time reference; establishing an agent behavior model library embedded with the chemical process mechanism and the safety interlocking logic; an intelligent agent decision strategy is optimized through deep reinforcement learning, and multi-target training is carried out by taking a safety index as a reward function; executing dynamic risk deduction and accident chain simulation in a digital twin environment, and quantifying risk probability and consequences in combination with Monte Carlo sampling; and finally, a visual thermodynamic diagram and an interpretable risk assessment report are generated, weak links are identified, and intervention suggestions are provided. According to the technical scheme, dynamic, accurate and prospective evaluation of the safety risk of the chemical industry park can be realized, and the early warning timeliness and the evaluation reliability are remarkably improved.

The invention discloses an equipment health state monitoring method and system based on the Internet of Things. According to the method, data in a multi-source sensor is obtained and preprocessed, a real-time operation data set of equipment is obtained, and a health quantification deviation value and a health state trend are determined through the real-time operation data set. And when the health state is abnormal, recording a starting point coordinate of an abnormal event, marking an abnormal event triggering timestamp, and analyzing the dependency relationship between the equipment by utilizing graph network modeling. Furthermore, the potential risk probability is analyzed through the long-short-term memory network, real-time monitoring, fault propagation prediction and risk assessment of the equipment health state are achieved, and the operation reliability and the maintenance efficiency of the industrial equipment are improved.

The invention discloses an urban intelligent water affair risk dynamic identification and early warning method based on deep learning, and the method comprises the following steps: S1, collecting the water pressure, flow, residual chlorine concentration, elevation, rainfall, valve state, pump station state and accident label of each node in a water supply network, and constructing a time alignment data sequence; s2, constructing a dynamic adjacency matrix according to the pipe network connection relation and the event state information; s3, inputting the data sequence and the dynamic adjacency matrix into an improved space-time diagram wavelet neural network to generate space-time feature representation; s4, multi-scale features are extracted and fused through the high-frequency branches and the low-frequency branches; s5, constructing a hyperedge set, executing graph structure propagation, and generating a risk representation tensor; s6, inputting the risk representation tensor into the risk prediction network, and outputting a node risk probability and a confidence interval; and S7, determining a risk level according to the risk probability and the confidence interval, and generating a corresponding early warning signal. According to the invention, fine modeling and dynamic early warning of urban water supply risks are realized.

The invention relates to a geological disaster early warning algorithm based on hyperspectral and Internet of Things data fusion analysis, and relates to the technical field of geological disaster monitoring and early warning, the geological disaster early warning algorithm comprises the following steps: S1, preprocessing hyperspectral data and Internet of Things data, and respectively extracting spectral-spatial features and dynamic topology time sequence features; s2, fusing the spectrum-space features and the time sequence features to generate cross-modal joint features; s3, performing parameter optimization on the fusion features through a quantum-classical hybrid optimization algorithm, and calculating a disaster risk probability; and S4, based on the optimization result and the risk probability, executing an edge-cloud collaborative early warning decision. According to the method, through non-negative tensor ring decomposition of the hyperspectral data and dynamic topology modeling of the Internet of Things, the limitation of a traditional single data source in temporal-spatial resolution and physical relevance is solved, multi-dimensional joint extraction of spectrum-space-mechanical characteristics can be realized, and the characterization precision of a rock-soil body deformation evolution law is remarkably enhanced.

The invention discloses a venous thrombosis risk assessment method based on a large language model, and relates to the technical field of medical artificial intelligence, and the method comprises the steps: collecting thoracic surgery diagnosis and treatment data of a patient, carrying out the space-time alignment, generating a standard diagnosis and treatment data flow, and carrying out the homomorphic encryption of the standard diagnosis and treatment data flow, and forming an encrypted patient data package; inputting the encrypted patient data packet into a multi-task large language model, performing feature extraction and semantic coding by a feature coding layer, performing time sequence modeling and risk probability calculation by a risk quantification layer, and outputting a venous thromboembolism risk level of a patient; and performing feature decoupling and potential space mapping on the encrypted patient data packet to obtain thrombus semantic potential features. Through the multi-task large language model, the dual machine learning algorithm and the homomorphic encryption, the accuracy of venous thrombosis risk early warning is improved, and the safety of the risk assessment process is enhanced.

The invention discloses a rainfall type landslide early warning method based on multi-source environment threshold and nonlinear fusion. The method comprises the following steps: constructing a landslide disaster-inducing factor set; introducing soil humidity, vegetation indexes and evapotranspiration, and calculating a threshold parameter corresponding to the rainfall type landslide event to obtain an environment threshold point set; according to the landslide disaster-inducing factor set, constructing and training a landslide susceptibility model based on progressive learning; adopting a double-layer nonlinear fusion model based on multi-source data fusion and quality perception gating, and combining a landslide susceptibility model to obtain comprehensive risk probability distribution; and performing graded early warning based on the comprehensive risk probability distribution to complete rainfall type landslide early warning. According to the method, on the basis of traditional rainfall parameters, early-stage disaster-pregnant environment factors are introduced, and a multi-source environment threshold value is formed; fuzzy logic reasoning is adopted to realize nonlinear fusion of the threshold information and the landslide susceptibility base map; and dynamically reflecting a regional environmental condition evolution process in combination with an annual iterative updated susceptibility layer, and realizing finer and more adaptive space grading early warning.

The invention discloses a real-time emergency prevention and control system and method for an electrochemical energy storage system based on digital twinning, and belongs to the field of energy storage safety. The system comprises a multi-source sensing unit, a digital twin deduction unit and a visual emergency decision unit. The sensing unit collects multi-source data to form a state vector; the deduction unit outputs a future thermal runaway evolution path containing a temperature field, a gas concentration field and a fire blast risk probability and uncertainty measurement thereof in real time through a pre-trained Gaussian process regression agent model; and the decision-making unit performs three-dimensional visual rendering, jointly determines a high-confidence risk area based on the risk level and the uncertainty level, and generates an emergency prevention and control instruction with accurate spatial positioning. The problems that in the prior art, disaster evolution paths cannot be predicted in real time, and prevention and control measures are extensive are solved, millisecond-level online deduction and accurate active prevention and control of the thermal runaway process are achieved, and the safety of an energy storage system is remarkably improved.

The invention discloses a customer information updating and management method and system based on big data, and relates to the technical field of big data and customer relationship management. Comprising the following steps: firstly, collecting multi-source customer data and constructing a multi-dimensional time sequence characteristic panel; then, through sharing an integrated model of a bottom layer, synchronously predicting a dynamic life cycle value and a continuous loss risk probability of the customer; the network influence weight of the customer is quantified by using an influence penetration model and Monte Carlo simulation; further, fusing the three dimensions, and generating a resource allocation coefficient through a multi-objective optimization function based on a customer type dynamic configuration weight; and finally, automatically updating the customer label based on the coefficient, and combining atomic operations through a strategy arrangement engine to form personalized strategy workflow execution and closed-loop optimization. According to the method, the problems of static isolation and strategy lag of traditional customer information management are solved, and accurate insight of customer values and dynamic intelligent distribution of management resources are realized.

The invention discloses a mild cognitive impairment screening and diagnosis system based on deep learning, and relates to the technical field of cognitive impairment screening and diagnosis, and the system comprises a data collection and extraction module which is used for collecting voice data and a digital drawing track of a subject; the low-frequency window determination module is used for determining a low-frequency window according to the voice envelope and the handwriting speed; the lag index calculation module is used for obtaining a speech writing low-frequency lag index based on the phase relation and the correlation intensity; the disturbance quantity extraction module is used for extracting a listening disturbance quantity and a motion disturbance quantity; the depolarization residual calculation module is used for calculating the depolarization residual of the speech writing low-frequency lag index; and the risk calculation and grading module is used for calculating the risk probability and determining a grading result. According to the method, the interference of non-cognitive factors on mild cognitive impairment screening can be eliminated, and the screening accuracy is improved.

The invention discloses an intelligent pipe network leakage active prediction and early warning system based on multi-technology fusion, and the system comprises a multi-source heterogeneous data fusion collection module, a spatial-temporal feature depth extraction module, a degradation trend prediction and residual life evaluation module, and a multi-stage early warning and decision generation module. The multi-source heterogeneous data fusion acquisition module acquires and fuses ultrasonic guided wave signals, pressure flow time sequence data and environmental factor data; the spatial-temporal feature depth extraction module extracts spatial-temporal fusion features through continuous wavelet transform and a CNN-LSTM hybrid network; the degradation trend prediction and residual life evaluation module determines a degradation level, predicts residual life and quantifies a pipe explosion risk probability; the multi-stage early warning and decision generation module generates graded early warning signals and maintenance strategy suggestions, the technology crossing from post-event detection to pre-event prediction is realized, and the scientificity and refinement level of operation and maintenance management of a pipe network are effectively improved.

The invention discloses a bridge structure risk safety identification method based on artificial intelligence, and relates to the technical field of bridge structure risk identification, and the method comprises the steps: building a topological graph model which reflects the geometric and mechanical connection relation of a bridge, taking piers, main beam sections and supports as graph nodes, and taking the physical connection between components as graph edges; structure response signals and environment load parameters in the service period of the bridge are collected, and the collected data are synchronized according to time and then mapped to corresponding nodes and edges in the topological graph model; based on the mapped data and a topological graph model, establishing a graph neural network embedded with structural dynamic constraints, and outputting the risk probability of a component corresponding to each node by jointly optimizing the consistency of a monitoring data fitting error and a physical rule; and performing causal relationship analysis on the structural response signal and the environmental load parameter, identifying a causal path between environmental interference and structural abnormality, and separating an abnormal component caused by structural degradation from the original response according to an identification result.

The invention provides a mineral resource overburden risk assessment method and system, and the method comprises the steps: obtaining multi-source heterogeneous geological data, carrying out the deep fusion of the multi-source heterogeneous geological data through employing a game theory gradient control robust neural network training method, and obtaining a fusion data set; performing deep mining and reasoning on high-dimensional geological data by utilizing the pattern recognition and correlation analysis capability of a large model and combining a manifold perception regularization technology, and recognizing geological structure features; establishing a dynamic safety mining depth evaluation model by using a fractional peak differential equation neural network with efficient accompanying parameter training, and calculating safety mining depth intervals and risk probabilities under different confidence degrees by integrating geological structure risks, mining disturbance effects and engineering safety thresholds; and constructing an assessment report generation module including core risk point analysis, key technology demonstration and prospective risk early warning, and generating an intelligent overburden risk assessment report. According to the invention, the accuracy and reliability of the pressing and covering risk assessment can be improved.

The invention discloses a vision-based industrial safety production full-cycle monitoring method and system, and belongs to the technical field of industrial safety, and the method comprises the steps: collecting the visual state data and interaction information of a production scene entity in real time, and generating an initial risk conduction link in combination with a pre-constructed working condition rhythm safety risk topological map; based on the initial risk conduction link, adopting a hidden Markov algorithm, a forward diffusion model and a Bayesian model to respectively obtain forward and reverse risk path sequences; inputting the forward and reverse risk path sequences into a risk monitoring map, and outputting risk probability, type and path characteristics through analogue simulation and risk threshold judgment; positioning a core risk node according to the path characteristics, and calling a matched initial prevention and control strategy; generating a secondary prevention and control strategy through an analogue simulation strategy effect, and carrying out iterative optimization until the risk probability is reduced to a safety threshold value; according to the invention, dynamic perception and accurate traceability of industrial production risks are realized, and the real-time performance and reliability of safety production monitoring are improved.

The invention discloses a BIM-based deep foundation pit collapse risk probability assessment method. The method relates to the technical field of risk assessment, and comprises the following steps of multi-failure mode coupling failure probability modeling, coupling risk dynamic regulation and control, and construction stage dynamic risk probability and dynamic risk closed-loop control and visualization. According to the method, the BIM model is constructed, the failure probability of multi-failure mode coupling is calculated, and whether chain type damage risk dynamic regulation and control are triggered or not is judged; calculating a dynamic risk probability value in combination with a construction monitoring data updating model, and judging whether self-adaptive closed-loop intervention is triggered or not; according to the method, a visual risk module is constructed in a BIM model, risk grading early warning and accurate management and control are realized, the deep foundation pit collapse risk probability assessment accuracy is improved, and the problems of model distortion caused by insufficient coupling mechanism cognition, flow stiffness caused by data flow-model flow-decision flow mutual separation, and risk assessment accuracy caused by deep foundation pit collapse risk probability assessment in the prior art are solved. And the deep foundation pit collapse risk probability assessment accuracy is low.

The invention provides a cooperative control method and device for a virtual power plant to participate in voltage safety of a power distribution network, and the method comprises the steps: collecting multi-source heterogeneous data, and carrying out the preprocessing of the data, and constructing a key feature set; inputting the key feature set into a trained artificial intelligence prediction model to output a dynamic prediction value; inputting the dynamic predicted value, the real-time operation data of the power distribution network and the available regulation capacity of the virtual power plant into a trained machine learning evaluation model, obtaining the voltage out-of-limit risk probability of each node of the power distribution network, and dividing the voltage safety risk level according to the probability; constructing a comprehensive cost index based on the grade, and solving to generate a cooperative control instruction set by using a reinforcement learning algorithm under the condition that constraints are met; and acquiring an actual operation response after the instruction is executed, and adjusting internal parameters of the model based on a deviation between the actual operation response and an expected target. Based on the method, the invention further provides cooperative control equipment. According to the invention, closed-loop management from data-driven prediction and intelligent risk assessment to collaborative optimization control is realized.

The invention relates to the technical field of mine safety monitoring and early warning, and provides a non-coal mine risk situation deduction system based on digital twinning and reinforcement learning, which comprises at least two twinning spaces; wherein the first twinborn space is used for building a mine digital twinborn body mapped by the current mine entity, and the second twinborn space is used for mapping a target dynamic event containing at least one risk factor in dynamic update of the mine digital twinborn body; the at least one reinforcement learning agent generates a third twinborn deduction model used for predicting the risk evolution trend of the mine; the risk early warning module is used for receiving the real-time multi-source data, determining a target dynamic event in the real-time multi-source data through the first twinborn space, judging whether the target dynamic event has a risk factor through the second twinborn space, and if the risk factor exists in the target dynamic event, performing early warning on the target dynamic event; and generating deduction data of the target dynamic event through a third deduction model, and determining a risk area and a risk probability.

The invention discloses a road and bridge support hidden danger monitoring and early warning method and system, and belongs to the technical field of road and bridge support hidden danger intelligent monitoring. Constructing a theoretical transfer relation model based on the synchronous vehicle load parameters and the synchronous support mechanical response parameters; generating a modified theoretical model based on the synchronous environment temperature parameters and the theoretical transfer relation model; obtaining a response residual error based on the corrected theoretical model, the synchronous vehicle load parameter and the synchronous support mechanical response parameter; acquiring a hidden danger characteristic mode based on the response residual error and the synchronous structure attitude parameter; acquiring a risk probability value based on the hidden danger feature mode; and obtaining graded early warning information based on the risk probability value and the hidden danger characteristic mode. According to the road and bridge support hidden danger monitoring and early warning method and system disclosed by the invention, the problems of false alarm and missing alarm in the prior art can be solved, and the accuracy and reliability of road and bridge support hidden danger monitoring are greatly improved.

The invention relates to the technical field of geotechnical engineering and geological disaster monitoring, in particular to a rockburst tendency dynamic discrimination method, and aims to solve the limitation caused by the fact that a dynamic evolution process is simplified into a quasi-static attribute in traditional rockburst tendency evaluation. According to the method, rockburst tendency is defined as a hidden state variable evolved along with time, a physical constraint state space model containing a hidden state kinetic equation and a multi-modal observation equation is constructed, continuous stress-strain and discrete acoustic emission data are fused, hidden state probability distribution is estimated in real time through online Bayesian inference, and the probability distribution of the hidden state is estimated in real time. And in combination with adaptive information weight updating and physical consistency constraint, predicting a future state trajectory, calculating a risk probability exceeding a critical threshold, and outputting a dynamic rockburst tendency level. According to the scheme, continuous, dynamic and prospective judgment of the rockburst tendency is realized, and the accuracy and reliability of early warning are improved.

The invention discloses a charging pile thermal runaway intelligent protection method and system based on edge calculation, and relates to the technical field of charging pile thermal runaway protection. Comprising the following steps: S1, collecting thermal runaway multi-mode sensing data in real time, and carrying out data preprocessing; a charging pile thermal runaway multi-mode abnormal state is judged, and an abnormal feature data packet is generated; s2, multi-modal abnormal feature vectors are constructed, the thermal runaway risk probability is evaluated, and thermal runaway risk grading protection of the charging pile is carried out; s3, the response effect of thermal runaway risk grading protection is quantified, and thermal runaway risk grading protection is adjusted; and S4, monitoring and feeding back the thermal runaway risk, and optimizing algorithm parameters and a thermal runaway risk grading protection strategy. The problems that an existing charging pile thermal runaway protection system is difficult to adapt to complex working conditions, multiple in false alarm and missing alarm, lagging in response and insufficient in protocol compatibility and data stability, and consequently the thermal runaway risk is difficult to recognize and protect timely and accurately are solved.

The invention relates to the technical field of geotechnical engineering safety evaluation, and discloses a collapsible water sensitivity index evaluation method based on loess engineering. The method comprises the following steps: acquiring time sequence data of multiple monitoring points of a loess engineering site to form a fusion feature vector; driving the collapsibility mode discrimination model, separating steady-state and transient collapsibility mode features from the collapsibility mode discrimination model, generating a water sensitivity evolution graph according to the features, and constructing a logic decision tree; and performing incremental dynamic coupling analysis, matching the decision tree path with a collapsing development path in a historical engineering case library, calculating a path similarity weight, and performing weighted correction on the water sensitivity evolution graph according to the path similarity weight to obtain a corrected graph. And deducing collapse risk probability distribution under a plurality of time nodes in the future based on the corrected map. Integrating the atlas, the decision tree path and the risk distribution to form a final loess engineering collapsibility water sensitivity evaluation report. According to the invention, mechanism separation and dynamic intelligent prediction of the collapsing process are realized.

The invention relates to the technical field of network public opinion monitoring, and discloses an emotion energy dynamic change-based public opinion inversion recognition method, which comprises the following steps of: constructing a weighted emotion potential energy field, and calculating effective emotion potential energy of nodes; and then calculating semantic field topological stress representing the viewpoint structure tearing degree and emotional potential energy field system entropy representing the energy distribution disorder degree, on this basis, constructing an entropy-stress two-phase state space and a dynamic hysteresis loop model, quantifying the social damping characteristics of the system, monitoring the system state in real time, and determining the state of the system. When the two-phase space yield judgment condition and the damping failure judgment condition are met at the same time, a public opinion inversion early warning signal and a risk probability index are output, the critical characteristics of public opinion system instability can be accurately recognized from the dimensions of structural mechanics and dynamics, and the false alarm rate of inversion early warning is effectively reduced.