55results about How to "Improve learning ability" patented technology

The invention relates to the technical field of batteries, and discloses a battery thermal runaway early warning and cooling system which comprises a multi-parameter monitoring module, a data processing and early warning model and a cooling system control module. Wherein the multi-parameter monitoring module is used for acquiring the internal resistance value of each battery monomer in real time in a battery charging process, calculating the internal resistance range, the internal resistance standard deviation and the variable coefficient according to the internal resistance value at a sampling time point, monitoring the working environment gas of the battery through a mass spectrometer, and acquiring the real-time mass spectrum monitoring data of the gas in real time; release characteristics, including hydrogen, hydrocarbon compounds and the like, of escaped gas are quantitatively analyzed on the basis of an equivalent characteristic spectrum method. According to the battery thermal runaway early warning and cooling system, various key parameters in the battery charging process are comprehensively monitored, early warning of the battery thermal runaway risk is achieved, timely intervention is conducted in combination with the cooling system, the thermal runaway risk is reduced, and vehicle and personal safety is guaranteed.

The invention relates to the technical field of big data analysis and artificial intelligence, in particular to a multi-source heterogeneous big data fusion and reasoning method and system based on a knowledge graph, and the method comprises the following steps: constructing mapping interfaces of various data sources, collecting and storing entities in the data sources, and constructing nodes in the knowledge graph; performing representation learning on a graph structure of the knowledge graph based on a graph neural network, extracting an embedded vector of a node, and extracting a fusion node through the embedded vector and a mapping interface; performing knowledge completion on the fusion nodes by using a neural network based on symbolic logic; generating candidate reasoning paths by adopting a depth-first search algorithm based on the knowledge graph; reasoning is carried out based on an attention mechanism and path similarity, candidate reasoning paths are screened and sorted to obtain an optimal reasoning path, a graph neural network is adopted to carry out representation learning of a knowledge graph, symbolic logic and the neural network are combined, the interpretability of the reasoning process is guaranteed, and the learning ability is high.

The invention relates to the related field of photovoltaic construction, in particular to a photovoltaic construction management system, which comprehensively analyzes video data and environmental sensor data, improves the accuracy and reliability of the monitoring effect, and can effectively reduce the blind area and false alarm rate of a single data source through the fusion of multi-modal data. According to the invention, a genetic algorithm and a decision tree algorithm are integrated, so that the system has strong learning ability, and the system can be continuously self-optimized according to historical data and operation logic to gradually adapt to different construction environments and working conditions; through regular updating and model training, the system can continuously improve the monitoring precision and the safety management level, and it is ensured that an efficient and stable operation state is always kept in long-term use; an analysis result is displayed through a visual platform, accurate defect positions and severity degree marks are provided, a detailed detection report can be automatically generated, long-term monitoring and data comparison are supported, and powerful technical support is provided for construction quality guarantee.

The invention relates to the technical field of software vulnerability detection, in particular to a source code vulnerability detection method and system based on an adaptive graph neural network. Extracting a code attribute graph from the normalized code by using an analyzer; converting the code attribute graph into a learnable vector representation by using CodeBERT; calculating a centrality index of each node in the code attribute graph, and splicing the centrality index with a node feature vector; learning global features and local features of the graph in combination with an adaptive attention convolution module and a GCN module; and fusing features learned by the adaptive attention convolution module and the GCN module, inputting the fused features into a full connection layer, and outputting a final vulnerability detection result. According to the method, firstly, based on a centrality method, structural representation of CPG on a function is enhanced, and CPG features are enhanced; and meanwhile, a CPG-based adaptive attention graph neural network is designed and realized, so that the accuracy and efficiency of software vulnerability detection are improved while the CPG feature analysis capability is enhanced.

The invention belongs to the technical field of fuel cells, and particularly relates to a fuel cell centrifugal air compressor irreversible loss prediction method and system, and the method comprises the steps: collecting performance data based on a fuel cell centrifugal air compressor comprehensive performance test platform, and building and verifying a multi-physics coupling CFD simulation model; defining an irreversible loss type and related structure parameters, constructing an integrated CFD simulation framework, generating a parameter combination table through an orthogonal test, and performing simulation calculation; an RL-GHEM method is provided, the weight and combination strategy of a base model are optimized, linear and nonlinear combination modes are dynamically switched, and irreversible loss prediction models under different working conditions are constructed. The hybrid integrated model based on reinforcement learning guidance is provided, the model combination strategy is dynamically optimized through the reward mechanism, the base model with the optimal performance is optimized, the prediction precision, stability and robustness are improved, precise prediction of the irreversible loss of the air compressor is achieved, and the problem that in the prior art, the prediction precision is insufficient under variable working conditions is solved.

The invention provides a text classification method based on quantum transfer learning, and belongs to the field of fusion of quantum computing and artificial intelligence. In consideration of complex feature space and nonlinear characteristics in text classification, quantum circuit design, a self-attention mechanism and a quantum transfer learning process are optimized, so that the learning efficiency and classification accuracy of the model are improved. The high-dimensional feature representation and a complex nonlinear decision process are converted into a quantum self-attention network structure, an optimal strategy of text classification is obtained by utilizing a complex quantum kernel self-attention network (CQKSAN) model, and a quantum superposition and entanglement mechanism is introduced to enhance the expression ability of the model. A simulation result shows that compared with a traditional deep learning algorithm and other mixed quantum-classical algorithms, the algorithm provided by the invention has higher classification precision and learning efficiency. The method not only shows the potential of a mixed quantum-classical method, but also provides a basis for further research of a future full-quantum learning system.

The invention discloses an electric vehicle charging load prediction method, which comprises the following steps: firstly, screening variables with high correlation through a maximum information coefficient, selecting features with high correlation as model input, and taking a charging load as output; reconstructing the data training set by adopting cross validation; a charging load integration model is constructed, the integration model is divided into a base learning device and a meta learning device, the base learning device comprises a DCformer model, a BiTCN model and an artificial neural network ANN model, and a lightweight gradient elevator LightGBM is used as the meta learning device; the parameters of the LightGBM model are optimized by adopting an improved star graffiti optimization algorithm INOA; an error sequence is obtained after a preliminary prediction value is obtained through model prediction, and a final prediction value is obtained by taking LightGBM as an error prediction model. According to the method, data features are effectively extracted by integrating a plurality of single models, the prediction accuracy is improved, and compared with a traditional model, the stability is better, and the generalization ability is higher.

The invention relates to the technical field of multi-modal sentiment analysis, in particular to a multi-modal sentiment analysis method and system based on pre-training and online learning. The method comprises the following steps: acquiring sentiment analysis multi-modal data, constructing a sentiment analysis model, namely constructing a multi-head attention mechanism to respectively encode text, image and audio modals, and splicing and fusing each encoded modal feature; mining an interaction relationship among all modes in the fusion features by using a feedforward neural network and a multi-head attention mechanism; evaluating the contribution degree of each mode in the joint features to the sentiment analysis task by constructing a full-connection neural network; according to the sentiment analysis method based on the multi-head attention mechanism, the multi-head attention mechanism is constructed to carry out coding processing on each mode, the coded mode features are spliced and fused, and therefore the sentiment analysis accuracy is remarkably improved. The multi-head attention mechanism based sentiment analysis method based on the multi-head attention mechanism has the advantages that the multi-head attention mechanism is constructed to carry out coding processing on each mode, and the coded mode features are spliced and fused.

The invention provides a highland cooperative control method fusing a graph convolutional neural network and reinforcement learning, through controlling urban road traffic signal lamps, an expressway ramp signal machine and a variable speed limit unit, expressway traffic congestion is relieved, the urban traffic passing efficiency is improved, road driving vehicles are controlled by means of the signal lamps and the speed limit unit, and the traffic efficiency is improved. Traffic flow scheduling optimization in a global road network is achieved; the whole structure adopts a distributed cooperative control framework, is controlled by a decision-making agent, and performs autonomous decision-making and adjustment according to real-time traffic conditions, so that the adaptability and robustness of the system are enhanced, and the defects of large data calculation amount, difficulty in processing, control strategy lag and the like caused by traditional centralized control are effectively avoided; the graph convolutional neural network model is constructed to aggregate the feature information of the peripheral decision intelligent agent, and the control weight is dynamically allocated, so that the information sharing capability is enhanced, the suboptimal decision caused by lack of comprehensive perception of the surrounding environment is prevented, and the cooperation efficiency of the whole system is improved.

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 discloses a high-voltage cable outer metal sheath grounding defect on-line monitoring method and monitoring system, which can work under the condition that a cable is not powered off, realizes on-line monitoring, and realizes on-line monitoring through the cooperation of a current sensor, a voltage sensor, a single-chip microcomputer and an upper computer. And the equipment can quickly detect when a fault occurs and is convenient for maintenance workers to timely eliminate obstacles, so that the risk of major electric power accidents is reduced. The algorithm is similar to an improved neural network algorithm and is used for diagnosing the defects of the outer metal sheath of the cable, the weight is obtained through training learning, and finally the defects of the outer metal sheath of the high-voltage cable are determined. The protection content covers each component of the technical scheme, and comprises a monitoring method, a device structure, a sensor technology, a data processing algorithm, a neural network learning algorithm and the like.

The invention relates to a method for determining an initial orbit of a satellite with an embedded physical knowledge neural network, which is used for determining an initial orbit of a short arc segment under an optical observation condition and providing support for subsequent precise orbit determination. The network comprises an encoder-decoder structure, an encoder predicts the motion state of a satellite in an orbit according to observation information, the decoder converts the predicted satellite motion state into an observation value, the prediction accuracy of the encoder is evaluated, physical information of satellite orbit dynamics is introduced into a loss function, and the prediction accuracy of the satellite orbit is improved. And satellite orbit determination based on the neural network is realized. The input of the encoder is the observation angle quantity (namely azimuth angle and pitch angle) of the satellite relative to an observer, the observation time and the position information (namely longitude, latitude and altitude) of the observer; the output of the encoder, namely the input of the decoder, is the absolute motion state quantity (namely the position and the speed of the satellite in a geocentric inertial coordinate system) of the satellite in the orbit and the position information of an observer, and the output of the decoder is the observed quantity of the satellite relative to the observer. When the network is trained, firstly, a decoder is trained by taking a mean square error between a predicted observation value and a real observation value as a loss function, and a transformation relation from a satellite motion state to the observation value is learned; fixing the structure and parameters of a decoder, training the whole encoder-decoder network, calculating the derivative of the predicted satellite absolute state quantity relative to the observation time, and calculating the physical information loss of a speed item and an acceleration item; adding the physical information item loss and the mean square error of the observed quantity, constructing a total loss function, and optimizing network parameters through error back propagation; in the reasoning stage, an encoder network is used for reasoning, and the absolute motion state quantity, predicted by an encoder, of the satellite is converted into an orbit element form, namely, the initial orbit is determined.

The invention discloses an electrocardiogram abnormal degree detection method based on artificial intelligence. The method comprises the following steps: removing low-frequency noise from original electrocardiogram data; constructing an electrocardiogram time sequence anomaly detection model according to the electrocardiogram signal after the low-frequency noise is removed; decomposing the electrocardiogram signal without the low-frequency noise into different low-frequency components and high-frequency components; performing threshold processing on the high-frequency component, and reconstructing an electrocardiogram signal; performing feature extraction on the electrocardiogram signal data subjected to noise interference removal to obtain an electrocardiogram feature sequence; inputting the electrocardiogram feature sequence into an electrocardiogram time sequence anomaly detection model for model parameter estimation, and outputting model parameters; and predicting a predicted value of the electrocardiogram feature sequence according to the estimated model parameters to obtain an electrocardiogram anomaly score. According to the method, after the electrocardiogram time sequence anomaly detection model is automatically constructed through the intelligent engine according to the electrocardiogram signal, the model parameters are adjusted, the accuracy and efficiency of electrocardiogram anomaly detection are improved, and the generalization ability is enhanced.

The invention discloses a power grid fault plan entity recognition method and device, equipment, a medium and a product, and relates to the technical field of entity recognition, and the method comprises the steps: enabling a preset to-be-recognized entity type, a target power grid fault plan text set and a to-be-labeled power grid fault plan text to form a first prompt word, and inputting the first prompt word into a large language model, obtaining a first entity set and the confidence of each entity in the first entity set; forming a second cue word by a preset to-be-identified entity type, related knowledge of each entity in the target entity set and the target entity set, and inputting the second cue word into the large language model to obtain a second entity set; and determining the entities with the confidence coefficient greater than or equal to a set threshold value in the first entity set and the entities in the second entity set as final entity results. According to the method and the device, the problems that a traditional deep learning model is poor in performance under the condition of small samples and the generalization of extraction of different types of power grid plans is poor can be solved; and the accuracy and the reliability in power grid fault plan entity identification are improved.

The invention discloses a neural network weight side channel recovery method based on deep learning, relates to the technical field of artificial intelligence and security, and solves the problems that in the prior art, power consumption traces cannot be perfectly synchronized with a data processing process, and errors may be introduced in acquisition and processing of each power consumption trace. The method comprises the steps of collecting power consumption traces and constructing a data set, and then determining a leakage interval to perform dimension reduction on the power consumption traces in the data set; then selecting a classifier, constructing the capability from side channel information to a correct weight model, and finally recovering the weight of a corresponding neuron in the MLP according to the trained model; the weight of each neuron in the MLP is deduced through a small number of power consumption traces while the high accuracy is kept, and the side channel analysis efficiency is greatly improved.

The invention discloses a multi-unmanned aerial vehicle (UAV) trajectory optimization method for user mobile communication in a heterogeneous network, and aims to improve the security of a considered communication system and suppress the eavesdropping capability of an air mobile eavesdropper by transmitting an interference signal from a friend UAV. The invention provides an optimization problem of a plurality of unmanned aerial vehicles under three-dimensional flight path flight conditions, ground mobile user scheduling and limited energy consumption constraints, and aims to maximize the total fair secrecy rate of ground mobile users. According to the invention, a multi-agent 3D flight path and power control algorithm is designed by using DRL, and the algorithm has a centralized training distributed execution framework. And a heterogeneous MADDPG is adopted to carry out reasonable 3D trajectory design and power control on the communication unmanned aerial vehicle and the interference unmanned aerial vehicle, so that the communication unmanned aerial vehicle and the interference unmanned aerial vehicle are used as different types of intelligent agents to independently interact with the environment. According to the method, due to the diversity of action variables between the intelligent agents, different types of intelligent agents need to utilize unique information of the intelligent agents and depend on the speciality of other intelligent agents to collaborate to find an effective strategy. The total fair secrecy rate in a communication system is maximized by jointly optimizing the 3D trajectories and transmitting powers of the communication unmanned aerial vehicle and the interference unmanned aerial vehicle.

The invention discloses an equipment fan control method and equipment based on reinforcement learning and a medium, and relates to the technical field of fan control. The method comprises the steps that an action space is constructed according to operation data of a fan, a state space is constructed according to operation data of target equipment, and the operation data of the target equipment comprises working temperature; obtaining fan power, calculating rewards according to the fan power and the working temperature, and constructing a strategy network and a value network according to the state space; obtaining the current sensor reading and the rotating speed of the fan, and carrying out online training on the strategy network and the value network; after training is completed, sensor readings are collected, and fan actions with the maximum probability are executed. By means of the method, the total power consumption of the fan meeting the temperature constraint condition is minimum while it is guaranteed that the equipment temperature is controlled within the ideal range.

The present application relates to the field of ultrasound examination report generation, and specifically to an ultrasound examination report auxiliary generation method, electronic device and storage medium, the method comprising: collecting ultrasound examination reports, obtaining structural data and diagnostic data; determining the data universality of ultrasound examination reports, obtaining the information contribution of ultrasound examination reports; dividing ultrasound examination reports into examination report clusters, obtaining the data quality excellence of examination report clusters; determining a training data set based on the data quality excellence of all examination report clusters to train a neural network model, obtaining an ultrasound examination report auxiliary generation model, and obtaining an ultrasound examination report auxiliary generation result based on the ultrasound examination report auxiliary generation model. The present application solves the problem of information redundancy corresponding to ultrasound examination reports, which leads to unreasonable auxiliary generation diagnostic suggestions of ultrasound examination report auxiliary generation models.

The invention provides a fatigue detection network for extracting blood flow motion features based on a face video. The network is composed of a face video preprocessing module, a feature extraction module, a feature constraint processing module and a classifier. The objective of the invention is to solve the problems of strong invasion of a biochemical index method, poor real-time performance of a physiological signal method, weak reliability of a behavior characterization method and the like in an existing fatigue detection method.

The invention provides a yarn vehicle code identification method, relates to the technical field of glass fiber production and manufacturing, and solves the technical problems that in the prior art, the efficiency of manual vehicle code identification is relatively low, errors are easy to occur after long-time identification, logistics conveying equipment frequently stops, and the production efficiency is influenced. The device comprises the following operation steps that when a yarn vehicle passes through a vehicle code recognition device, the vehicle code recognition device can obtain an image of a yarn vehicle code plate on the yarn vehicle; image information of the yarn vehicle code plate is uploaded to an ROI area detection network, and the ROI area detection network obtains an information section of the yarn vehicle code plate from the image of the yarn vehicle code plate; the information section is uploaded to a vehicle code identification network, and the vehicle code identification network obtains the vehicle code information contained in the information section and verifies the vehicle code information; if the verification is passed, vehicle code information is uploaded to an upper computer system for distributing the direction of the yarn vehicle; and if the verification is not passed, giving an alarm.

The application discloses a kind of based on multimodal data fusion pumped storage unit fault diagnosis method, the voiceprint signal of pumped storage unit, infrared thermal image and historical operation data are collected, improved unscented Kalman filtering algorithm is used to denoising processing voiceprint set, the voiceprint signal after denoising and historical operation data are trained using COMRes+Model, future voiceprint signal is predicted, improved Deeplabv3+Model is used to train image set, and the features of infrared thermal image are extracted;Voiceprint feature, historical operation data text feature and image feature are input into improved CentralNet model for multimodal data fusion, and the fault category is output through classification identification module, so as to complete the diagnosis of pumped storage unit fault;The method can effectively improve the accuracy and efficiency of fault diagnosis by the fusion of multimodal data and the optimization of deep learning model, and provide strong guarantee for the safe operation of pumped storage unit.

The present invention relates to a multi-sensor error compensation method based on ISSA-BP neural network, belonging to the field of sensor compensation. The method uses a trained ISSA-BP neural network to perform environmental error compensation on three sensors of temperature, humidity and pressure carried by the system respectively, thereby eliminating the detection errors caused by environmental temperature, humidity and air pressure. The method solves the problem that the prior art does not fully consider the influence of multiple environmental factors such as temperature, humidity and air pressure in the detection environment of the multi-sensor system on the detection data, resulting in poor robustness of the compensation method, and the problem that the traditional sensor error compensation algorithm has low accuracy and slow time.

The invention discloses an intelligent traffic light identification and real-time prompting system for color blindness drivers. The system comprises an environment sensing module; the acquisition module is responsible for acquiring surrounding environment information; the parameter self-adaptive module is responsible for dynamically adjusting identification parameters of the system; the dynamic image processing module is responsible for processing and analyzing images acquired in real time; the signal lamp identification module is responsible for identifying colors and states of traffic lights; the decision and prompt module is responsible for making a corresponding driving decision and prompting a driver; according to the system, through the environment sensing module, the system can obtain and process the information of the surrounding environment in real time, the information comprises illumination conditions, weather conditions and the like, and basic data is provided for subsequent parameter adjustment and image processing. And the parameter adaptive module dynamically adjusts image processing parameters and signal lamp identification parameters according to the environment information, so that the adaptability of the system to different environments is improved.

The invention relates to the technical field of data detection, and discloses a Botnet malicious software detection method based on a download link, which comprises the following steps: firstly, analyzing original traffic, capturing a pcap packet in an environment, and extracting IP, port and url information of each frame in the pcap packet; performing data set arrangement on the data of the pcap packet in the environment; training detection is carried out, firstly, an adjacent matrix is constructed, and then parameter initialization is carried out; carrying out matrix operation on the initialized data, then carrying out multi-channel training, carrying out image convolution layer input, and training a recognition model; performing model training optimization on the trained identification model; and applying the trained model to the data based on the download link to detect the botnet malicious software. The detection method is flexible in design and can adapt to constantly changing malicious software attack means and modes. By dynamically adjusting detection strategies and parameters, the method can quickly respond to and adapt to newly appearing threats, and the efficient detection capability is kept.

The invention discloses a conductor equipment core component cross-scale near-net shape manufacturing method, and relates to the technical field of additive manufacturing, and the conductor equipment core component cross-scale near-net shape manufacturing method comprises the following steps: S1, in a forming process, synchronously executing macro-scale and micro-scale monitoring, and associating two types of data through coordinate calibration to obtain cross-scale associated data; and S2, based on the cross-scale associated data, performing fusion analysis with real-time process and environmental parameters, and outputting a process parameter adjustment direction according to conductor exclusive analysis logic. According to the cross-scale near-net-shape manufacturing method for the core component of the conductor equipment, the overall manufacturing quality and production efficiency of the core component of the conductor equipment can be remarkably improved, the deviation of the size and the organization structure is recognized in real time in the forming process through synchronous monitoring and data fusion of the macroscale and the microscale, and the manufacturing quality of the core component of the conductor equipment is improved. And intelligent analysis is carried out based on the characteristics of the conductor material, and dynamic optimization and closed-loop regulation and control of process parameters are realized.

The invention discloses a quantum self-attention neural network model and a construction method thereof. The model comprises a quantum mixed state embedding module, a quantum self-attention module, a quantum position coding module and an output module. According to the method, the expression ability of a quantum system is expanded through mixed state operation, so that the model can capture the similarity between query and keys more comprehensively and finely, meanwhile, more efficient data representation and processing are achieved, the number of parameters is remarkably reduced under the same input sequence condition, and more excellent performance is shown; and higher learning ability is shown.

This invention discloses a multi-target tracking method based on Transformer and graph embedding, comprising: acquiring a video sequence; reading the video sequence frame by frame to obtain all frames; setting a counter cnt1 = 1; determining whether cnt1 is equal to the total number of frames in the video sequence; if not, inputting the cnt1-th frame and the cnt1+1-th frame of the video sequence into a pre-trained multi-target tracking model to obtain an allocation matrix between the target in the cnt1-th frame and the corresponding target in the cnt1+1-th frame, where the cnt-th frame is the previous frame and the cnt+1-th frame is the next frame; obtaining all targets associated with each target in the cnt+1-th frame based on the obtained allocation matrix between the target in the cnt1-th frame and the corresponding target in the cnt1+1-th frame; and constructing the tracking trajectory of the target in the cnt-th frame together with all the obtained targets. This invention can solve the technical problem that the global correlation learning used in existing Transformer-based multi-target tracking methods not only increases computational complexity but also suffers from excessive redundant computation.

The invention belongs to the technical field of contract auditing, and discloses a contract comparison and recognition method based on deep learning, and the method comprises the specific steps: 1, importing a recognition system; step 2, training based on a deep learning OCR model; step 3, performing OCR operation on the picture; 4, identifying and uploading the content; 5, contract output or modification is judged; and step 6, risk prompting. By automatically comparing contract texts, the system can quickly identify key information, the complexity of manual operation is reduced, the system has good adaptability, contracts of different fields and types can be processed without manually adjusting parameters, the manual intervention requirement is greatly reduced due to the high automation degree, contract auditing is more efficient, and the contract auditing efficiency is improved. According to the method, multi-language texts can be easily processed, the automatic data extraction function provides great convenience for subsequent data analysis, and compared with manual text transcription, the OCR technology has better cost benefits in large-scale document processing.

The invention discloses a man-machine interaction system and a man-machine interaction method for training the phoneme distinguishing capability of developmental reading disorder. The man-machine interaction system comprises an evaluation unit which is used for collecting personal information of a user and evaluation results of voice consciousness; the parameter configuration unit is connected with the evaluation unit, is provided with a preset element library and a preset rule library, and is used for configuring a practice interaction task and a formal interaction task with preset difficulty for the user based on the evaluation result; the exercise task unit and the formal task unit are both connected with the parameter configuration unit so as to perform man-machine interaction with a user according to the sequence of first exercise and then formal; the learning unit is connected with the formal task unit so as to perform learning reinforcement when the user answers an error and perform formal interaction again after learning is completed; and the feedback unit is connected with the formal task unit and is used for feeding back the interaction result to the user. According to the invention, learning and practice are combined, so that the knowledge boundary of children is expanded, and the confidence and learning ability of children are improved.

The invention belongs to the technical field of product tracking, and particularly discloses an industrial product unmarked tracking method and device based on shearing interference and a neural network, and the method comprises the steps: obtaining an unmarked interference image of the surface of an industrial product; preprocessing the interference image, and carrying out binarization processing on the preprocessed interference image to obtain a binarized image; and extracting key features of the binarized image by using a trained neural network model, and matching the key features based on a similarity measurement function to obtain a feature similarity result so as to realize automatic tracking of a product. According to the invention, the tracking efficiency and the identification efficiency of the industrial product are improved.