206 results about "Deep belief network" patented technology

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

According to the cooling tower early fault early warning method based on vibration state monitoring, vibration signals and working condition labels of key parts of the cooling tower are synchronously collected through multiple channels, and data quality is improved through preprocessing operation such as band-pass filtering and normalization; time-frequency features are extracted in a multi-scale mode through self-adaptive variational mode decomposition and wavelet packet transformation, signal complexity is quantized through energy entropy, and weak fault detection capacity is enhanced; the obtained features are input into a deep belief network after being subjected to principal component analysis dimensionality reduction, and automatic classification and recognition of the equipment operation state are achieved; dynamic early warning grade adaptation is carried out according to an identification result in combination with a working condition label, the environmental adaptability and stability of early warning are effectively improved, the method further has the functions of early warning sample recording and periodic model iterative optimization, and the fault identification precision and robustness in a complex noise environment are remarkably improved.

The invention belongs to the technical field of metallurgical equipment intelligent control and digital twinning, and provides a metallurgical equipment twinning HMI control system based on multi-source data, and the system comprises a multi-source data collection module which is used for collecting the multi-source data; the data fusion processing module is used for performing data fusion processing on the multi-source data by adopting a space-time alignment algorithm and a feature extraction network; the digital twinborn modeling module is used for constructing a metallurgical equipment digital twinborn model by utilizing an improved deep belief network algorithm based on the fusion processing data; the HMI interaction module is used for dynamically presenting the fusion processing data and the operation state data of the metallurgical equipment digital twinning model based on the output of the digital twinning modeling module; and the intelligent control module is used for performing closed-loop control on the metallurgical equipment by adopting a self-adaptive predictive control algorithm. According to the invention, full-process intelligent management of the metallurgical equipment can be realized, and the operation efficiency, the control precision and the reliability of the metallurgical equipment are remarkably improved.

The invention relates to the technical field of point cloud processing, and discloses a point cloud data processing method, system and device for industrial three-dimensional model construction. The method comprises the following steps: carrying out denoising and down-sampling processing on original point cloud data of an industrial part to obtain preprocessed point cloud data, calculating a local geometric descriptor, converting the local geometric descriptor into an industrial feature recurrent plot through a recurrent plot algorithm, inputting the recurrent plot into a convolutional neural network to extract a high-dimensional geometric feature vector, and mapping the feature vectors into B-spline curved surface control parameters through a deep belief network, and performing point cloud reconstruction quality evaluation according to the control parameters to obtain geometric accuracy error data and curved surface reconstruction integrity data. According to the method, the problems that in existing industrial three-dimensional model construction, a point cloud data processing method lacks intelligent feature recognition, the reconstruction precision is insufficient, and manufacturing constraints are not fully considered are solved, and the recognition accuracy of geometric features of complex industrial parts and the precision of three-dimensional model reconstruction are improved.

The invention provides a full-link electricity consumption monitoring method, system and equipment based on intelligent Internet of Things, and relates to the technical field of power system management. The method comprises the following steps: acquiring operation data acquired by an intelligent sensor group deployed at a power network node in real time, and executing localized carbon flow accounting according to the operation data through an edge computing node; constructing a joint probability model based on Monte Carlo simulation and a deep belief network, and generating a node carbon flow density matrix under multiple scenes; dynamically updating an electricity-carbon conversion coefficient according to real-time energy structure data, and calculating node-level carbon emission: optimizing a power grid topology and an energy storage scheduling strategy by adopting a double-delay depth deterministic strategy gradient algorithm TD3 and taking a carbon flow density matrix as a constraint condition; and generating an intelligent report including a carbon footprint thermodynamic diagram, emission reduction potential evaluation and block chain evidence storage. According to the invention, the capability of multi-target collaborative optimization of the security and economy of the power grid can be improved.

The invention relates to the technical field of tunnel monitoring, and discloses a tunnel fan health diagnosis and early warning system and method based on multi-source fusion, comprising a sensing layer, a data acquisition and transmission layer, a data processing and fusion layer, an intelligent diagnosis and early warning layer and an application layer which are arranged in sequence from bottom to top by adopting a layered architecture, all the layers cooperate to achieve tunnel fan collision safety and equipment health two-dimensional management and control. Vibration, displacement, strain, temperature and power multi-source monitoring data are fused, single-parameter monitoring limitation is broken through, wind turbine typical faults such as bearing damage, coupling misalignment and foundation looseness and specific risks such as over-limit vehicle collision are covered, and a full-dimension monitoring system is constructed. By means of a deep belief network (DBN), a convolutional neural network (CNN) and an MATLAB core algorithm, and in combination with a fault-feature mapping library and a multi-evidence combined judgment mechanism, the fault diagnosis accuracy is greatly improved.

The invention provides an electromechanical equipment fault prediction method and system based on multi-source information fusion, and the method comprises the steps: collecting operation data, including vibration data, temperature data and current data, during the operation of electromechanical equipment; performing feature extraction on the operation data based on a principal component analysis algorithm to obtain a fusion feature vector; inputting the fusion feature vector into a fault prediction model based on a deep belief network, and outputting a prediction result; wherein the deep belief network adopts a small-batch stochastic gradient descent algorithm combined with an adaptive learning rate adjustment strategy during training; and judging whether the electromechanical equipment has a fault hidden danger or not according to the prediction result. According to the method, the relevance between different types of data is mined, and the defect of low prediction precision is overcome.

The invention belongs to the technical field of wind turbine generator state monitoring and fault diagnosis, and relates to a wind turbine generator low-speed bearing defect diagnosis and early warning method based on multi-source data. Comprising the following steps: acquiring vibration, temperature, load and working condition data of a bearing; performing noise reduction on the vibration signal and extracting the vibration kurtosis; constructing a three-dimensional temperature field according to the temperature data and calculating a gradient entropy; calculating an asymmetric index from the load data; obtaining a rotating speed modulation factor according to the working condition data; fusing the features to construct a composite feature vector, and inputting the composite feature vector into a pre-trained deep belief network model; the model outputs the defect grade and residual service life probability distribution of the bearing; and according to the output result and the dynamic early warning threshold, triggering multi-stage early warning and generating a diagnosis decision. According to the method, accurate recognition of early defects of the bearing and intelligent early warning of the fault evolution trend are realized, and the diagnosis accuracy and the operation and maintenance decision timeliness are remarkably improved.

The invention relates to the technical field of smart park data monitoring, and discloses an artificial intelligence smart park multi-source data monitoring and analysis method. The method comprises the following steps: constructing a park operation state basic model; when the model is identified to be abnormal, data dynamic change characteristics are extracted by utilizing a spatio-temporal data fusion technology, and an abnormal event mode in a specific region is identified by combining deep belief network deep analysis; calculating data complexity by using permutation entropy, optimizing and analyzing an abnormal propagation path in combination with an A star algorithm, and evaluating a diffusion range and an influence path; recognizing a comprehensive risk area, analyzing the environment and equipment state characteristics of the area through a multispectral imaging technology, and evaluating the operation abnormity in combination with real-time multi-source data; and performing accurate intervention on the comprehensive risk area based on the abnormal condition. According to the method, effective integration and analysis of multi-source data of the smart park are realized, abnormity is accurately identified, risks are mastered, a scientific means is provided for park management, and stable and efficient operation of the park is guaranteed.

The invention discloses a substation primary and secondary equipment communication prefabricated optical cable online monitoring method and system, and belongs to the technical field of communication prefabricated optical cable monitoring. The system comprises an optical cable parameter acquisition module, a communication quality evaluation module, a physical parameter analysis module, a first-aid repair path planning module and a fault database. The method comprises the following steps: optical cable parameter acquisition: acquiring data in real time, and converting high-dimensional data into low-dimensional data through a principal component analysis algorithm; communication quality evaluation: identifying communication characteristics in the low-dimensional signal parameter matrix based on a fuzzy comprehensive evaluation algorithm, and outputting a communication quality score; the physical parameter analysis module extracts physical deep features of the optical cable by using a deep belief network DBN, and inputs an improved Bayesian classifier to output a state classification result; according to the first-aid repair path planning, OTDR detection parameters are dynamically adjusted, and an optimal first-aid repair path is planned in combination with the position of the inspection robot; according to the invention, optical cable communication quality evaluation, accurate physical state classification and efficient fault repair planning are realized.

The invention provides an argon flow and micro-positive pressure linkage control method for an electroslag furnace. The method comprises the steps that the oxygen content and micro-positive pressure signals in the furnace are collected; the future furnace condition is predicted through the deep belief network model; optimizing initial parameters of the fuzzy PID controller in an off-line manner by using a particle swarm algorithm; on the basis of the real-time signal and the prediction result, adaptively adjusting controller parameters on line; and finally, an argon flow and dust removal valve opening control instruction is output in a linkage mode. According to the method, future furnace conditions are predicted through the deep belief network model, the control advancement is realized, and the problem of large lag of the system is solved; offline parameter optimization is carried out on the fuzzy PID controller through a particle swarm algorithm, and excellent initial performance adapting to different working conditions is provided for the fuzzy PID controller; the parameters of the controller are adaptively adjusted through online fuzzy reasoning, so that the system has robustness for coping with a dynamic process; finally, accurate and stable linkage control of the oxygen content in the furnace and the micro-positive pressure is achieved through linkage calculation of the argon flow and a dust removal valve opening instruction.

The invention discloses a virtual power plant regulation capability test method and system based on a deep belief network, and the method comprises the steps: deploying an intelligent terminal system at an aggregation resource side in a virtual power plant, and collecting resource operation data in real time; dynamically loading market rule parameters of the target transaction variety through a rule configuration engine; the method comprises the following steps: preprocessing historical scheduling data of a virtual power plant, collecting resource-level data and virtual power plant-level data, cleaning abnormal data, and interpolating and filling missing values; calculating an adjustment capability index based on the preprocessed data; inputting the indexes and the rule parameters into a pre-trained deep belief network DBN model, and outputting a virtual power plant overall regulation capability predicted value and a marginal contribution weight of each aggregation resource; generating a virtual power plant admission judgment report and a resource optimization suggestion according to a comparison result of the predicted value and the market rule parameter; according to the invention, differential evaluation of the virtual power plant regulation capability indexes is realized, and the access test requirements under diversified power market rules are met.

The invention discloses an intelligent control system for a tea processing process and a control method based on technological parameter optimization, and relates to the technical field of tea processing. By integrating the high-precision sensor and the intelligent decision-making module, comprehensive monitoring and real-time optimization of key technological parameters of tea processing are realized, data accuracy is ensured through pyroelectric infrared and SAW humidity sensors and the like, and the intelligent decision-making module automatically adjusts the processing parameters by using a deep belief network and an ant colony algorithm, so that the processing accuracy is improved. The production efficiency is improved, manual errors are reduced, meanwhile, technological parameters are accurately controlled by adopting an advanced data processing algorithm, variation mode decomposition and independent component analysis ensure stable tea quality, in addition, external and internal data are integrated to optimize the processing technology, heating, ventilation and other parameters are accurately controlled, and energy consumption and cost are reduced.

The invention relates to the technical field of market trend prediction, and discloses a market trend prediction system driven by macroeconomic indicators. An index acquisition module of the system dynamically acquires core economic indexes such as GDP growth rate, CPI, PMI and currency supply; the data preprocessing module is used for carrying out layered noise reduction processing on the multi-source heterogeneous data; the feature engineering module constructs a market sensitive feature set through spatio-temporal feature fusion; the prediction model building module is used for building a multi-layer nonlinear prediction model based on a deep belief network; the dynamic adjustment module adopts reinforcement learning to optimize a decision threshold value and combines a Markov chain to carry out state transition planning; and the feedback iteration module analyzes and predicts deviation through Bayesian filtering and realizes strategy updating. According to the method, deep learning and reinforcement learning technologies are creatively fused, the prediction precision is remarkably improved through a dynamic calibration mechanism, and the method can be widely applied to the macroeconomic analysis fields of financial investment, industrial planning and the like.

The invention relates to the technical field of mine equipment intelligent operation and maintenance and augmented reality, in particular to a forklift predictive maintenance and repair guidance system based on augmented reality, which comprises a multi-source data acquisition module, a mining area operation analysis module, a predictive maintenance engine, an AR intelligent repair guidance module and a resource dynamic scheduling center, a multi-source sensor is used for collecting equipment state and environment data, and mineral characteristics, path characteristics and load changes are dynamically analyzed in combination with working conditions of a mining area; the predictive maintenance engine constructs a fault prediction model based on a deep belief network, and dynamically corrects a working condition adaptive threshold; the AR module is used for positioning and superposing three-dimensional maintenance guidance through SLAM (Simultaneous Localization and Mapping), and adapting to the skill And the resource scheduling center generates spare part, personnel and alternative equipment scheduling schemes as required. According to the method, the whole process from fault prediction to maintenance execution is intelligentized, the fault early warning accuracy and the maintenance efficiency are improved, and the method is suitable for operation and maintenance management of mining forklifts under complex working conditions.

The invention discloses a management decision support method for intelligent operation and maintenance and fault prediction of engineering equipment, and belongs to the technical field of operation and maintenance management and intelligent decision of the engineering equipment. According to the method, equipment fault features are extracted and classified through a deep belief network (DBN), and text diagnosis is refined in combination with TF-IDF and cosine similarity; analyzing the importance and cause of the fault by using a Bayesian network; predicting a fault and a decline trajectory based on the decision tree and logistic regression; and constructing an intelligent operation and maintenance decision support system of ontology integration. According to the method, multi-source data are integrated, accurate fault diagnosis, advanced prediction and intelligent decision making are achieved, the problems that traditional operation and maintenance depend on experience, precision is low and cost is high are solved, and the operation and maintenance efficiency and reliability of engineering equipment are improved.

The invention discloses a rapid early warning method and system for water quality of a drinking water source. The system integrates a multi-source monitoring module, a data fusion processing module, a water quality prediction analysis module, a dynamic early warning decision module and a digital twinborn simulation platform, and acquires multi-dimensional data such as physical and chemical parameters, biological behavior indexes and spectral characteristics; a self-adaptive weighted deep belief network (AW-DBN) model is used for pollutant identification, diffusion simulation and overproof time prediction, an early warning threshold value is dynamically adjusted by combining a Bayesian optimization algorithm so as to reduce the false alarm rate and the missing report rate, and visual simulation and emergency disposal scheme evaluation of the pollution diffusion process are realized based on the digital twinborn technology. Rapid and accurate early warning and scientific emergency decision making of the water quality risk are realized, and the safety guarantee capability of the drinking water source is remarkably improved.

The invention discloses a multi-source information fusion fault diagnosis method based on an improved DS evidence theory, and relates to the technical field of industrial equipment state monitoring and intelligent fault diagnosis, and the method comprises the steps: extracting multi-scale vibration energy and impact characteristics, a thermal load change rate and oil physical and chemical characteristics through synchronously collecting vibration, temperature and lubricating oil multi-source operation information; and constructing a unified feature vector and inputting the unified feature vector into the deep belief network to realize initial probability distribution of normal, early warning and fault working conditions. The cross-modal evidence conflict is evaluated and corrected by constructing a feature coupling index, a conflict degree and a confidence entropy index, and the interference of inconsistent evidences on a diagnosis result is inhibited; and further introducing a time sequence evidence library and improving a DS recursive fusion mechanism, depicting time evolution of an equipment operation state, and constructing a health trend index to realize evolutionary fault early warning. And finally, the model is updated in combination with diagnosis result feedback, self-learning optimization and closed-loop fusion of fault diagnosis are realized, and the diagnosis accuracy and stability are improved.

The invention relates to the technical field of thermoelectric pipe network inspection, and discloses a thermoelectric pipe network fault inspection system and method based on deep learning. The system comprises a pipe network partition module, a multi-mode sensing module, a fault prediction module, a grading response module and a dynamic control module. The pipe network partition module is divided into a high-temperature steam pipeline area, a medium-temperature hot water pipeline area, a low-temperature water return pipeline area and a key valve connecting area; the multi-modal sensing module deploys temperature, pressure and sonic sensor arrays in each area; the fault prediction module extracts spatial-temporal characteristics through a deep belief network model and outputs a fault probability value; the hierarchical response module triggers different levels of response instructions according to a comparison result of the fault probability value and a preset threshold interval; and the dynamic control module controls a moving path of the inspection robot and detection equipment parameters according to the response instruction. According to the system, accurate prediction and dynamic response of the thermoelectric pipe network fault can be realized, and the inspection efficiency and the pipe network operation reliability are improved.

The invention relates to the related technical field of robot fault detection, in particular to an industrial robot real-time fault detection method and system, and the method comprises the steps: analyzing the operation state of an industrial robot based on a communication state signal, determining a motion domain and sensing domain synchronization factor, configuring a cross-domain feature vector and a performance feature vector, and carrying out the fusion, the deep belief network is used for drawing up the fault probability graph and identifying the feature identifier to achieve fault reminding, the technical problems that a fault response strategy is fixed, complex and changeable industrial scene requirements are difficult to adapt, and fault reminding cannot be effectively conducted in time are solved, cross-domain feature vectors and performance feature vectors are constructed, and the fault reminding efficiency is improved. The method has the technical effects that the serial control sequence is dynamically verified, multi-scale decomposition is combined, signal delay fluctuation and retransmission frequency are accurately extracted, a deep belief network is used for fusing time sequence and spatial features to identify fault types, fault reminding is carried out through a fault probability distribution map, and the operation safety of the industrial robot is guaranteed.

The invention relates to the technical field of network security situation awareness and early warning, and discloses a photovoltaic power station network security situation awareness and early warning method and system, and the method comprises the steps: carrying out the multi-source heterogeneous data collection of a photovoltaic power station and a social network, and carrying out the distributed preprocessing and feature extraction of the collected data, carrying out weighted fusion analysis on the comprehensive feature data through a situation awareness model, predicting potential attacks through an attack prediction model, calculating a network security situation in real time through a deep belief network, and carrying out automatic attack blocking by a centralized control center and a plant station operation and maintenance terminal; encryption protection is carried out through a forward isolation device and a VPDN tunnel technology. According to the invention, situation awareness and risk prediction are carried out through a deep learning algorithm, automatic attack blocking and headquarters-plant-station intensive management and control are realized in combination with a dynamic early warning mechanism, a trapping system and a block chain technology, and the monitoring, early warning and protection capabilities of the network security of the photovoltaic power station are significantly improved.

The invention relates to the technical field of wind power plant equipment monitoring, and discloses a wind power plant equipment state online monitoring system. The system comprises a real-time acquisition module, a multi-modal fusion module, a dynamic segmentation module, a preliminary evaluation module, a deep analysis module and a maintenance trigger module. The real-time acquisition module acquires a multi-source sensor data stream, and the multi-modal fusion module performs adaptive weighted fusion on the multi-source sensor data stream to generate a comprehensive signal feature set. The dynamic segmentation module segments a feature set according to a load rate to generate a running state feature sequence, and the preliminary evaluation module performs health degree scoring by using a deep belief network. The deep analysis module performs modeling on abnormal time period data through a space-time diagram neural network to generate an equipment state label, and the maintenance triggering module triggers a real-time maintenance strategy instruction according to the equipment state label. The system can accurately monitor the equipment state of the wind power plant in real time, improve the fault diagnosis accuracy, realize intelligent maintenance decision, and improve the operation reliability and economy of the wind power plant.

The invention relates to the technical field of wind power plant control, and discloses a wind power plant control system. The system comprises a data acquisition module, a feature extraction module, a decision instruction generation module, an optimization model construction module and a hierarchical control execution module. The data acquisition module acquires data through a multi-source sensor; the feature extraction module performs multi-modal feature extraction by using a deep belief network; the decision instruction generation module generates a regulation and control instruction by means of a pre-trained collaborative decision model; the optimization model construction module constructs a multi-target dynamic optimization model, and adopts a decomposition coordination optimization algorithm to adjust operation parameters; the hierarchical control execution module executes a regulation and control instruction through a hierarchical control architecture, and the decision layer, the coordination layer and the execution layer respectively adopt corresponding algorithms to realize global regulation and control, local parameter adaptation and accurate tracking of power and pitch angles. The system can improve the power generation efficiency of the wind power plant, balance the equipment loss, enhance the cooperative operation capability with the power grid, and guarantee the stable and efficient operation of the wind power plant.

The invention relates to the technical field of medical data supervision and safety. The medical anti-statistic behavior monitoring method based on deep learning comprises the following steps: performing multi-source heterogeneous data acquisition on hospital anti-statistic system data, database audit data and access control data to obtain an original operation log set; performing feature fusion and standardization processing based on the original operation log set to generate a multi-dimensional time sequence feature matrix; constructing a deep belief network model based on the multi-dimensional time sequence feature matrix, and generating a statistical behavior risk scoring model through hierarchical pre-training and fine tuning; performing cross validation and dynamic parameter adjustment by using the annotation data set based on the statistical behavior risk scoring model, and generating an optimized risk grading model; and deploying the optimized risk grading model to a hospital database auditing system, and generating a real-time monitoring and early warning signal so as to achieve the technical effects of improving the monitoring efficiency and accuracy and preventing privacy leakage.

The invention relates to the technical field of industrial electric control optimization, and discloses an intelligent industrial electric control optimization system based on a deep learning algorithm. The system comprises a multi-modal sensing module which integrates a temperature sensor, a flow sensor and a pressure sensor to collect multi-modal data flow; a feature extraction module extracts three types of features to construct a multi-modal industrial feature set; the state comparison module generates normal and abnormal operation state comparison characterization by means of a generative adversarial network; the contribution degree evaluation module takes the system load rate as a tool variable, and calculates the contribution degree score of each sensing channel to the system stability through a Bayesian network; the dynamic screening module fuses the two types of indexes, and adjusts threshold screening through a deep belief network to generate optimization target representation; the time sequence grouping module performs phase grouping and weighting according to an operation cycle; and the fusion decision module generates a regulation instruction through hierarchical attention mechanism fusion, so that the operation stability and efficiency of the industrial electronic control system are effectively improved.

The invention discloses an ultra-high voltage transmission line fault analysis system based on multi-dimensional data, and belongs to the technical field of big data analysis. The method is used for solving the technical problem that the robustness of a system is poor when an existing single-algorithm technical scheme is implemented. Through multi-source data synchronous acquisition and improved EEMD denoising, noise interference of an extra-high voltage line in a complex electromagnetic environment is suppressed; signal characteristics are dynamically tracked through adaptive Kalman filtering, space-time alignment and standardization processing are carried out, dimensional difference and time delay are eliminated, improved wavelet packet transformation, a deep belief network and adaptive morphological filtering are carried out in parallel, multi-dimensional characteristics are dynamically weighted and fused through an attention mechanism, a fault characteristic vector is constructed, and fault diagnosis is carried out. According to the method, efficient extraction and optimization of fault features are achieved, accurate reasoning of fault types and positioning can be achieved through the improved fuzzy Bayesian network, the particle swarm optimization algorithm adaptively updates parameters based on real-time errors, and the adaptive robustness of a complex power grid environment can be effectively improved.

The invention relates to a modular intelligent power distribution cabinet safety management method based on the Internet of Things, particularly discloses a modular intelligent power distribution cabinet safety management method based on the Internet of Things, and aims to solve the problems that a traditional power distribution cabinet monitoring system is poor in expansibility, weak in fault recognition capability and rigid in protection strategy. The method comprises the following steps: deploying a modularized sensing unit with a standardized interface to realize plug and play; multi-dimensional data of current, voltage, temperature and arc light are synchronously acquired at a high sampling rate; data are fused on the edge side through Kalman filtering, and key features such as harmonic distortion rate, voltage sag depth, temperature gradient change rate and electric arc energy accumulation value are extracted; performing dynamic risk assessment by using a five-layer deep belief network model, and triggering early warning when the high risk probability exceeds a threshold value; adaptively adjusting an overcurrent protection constant value, opening delay and isolation logic according to an evaluation result; and the edge-cloud coevolution is realized by continuously optimizing the model and the strategy through cloud reinforcement learning.

A method in an illustrative embodiment comprises configuring a machine learning system with a multi-layer network architecture comprising at least one neural network and one or more additional network layers, training the neural network at least in part utilizing quantum sampling performed by a quantum computing device, obtaining data characterizing a monitored system, processing at least a portion of the obtained data through at least a portion of the multi-layer network architecture of the machine learning system to generate a prediction of at least one characteristic of the monitored system from the obtained data, and executing at least one automated action relating to the monitored system based at least in part on the generated prediction. The neural network may comprise, for example, a deep belief network (DBN) that includes at least first and second restricted Boltzmann machines (RBMs) of respective first and second different types, or at least one conditional restricted Boltzmann machine (CRBM).

The invention discloses a tower crane bolt axial force intelligent ultrasonic detection method and system based on multi-modal data fusion. Firstly, a multi-stage influence factor system is constructed based on a fuzzy hierarchical comprehensive evaluation theory, and a high-risk region identification model is established; and calculating a regional risk value based on the model, and determining a monitoring point distribution scheme. A heterogeneous network composed of a fiber grating strain sensor and an ultrasonic sensor is arranged in a selected area, and strain and ultrasonic data are synchronously collected. And inputting the multi-modal data into a deep belief network for feature fusion, and realizing health state dynamic evaluation through a fuzzy recognition model. And based on an evaluation result, performing fatigue life prediction by adopting an improved rain flow counting method and an accumulative damage theory. And finally, data fusion display and intelligent early warning are realized through the remote monitoring platform. According to the invention, full-chain intelligent health management from risk identification, real-time monitoring to life prediction is realized.

The invention relates to the technical field of data processing, in particular to a concrete gravity dam operation period safety risk value calculation method and system.The method comprises the steps that structure monitoring data, environment data and operation data of a dam body in the operation period are continuously collected through a safety monitoring system; carrying out preprocessing and standardization, coordinate conversion and risk feature extraction on the collected data, and obtaining fusion state data through data fusion processing; constructing a dam body operation environment parameter set according to the environment data, and calculating a safety risk value by using a deep belief network model in combination with the environment parameter set and the fusion state data; and dividing risk levels according to the security risk values and outputting early warning information. According to the method, dynamic quantitative evaluation of the safety risk of the concrete gravity dam in the operation period is realized through multi-source data fusion and a deep learning technology, and a scientific basis is provided for dam body safety management and risk early warning.