303 results about "Deep belief network" patented technology

The invention discloses a wind power plant booster station multi-source data fusion anti-misoperation locking intelligent decision-making and early warning method, and relates to the technical field of intelligent misoperation prevention of a power system, and the method comprises the following steps: collecting multi-source heterogeneous data, obtaining the data through a distributed sensor network, and carrying out the edge calculation preprocessing; performing data space-time alignment and fusion, performing equipment state evaluation, and constructing a deep belief network and Bayesian network hybrid model to calculate a health index; anti-misoperation rule modeling is carried out, and operation logic verification is carried out based on a Petri network and an expert knowledge base; risk early warning decision making: fusing multi-source early warning information to divide risk levels; intelligent locking control is carried out, and a locking strategy is optimized through reinforcement learning; and performing decision support and visualization, constructing a three-dimensional digital twinborn model, and displaying operation guidance and risk early warning in combination with an AR technology. Through multi-source data fusion and intelligent decision making, the anti-misoperation locking accuracy and efficiency are improved, and the safety and the operation and maintenance level of the booster station are remarkably enhanced by equipment fault early warning three months ahead of time.

The invention discloses a substation equipment state monitoring and intelligent fault early warning method based on deep learning. The method comprises the following steps: S1, obtaining a preprocessed multi-source state data set; s2, generating a high-dimensional equipment state feature matrix; s3, a fault sensitive deep belief network model is adopted to form a preliminary fault state recognition result; s4, obtaining an optimized sensitive depth belief network model; s5, performing online analysis on the multi-source state data acquired in real time by using the optimized sensitive deep belief network model, generating a real-time fault prediction result of the equipment state, and classifying and grading fault risks; and S6, according to a real-time fault prediction result, triggering a remote fault early warning mechanism, and sending fault early warning information including a fault risk level, an early warning signal and an emergency processing suggestion to a substation operation and maintenance center. According to the invention, intelligent alarm linkage and hierarchical control of the scheduling system are effectively supported.

The invention relates to a water conservancy equipment service life prediction and fault monitoring method. The water conservancy equipment service life prediction and fault monitoring method comprises the steps of performing comprehensive digital modeling on water conservancy equipment, determining a fault sensitive area and a key monitoring point, generating a sensor deployment scheme, preprocessing analog and simulated sensor data, and ensuring that the data is comprehensive and targeted; performing multi-domain fusion feature extraction, deep feature learning and correlation analysis on the data, screening out important features to form a feature subset, and capturing equipment fault features in all directions; a mixed life prediction model is constructed, parameter initialization, pre-training and formal training are completed, a multivariate Gaussian mixture model and a deep belief network are constructed, and then a fault monitoring model is obtained; sensor data arranged according to a deployment scheme is obtained in real time, features are extracted after preprocessing, real-time feature vectors are input into a life prediction model and a fault monitoring model respectively, life prediction and fault monitoring of the water conservancy equipment are achieved, powerful decision support is provided for equipment maintenance, reliable operation of the water conservancy equipment is guaranteed, and fault loss is reduced.

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.

The invention relates to the technical field of water quality detection, and discloses a water quality pollutant detection method, system and equipment based on spectral analysis, and the method comprises the following steps: carrying out real-time spectral data acquisition on a water body sample in a vehicle driving process through a multispectral sensor array in a vehicle-mounted water quality detection device; obtaining a water body spectrum three-dimensional data cube; carrying out anti-vibration wavelength calibration and ambient light source interference elimination processing to obtain a preprocessed spectrum data set; performing standard multivariate decomposition processing and horizontal local constraint optimization of a pollutant target wave band on the preprocessed spectrum data set to obtain enhanced feature fingerprint spectrums for different types of water quality pollutants; and inputting the enhanced feature fingerprint spectrum into a deep belief network model to perform pollutant feature matching analysis to obtain various pollutant types and corresponding concentration values in the water body sample, thereby effectively eliminating ambient light changes and other background interferences, not only determining the pollutant types, but also accurately quantifying the concentration values.

The invention provides a transformer substation fault intelligent analysis and diagnosis method and system based on big data, and the method comprises the steps: obtaining the surface and internal temperature data flows of all power equipment in a transformer substation, and the environment temperature and humidity data flow of the transformer substation in the operation environment of the transformer substation; performing cross-time-scale dynamic coupling on the temperature data stream and the environment temperature and humidity data stream to generate a heterogeneous temperature characteristic stream; based on the heterogeneous temperature characteristic flow, dynamically adjusting an association rule of the temperature anomaly of the power equipment and the operation state of the transformer substation; performing cross-device coupling analysis on the heterogeneous temperature characteristic flow through a deep belief network to generate a dynamic conduction topological structure; and generating a transformer substation fault diagnosis result in combination with the adjusted fault association rule and the dynamic conduction topological structure. According to the invention, the early warning accuracy and diagnosis efficiency of the substation fault are improved.

The invention relates to the technical field of burn wound treatment, and discloses an intelligent burn wound dynamic monitoring and evaluation system. The system comprises a data acquisition module for acquiring real-time physiological data of a burn wound by using a multi-source sensor; the multi-modal feature fusion module is used for generating wound comprehensive state features based on a deep belief network; the dynamic evaluation module outputs a wound healing level and a risk early warning signal through a pre-trained collaborative evaluation model; the optimization decision generation module is used for constructing a multi-target dynamic optimization model to adjust a treatment scheme according to risk early warning; and the hierarchical execution control module realizes distributed execution of treatment instructions through a hierarchical control architecture. The system can comprehensively monitor the state of the wound surface, accurately evaluate the healing risk, optimize the treatment scheme and accurately execute the treatment scheme, effectively improve the treatment effect of the burn wound surface, reduce the infection risk and shorten the healing period, and has important clinical application value.

The invention discloses a product collaborative design management method and system based on data driving, and relates to the technical field of product design management, and the method comprises the steps: constructing a double-flow heterogeneous quantitative collection network; establishing an incidence matrix and calculating dynamic coupling strength among the parameters; designing a recursive deep belief network based on the incidence matrix and the dynamic coupling strength, encoding product structure parameters and design process data into a probability graph model, and constructing a design knowledge base; dynamically distributing the design rules in the design knowledge base by adopting a swarm intelligent optimization algorithm, generating a collaborative decision-making unit, and establishing a constraint propagation link; and generating a multi-target collaborative optimization scheme group, and screening an optimal scheme group based on Pareto frontier. According to the invention, omnibearing acquisition of product structure parameters and design flow data is realized through the double-flow heterogeneous quantitative acquisition network, and the data integrity is improved.

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 discloses an operation state and characterization state parameter method of a GIS isolation switch, and relates to the field of GIS isolation switch operation. Multi-source heterogeneous sensors including vibration, temperature, strain gauges, fiber bragg gratings, acoustic cameras and the like are deployed at key parts, and data are collected in a self-adaptive mode according to working conditions; processing data by using a deep learning noise reduction auto-encoder, adaptive weighted fusion and DBSCAN; deeply mining features by using empirical wavelet transform, a heat conduction model and the like; constructing a composite state parameter by means of a deep belief network and principal component analysis in combination with particle swarm optimization; the state is evaluated through a convolutional neural network of transfer learning, LSTM prediction and early warning are carried out, and a fault tree and a Bayesian network are combined to locate a fault. According to the invention, data is comprehensively collected, intelligent processing and deep mining are carried out, scientific state parameters are constructed, and accurate state evaluation and early warning are realized in combination with transfer learning; the adaptability is improved through self-learning and multi-modal fusion, the operation and maintenance efficiency is improved through a VR / AR visual platform, the risk of the power system is reduced, and benefits are remarkable.

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 discloses a low-voltage equipment management method and system for a power distribution network, and the method comprises the steps: S1, collecting the operation parameters of all low-voltage equipment in real time through an Internet of Things terminal node disposed at the low-voltage side of the power distribution network; s2, on the basis of the real-time data in the S1, combining dynamic change characteristics of the power distribution network topology, constructing an adaptive topology network model through edge computing nodes, and dynamically updating a connection relation and a load path between equipment by the model by adopting a graph theory algorithm; s3, inputting the real-time data in the S1 into a preset fault diagnosis model, identifying abnormal equipment and positioning a fault type by the model through fusion of a deep belief network and a random forest algorithm, and predicting a health degree attenuation trend of the equipment at the same time; and S4, according to the topology model in the S2 and the fault diagnosis result in the S3, generating a resource optimization allocation strategy based on a dynamic programming algorithm, including adjusting the switching priority of standby equipment, reconstructing a load allocation path, and sending a control instruction to a specified terminal through a communication module.

The invention relates to the technical field of ultrasonic detection, and provides an aero-engine fuel control valve leakage amount ultrasonic detection method, which forms a coherent technical link through leakage ultrasonic signal acquisition, leakage signal separation, leakage feature extraction, leakage area positioning, offset compensation and cross validation of a sensor, and the links are mutually verified and optimized. A leakage signal is separated through a dynamic threshold value, the identification degree of a deep belief network is enhanced through multi-dimensional features, environment interference is compensated and corrected through working condition parameters, positioning deviation caused by single-link error accumulation in a traditional method is effectively restrained, accurate capturing and positioning of a micro leakage signal are achieved, and the method is suitable for large-scale popularization and application. The leakage thermodynamic diagram integrates leakage rate grading, coarse positioning area and micro leakage point information, the analysis and decision-making time of maintenance personnel is greatly shortened through a visual presentation mode, and rapid interpretation of fault positions and severity is achieved.

The invention discloses a target recognition method based on multi-view vision and a target recognition model training method, belongs to the technical field of target recognition, and solves the problem that global information is easy to lose due to the fact that a binocular vision method adopted in an existing method mainly depends on target detection after disparity maps and image splicing in the aspect of feature extraction. The method comprises the steps of performing pre-recognition processing on a target scene image based on an improved deep belief network model, responding to an auxiliary adjustment instruction by a multi-view vision acquisition system, fusing a pre-recognition result and a scene compensation image based on a spatial stamp alignment algorithm, and performing scene compensation. The target recognition model extracts global information of a scene fusion set in a parallel mode of combining a bidirectional feature pyramid network architecture with a YOLO algorithm; according to the invention, accurate identification of the target object type and the target object position in the three-dimensional space of the target scene is realized by adopting a cooperative cooperation mode of the improved deep belief network model and the target identification model, and the problems of global information loss and information insufficiency of the scene fusion set during target identification are avoided.

The invention provides a capacitive equipment resistive current taking-free PT signal measurement method suitable for a lightning arrester, and relates to the technical field of capacitive equipment resistive current measurement. The method comprises the following steps: deploying a non-contact electric field coupling sensing array; acquiring a multi-channel main induction signal and an equipment high-voltage side induction electric field signal; performing cascade filtering processing to obtain a filtering reference signal; dynamically correcting the phase deviation of the filtered reference signal to obtain a calibration reference signal; collecting a leakage current signal, and separating to obtain a resistive current component; and constructing a dielectric loss factor multivariable dynamic compensation model based on the deep belief network, embedding a capacitive equipment operation duration cumulative effect factor, and updating parameters in real time through an online learning algorithm to obtain a resistive current measurement result. High-precision measurement of resistive current is realized through an advanced technology, a model is established in combination with multiple environment parameters and equipment operation duration, adaptability is improved, and long-term accurate monitoring is guaranteed through online parameter updating.

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.

An intelligent grain storage control system based on a wireless sensor network comprises a grain storage environment monitoring subsystem which comprises a plurality of sensor arrays used for monitoring at least one grain storage environment parameter; the grain condition early warning subsystem is used for processing all the grain storage environment parameters by using a deep belief network and a long-short term memory network to obtain a real-time grain condition state; the intelligent control subsystem is used for generating an equipment control instruction according to the real-time grain condition state; and the overrun execution subsystem comprises a plurality of environment control devices, and the environment control devices are used for operating according to the device control instructions to change the environment of the grain depot. According to the invention, the grain storage environment parameters in the grain depot can be more flexibly collected, and the real-time grain condition state is predicted through the grain condition early warning model comprising the deep belief network and the long-short term memory network based on the grain storage environment parameters, so that the grain depot environment can be controlled with high precision, and full protection of grains is realized.

The invention discloses a track fault identification method and system based on HSROA, and the method comprises the steps: calculating a frequency band entropy value of at least one IMF component signal, and selecting a sensitive IMF component signal from the at least one IMF component signal according to the frequency band entropy value; filtering the sensitive IMF component signal for the second time according to a band-pass filter to obtain a target signal, and performing envelope power spectrum analysis on the target signal to obtain a fault characteristic frequency; fusing the fault feature frequency with a time domain feature, a frequency domain feature and a time-frequency domain feature in the track vibration signal to obtain a fusion feature vector, and constructing a fusion feature matrix according to the fusion feature vector; and inputting the fusion feature matrix into a preset deep belief network for iterative training to obtain a track fault recognition model, inputting the obtained real-time track vibration signal into the track fault recognition model, and outputting the track fault recognition model to obtain a track fault recognition result.

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 provides an industrial and commercial photovoltaic power station intelligent management system based on the Internet of Things, and belongs to the technical field of electric power. The system comprises a data acquisition unit used for acquiring real-time data of a photovoltaic power station; the edge computing node is internally provided with a fault prediction model and an anomaly detection model, and is used for performing fault prediction and anomaly detection on the photovoltaic power station according to the collected real-time data of the photovoltaic power station, and selecting an optimal maintenance scheme according to the results of fault prediction and anomaly detection; and the cloud server is used for constructing and training a fault prediction model based on a deep belief network and constructing an anomaly detection model based on a density spatial clustering algorithm. According to the method, the fault is accurately pre-judged through the fault prediction model, the operation state of the equipment is comprehensively monitored by using the anomaly detection model, and the optimal maintenance scheme can be selected according to the detection result, so that the power generation loss and high maintenance cost caused by sudden faults of the equipment are effectively reduced, and the maintenance benefit of the power station is maximized.

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 an intelligent photovoltaic sunshade method and system based on cloud cover recognition, and aims to improve the power generation efficiency of a photovoltaic system and achieve typhoon resistance. According to the method, cloud amount data are collected in real time through a multispectral camera and a meteorological station sensor, and feature values of cloud are extracted after preprocessing. A deep belief network (DBN) model of a double-layer RBM structure is used for power supply prediction, and model parameters are optimized through particle swarm optimization (PSO). And intelligently adjusting the angle of the sun shield or starting an anti-typhoon mode according to a comparison result of the predicted power supply quantity and a dynamically adjusted threshold value. The system further comprises an environment parameter prediction module and a user interaction interface, and the comfortable and energy-saving indoor environment of the near-zero-carbon building is achieved. The method and the system provided by the invention can effectively deal with extreme weather, optimize energy utilization and have a wide application prospect.

A health intervention method for healthy and healthy people based on compliance prediction relates to the technical field of health intervention, and mainly comprises the following steps: collecting training sample data to perform model pre-training on a deep belief network, collecting actual data of healthy and healthy people to be intervened, and sending the actual data to the pre-trained deep belief network to perform re-diagnosis compliance prediction; and outputting a re-visit compliance prediction classification result, adjusting and optimizing a health intervention strategy for the crowd according to the prediction classification result, and sending intervention knowledge content according to the intervention strategy and the patient condition. The invention further provides a health intervention system for healthy and healthy people based on compliance prediction. The system comprises a data synchronization module, a compliance prediction module, an intervention knowledge base, an intervention rule updating module and an intervention content sending module. According to the invention, a compliance prediction model for healthy and healthy people is constructed based on the deep belief network, and is used for guiding, adjusting and optimizing a health intervention strategy for people.