2594 results about "Data collecting" patented technology

The invention discloses a network security analysis early warning system based on artificial intelligence, and the system comprises a data collection layer which captures full flow based on DPI, aggregates firewall logs, terminal behaviors and threat intelligence, and constructs a structured data pool; through TLS fingerprint identification of AI driving, the encrypted traffic is penetrated, and a sampling strategy is dynamically adjusted in combination with reinforcement learning. The intelligent analysis layer is used for carrying out cross validation on known threats and abnormal behaviors; the time sequence CNN extracts encrypted traffic features, and a novel threat detector is rapidly generated by using historical attack fragments in combination with a meta-learning framework; sHAP value driving dynamic feature selection and optimization feature vector input; the decision-making early warning layer is used for fusing multi-source features through a Bayesian network and generating 0-100 score risk scores; a self-adaptive threshold module is combined to adjust a score threshold in real time, and a high-risk event is pushed; the collaborative response layer is used for triggering a preset decision tree, deploying a GAN dynamic honeypot to trap an attacker and reversely tracing; the Neo4j visually restores the attack path, and blocking is executed after the threat is confirmed by a progressive response mechanism.

The present invention belongs to the technical field of digital mine safety, and particularly relates to a blasting area surface morphology inversion method based on an unmanned aerial vehicle. The method comprises: S01, on-site data collection; S02, blasting area morphology inversion; S03, a muck pile throw distance; and S04, a muck pile surface fragmentation distribution. In the present invention, oblique photography by an unmanned aerial vehicle is used, and three-dimensional model reconstruction is performed by capturing a blasting area image, so that a feasible aerial survey scheme is formulated, and the integrity of collected data is good; and reverse modeling of the blasting area is performed by means of the steps of feature point extraction, spatial information conversion, point cloud generation, grid generation, etc.; voxel grid downsampling, point cloud matching and error detection are used to perform registration on point clouds of a target area before and after blasting, so that a good effect is achieved; and a color-based region growing algorithm is used to perform coarse segmentation of point cloud features on an ore-rock block on the surface of a muck pile, and a PointNet++ algorithm is used to perform fine segmentation of point cloud features on the ore-rock block on the surface of the muck pile, so that the muck pile throw distance and the muck pile surface fragmentation distribution are calculated.

The invention discloses an intelligent management system for nuclear power plant personnel situation prediction and risk assessment, and relates to the field of intelligent safety management systems, and the system comprises a data collection unit, a multi-dimensional situation awareness unit, a risk prediction and assessment unit, an intelligent decision intervention unit and a visual interaction unit. And multi-source data acquisition, real-time situation construction, dynamic risk prediction and evaluation, intelligent early warning intervention and information visualization are realized. According to the invention, real-time monitoring, dynamic risk prediction and intelligent management of the safety state of the operating personnel can be realized, and the defects of real-time monitoring, dynamic prediction and intelligent management of the operating personnel in a high-risk area in the prior art are overcome, so that the safety management level is improved, the life safety is guaranteed, and the accident occurrence probability is reduced.

The invention discloses a power distribution room anomaly detection system based on cloud side-end cooperation, and belongs to the technical field of intelligent power grids. In order to solve the problems of high network bandwidth pressure, insufficient edge computing capability, low anomaly detection accuracy, difficulty in multi-source data fusion and the like caused by the adoption of an end-cloud direct connection architecture in an existing power distribution room monitoring system, the system comprises: a data acquisition layer configured with various heterogeneous sensors to acquire operating parameters and environmental data in real time; the edge storage and calculation layer carries out local real-time processing, anomaly detection, model training and visual display, an anomaly detection module of the edge storage and calculation layer carries out research and judgment on real-time data to generate early warning information, and a prediction and detection linkage module monitors an anomaly probability trend and adjusts a sampling frequency; the edge gateway realizes protocol conversion and data forwarding; and the cloud decision-making layer aggregates multi-edge node data, optimizes a global model through federal learning, and issues and updates a local model. The system is used for improving the accuracy, real-time performance and reliability of anomaly detection of the power distribution room, reducing the operation and maintenance cost and realizing intelligent operation and maintenance.

The invention relates to the technical field of airport safety monitoring, and discloses an airport video data real-time analysis system. The system comprises a video stream spatial-temporal feature modeling module, a behavior trajectory map construction module, an abnormal region association analysis module, a risk level semantic judgment module and a situation structure visualization module. According to the method, multi-scale spatial-temporal feature analysis is carried out on an airport monitoring video stream, a multi-dimensional behavior trajectory map is established, abnormal behavior region association is analyzed, risk level semantics are judged, and finally an airport global risk situation thermodynamic distribution map is generated. According to the system, the whole process processing from video data acquisition to risk situation visualization is realized, the abnormal behavior area can be accurately identified, the risk level and category are clear, comprehensive and visual situation information is provided for airport safety management, and the intelligent level of airport safety management is improved.

The invention provides a block chain-based electronic evidence full-life-cycle evidence storage and tracing method and system, and relates to the technical field of block chains. The method comprises the steps of metadata collection, initial evidence storage, incremental evidence storage, operation chain construction, traceability request analysis, target block positioning, intelligent contract verification and the like, and through Byzantine consensus, a Merkel tree structure and judicial node participation, credible recording on a whole process chain of evidence from generation to verification is achieved. Therefore, the electronic evidence has the capabilities of right confirmation, tampering prevention, traceability and judicial credit collection.

Disclosed are a three-dimensional laser point cloud-based method and system for in-depth vegetation management in transmission corridors. The method comprises: collecting multi-source data by means of an unmanned aerial vehicle- and helicopter-mounted multi-sensor system, and preprocessing the multi-source data; using the processed multi-source data to create a digitalized power grid corridor and construct a vegetation management analysis model; on the basis of data analyzed by the vegetation management analysis model and geofencing technology, designing a hazard vegetation clearing-cost-compensation prediction model; and developing a mobile application for analyzing hazard vegetation data flows and the closed-loop management of vegetation management service flows. The described method achieves high-precision and high-coverage data collection, precisely identifies transmission line digital twins and hazard vegetation, standardizes and automates the management of hazard vegetation clearing and related compensation, and develops a mobile application for analyzing hazard vegetation data flows and the closed-loop management of vegetation management service flows, thereby achieving the end-to-end digital and mobile management of vegetation management. The organic combination of steps comprehensively improves the intelligence and efficiency of transmission line vegetation management.

The invention relates to the technical field of chemical industry park risk control, and discloses a chemical industry park multi-source risk coupling evaluation system and method. A data acquisition and preprocessing unit of the system acquires and processes dangerous source state, environment and emergency resource information in real time in a standardized manner; the multi-source risk coupling evaluation model simulates risk evolution based on a coupling mechanism and outputs a quantitative index; the dynamic risk situation assessment unit calculates risk space-time propagation in combination with real-time meteorological geographic information to generate a dynamic map; the risk intention deduction unit deduces a risk evolution trend in combination with a historical accident scene library, and identifies short-term and long-term risks; the emergency strategy generation unit is combined with the emergency resource layout to generate a multi-level emergency response strategy; and the visual interaction unit comprehensively displays the dynamic graph and the emergency strategy. According to the system, accurate assessment and dynamic management and control of multi-source risks are realized, and the risk management and control and emergency response capabilities of the park are improved.

The invention discloses a rubber product quality detection method and system, and relates to the related technical field of rubber quality detection.The method comprises the steps that rubber products are conveyed to a target detection table, and data acquisition of the rubber products is executed; inputting the wave impedance distribution data and the surface heat distribution gradient field into a defect causal atlas channel to execute phase inversion fusion processing, establishing a cross-modal coupling phase tensor, and identifying deep defect response characteristics by solving a phase difference matrix; activating a pressure spectrum sensing unit to apply control pressure, reading a local mechanical response spectrum, sending the local mechanical response spectrum to a cross-modal calibration layer, and executing joint error authentication; and performing quality detection report according to a self-calibration defect mapping result. The technical problems that in the prior art, multi-modal sensing data fusion efficiency is low, deep defects are difficult to comprehensively recognize, and consequently the quality detection precision and reliability of rubber products are insufficient are solved, and the technical effects that cross-modal cooperative detection is achieved, and the precision and reliability of defect recognition results are improved are achieved.

The invention relates to the technical field of water conservancy management, and discloses a water conservancy dynamic decision-making method and system based on digital twinning and multi-source heterogeneous data, and the method comprises the steps: carrying out the real-time information capturing of a target water conservancy system, and generating a system operation information scheduling matrix based on a grid geographic model and a time dislocation mark; performing hierarchical slicing on the system operation information scheduling matrix according to a multi-scale time window to form a multi-channel flow framework of regional hydrological dynamic behaviors; analyzing the evolution trends of the water level, the flow and the water quality by using a multi-channel flowing framework and a dynamic fusion analysis operator; dividing a river reach, a reservoir and a regulation and control facility corresponding to the potential risk event as candidate scheduling areas; and based on the local risk coupling network, dynamically optimizing a scheduling strategy, a pump gate operation sequence and a data acquisition frequency, and generating a real-time scheduling and emergency response scheme of the candidate region. The method has the advantage of improving flood early warning accuracy and water supply scheduling efficiency.

The invention discloses a cold region tunnel freeze injury diagnosis, risk grading and control system and method, and relates to the technical field of tunnel engineering. The system comprises a basic data layer for storing disease, index and measure databases; the core analysis layer is used for diagnosing a frost heaving mechanism, identifying single-factor, double-factor and three-factor frost heaving types, calculating freezing depth and frost heaving force and analyzing sensitivity; the risk level evaluation layer is used for dividing a disease zone, a frost heaving level, a freezing injury risk and a freezing injury risk response level; and the intelligent decision-making layer constructs an intelligent comprehensive decision-making analysis platform to realize risk-measure accurate matching and closed-loop management and control. The method comprises the four steps of data acquisition and storage, frost heaving mechanism diagnosis and quantification, risk grade evaluation and intelligent decision measure matching, through multi-source data fusion, multi-factor coupling analysis and zoning and grading prevention and control, the whole-process accurate management and control of the cold region tunnel frost damage is realized, and the frost damage treatment efficiency and the tunnel operation safety are improved.

The invention provides an intelligent detection method and system for the tensile property of a plastic cable tie. Gradient temperature rise control is executed through the carbon fiber heating tube array, microscopic deformation fluctuation characteristics are captured through the double-grating displacement sensor, and temperature field distribution is collected through the infrared thermal imager. Establishing dynamic correlation between deformation and a temperature field to generate a three-dimensional space-time atlas, identifying a migration path of a thermal aggregation area, recording a standard path, and calculating a thermal anomaly coefficient in combination with pressure data; analyzing temperature conduction hysteresis to generate a heat conduction parameter, and dynamically correcting a threshold value by cross-modal correlation with an abnormal coefficient; a structure degradation index is generated based on the migration path, mapping between the structure degradation index and a temperature field is established to generate a thermal damage compensation coefficient, an abnormal coefficient is calibrated accordingly, and grading early warning is triggered in combination with the path deviation degree. According to the technical scheme provided by the invention, multi-scale damage evaluation from thermodynamic associated data acquisition to molecular chain fracture inversion is realized, and the high-temperature tensile failure critical point is accurately pre-judged through a dynamic calibration early warning mechanism.

The invention discloses an agricultural decision reasoning large model based on a large language model and a construction method thereof. The method comprises the following steps: constructing a space-ground-air integrated multi-modal agricultural data acquisition and fusion module, integrating data of a ground sensor, unmanned aerial vehicle remote sensing, meteorological sensing and Internet of Things, solving the problem of data isomerism through reinforcement learning and a Transform architecture, and ensuring real-time integrity of the data. In combination with crop simulation model mechanisms such as DSSAT and the like, the inference rule base is dynamically updated to realize rapid iteration of agricultural knowledge; an agricultural feature extraction module and an adaptation layer are integrated, so that professional data understanding is enhanced; a multi-stage training strategy is adopted, and large-scale agricultural text fine tuning is carried out; reinforcement learning is introduced and expert feedback is combined to optimize decision generation, and a causal inference module is innovatively added to improve the inference precision. The model fuses the language ability of a large language model and agricultural knowledge, can accurately predict and diagnose agricultural problems and provide decision suggestions, and significantly improves the intelligence, precision and high efficiency level of agricultural production.

The invention relates to the technical field of water supply pipeline detection, and discloses an underground water supply pipeline health grade evaluation and risk prediction method, which comprises the following steps: sensor arrangement: arranging a flow sensor, a pressure sensor and a sonic sensor at key positions of an underground water supply pipeline; and data acquisition: acquiring signals of the sensor in real time through a data acquisition module, wherein the signals comprise flow, pressure and sound wave signals. Preprocessing the data: carrying out preprocessing such as denoising and normalization on the collected signals; and multi-source data fusion: inputting flow, pressure and sound wave signals into a deep neural network model, and performing feature extraction and fusion analysis. And health level assessment: assessing the health level of the pipeline based on the output result of the deep neural network model. And risk prediction: predicting abnormal working conditions possibly occurring in the future and risk levels of the abnormal working conditions by analyzing the current pipeline state and historical data. And abnormal positioning: accurately positioning an abnormal position in combination with the propagation time of the sensor signal and a positioning model of the deep neural network.

The invention relates to the technical field of image recognition, and discloses a carotid plaque stability and stroke risk prediction system fusing multiple modes, and the system comprises a data collection and fusion module, a feature extraction and association module, a risk assessment and layering module, an intervention decision module, and a report generation and feedback module. When carotid plaque stability and cerebral apoplexy risk prediction is carried out, plasma proteomics, radiomics and clinical data are synergistically integrated by establishing a multi-modal data fusion analysis framework, so that the limitation that a traditional method depends on a single data source is overcome; the plaque risk can be evaluated from multiple dimensions of biological activity and morphological features, the comprehensiveness and accuracy of risk prediction are improved, a more reliable diagnosis basis is provided for clinic, interaction and sensitivity influence among different modal features can be adaptively quantified by introducing dynamic feature correlation modeling and a real-time weight calibration mechanism, and the risk prediction accuracy is improved. And objectivity and consistency of risk assessment results are ensured.

The invention provides a complex data blood relationship-oriented intelligent tracking method and system, and relates to the field of data management, and the method comprises the steps: collecting a data flow record to generate a data fingerprint identifier, setting a data collection point to obtain a data flow segment, and calculating a monitoring feature hash value; and matching the monitoring feature hash value with the fingerprint identifier to determine a blood relationship link set, calculating a link association degree to identify a complete / partial blood relationship so as to construct a multi-level blood relationship map, and detecting data quality abnormality and tracing a propagation path in combination with historical state information. According to the invention, accurate tracking and abnormal traceability of the data blood relationship in a complex environment are realized.

The invention discloses a sewage data management system and method based on kitchen garbage treatment, and relates to the technical field of data management. The system comprises a data acquisition and historical database construction module, a correlation analysis and dynamic response rule construction module, a sewage data trend pre-judgment module and an operation regulation and data updating module. The data acquisition and historical database construction module is used for acquiring garbage treatment load and sewage data and constructing a historical database covering time period fluctuation and seasonal periods; the correlation analysis and dynamic response rule construction module constructs a dynamic response rule and determines the time delay duration; the sewage data trend pre-judging module is used for acquiring load data in real time and pre-judging a future sewage change trend; and the operation regulation and data updating module adjusts parameters of the sewage treatment system, collects effluent data to construct a new sample, supplements a database and updates a dynamic response rule. According to the invention, accurate sewage data management is realized, and the operation efficiency and stability of the sewage treatment system are improved.

The invention discloses an intelligent wheat yield prediction method and system, and relates to the technical field of agricultural information. According to the method, basic geography, climate, soil, crop physiology and agricultural management multi-source data are collected, and an input feature set is obtained through preprocessing and feature engineering; constructing an Attention-LSTM-CNN model fused with an improved attention mechanism, extracting local features through CNN, capturing time sequence association through LSTM, highlighting key contribution through an attention layer, and finishing model training in combination with an RMSE loss function and an Adam optimizer; and dynamically updating data and a prediction result in a wheat growth cycle, and outputting and triggering early warning in multiple forms. The system correspondingly comprises a data acquisition module, a data storage module, a data preprocessing module, a model calculation module, a prediction output and early warning module and a communication module. The method solves the problems that a traditional method is single in data, poor in model adaptability and lack of dynamic prediction, high-precision full-period prediction is achieved, and scientific support is provided for agricultural decision making.

The invention relates to an intrusion detection method based on cross-domain security management and a shared behavior model. The method comprises the following steps: acquiring multi-dimensional original data according to a preset cross-domain data acquisition rule and multi-domain node deployment; performing compliance, integrity and format matching degree verification on the original data, shielding sensitive information by using a dynamic desensitization technology based on a verification result, converting a heterogeneous data format, filtering missing field abnormal data, and obtaining compliance data; the method comprises the following steps: extracting a multi-dimensional feature set of user cross-domain access, constructing a shared behavior feature vector through weighted calculation, constructing a reference behavior model library in combination with a cross-domain security policy, and screening out intrusion behaviors and feature deviation data through feature comparison and behavior deviation calculation; according to the method, cross-domain security audit logs are fused for correlation analysis, intrusion behavior types and risk levels are judged by means of a Bayesian network model, differential security response strategies are generated and executed, and cross-domain intrusion detection and protection are achieved.

The invention provides a multi-source geophysical prospecting data fused Zenzenite rock mass contact zone anomaly method and system, and relates to the technical field of geophysical prospecting, the method comprises the following steps: executing joint denoising processing and time-frequency feature enhancement processing on a TFEM data set in a three-dimensional fused data volume, and outputting the optimized three-dimensional fused data volume; and based on the optimized three-dimensional fusion data body, by establishing a mapping relationship between magnetic, electric and polarization anomalies and a geologic body, quantifying the correlation between a multi-parameter combination and a mineralization index and screening an up-to-standard abnormal region, and finally delineating a magnetic anomaly gradient zone, a low-resistance region and a high-polarization anomaly co-occurrence ore prospecting target region three-dimensional distribution diagram. Through multi-source geophysical prospecting method collaboration, data deep fusion, quality optimization and accurate mapping, full-chain technology upgrading from data acquisition to target area delineation is realized, and the high efficiency of mineral exploration is improved.

The invention relates to the technical field of environment intelligent supervision, and discloses a visual river and lake water area health ecological supervision and management system. A data acquisition and feature extraction module of the system is responsible for acquiring multi-source ecological monitoring data of water areas of rivers and lakes, extracting spatial change features of motion trails of aquatic organisms and a time sequence of water quality parameters from the data, and identifying abnormal features according to an index variation mode corresponding to a predefined ecological health level; and generating an ecological health abnormity response label set associated with the specific geographic position. And the health state analysis module obtains key information of the marked water area section based on the label set, analyzes a change track and a deviation interval of the health state in the supervision time frame, and determines a health state lagging time period. And the evaluation delay processing module is used for extracting related time information to calculate a time difference according to the water area subareas related to the lagging time period, screening out the water area subareas which are not evaluated completely and sorting the water area subareas, and generating an adjustable configuration scheme of the monitoring time period of the water area subareas.

The invention discloses a Beidou-based electric power tower attitude change on-line monitoring method, which comprises the following steps: A, setting a tower attitude acquisition system, and respectively acquiring multi-source data of a tower in real time by using the tower attitude acquisition system; b, constructing a meteorological database; c, constructing a tower attitude analysis model; the tower attitude analysis model comprises a data acquisition and preprocessing module, a multi-source data fusion module, a comprehensive analysis module, a situation diagnosis and risk assessment module, an intelligent prediction module, an early warning decision module and a visualization and feedback module; and D, on the basis of the trained constructed tower attitude analysis model, acquiring multi-source data of the tower in real time by using a tower attitude acquisition system, and performing online monitoring on the attitude change of the power tower in combination with a meteorological database. According to the invention, the posture change of the power tower can be obtained more accurately, and further prediction and early warning are carried out on the posture change of the power tower in combination with historical and predicted environmental data.

The invention relates to the field of traditional Chinese medicine diagnosis, and discloses a traditional Chinese medicine intelligent diagnosis system based on multi-source data fusion and an AI technology, and the system comprises a tongue picture collection and classification unit which is used for collecting tongue picture data of a patient through imaging equipment, carrying out the feature extraction of the tongue picture data, and obtaining a tongue picture feature data set; performing tongue picture pathological mode classification on the tongue picture feature data set based on a deep convolutional neural network to obtain tongue diagnosis identification result data; and the face diagnosis feature correlation analysis unit is used for inputting the tongue diagnosis identification result data into a multi-modal data fusion engine and carrying out face diagnosis pathological feature correlation analysis based on a graph attention network to obtain face diagnosis identification result data. The deep semantic understanding is realized by introducing a plurality of data acquisition modes such as three-dimensional imaging, thermal imaging, micro-expression recognition, spectral information and pulse condition harmonic analysis and combining advanced models such as a graph neural network, U-Net and Bi-LSTM.

The invention discloses a data security protection method and system combined with big data analysis, and relates to the field of data security protection, and the method comprises the steps: building a multi-dimensional data collection and fusion data set according to API access records, external threat intelligence and context metadata association data; based on an isolated forest algorithm, calculating a sample path length to evaluate a behavior anomaly probability score; risk indexes of external threats, data sensitivity and permission exceptions are quantified respectively; constructing a data security risk comprehensive assessment model based on a weighted summation algorithm, and updating and optimizing the weight in the model by using a gradient boosting tree algorithm; and according to an output result of the data security risk comprehensive assessment model, setting a security risk level, and according to the security risk level, dynamically responding to a protection measure. The method has the advantages that continuous and dynamic risk assessment and automatic response to third-party data access behaviors are realized by fusing multi-source data and an intelligent algorithm.

The invention relates to the technical field of tunnel engineering automation and intelligent control, and discloses a TBM tunneling parameter intelligent optimization decision-making system based on an LSTM network, and the system comprises a data collection and preprocessing module which obtains external data and outputs a tunneling parameter sequence; the probabilistic tunneling trend prediction module is used for outputting a prediction expected value and prediction uncertainty; the prospective geological precursor sensing module is used for matching and identifying known risks and outputting alarm events; and determining an optimal tunneling mode by the dynamic risk avoidance decision matrix. When the prediction uncertainty is too high, activating the prospective template driven by the uncertainty to excavate a new precursor template and update the template library; meanwhile, the decision-efficiency relevance evaluation and strategy self-optimization engine optimizes the decision rule according to the actual tunneling efficiency. According to the method, decision making is carried out through quantitative risk prediction and fusion of multi-source information, and a double learning closed loop of knowledge discovery and strategy optimization is established, so that the reliability, the adaptability and the long-term efficiency of system decision making are remarkably improved.

The invention discloses a three-phase imbalance compensation method and system based on negative sequence current and a medium, and relates to the technical field related to power imbalance compensation, and the method comprises the steps: executing the three-phase current, voltage and reactive power data collection of a power distribution network, and carrying out the three-phase imbalance recognition through negative sequence current analysis; carrying out additional acquisition of a load mode and environment data on the power distribution network; taking the data acquisition result and the additional data set as input data to execute negative-sequence current change trend prediction; a double-layer compensation device is configured for cooperative control optimization; power distribution network balance compensation is executed through a time sequence compensation strategy, and time sequence window feedback is established; and carrying out adaptive strategy correction. The technical problems that in the prior art, a three-phase imbalance compensation strategy lacks perspectiveness and self-adaptive capacity and cannot cope with a dynamically-changing power grid scene, and consequently compensation precision and stability are poor are solved, and the technical effects that perspectiveness and predictive compensation is achieved, and three-phase imbalance compensation precision and stability are improved are achieved.

The invention provides an electric power system cross-source safety intelligent monitoring and dynamic response method and system based on mimicry defense, and relates to the technical field of electric power system network safety. According to the method, multi-source data are acquired by deploying an acquisition agent and a sensor and are encrypted and transmitted, parallel analysis and threat judgment are realized by utilizing a heterogeneous executor pool and dynamic scheduling, an attack chain is identified by combining cross-domain association modeling, disposal is performed according to grades, credible recovery is supported, and reinforcement learning and federated learning are assisted to optimize a strategy and resource allocation. And lightweight adaptation is realized through intensity control in a resource limited scene, a closed-loop safety system covering data acquisition, threat analysis, dynamic disposal and continuous evolution is formed, and the detection accuracy, response efficiency and robustness of a power system are improved.

Methods and systems for a monitoring system for data collection in an industrial environment including a data collector communicatively coupled to a plurality of input channels connected to data collection points operationally coupled to at least one industrial component in at least one of an industrial boiler system or industrial pipeline system; a data storage structured to store a library of stored noise patterns associated with operation of the at least one industrial component; a data acquisition circuit structured to interpret a plurality of detection values from the collected data; and a data analysis circuit structured to: analyze the collected data, determine a measured noise pattern for the at least one industrial component, and compare the measured noise pattern to the library of stored noise patterns to identify a changed condition of the at least one industrial component.

The invention discloses a coal drop pipe full-life-cycle operation and maintenance decision-making method. The method comprises the steps of S1, coal drop pipe full-life-cycle data collection; s2, constructing and updating a digital twinborn body of the coal drop pipe: S2.1, constructing a multi-dimensional digital twinborn body comprising a geometric model, a physical model, a behavior model and a rule model, and comprehensively describing the characteristics of each aspect of the coal drop pipe; s2.2, dynamically updating the twinborn body, and dynamically updating the twinborn body through a data driving algorithm by using real-time monitoring data; s3, health state evaluation and reliability prediction, including short-term health diagnosis and long-term reliability prediction; s4, carrying out dynamic operation and maintenance decision making based on risks and cost; and S5, performing decision issuing execution and closed-loop feedback. According to the method, the design data, the real-time monitoring data, the environment data and the operation and maintenance historical data are integrated, the coal drop pipe digital twinborn body is constructed and dynamically updated, it is ensured that the model is consistent with the physical entity state, and the comprehensiveness and timeliness of the data are improved.

The invention discloses a hydraulic reclamation foundation secondary disaster detection method and system based on multi-source fusion, and belongs to the technical field of geological engineering and disaster monitoring, and the method comprises the steps: S1, synchronously deploying a plurality of data collection devices, and carrying out the omnibearing data collection of a hydraulic reclamation foundation region; s2, preprocessing the collected multi-source data; s3, fusing the preprocessed data, and dynamically adjusting the weight distribution of each data according to the detection depth; s4, inputting the fused feature data into a deep learning model, and identifying an abnormal geological structure in the hydraulic reclamation foundation; s5, the risk level of the secondary disaster is calculated in combination with the recognition result and the environmental parameters; s6, when the risk level exceeds a set threshold value, triggering a grading early warning mechanism; the distribution, the type and the risk level of the disaster are displayed; the hydraulic reclamation foundation secondary disaster detection method and system based on multi-source fusion are suitable for secondary disaster detection and early warning of hydraulic reclamation foundation areas such as ports, artificial islands and reclamation areas.