574results about How to "Reduce dimensionality" patented technology

The invention relates to the technical field of multi-axis linkage numerical control machining, and discloses a multi-axis linkage numerical control machining state monitoring method and system.The method comprises the steps that vibration, acoustic emission and cutting force signals in the multi-axis linkage numerical control machining process are collected and preprocessed, and first multi-source sensor data are obtained; performing feature extraction and compressed sensing to obtain a low-dimensional feature subset; performing fusion judgment to obtain an abnormal state detection result; training a deep learning model comprising a backbone network and a plurality of branch networks to obtain a state evaluation model; and inputting second multi-source sensor data collected in real time into the state evaluation model for state evaluation to obtain a processing state classification result and a parameter estimation value. According to the method, cooperative state evaluation and prediction simulation of multiple machine tools are realized.

The invention belongs to the technical field of learner cognitive state interpretation, and particularly discloses a brain-language-vision large model-based cognitive state interpretable method, which comprises the following steps of performing multi-modal data collection for the cognitive state of a learner; performing learner cognitive state characterization based on a multi-scale map convolutional network; brain-vision-language mixed multi-modal time sequence feature fusion based on hierarchical mutual information maximization is carried out; embedding knowledge graph nodes for enhancing the complex task understanding ability of the multi-modal large model; training a brain-vision-language multi-modal large model guided by knowledge; recognizing a learner cognitive state based on a multi-modal large model; the cognitive state of the learner can be explained and analyzed under the support of multi-mode owned reasoning. According to the cognitive state interpretable method based on the brain-language-vision large model, the input multi-modal fusion features are converted into high-quality prompts so as to guide the large language model to extract implicit knowledge, and important support is provided for reasoning generation.

The invention discloses a network security active defense method and system for detecting abnormal network behaviors. The method comprises the following steps: S1, constructing a virtualized isolation network environment through a software defined network technology; s2, deploying intelligent dynamic camouflage nodes in the virtualized isolation environment; s3, acquiring a disguised node attack behavior, and identifying a network abnormal behavior through a deep learning technology; s4, extracting network abnormal behavior features, and identifying potential threats through a deep learning technology; s5, a defense mechanism is automatically triggered, a honeynet is started, and abnormal behaviors and potential threats are isolated; and S6, tracing an attack path, and carrying out source positioning. The network security active defense system comprises a network construction module, a deployment module, an identification module, a honeynet, an isolation module and a tracing module. The disguised nodes are deployed through the deployment module to detect abnormal network behaviors, active defense of network security is realized through the honeynet, the isolation module and the tracing module, and the network security is improved.

The invention belongs to the technical field of electromagnetic modeling of a target in a layered medium, particularly provides a planar multilayer medium electromagnetic scattering modeling method based on a layered medium Green function, and is used for solving the problems that the number of unknown quantities of a system is large and the calculation efficiency is low when electromagnetic scattering characteristics of the target are analyzed in the layered medium. The method comprises the following steps: firstly, carrying out mesh generation on a medium target in a planar multilayer medium; then, a layered medium Green function is calculated based on a space list and an interpolation method, a surface integral equation of the target is solved by adopting a moment method, and a matrix equation of target current and magnetic current is constructed; and finally, solving a matrix equation by applying an improved laminated off-diagonal low-rank matrix, and calculating a target scattering field. According to the method, a direct solving calculation method is adopted, and the problem that an ill-conditioned matrix is difficult to converge in a conventional iteration method is effectively solved; especially for a multi-port excitation problem, repeated calculation is not needed, the solving efficiency of a large-scale problem is improved, and calculation resource consumption is reduced.

The invention relates to the technical field of biological information, in particular to a method and system for online integration of multi-source single cell data. The method comprises the following steps: acquiring multi-source single cell transcriptome data; performing data quality evaluation based on gene expression quantity, mitochondrial gene proportion and cell data depth on the single cell transcriptome data to obtain initial quality control data; setting standardized filtering parameters according to the initial quality control data, and performing quality control parameter filtering on the single cell transcriptome data to obtain filtered cell data; performing gene expression characteristic analysis on the filtered cell data to generate high-variation gene expression characteristic data; and performing gene expression spatio-temporal distribution statistics according to the high-variation gene expression feature data to generate gene expression statistical feature data. According to the method, through refined quality control and batch effect correction, data from different sources can be seamlessly integrated on the premise of ensuring data quality, so that noise caused by different batches and technical platforms is eliminated.

The invention discloses a rice spectrum stress index construction method and system, computer equipment and a storage medium, and relates to the technical field of rice stress classification, and the method comprises the steps: regularly shooting different groups of rice leaves to obtain original hyperspectral images, and carrying out black and white correction; extracting regions of interest from rice leaves under different stresses, calculating average spectral data and performing SG smoothing filter processing; using a generative adversarial network to extract 50 characteristic wave bands with the highest contribution degree; inputting the average spectral data corresponding to the characteristic wave band as model input data into a random forest classification model to obtain a model classification result; and meanwhile, screening the first five characteristic wave bands and corresponding weights to construct spectral indexes, obtaining stress indexes, dividing stress categories, and outputting stress reasons of the rice. According to the method, the nonlinear relationship in the data is more efficiently captured, the classification model performance is improved, the calculation efficiency is high, the adaptability is high, the dimensionality of the hyperspectral data is greatly reduced, and the model calculation complexity is effectively reduced.

The invention relates to the technical field of spectral analysis, and discloses a spectral analysis-based gas component detection method, device and equipment and a medium, the method comprises the following steps: carrying out axial scanning on a gas storage device to obtain hyperspectral image data; performing preprocessing and spectrum standardization on the hyperspectral image data to obtain preprocessed spectrum data; carrying out waveband selection on the preprocessed spectral data to obtain a characteristic waveband; performing abundance estimation on the characteristic wave band to obtain initial gas component content data; optimizing the initial gas component content data to obtain target gas component content data; and performing gas component anomaly detection on the target gas component content data to obtain a gas component anomaly detection report, thereby improving the accuracy of gas component anomaly detection.

The invention discloses a power prediction method considering photovoltaic power clustering, which comprises the following steps of: firstly, acquiring historical data of a photovoltaic power station, decomposing a photovoltaic power time sequence by using an STL time sequence decomposition method, performing DBSCAN clustering on the decomposed photovoltaic power data, identifying a low-power cluster and a high-power cluster, and predicting the power of the photovoltaic power station according to the low-power cluster and the high-power cluster; establishing a photovoltaic power prediction model based on a time convolutional network and Times Net for low power and high power respectively; secondly, a badger algorithm is improved, Chebyshev chaotic mapping is adopted to improve initialization of a badger population, and a Gaussian mutation strategy and a lens imaging reverse learning strategy are introduced to obtain MHBA; and optimizing hyper-parameters of the TCN photovoltaic low-power prediction model and the TimesNet photovoltaic high-power prediction model by using an improved badger algorithm, and predicting photovoltaic power by using the optimized models to obtain a final prediction value. Compared with the prior art, the precision and effectiveness of the photovoltaic power prediction model can be improved.

The invention discloses a parallel iteration Runge-Kutta method for simulating a second-order nonlinear differential equation. The parallel iteration Runge-Kutta method comprises the steps that a simulated ordinary differential equation is converted into a target nonlinear implicit differential equation, and a system differential algebraic equation is established; judging whether a system matrix and a decomposition iteration matrix in the system differential algebraic equation are updated or not; a four-order extrapolation method is adopted to obtain a predicted differential variable derivative, and if a prediction result is not converged, parallel iterative operation is carried out through a four-stage four-order Runge-Kutta method; after the iteration process is converged, judging whether an operation result accords with a preset error range, if not, updating a system matrix according to a step size reduction strategy and re-decomposing an iteration matrix, and if yes, updating the operation result; and storing and displaying an operation result when judging that simulation is finished. The algorithm stability and the calculation efficiency of the rigidity problem can be considered.

The invention relates to the technical field of battery management, and discloses a lithium battery thermal runaway feature recognition algorithm based on a BP neural network, and the algorithm comprises the following steps: data collection and preprocessing: employing a temperature sensor and a gas sensor to monitor the temperature and gas information of a lithium battery system in real time; the temperature change of the temperature sensor before and after the thermal runaway and the temperature change slope at the moment of the thermal runaway event are used as main characteristic parameters; for a gas sensor, gas concentration change before and after thermal runaway and gas concentration change slope are considered as main characteristic parameters; and taking the collected temperature, gas data and characteristic parameters as a data set. According to the invention, the temperature and gas changes of the lithium battery are monitored in real time through the temperature sensor and the gas sensor, the characteristics of thermal runaway can be quickly identified, and early warning can be timely given out, so that the real-time monitoring capability of the system is improved, and the accuracy and timeliness of thermal runaway prediction are enhanced.

The invention relates to an enterprise park meteorological fire risk assessment method based on an adaptive random forest model, and the method comprises the steps: building a risk assessment model and a visual early warning platform, enabling the risk assessment model to collect alarm data, meteorological data, enterprise data and park division data, and forming a risk assessment data set, and integrating internal characteristics and external data of the park into a data set, inputting new data into the self-adaptive random forest model by means of a real-time data flow technology, updating and evaluating a fire risk index value of the park in real time, and optimizing a model decision tree to automatically adjust a risk evaluation model and adapt to changes of environment and operation. Accurate early warning information is provided for a visual early warning platform, the risk level of the park is displayed in real time, and a risk alarm is given out in time.

The invention discloses a water quality prediction method based on double decomposition and a hybrid model and a related device, and relates to the technical field of water quality monitoring, and the method comprises the following steps: preprocessing obtained original water quality time sequence data; determining to-be-processed data based on a Pearson's correlation coefficient method and the preprocessed data; performing complete ensemble empirical mode decomposition on the to-be-processed data to obtain a plurality of intrinsic mode functions; k-means clustering is carried out on the plurality of intrinsic mode functions; the clustered intrinsic mode function comprises a high-frequency mode, an intermediate-frequency mode and a low-frequency mode; variational mode decomposition is carried out on a high-frequency mode in the clustered intrinsic mode function, and a plurality of high-frequency sub-modes are obtained; and respectively inputting the plurality of high-frequency sub-modes, the plurality of intermediate-frequency modes and the plurality of low-frequency modes into the Parallel Transform-LSTM model to obtain a final prediction result. The precision and stability of water quality parameter prediction can be improved.

The invention provides a multi-stage structure topological optimization method based on a potential diffusion model, and relates to the technical field of engineering design and structure design optimization. Creating a structure topology optimization image data set by using a traditional SIMP method and performing preprocessing; mapping the structure topological optimization image to a low-dimensional potential space for representation so as to reduce the data dimension and improve the calculation efficiency; a forward diffusion process and a reverse diffusion process are carried out on the initial potential variable in the low-dimensional potential pair, a denoising network is added in the reverse diffusion process, and a cross attention mechanism and a condition mechanism are introduced, so that a topological structure is adjusted and designed according to physical and functional requirements, and the structural fidelity is ensured; after training is completed, potential variables of a low-dimensional space are mapped back to a high-dimensional space, a topological structure is generated, few-step SIMP direct optimization is carried out on the topological structure, the structure is rapidly refined, physical information is directly injected into the design so as to adapt to given boundary conditions, and generation of the optimal topological structure can be completed through few-step iteration.

The invention relates to the technical field of hydraulic engineering operation prediction, in particular to a hydraulic engineering operation trend prediction method and system. The method comprises the following steps: acquiring multi-source hydraulic engineering data, and generating a hydraulic engineering three-dimensional model through multi-source space fusion processing and three-dimensional model simulation; performing geometric complexity identification and stress path analysis on the three-dimensional model to determine key nodes in the water conservancy project; historical water conservancy data are obtained, time sequence conversion and potential mode mining are carried out, and potential modes related to engineering are obtained; performing stress superposition comparison on the key nodes of the water conservancy project and the potential mode to generate stress superposition comparison data, and iteratively adjusting the key nodes according to a result to obtain adjusted stress nodes; constructing a prediction model based on the adjusted stress node and the potential mode, performing operation trend prediction, and generating an operation trend prediction result of the water conservancy project; the high-precision and high-adaptability water conservancy project operation trend prediction method is realized.

The invention provides a power distribution network dynamic network reconstruction method based on constraint enhanced safety reinforcement learning, and the method comprises the steps: describing a DDNR task of a power distribution network as a constraint type Markov decision process (CMDP); then, soft constraints (node voltage and line current constraints) and hard constraints (radial topology constraints) in the DDNR process are processed by adopting an interior point strategy optimization (IPO) algorithm and a mode of adding a safety layer based on a branch switching method respectively; meanwhile, the invention provides a space-time perception neural network model to increase the dynamic space-time change characteristics of the reinforcement learning agent to the power flow of the power distribution network in the DDNR process. The method plays an important role in reducing the energy loss of the power distribution network, optimizing the voltage distribution and increasing the bearing capacity of new energy.

The invention relates to an intelligent industrial data clustering and grouping method, system and device and a medium, and the method comprises the steps: carrying out the standardization preprocessing of collected original industrial data, obtaining standard industrial data, and constructing a high-dimensional feature space according to the standard industrial data; performing industrial feature extraction and dimension reduction on the high-dimensional feature space to obtain a low-dimensional feature vector; performing preliminary grouping on the low-dimensional feature vectors according to a preset clustering algorithm to obtain a first clustering result; performing secondary adjustment on the first clustering result to obtain a second clustering result; and performing industrial scene simulation verification on the second clustering result to obtain a verification result, and performing optimization adjustment on the second clustering result according to the verification result to obtain a target clustering combination. According to the method, a more reasonable clustering result can be obtained, and the accuracy and applicability of the clustering effect are improved.

The invention discloses a power load prediction method and system, and the method comprises the steps: obtaining multi-element load data, and carrying out the comprehensive correlation analysis of the multi-element load data, so as to obtain a high-correlation feature; the comprehensive correlation analysis comprises correlation analysis between meteorological factors and multi-element loads, coupling analysis between the multi-element loads and respective correlation analysis of the multi-element loads; constructing a hybrid deep learning prediction model through fusion of a convolutional neural network, a bidirectional long-short-term memory network and an attention mechanism; and inputting the high correlation feature into a mixed deep learning prediction model for prediction to obtain a predicted multi-element load value. According to the method, the dimensionality of the input layer of the multi-element load prediction model is reduced, and the input data quality is improved. The over-fitting phenomenon of a prediction algorithm can be effectively prevented, and the model prediction precision is improved; and the scheduling requirement of a system in actual engineering is met.

The invention discloses a brain decoding system based on a functional magnetic resonance image and a potential diffusion model. The brain decoding system comprises a functional magnetic resonance image feature extraction module, an image generation module and an image output module, the functional magnetic resonance image feature extraction module combines spherical convolution and a mask auto-encoder, neural activity features related to visual stimulation are extracted from functional magnetic resonance imaging fMRI data, and the image generation module generates an image with high semantic information by using the neural activity features as conditions. And the output module processes and outputs the generated image. The method solves the major challenges of reconstructing visual stimulation from fMRI data, including the problems of low signal-to-noise ratio and high dimension, effectively reduces the dimension of fMRI data, accurately captures the distance between brain regions, and learns local and global brain activation modes by introducing a feature learning algorithm based on surface fMRI data; besides, the design of an anti-noise potential diffusion model (NR-LDM) further improves the quality of a reconstructed image by reducing the influence of fMRI noise, an image with more accurate semantic information can be generated, and improvement of the accuracy of brain decoding based on fMRI data is facilitated.

The invention relates to the technical field of computer data management, and discloses a data management method and system capable of reducing computing power occupancy, and the method comprises the steps: S1, based on data distribution analysis, employing a sparse representation algorithm and a principal component analysis method, carrying out the operation elimination of the redundancy of high-dimensional data features through setting an L1 regularization constraint condition, and obtaining an L1 regularization constraint condition; decomposing the data correlation matrix in combination with a matrix operation mode, extracting feature vectors with top-ranked contribution values, and converting the feature vectors into a sparse low-dimensional matrix; and generating a sparse low-dimensional data matrix. Through combination of sparse representation and principal component analysis, L1 regularization is creatively applied to perform feature screening on high-dimensional data, redundant data items are removed, and principal component features with high correlation are extracted, so that the data dimension is effectively reduced, consumption of computing resources is reduced, and meanwhile, main information features of the data are reserved; the efficiency and accuracy of subsequent processing are improved, and distributed scheduling of tasks and efficient utilization of equipment computing power are realized.

The invention discloses a sewage treatment fault diagnosis method and system based on data analysis, and the method comprises the steps: continuously optimizing a diagnosis strategy in the input and training process of a large amount of data through introducing a fault diagnosis model, and improving the accurate recognition capability of a fault under a complex working condition; therefore, the system not only has strong learning ability, but also can perform self-optimization through a reward mechanism, thereby reducing the occurrence rate of false report and missing report. An adaptive weight adjustment mechanism is introduced, so that the model can automatically adjust weight distribution and increase response to a new fault when the system has a novel fault or few faults; by dynamically adjusting the weight vector, the diagnosis model of the invention has strong self-learning ability and can continuously adapt to new fault modes and working condition changes, and the weight adjustment mechanism greatly improves the generalization ability and adaptability of the model, and especially has significant advantages in the face of fault scenes with strong uncertainty.

The invention provides a fan bearing diagnosis method and device based on a gradient weighting type activation thermodynamic diagram, and relates to the technical field of wind driven generator bearing fault diagnosis, and the method comprises the steps: obtaining a bearing vibration signal of a wind turbine generator; carrying out noise reduction processing on the obtained bearing vibration signal; the bearing vibration signal after noise reduction is converted into a time-frequency image through short-time Fourier transform, the obtained time-frequency image serves as input, and a gradient weighting type activation thermodynamic diagram is obtained through a feature extraction unit of a fault diagnosis model; taking the obtained gradient weighting type activation thermodynamic diagram as input, performing fault diagnosis through a fault diagnosis unit of a fault diagnosis model according to the input gradient weighting type activation thermodynamic diagram, and obtaining a diagnosis result; and the fault diagnosis unit is obtained by training a preset machine learning algorithm according to historical gradient weighted class activation thermodynamic diagrams corresponding to different bearing faults of the wind turbine generator. The data processing calculation amount can be effectively reduced, and the fault diagnosis accuracy is improved.

The embodiment of the invention provides a virtual power plant scheduling strategy based on a consistency algorithm. The method comprises the following steps: acquiring a plurality of power dispatching instructions sent by a power grid dispatching center; according to the plurality of power scheduling instructions, determining a total target allocation power of the plurality of distributed resource clusters and a target allocation power of a target distributed resource cluster, the target distributed resource cluster being any one of the plurality of distributed resource clusters; according to the target allocation power of the distributed resource cluster, the corresponding cooperative adjustment contribution parameter and the power error adjustment parameter, determining the target allocation power of other distributed resource clusters by adopting a consistency algorithm; and scheduling the distributed resources in the corresponding distributed resource cluster according to the plurality of target allocation powers. According to the method, through two-stage hierarchical distribution and adjustment of power, the dimensionality of scheduling instruction dynamic tracking optimization can be significantly reduced, the communication capacity is increased, and the economical efficiency and rapidity of virtual power plant power adjustment are ensured.

The invention belongs to the technical field of stratum recognition, and particularly relates to a shield parameter geological recognition calculation method and system based on an unsupervised clustering algorithm, and the method comprises the steps: selecting a plurality of groups of data containing factors affecting stratum category recognition; pCA converts a group of complex related variables into fewer and irrelevant variables, and the new variables are called as main components and can capture most of information in original data; determining the clustering number of K-means + + by adopting an elbow method; determining the clustering number of a hierarchical clustering method by adopting a hierarchical tree; k-means + + and hierarchical clustering are respectively used for clustering the data, and evaluation indexes such as identification precision or contour coefficients of the data and the data are calculated, so that potential modes in the data are analyzed and mined more comprehensively. The invention aims to deeply explore the internal relevance between shield tunnel parameters and geological types, provides an innovative and robust solution for real-time detection of geological features in shield construction by overcoming the limitation of the prior art, and has wide application potential.

The invention belongs to the technical field of medical data processing, and discloses a temporal-mandibular joint disease automatic diagnosis method and system based on artificial intelligence, and the method comprises the steps: collecting and preprocessing data; expanding the patient data, dynamically adjusting the structure and parameters of a data generator, and generating the patient data; performing feature extraction on the patient data, and automatically adjusting an evolution rule of a feature extraction network to obtain patient data features; performing dimensionality reduction on the data features to obtain low-dimensional patient data features; inputting the low-dimensional patient data features into a classifier, and dynamically adjusting the configuration and weight of hidden layer nodes of the classifier according to the low-dimensional patient data features to obtain a classification result; and defining a loss function, and training the model. Parameters can be automatically adjusted in the data feature extraction process, the data dimension reduction process is optimized by adopting quantum state mapping and an adaptive adjustment mechanism, the classifier can dynamically adjust the number of hidden layer nodes and an activation function, and the accuracy and robustness of a diagnosis result are improved.

The invention relates to the technical field of circuit data processing, in particular to a device simulation method for an integrated circuit. The method comprises the following steps: acquiring integrated circuit device data, and generating a device main feature matrix through multi-dimensional topological feature extraction and redundant feature elimination; performing hierarchical analysis on the main feature matrix of the device, generating a device hierarchical embedding graph matrix, and determining a device hierarchical structure through hierarchical clustering of a module graph theory; predicting a potential element link probability in the main feature matrix of the device, and carrying out topology analysis to generate an element link relationship; based on the device hierarchical structure and the element link relationship, constructing a device unit, and generating a simulation circuit device unit; performing circuit integration simulation on the simulation circuit device unit, performing defect optimization in combination with the data of the integrated circuit device, and finally obtaining the optimized data of the integrated circuit device; according to the invention, a more accurate and more reliable device simulation method for an integrated circuit can be realized.

The invention relates to the technical field of automobile fault diagnosis instruments, in particular to an automobile fault diagnosis method and system based on deep learning, and the method comprises the steps: firstly obtaining fault code data of a target automobile, and then grouping a plurality of fault codes based on a sequence represented by collection time sequence data, and converting all the fault codes in the multi-frame fault code set into word vectors based on a preset word embedding matrix, combining the word vectors in the same frame of fault code set to obtain a context vector of each frame of fault code set, and finally inputting a plurality of context vectors into a preset neural network diagnosis model to obtain a fault diagnosis result of the target automobile. Compared with the prior art, on one hand, the dimension of the fault codes on the time scale is reduced by grouping the multiple fault codes, and on the other hand, the dimension of the fault codes on the category scale is reduced by utilizing the word embedding technology; the problem that dimension explosion exists in an automobile fault diagnosis method based on deep learning in the prior art is solved.

The invention relates to the field of intelligent manufacturing, and discloses an unmanned workshop equipment upgrading intelligent scheduling optimization system based on reinforcement learning, and the system comprises a feature collection module which collects workshop equipment data, extracts corresponding data features, and obtains production feature data; the space optimization module is used for performing clustering operation on the production feature data to obtain feature clustering tags, and constructing an optimized multi-dimensional state space through the feature clustering tags; the strategy optimization module is used for learning optimal equipment scheduling action strategies in different states in a multi-dimensional state space through a neural network model with an optimized structure to obtain a series of equipment scheduling strategy sets for different states; and the scheduling acquisition module is used for constructing an improved depth # imgabs0 # network model, inputting the equipment scheduling strategy set into the improved depth # imgabs1 # network model, and obtaining optimal scheduling modes in different states. Compared with the traditional method, the method has the advantages that the rule distribution is simple, and the equipment scheduling of the unmanned workshop can be better optimized.

The invention relates to a human body behavior recognition method, device and system based on WiFi and vision multi-mode fusion. The human body behavior recognition method processes WiFi channel state information and video data through two independent network models. The method comprises the following steps: firstly, carrying out feature extraction and recognition on WiFi channel state information by utilizing a WiFi deep learning model; then, performing feature extraction and recognition on the video data by using a visual deep learning model; and finally, splicing the identification results of the two and carrying out decision fusion. According to the method, multi-modal data fusion in a feature layer is avoided, and the influence of modal information change on a prediction result is reduced, so that the robustness of the human body behavior recognition method under the conditions of data noise, modal failure and the like and the accuracy of human body behavior recognition are improved.

The invention relates to the technical field of data mining, in particular to a pediatric clinical intelligent medical decision model training method. The method comprises the following steps: acquiring historical pediatric clinical data and desensitizing to obtain historical pediatric clinical desensitization data; performing data fusion according to the historical pediatric clinical desensitization data to obtain historical pediatric clinical fusion data; performing pediatric health care detection on the historical pediatric clinical fusion data to obtain abnormal pediatric health care data; calculating health recovery influence factors of the abnormal pediatric health care data to obtain the health recovery influence factors; performing pediatric doctor recommendation priority division on the historical pediatric clinical sensitivity reduction data to obtain pediatric doctor recommendation priority data; and according to the pediatric doctor recommendation priority data and the health recovery influence factors, constructing an intelligent pediatric medical decision model. The decision-making efficiency of pediatric medicine can be improved.

The invention provides a radiation source individual identification method based on multi-domain feature fusion, which mainly realizes radiation source individual identification through signal acquisition, signal preprocessing, signal pre-classification, feature extraction and model pre-training. Compared with a traditional method, the method has the advantages that the fused multi-domain features can better reflect subtle differences of radiation sources, and the recognition effect is obviously improved; by respectively extracting the features of each domain, the dimension of data can be effectively reduced, so that the complexity of calculation and storage is reduced; meanwhile, pertinent processing and fusion are allowed to be carried out on the features of different domains, and the classification precision is improved; the features of different domains can be selected and weighted, the influence of important features is enhanced, and the model is more flexible and accurate; common computer hardware is used for configuration, practicability is high, and economical efficiency is good.