164results about How to "Efficient forecasting" patented technology

The invention discloses a method for realizing high-efficiency low-consumption micro-aerobic hydrolytic acidification of petrochemical wastewater by regulating and controlling the aeration rate in combination with a space-time diagram neural network and reinforced learning, and relates to a method for realizing high-efficiency low-consumption micro-aerobic hydrolytic acidification of petrochemical wastewater by regulating and controlling the aeration rate. The method comprises the following steps of: 1, collecting and cleaning data; 2, constructing a graph data set; 3, training the ST-GNN model; 4, constructing a deep Q reinforcement learning model; 5, the trained ST-GNN model is embedded into an automatic control system of a sewage treatment plant, and the model outputs the optimal aeration rate of the current time step and predicts the micro-aerobic hydrolytic acidification effect through interaction of the current working condition and historical data; and adjusting operation parameters of aeration equipment according to the output of the model, and carrying out hydrolytic acidification treatment. The method is high in regulation and control efficiency and low in operation error, and can be applied to the field of digital and intelligent management of sewage treatment plants.

The invention discloses a transformer noise detection method based on season trend decomposition and time sequence prediction, and belongs to the technical field of transformer noise anomaly detection. Comprising the following steps: S1, acquiring noise data of a transformer, and preprocessing the noise data; s2, adopting an STL decomposition model to obtain a seasonal component, a trend component and a residual component; s3, performing Transform time sequence prediction on the seasonal components and the trend components to obtain prediction results of the seasonal components and the trend components; s4, performing anomaly detection on prediction results of the seasonal components and the trend components, and outputting a detection result; and S5, carrying out weighted composition on the detection results of all the components, and outputting a diagnosis result. According to the method, the time sequence data of the noise of the transformer substation is decomposed, predicted and subjected to abnormal point detection, and reliable technical support is provided for state monitoring and fault prevention of transformer substation equipment.

The invention relates to a fractured coal and rock mass strength intelligent prediction method based on image recognition, and belongs to the technical field of rock mass mechanics prediction. The method comprises the following steps: (1) carrying out an indoor test according to different fracture parameters and lithology, and establishing an initial training data set; (2) introducing numerical simulation data, supplementing laboratory data through the simulation data, integrating the data into a data set, and constructing a sample database of coal and rock mass fractures and strength; (3) training a machine learning model by using the sample database, wherein the machine learning model is trained by adopting a gradient lifting decision tree model; (4) testing the precision of the machine learning model by using test set data; (5) acquiring a working face image; and (6) predicting the strength of the to-be-measured coal rock mass. According to the method, a traditional rock strength measurement method is improved, an accurate prediction model can be constructed by combining the nonlinear relationship between the geometrical characteristics and the strength of the coal-rock mass fractures with different lithology, and efficient prediction is realized.

The invention discloses a PM2.5 and ozone combined pollution intelligent prediction method and system and a medium. The method comprises the following steps: collecting historical air quality data, environmental meteorological data and atmospheric pollution source emission data of a to-be-predicted area; collecting cross characteristic data formed by combining different environmental meteorological conditions and different associated pollution sources; enabling the collected data to form a training data set, and carrying out the data matching; training a multi-task neural network model; selecting an optimal input feature parameter according to the predicted increment contribution degree of each input feature to the trained multi-task neural network model, and constructing and forming a final combined pollution identification model; and acquiring real-time air quality data, environmental meteorological data and atmospheric pollution source emission data of a to-be-predicted area, and inputting the data into the combined pollution identification model to predict PM2.5 and ozone concentration data. The method has the advantages of being simple in implementation method, low in cost, high in prediction precision and efficiency, high in robustness and the like.

The invention relates to the technical field of flash memories, and discloses a NAND FLASH service life prediction and management method and related equipment. The method comprises the following steps: performing multi-dimensional acquisition and feature extraction on physical and electrical parameters and environmental error parameters of a target flash memory, and performing time sequence alignment and preprocessing on obtained basic feature data to obtain multi-modal parameter data; and through multi-level feature extraction and correlation analysis, constructing a multi-dimensional feature space, generating an initial fusion feature matrix based on feature pairing and fusion analysis, and through error coefficient and performance degradation calculation and performance prediction of a multi-P / E cycle scale, generating a life prediction result. Based on the performance state parameters, threshold division and deviation calculation are carried out, weighting calculation and state evaluation of margins are carried out, and safety operation margin and health state evaluation results are obtained; and finally, generating an operation management result based on state grade division and parameter mapping. And efficient and accurate prediction and management of the service life of the flash memory are realized.

The invention discloses a shaft surrounding rock deformation prediction method and device based on fluid-solid coupling linkage simulation, and the method comprises the steps: 1, describing the influence of rainwater permeation on surrounding rock deformation through the change of water content, constructing and forming a permeation-surrounding rock deformation model, and constructing and forming a scouring-surrounding rock deformation model, building a vertical shaft excavation model under the action of rainwater; 2, performing multi-scale linkage simulation on the permeation-surrounding rock deformation model, the scouring-surrounding rock deformation model and the vertical shaft excavation model to obtain surrounding rock deformation data; 3, taking the surrounding rock deformation data as a training set to train a TCN-LSTM neural network model, and obtaining a shaft surrounding rock deformation prediction model under the rainwater effect; and 4, acquiring real-time surrounding rock deformation data, inputting the real-time surrounding rock deformation data into the shaft surrounding rock deformation prediction model, and outputting a prediction result. The shaft surrounding rock deformation prediction precision, efficiency and application flexibility can be improved, and meanwhile the implementation complexity and the calculation amount are reduced.

The invention relates to the technical field of leakage traceability, in particular to a water transfer project leakage traceability identification method and system, and the method comprises the following steps: setting monitoring points in a water transfer project, collecting the temperature, pressure and flow velocity data of the key monitoring points, carrying out the time sequence analysis of parameters, analyzing the water flow dynamic state and environment change in real time, and generating state monitoring data. According to the method, the environment change near the leakage is analyzed by using the state monitoring data, the abnormal change is identified through simulation comparison analysis, the leakage point is predicted and positioned more accurately, the energy state before and after the leakage is evaluated through entropy increase calculation, and the leakage position and time are further predicted accurately; by simulating energy flow under differentiated environment conditions, the accuracy of seepage path identification is improved, and more efficient seepage source positioning is realized, so that the whole seepage traceability identification process is more systematic and scientific, and the safety supervision and maintenance efficiency of water conservancy facilities is greatly enhanced.

The invention provides a GNSS data quality prediction method and device based on a fisheye camera, and a medium, and belongs to the technical field of data processing, and the method comprises the steps: determining the satellite orbit information of a future time period with a to-be-evaluated observation station as a reference point based on ephemeris prediction; according to the fisheye sky image shot at the to-be-evaluated observation station and the satellite orbit information, determining satellite feature data of a future time period and the occlusion porosity of the future time period; respectively inputting the occlusion porosity of the future time period and at least part of satellite feature data of the future time period into a general prediction model and an observation station prediction model corresponding to the observation station to be evaluated for data quality evaluation, and obtaining a first data quality evaluation index and a second data quality evaluation index; and determining a data quality prediction result based on the first data quality evaluation index and the second data quality evaluation index. A general prediction model and an observation station exclusive prediction model are introduced to carry out double-model evaluation, and accurate prediction of GNSS observation data quality is realized.

The invention relates to the technical field of equipment fault detection, and discloses an equipment fault prediction and diagnosis method which is applied to an ATE system, and the method comprises the steps: collecting the real-time operation data of equipment through a plurality of sensors, and dynamically scheduling the sensors through an adaptive algorithm, so as to adjust the data collection frequency and range according to the state of the equipment; the collected data is preprocessed; training a plurality of machine learning models based on the historical fault data and the operation data, and deploying the models to an ATE system for real-time analysis; predicting an equipment fault according to the model, generating alarm information when a potential fault is detected, and sending the alarm information to an operator through a user interface or a notification system; generating a fault diagnosis report according to an analysis result and providing a repair suggestion; recording a fault instance and data, and updating and retraining the machine learning model to improve prediction and diagnosis accuracy; and generating a digital twin model of the equipment based on real-time and historical data, simulating the running state of the equipment, predicting future faults, and evaluating the health condition of the equipment.

The invention provides a grouting curtain parameter design method for structure protection under the blasting vibration effect, and relates to the technical field of underground engineering and blasting engineering, and the grouting curtain parameter design method comprises the following steps: S1, determining main factors influencing the vibration velocity of a protection structure, and constructing a vibration velocity prediction model containing an undetermined coefficient by adopting a dimension analysis white golden-han theorem; s2, establishing a finite element blasting numerical model, and obtaining structural vibration velocity data under different parameter combinations; s3, fitting the structural vibration velocity data by using the vibration velocity prediction model, and determining an undetermined coefficient in the vibration velocity prediction model; and S4, a relational expression between the structural vibration velocity and stress is established, and grouting curtain design parameters meeting structural safety requirements are determined in combination with engineering conditions. By means of the method, the problem that the grouting curtain damping effect and grouting curtain parameters are difficult to determine in actual engineering is solved, the blasting vibration peak value of the protection structure under the blasting vibration effect is effectively reduced, and a guarantee is provided for underground and ground structure protection and blasting engineering safety construction.

The invention belongs to the technical field of dynamic data forwarding, and discloses a dynamic data forwarding method and system based on an intelligent gateway. Comprising the following steps: collecting multi-dimensional information (CPU utilization rate, memory utilization rate, network flow, equipment operation time, environment temperature, power supply voltage and equipment type) of a gateway, and combining a support vector regression (SVR) model to predict a future energy consumption value. And the energy consumption is predicted in real time based on the updated feature vector. Establishing a topological node of the gateway, and comprehensively considering factors such as predicted energy consumption, node electric quantity, working voltage, environment temperature and the like to obtain a state score; during path selection, the path with the highest score is selected according to the state score of the node so as to ensure that the energy consumption in the data transmission process is minimized, and the service life of the gateway is prolonged.

The invention provides a prediction model generation method and prediction method for the final recoverable reserves of shale gas, and the prediction method comprises the steps: obtaining a parameter type and a parameter value of a to-be-predicted shale gas well; according to the parameter type and the parameter value of the to-be-predicted shale gas well, determining the approximate recoverable reserves of the to-be-predicted shale gas well by using a preset rule; performing normalization processing on the to-be-predicted shale gas well parameter values and the approximate recoverable reserves to obtain normalized shale gas well parameter values and normalized approximate recoverable reserves; calculating the correlation between the normalized shale gas well parameter value and the normalized approximate recoverable reserves; the to-be-predicted shale gas well parameter types with the correlation exceeding a preset fourth threshold value serve as main control factors of the to-be-predicted shale gas well; and substituting the main control factors of the to-be-predicted shale gas well into a preset prediction model of the final recoverable reserves of the shale gas to obtain the final recoverable reserves of the to-be-predicted shale gas well. According to the prediction method, the final recoverable reserves of the shale gas well can be accurately and efficiently obtained.

The invention relates to the field of water supply systems, and discloses an urban pipeline leakage monitoring system based on a DMA partition management technology, and the system comprises a data collection module which is used for collecting the data of a flowmeter, a pressure sensor and a hydrophone in a DMA partition; the data preprocessing module is used for preprocessing the data; the time sequence analysis module is used for performing seasonal and trend decomposition and nonlinear time sequence analysis on the preprocessed data; the leakage detection module is used for predicting and detecting leakage; the noise analyzing and positioning module is used for analyzing and determining the specific position of the leakage point of the pipe section through the noise data of the hydrophone; and the real-time prediction alarm module is used for processing data in real time and triggering alarm when leakage is detected. The system not only can predict a potential leakage event in advance, but also can quickly position and give an alarm when leakage occurs, so that the operation efficiency and safety of the water supply system are greatly improved.

The embodiment of the invention discloses a model training method, a path planning method and a related device. The method comprises the steps of obtaining an environment data set in a current environment according to a preset sample type set; the preset sample type comprises a map size, an obstacle position point, and a starting position point and a target position point of the robot; based on a path planning algorithm, according to the environment data set, generating a path data set comprising the shortest obstacle avoidance path between the position points; the path data set comprises a path point sequence and a path length; forming a training data set according to the environment data set and the path data set; training the initial neural network model based on the training data set and an improved teaching and learning optimization TLBO algorithm to obtain a pre-trained neural network model; wherein the improved TLBO algorithm is an algorithm for selecting two hyper-parameter combinations with the highest fitness to carry out cross learning. According to the embodiment of the invention, the path planning accuracy and reliability can be improved, and the dynamic environment adaptability is enhanced.

The invention discloses a converter station energy efficiency prediction system based on deep learning, and the system comprises the steps: S1, collecting the data of all equipment in a converter station, and generating an original data set; s2, processing the original data set to generate a standardized data set; s3, constructing an equipment network diagram based on the topological relation of the converter station equipment; s4, the graph neural network module constructs a graph neural network model based on the equipment network graph, extracts feature information of nodes and edges, and generates a multi-dimensional embedded vector; s5, the meta learning module updates model parameters by adopting a hierarchical optimization strategy based on the multi-dimensional embedded vector generated by the graph neural network module, and generates a model parameter set capable of adapting to different input feature distributions; and S6, based on the model parameter set generated by the meta learning module, predicting the energy efficiency related indexes of the converter station. The method has the advantages of strong modeling capability, high prediction precision, strong adaptability and good real-time performance.

The invention relates to a mine local fan required air volume advanced adjustment method, which comprises the following steps of: 1, establishing a physical model of a local fan air duct by using numerical simulation software, performing simulation operation, and generating local fan air duct opening air volume simulation data under various working conditions; 2, carrying out data screening processing on the generated air volume simulation data of the air duct opening of the local fan under each working condition; 3, training a neural network according to the screened data, and performing performance prediction on the air volume of the air duct opening by using verification set data in the trained neural network; and 4, target parameters are set according to the predicted air volume value, and the output air volume is adjusted in advance through a closed-loop PID feedback control mechanism. According to the advanced adjustment method for the needed air volume of the mine local ventilator, the prediction efficiency and precision of the air volume of an air duct opening of the ventilator are greatly improved.

The invention discloses a wind power plant wake flow prediction method based on an improved physical information neural network. The wind power plant wake flow prediction method comprises the following steps: constructing a basic physical information neural network fusing a laser wind finding radar module, a two-dimensional Navier-Stokes equation and an actuating disc model; a cosine annealing algorithm and a dynamic loss function weight are adopted for the basic physical information neural network, and a step-by-step time saving strategy is introduced for training to construct an improved physical information neural network; constructing a training data set based on the radial wind speed data of the sparse measurement points of the laser wind finding radar, and training the improved physical information neural network to obtain a wind power plant wake flow prediction model; and inputting spatial position data and time data to obtain the predicted wake wind speed and air pressure of the wind power plant. According to the invention, the improved physical information neural network combines the measurement data with the physical law, and achieves the accurate and efficient prediction of the wake flow of the whole wind power plant through employing a precision improvement strategy and a step-by-step time saving strategy.

The invention discloses a groundwater environment quality prediction method and system, and relates to the technical field of prediction data processing, and the method comprises the steps: obtaining historical observation data and real-time observation data of a to-be-predicted solute; obtaining a boundary condition, dividing the prediction region into a plurality of sub-regions based on the boundary condition, and generating a plurality of three-dimensional grid points; acquiring geological data corresponding to the three-dimensional grid point, acquiring an influence factor between the solute to be predicted and the geological data based on the correlation model, judging whether the influence factor is within a preset threshold range, and if not, taking the three-dimensional grid point as a grid point to be processed; acquiring a first boundary value and a second boundary value; performing calculation processing on the first boundary value and the second boundary value based on a dispersion calculation model to obtain longitudinal dispersion and transverse dispersion; and obtaining prediction moment data, obtaining a grid prediction result, obtaining a sub-region prediction result, and obtaining a target prediction result. The method has the advantages of being comprehensive, accurate, high in prediction efficiency and high in adaptability.

The invention discloses a 42CrMo fatigue crack propagation rate prediction method based on machine learning, and the method comprises the steps: obtaining a corresponding relation among a stress intensity factor, a stress ratio and a fatigue crack propagation rate of a 42CrMo material through a fatigue crack propagation test, and taking the corresponding relation as an original data set; dividing the preprocessed original data set into a training set and a test set, constructing a fatigue crack growth rate prediction model, training the model by adopting the training set, and evaluating the trained model by adopting the test set; and inputting the actually measured stress intensity factor and stress ratio of the 42CrMo material to be predicted into the prediction model obtained in the step 3, and outputting through the prediction model to obtain a predicted value of the fatigue crack growth rate of the 42CrMo material to be predicted. According to the method, the fatigue crack growth rate of the 42CrMo material can be efficiently and accurately predicted at low cost, so that the service life of the wind power bearing is prolonged, and potential safety hazards are reduced.

The invention relates to an intelligent card chip energy consumption prediction and optimization method and a related device. The method comprises the following steps: acquiring operation data based on a target working mode acquired by an intelligent card chip in a target time window, and performing time slice division processing and clustering processing on the operation data to obtain a multi-dimensional feature vector in the target time window; obtaining a trained energy consumption prediction model, inputting the multi-dimensional feature vector into the energy consumption prediction model for processing, and predicting to obtain a dynamic energy consumption distribution diagram corresponding to the energy consumption parameters of the smart card chip; and determining each energy consumption unit of the smart card chip, and optimizing the energy consumption parameters based on the association relationship between each energy consumption unit and the dynamic energy consumption distribution diagram to obtain optimized energy consumption parameters. By adopting the method, energy consumption prediction and optimization can be efficiently and effectively carried out on the intelligent card chip.

The invention discloses a rusted high-strength bolt connection pretightening force intelligent self-optimization prediction method and system, and belongs to the technical field of bolt pretightening force detection.The method specifically comprises the steps that a finite element database of bolt connection pretightening force loss is established, and a machine learning prediction model based on a neural network is trained; when the recognition accuracy of the machine learning prediction model reaches a preset value, pre-tightening force loss of the target bolt is monitored in real time, and real-time geometric feature information of connection of the target bolt is obtained; when the similarity between the real-time geometric feature information of the target bolt connection and a finite element model in the finite element database is greater than or equal to a preset value, calling the pre-tightening force loss of the existing finite element database; when the similarity is lower than a preset value, the trained machine learning prediction model is utilized to output actual bolt connection pre-tightening force loss in real time; the bolt looseness detection device realizes automatic and marker-free bolt looseness detection, is high in bolt image acquisition angle and distance tolerance, and can meet bolt looseness detection at different positions.

The invention discloses a tensor-kernel neural network for efficient prediction modeling and a modeling method thereof, and the tensor-kernel neural network comprises a kernel function mapping module which is used for carrying out the nonlinear kernel mapping of input data to obtain high-dimensional data; the tensor decomposition module is used for performing low-rank reconstruction on high-dimensional data and extracting low-dimensional features; and the deep neural network is used for training and predicting the extracted data low-dimensional features and outputting a prediction result. The loss function can effectively balance the reconstruction error and the prediction error in the model training process, and the generalization ability of the model is improved. In combination with Gaussian radial basis kernel function nonlinear kernel mapping, nonlinear input data is mapped to a high-dimensional feature space, and low-rank reconstruction is performed on high-dimensional data through a CP decomposition method; and finally, training and predicting the extracted low-dimensional features through a deep neural network. According to the method, the calculation efficiency and the prediction precision can be remarkably improved in high-dimensional data modeling; and the calculation complexity is reduced, and meanwhile, accurate extraction of data features and efficient training of the model are ensured.

The invention relates to an intelligent vibration prediction method and system for a large space unfolding mechanism. The method comprises the following steps: acquiring grating reflection wavelength change data caused by small structural deformation caused by vibration; the change of wavelength is calculated by receiving light waves reflected by the fiber grating sensor, and then the change of wavelength is converted into space and time information of vibration through signal demodulation, and local vibration displacement data are analyzed and processed; taking the analyzed and processed local vibration displacement data as the input of a GCN-LSTM neural network; and performing prediction calculation on the overall vibration state of the large space expansion mechanism by combining a space-time coupling relationship to obtain an overall vibration displacement result of the mechanism. The overall vibration of the large space unfolding mechanism can be monitored in real time, the method has the advantages of being high in precision and reliability, meanwhile, a decision basis can be provided for control over the on-orbit state of a spacecraft, and the high-precision attitude and stability of the spacecraft in the complex space environment are ensured.

The invention relates to the technical field of power grids, and discloses a power prediction method and device for photovoltaic power generation, equipment, a medium and a product. The method comprises the steps of obtaining a multi-modal historical data set corresponding to a photovoltaic station, and performing modal decomposition on the multi-modal historical data set by adopting a multivariate variational modal decomposition method to obtain a plurality of first modal components; obtaining a modal prediction result corresponding to each first modal component according to the plurality of first modal components through a kernel extreme learning machine prediction model optimized by a multi-target white shark optimization algorithm; and searching a prediction weight value corresponding to each first modal component, and obtaining a generation power prediction result corresponding to the photovoltaic station according to the modal prediction result and the prediction weight value corresponding to each first modal component. According to the scheme of the embodiment, multi-modal data feature mining and efficient prediction of the photovoltaic station can be realized, and the accuracy of power prediction of photovoltaic power generation can be improved.

The invention discloses a sequential causal-driven meteorological disaster prediction method, and belongs to the field of causal relationship discovery and meteorological disaster prediction. According to the specific scheme, the method comprises the following steps: preprocessing time series data of meteorological disasters to obtain an optimal lag window and an optimal data length; dividing the meteorological data into a plurality of meteorological data segments with the same length according to the optimal lagging window and the optimal data length; respectively applying a time sequence causal relationship discovery method to the divided meteorological data segments to obtain window causal structure diagrams; and fusing the causal structure diagrams in the plurality of data fragments in an ensemble learning mode to obtain a globally consistent time sequence causal structure diagram. In meteorological disaster prediction, the time sequence causal relationship between disaster risk levels and key meteorological factors in meteorological data can be mined, the accuracy of meteorological disaster prediction by a meteorological institution is improved, and effective prevention of meteorological disasters is realized.

The invention discloses a wind power prediction system fusing ensemble learning and time sequence analysis, and relates to the technical field of new energy, and the method comprises the steps: extracting a characteristic variable matrix of wind power, determining a weight identifier, carrying out the mining of a time-phase linear law based on the characteristic variable matrix, and constructing a multivariate time sequence curve. And constructing a power prediction model integrating a plurality of base predictors by combining a multivariate time sequence curve and adopting a data-driven training method. And on the basis of the model, monitoring and returning multiple paths of feature data, analyzing a prediction task type, and determining final power prediction data. And displaying a power prediction result to a terminal user through visual display, and reversely guiding operation and management of the wind turbine generator through data, so that efficient prediction and real-time optimization are realized. The technical problems of inaccurate wind power prediction, insufficient timeliness and poor response to complex environment change in the prior art are solved, and the technical effect of improving the accuracy and timeliness of wind power prediction is achieved.

The invention discloses a method for predicting the SOH of a battery based on an MH-MoE model and EIS data. The method comprises the steps that S1, the EIS data and the SOH data of the battery are tested and collected; s2, constructing an MH-MoE model, wherein the MH-MoE model comprises three expert sub-models and three gating network models, and each sub-model is a CNN-LSTM model; the CNN model extracts spatial features of input data, and the LSTM model extracts time sequence features of battery EIS data; s3, training and verifying the MH-MoE model; and S4, predicting the SOH of the battery. The method is based on the MH-MoE model and uses the battery impedance to predict SOH, has the advantages of diversified feature extraction brought by a multi-head mechanism, sparse activation to improve calculation efficiency, capability of focusing on different modes by an expert network, and flexible adaptation to a multi-task environment, can predict the health state of the battery more efficiently and more accurately, and adapts to complex working conditions.

The invention discloses a method and a system for rapidly estimating the state of health of a battery based on frequency domain analysis. The method comprises the following steps: acquiring lithium battery operation data in a plurality of discharge cycles; inputting the lithium battery operation data into a frequency domain prediction neural network to perform battery health state prediction so as to obtain a prediction result; wherein the frequency domain prediction neural network is obtained by obtaining historical lithium battery operation data and corresponding lithium battery health state data as a sample set to train the neural network, and the neural network comprises a data processing layer, a Fourier transform layer, a filter, a complex value linear layer, a zero filling layer and an inverse Fourier transform layer which are connected in sequence; and outputting the prediction result. By implementing the method provided by the invention, effective combination of frequency domain analysis and low resource consumption can be realized, and the SOH prediction efficiency and accuracy of the battery management system are improved.

The invention relates to a multi-scale optimization intelligent design method for a special-shaped structure, and belongs to the field of spacecraft structure mechanism additive manufacturing. Comprising the following steps: constructing a special-shaped lattice structure by adopting a level set function, and performing interpolation and correction; setting relative density sample points and performing polynomial interpolation to establish a geometric agent model; obtaining a constitutive matrix through numerical value homogenization and establishing a constitutive agent model; based on a constitutive agent model and an isogeometric topological optimization method, performing iterative solution by taking minimization of the flexibility of the special-shaped sandwich structure as a target to obtain optimal density distribution; and the optimal density distribution is substituted into a geometric agent model to output an interpolation coefficient, and an implicit model of the special-shaped sandwich structure is generated through gradient lattice filling. According to the method, the implicit model of the special-shaped sandwich structure filled with the conformal continuous gradient special-shaped lattice structure is constructed, and the special-shaped sandwich structure is generated through additive manufacturing based on the implicit model, so that the problem that a multi-scale optimization design method for the gradient lattice filled special-shaped sandwich structure is lacked at present is solved.

The invention relates to a deep learning-based obsessive-compulsive disorder recognition intervention method and intervention system, and the method comprises the steps: carrying out the segmentation training of a collected video image of a target object when the target object appears in a visual field, predicting the obsessive-compulsive disorder correlation degree, and carrying out the segmentation training of the collected video image of the target object when the target object is not in the visual field; multiple physiological index data of obsessive-compulsive disorder persons are collected through the bracelet, a fuzzy neural network is adopted for prediction, obsessive-compulsive disorder relevancy detected under two scenes can achieve data fusion, finally, a target control function is judged, a remote control instruction is sent to the bracelet in time when an obsessive-compulsive disorder behavior occurs, and the obsessive-compulsive disorder behavior is controlled. And meanwhile, the bracelet display screen is used for reminding the user of'relaxing ', so that the obsessive-compulsive disorder person is postponed physically and mentally and jumps out of the obsessive-compulsive disorder mode. Therefore, according to the technical scheme, the obsessive-compulsive disorder behavior of the person is accurately and efficiently predicted, and the obsessive-compulsive disorder behavior is timely intervened and guided in an audio-visual mode on the basis of prediction.