87results about How to "Efficient learning process" patented technology

The invention discloses a content recommendation method and system based on semantic recognition, and the method comprises the steps: analyzing the text, voice or video input of a user through a natural language processing technology, and generating user semantic representation data; the method comprises the following steps: dynamically constructing a user interest graph by combining user historical behavior data, and performing representation learning of nodes and edges through a multilayer graph convolutional network to generate time sequence user interest graph representation data; a self-attention mechanism and a multi-head attention mechanism of a Transform model are utilized, the matching degree of user interest representation and recommended content is optimized in combination with a contrast learning module, and personalized recommended content data are generated; collecting multi-modal information of the user, fusing the multi-modal information with the personalized recommendation content data, and generating optimized multi-modal recommendation content data through a multi-modal fusion algorithm; and collecting user feedback feature data, and dynamically adjusting and optimizing a generation strategy of recommended content through an adaptive learning algorithm. According to the invention, efficient, accurate and personalized recommendation of the recommendation system is realized, and the user experience is improved.

The invention discloses a course recommendation method based on a learning track and a knowledge graph, and particularly relates to the technical field of education course recommendation, and the method comprises the following steps: collecting and integrating learning track data of students, mapping the learning track data to the knowledge graph to generate a learning track-knowledge joint graph, and carrying out knowledge point mastering and learning rate analysis; obtaining a knowledge mastering value and a learning rate of each knowledge point; based on the knowledge mastering value, the system divides the knowledge points into strong items and weak items; for weak knowledge points, weak recommendation degrees are calculated in combination with knowledge mastering values and learning rates, and courses are preferentially recommended according to the recommendation degrees; for the strong knowledge points, the strong recommendation degree is calculated based on the learning rate, advanced courses are recommended, and the learning effect and personalized learning experience of students are improved; according to the method, strong and weak knowledge points of students can be accurately identified, so that learning recommendation with extremely high pertinence is provided, the students can more efficiently learn on a personalized learning path, and the learning result is improved.

The invention belongs to the technical field of data processing, and discloses an enterprise multi-type data labeling method and system based on feature engineering, and the method comprises the steps: collecting initial data of an enterprise internal information system, and dividing the initial data to obtain a text data set, a table data set and an image data set; feature vectors of different types of data are fused after features are extracted and screened, and a multi-dimensional feature space is constructed; constructing an annotation model according to the feature information of the feature space, constructing similar sample pairs as positive sample pairs and irrelevant sample pairs as negative sample pairs for unannotated new data, and comparing, learning and optimizing the similarity between the similar sample pairs; and carrying out label inference based on the feature vectors obtained by comparative learning, and generating a labeling result. According to the method, efficient and automatic labeling of various types of data can be achieved, the method is suitable for various heterogeneous data such as texts, tables and images, and high labeling precision and expansibility are achieved.

The invention discloses a self-adaptive memory enhancement imitation learning method for a robot control task, which comprises the following steps of: performing standardization processing and time sequence feature extraction on original multi-modal data, and constructing hierarchical feature representation; establishing a layered structure including short-term memory, working memory and long-term memory, and evaluating memory importance and dynamically updating memory content based on use frequency, task correlation and novelty; retrieving related memories according to the current state and carrying out multi-modal feature fusion; a shaping reward is calculated based on neighbor sample search; and optimizing strategy network parameters through a multi-loss function combination. Through adaptive memory management and a multi-modal feature fusion mechanism, the learning efficiency and execution performance of the robot control system are improved.

A mobile robot path planning method based on an SAC algorithm belongs to the technical field of deep reinforcement learning and mobile robot navigation, integrates the thought of hierarchical reinforcement learning skill combination into the SAC algorithm, and uses a time sequence abstraction method to integrate low-level fine-grained actions, so as to improve the precision of the SAC algorithm. A higher-level coarse-grained action with definite semantics can be learned; in reinforcement learning training, a distributed soft strategy iteration method is used, maximum entropy reinforcement learning is expanded into a distributed learning version, and variance and overestimation of a Q value are reasonably controlled through a learning state-action return distribution function, so that the overall strategy quality of robot path planning is improved. According to the method, training and testing are carried out in a simulation environment Image-Env, and the performance of the algorithm is verified on Turtlebot2. The method can efficiently avoid obstacles, is short in path, and has a good obstacle avoidance effect in a complex dynamic environment.

The invention relates to the technical field of circuit board quality monitoring, in particular to a circuit board quality anomaly prediction system based on machine intelligent learning, which comprises an environmental parameter simulation and analysis module, an environment parameter simulation and analysis module, an environment parameter simulation and analysis module, a circuit board quality anomaly prediction module and a circuit board quality anomaly prediction module, and generating an environmental parameter dynamic simulation result. According to the invention, through the environment parameter simulation and analysis module, the system can simulate and track tiny changes of key environment parameters such as temperature, humidity and material attributes along with time and influences of the key environment parameters on the quality of a circuit board in detail; the prediction accuracy of the system on the change trend of the future environmental parameters is improved, and the environmental parameters which have the greatest influence on the quality of the circuit board can be accurately identified, so that targeted quality control and optimization are realized.

The invention discloses a molecular property prediction method based on a molecular line graph structure. The method comprises the following steps: firstly, collecting SMILES molecular formulas of drug molecules, and carrying out pretreatment to extract molecular fingerprints and molecular descriptors; then, the molecular line graph structure serves as model input, feature representation of a molecular line graph is calculated through a graph neural network MolGraphTrans based on Transformer, and molecular properties are predicted. According to the method, substructure masks, a multi-head attention mechanism, path coding and distance coding are introduced, so that molecular structure characteristics can be better captured. And finally, the prediction performance is optimized through a low-rank linear layer and a multi-head attention layer. Compared with the prior art, the accuracy and efficiency of molecular property prediction are improved, and the method is suitable for drug screening and optimization.

The invention discloses an audio signal processing method and device, computer equipment and a storage medium, and belongs to the technical field of artificial intelligence. The audio signal processing method is applied to a pre-built vocoder system, and the vocoder system is built based on a variational auto-encoder and comprises a prior encoder, a posterior encoder and a decoder. First, a loss function is constructed, including reconstruction loss and difference loss between encoders. And then, inputting the original audio signal into a posterior encoder to generate Gaussian distribution, sampling the distribution to generate an intermediate hidden variable, and inputting the hidden variable into a decoder for audio reconstruction to generate a restored audio signal. A pre-trained vocoder system is obtained by calculating loss and carrying out optimization and iterative training. And finally, processing the to-be-restored frequency spectrum by using the pre-trained vocoder system to generate a restored audio signal. According to the method, the stability of the model is effectively improved, the training difficulty is reduced, and the generalization ability of the model is enhanced.

The invention relates to the technical field of mixed learning, in particular to an online and offline combined digital interactive teaching method and system, and the method comprises the following steps: collecting online and offline learning data of students, carrying out the statistics of the number and frequency of various interaction types, including the number of discussion times, the number of question answering times and the group cooperation frequency, and generating an initialized interactive data set. According to the method, redundancy and deficiency of various interactions are calculated through the information entropy technology, information vacancy in learning is helped to be recognized, teaching content is optimized, student requirements are matched, the optimized design defines a learning target, an expected interaction frequency is set, the interaction adjustment effect is verified through a simulation test, it is ensured that teaching activities are more targeted, and the teaching efficiency is improved. The interaction frequency is dynamically adjusted in real-time teaching, the sense of participation of students is improved, the learning effect can be monitored in real time, the learning motivation and achievement of the students are improved, and implementation of personalized learning is promoted.

The invention discloses an ultrasonic image few-sample target detection method and system based on a Mangbar model, and the method comprises the following steps: extracting the image features of an ultrasonic image, and generating a suggestion box; performing position coding on the picture features, obtaining new picture features by using a global information enhancement module, and generating suggestion box features in combination with the generated suggestion box; obtaining new suggestion box features by using a category relationship learning module; generating a suggestion box classification score and a coordinate prediction value; parameter updating is carried out, and if the training round reaches the maximum number of iterations, the process is ended; otherwise, repeating the steps. A Mamba model is used for obtaining global information, and the Mamba model shows deeper semantic understanding and performance enhancement potential in context learning by means of the capabilities of context modeling and long-range dependency relationship capturing.

The invention is applicable to the technical field of online learning platforms, and particularly relates to a self-adaptive learning content generation method of a smart learning cloud platform, which comprises the following steps: acquiring course learning data of a learner; wherein the course learning data of the learner is used for representing learning data of the learner in a certain course; the course learning data of the learner comprises learning interest data, course progress data, learning behavior data and test evaluation data; determining course learning features of the learner according to the course learning data of the learner; performing feature extraction on the course learning content in the smart learning cloud platform to obtain learning content features; and determining the adaptive learning content of the learner from the course learning content of the smart learning cloud platform according to the course learning features of the learner and the learning content features. According to the method, the adaptive learning content can be dynamically generated, so that the learning efficiency of the learner is improved.

The embodiment of the invention provides an object placement method and device based on a mechanical arm, electronic equipment and a storage medium, and belongs to the technical field of imitation learning. The method comprises the steps of obtaining target expert teaching data for executing a target task; performing grabbing action information generation on the environment state image based on a grabbing action generation network to obtain grabbing action information; performing placement action information generation based on a placement action generation network to obtain first placement action information, the placement action generation network being a network constructed based on a twin network; inputting the first placement action information and the grabbing action information into a preset local structure learning network for position optimization to obtain second placement action information; and determining target placement action information based on the first placement action information and the second placement action information, and placing the target object according to the target placement action information. According to the embodiment of the invention, the object placement accuracy can be improved when the mechanical arm executes the task.

The invention relates to the field of robot path planning, and discloses a service robot path planning system with a self-repairing function and a method thereof. The voice recognition module is used for carrying out voice activity detection and transcription and realizing accurate understanding of natural language commands; the object recognition module is used for carrying out object detection and classification and simultaneously receiving text input so as to provide recognition prompts and enhance the perceptual ability of the robot in the environment; the task planning module is used for converting a natural language command into an executable format and realizing automatic execution of a complex task by executing combination of skill functions; and the semantic mapping module integrates the environment information into a 3D semantic map, carries out image and text coding, and provides rich semantic information for task planning with a self-repairing function of the robot. According to the invention, efficient path planning and task execution in a dynamic and huge service environment are realized.

The invention discloses a dynamic cross-modal Hash retrieval method and system based on concept vector learning. The method comprises the following steps: constructing a concept vector matrix based on current label information and a Hadamard matrix; concept vectors in the concept vector matrix are used for representing that the samples with the same label have the same and unchanged vectors; constructing a data similarity matrix based on the current label information and the historical label information; obtaining a target hash code based on the concept vector matrix and the data similarity matrix; obtaining a target hash function based on the target hash code; analyzing the to-be-queried data according to the target hash function to obtain a to-be-queried hash code corresponding to the to-be-queried data; and performing similarity calculation on the to-be-queried hash code and a target hash code in a database to obtain a target retrieval result. According to the method, the defect of the concept drift problem in the dynamic data environment can be overcome, the adaptability of the cross-modal Hash model to the concept drift is improved, the dynamic cross-modal Hash retrieval performance is improved, and the method can be widely applied to the technical field of information processing.

The invention discloses a metal welding spot form detection method, and relates to the field of welding form identification, and the method comprises the following steps: constructing a sampling system for multi-angle progressive and multi-angle welding spot cloud collection of welding spots; firstly, a main point cloud is established at a main view angle, an algorithm is designed to judge the quality of the main point cloud, if data of the main point cloud is insufficient, auxiliary point clouds continue to be collected from other view angles, and then secondary judgment is carried out; designing a rapid 3D point cloud fusion scheme according to the set features of the to-be-detected sample; and finally, outputting the form description of the welding spot through machine learning. The invention further discloses a system for detecting and identifying the quality after welding spot welding, and the system is based on the method, the output welding spot form information is matched with the standard qualified welding spot, and the quality evaluation result of the welding spot is given in real time. The method is high in collection efficiency, can effectively overcome the environmental influence and the interference of sensor noise, improves the accuracy and integrity of the point cloud file, and improves the accuracy of the neural network recognition data.

The invention discloses an efficient progressive strategy enhancement (EPPE) framework based on deep reinforcement learning (DRL) and used for a path planning scene, and the framework assists a DRL algorithm and is used for solving dual challenges of training efficiency and combinatorial optimization faced by DRL in path planning. The framework combines the advantages of sparse rewards and process rewards, the sparse rewards help the intelligent agent to realize a global optimal strategy, and the process rewards provide real-time feedback, so that the intelligent agent can adjust the strategy in time. The strategy learning efficiency is remarkably improved, and the reward coupling problem caused by process rewards is effectively solved, so that the global optimization of the strategy is realized. The method can be applied to the research field of robot path planning, and plays an important role in the application fields of automatic driving, logistics distribution and the like.

The invention relates to the field of artificial intelligence, and provides an information recommendation method and device, equipment and a storage medium. The method comprises the steps that a recommendation model conducts recommendation estimation on multiple pieces of candidate recommendation information obtained through retrieval, target recommendation information recommended to a target object is determined based on corresponding information recommendation estimation values, and the recommendation model is obtained by adopting a loop iteration mode and based on first training samples of classification targets with the positive and negative sample number greatly different. In each round of iterative training, each first training sample is divided into a common sample or a difficult sample, a first weight value with a relatively low weight value is given to a target weight of the common sample, a second weight value with a relatively high weight value is given to a target weight of the difficult sample, and the second weight value is in positive correlation with the learning difficulty of the corresponding difficult sample; based on the first recommendation estimated value of each training sample and the target weight, model parameters are adjusted, and a model capable of correctly distinguishing positive and negative sample interfaces is adopted, so that the recommendation accuracy is improved.

The invention belongs to the technical field of aerospace, and particularly discloses a spacecraft cluster game method and system based on generative adversarial reinforcement learning, and the method comprises the steps: building a relative motion dynamics model of pursuit and escape parties, and building a game relation model of the pursuit and escape parties based on a two-person zero sum game model; solving a Nash equilibrium solution of the pursuit game by using a differential game method based on the game relationship model of pursuit and escape parties, and generating an expert database; designing and generating an adversarial reinforcement learning algorithm based on the expert database; and solving the game relationship between the pursuing and escaping parties by using a generative adversarial reinforcement learning algorithm to obtain an optimal game strategy. According to the method, the optimal control method and the artificial intelligence method are combined to solve the approaching strategy of the spacecraft cluster virtual main satellite to the non-cooperative target, the problems that convergence is difficult, reward setting is difficult and the effect is poor when the artificial intelligence method is used for solving the orbit game problem are solved, and finally efficient and accurate approaching to the target is achieved.

The invention discloses a semi-supervised adaptive unstructured scene segmentation method, and relates to the technical field of computer vision. The method comprises the following steps: acquiring label-free image data of an unstructured scene; inputting the weakly enhanced image into a teacher model to generate a first prediction result; inputting the strong enhancement image into the student model to generate a second prediction result; calculating the JS divergence between the first prediction result and the second prediction result, and dynamically adjusting the attenuation coefficient of the index moving average according to the JS divergence; updating teacher model parameters according to the adjusted index moving average attenuation coefficient to obtain an unstructured scene segmentation model; and inputting a to-be-predicted unstructured scene image into the trained unstructured scene segmentation model, and outputting an unstructured scene segmentation result. According to the method, the teacher model weight updating rate is reduced in the model divergence significant stage such as the initial training stage or the difficult sample processing stage so as to suppress noise propagation, and the prediction precision of unstructured scene segmentation is effectively improved.

The present invention provides a personalized exercise resource recommendation method and system based on solution strategy, wherein the method first obtains a set of knowledge blind spots of learners, then constructs a strategy-level knowledge graph, closely combines learning strategies with knowledge concepts, and generates a multi-level, strategy-oriented knowledge network. Then, knowledge concept learning paths based on the strategy-level knowledge graph are generated to generate a personalized learning path for students that takes into account the logical order and difficulty of knowledge, so as to assist students in learning and mastering relevant knowledge in the shortest possible time and with the most efficient learning path. Finally, exercise resource recommendation based on solution strategy is performed to recommend exercise resources that match the current learning path to students. Through a two-way mapping relationship, the final recommended exercise resources not only cover the knowledge concepts that students need to master, but also incorporate exercise solution strategies, thereby greatly improving the recommendation effect of personalized exercise resources.

The invention discloses a human body orientation and action form identification method, system and device and a medium, and relates to the technical field of human body orientation and action form identification, and the method comprises the steps: obtaining analog signals outputted by a sensor under different conditions; converting the preprocessed analog signals into waveform signals, and analyzing each waveform signal in a time domain and a frequency domain to obtain first waveform data corresponding to each waveform signal; training the preset initial model until the preset initial model reaches a preset training ending condition; acquiring second waveform data corresponding to the to-be-detected signal, and inputting the second waveform data into the identification model for processing to obtain a target distance and a target form of the to-be-detected signal; by means of the mode, while action form and distance recognition is achieved, the cost and power consumption of the system are greatly reduced, and the problems that an existing video image form recognition mode is high in cost, complex in algorithm, difficult to maintain in the later period and the like are effectively solved.

The application discloses a LIBS element quantitative analysis method based on double-branch feature fusion and electronic equipment, the method comprises the following steps: preprocessing the collected LIBS spectrum signal, including baseline correction and spectrum resampling; inputting the spectrum signal data after preprocessing into a double-branch feature extraction network, extracting local features through a CNN branch, and extracting global features through an MLP branch in parallel; performing adaptive weighted fusion on the local features and the global features through a gating fusion module to obtain fusion features; inputting the fusion features into a WMA-MLP model, the WMA-MLP model is based on MLP, integrates a multi-head self-attention mechanism and a residual module, is used for modeling the global dependency relationship between features, and outputs a final element quantitative analysis result. Through the parallelly arranged CNN and MLP branch feature extraction structures, more comprehensive spectrum feature representation can be obtained, and the accuracy and robustness of the spectrum analysis model are improved.

The invention relates to the technical field of yoga training, and discloses a yoga training auxiliary guidance projection correction integrated training device which comprises a yoga training auxiliary guidance box and a base, a groove is formed in the surface of the top of the base, a rotating air cylinder is installed at the bottom of the inner wall of the groove, and a plurality of control buttons are installed on the left side of the base; real-time analysis and feedback can be carried out according to actions of a user, personalized teaching guidance can be provided, action deviation of the user can be accurately recognized through the action capture technology and device, instant correction is carried out through visual or sound feedback, the user can quickly master a correct yoga posture, and the yoga effect is improved. The visual projection teaching and dynamic identification feedback enable the learning process to be more efficient, the user can autonomously complete practice and continuously make progress without frequently checking teaching videos or depending on real-time guidance of a coach, and the device can be conveniently adjusted according to the use condition of the user, so that the training experience of the user is improved.

The invention discloses a chrysanthemum image intelligent screening method based on deep multi-scale fusion under a meta-learning framework, and the method comprises the steps: obtaining deep fusion features through a deep feature extraction module based on dense connection; taking the deep fusion features as feature input of a semantic analysis module to obtain semantic features; a deep feature extraction module, a semantic analysis module and a linear connection layer jointly form a backbone network, a parameter updating mode of the backbone network is learned and simulated through a meta-learning framework, and obtained priori knowledge is stored; and inputting the chrysanthemum distorted image into the backbone network, fusing the chrysanthemum distorted image into the backbone network through priori knowledge, and then performing fine tuning to obtain a model suitable for chrysanthemum quality evaluation. According to the method, prior knowledge can be effectively obtained from different types of distorted images and fused into a chrysanthemum quality evaluation task, meanwhile, the chrysanthemum quality can be accurately predicted by using a small amount of label data, so that quality screening is carried out, and the method has the advantages of high efficiency, robustness and high availability.

The invention discloses a public security event identification method based on small sample learning and a knowledge graph, which comprises the following steps: generating a virtual scene similar to a real event scene through the combination of small sample data and the knowledge graph, and dynamically activating nodes related to the event through a knowledge activation threshold value, freezing and storing the unrelated nodes, and dynamically releasing frozen nodes; identifying dynamic evolution of the event by monitoring node relation fluctuation in the knowledge graph; a multidimensional event analysis result is generated through mutual reinforcement of a graph fission mechanism and an interactive training mechanism; generating a risk path identification result through the event aggregation trigger point; the scheme has the characteristics of realizing efficient learning of small samples, enhancing semantic understanding ability, dynamically adapting to diversified scenes, improving event identification flexibility and constructing a global risk assessment framework.

The application discloses a kind of brain-like reinforcement learning method and system based on layered experience playback, it is related to reinforcement learning and brain-like computing technical field.The method comprises the following steps: S1, collecting observation data and pre-processing;S2, initialize experience buffer pool and actor network, critic network and corresponding target network, parameter initialization;S3, initialize exploration noise and select action from actor network according to current state and execute, store the obtained experience sample to experience buffer pool;S4, obtain new sample from experience buffer pool, carry out short-term memory pool update;S5, use attention discrimination module to determine whether part of experience in short-term memory experience pool is transferred to long-term memory experience pool;S6, the parameters of actor network, critic network and corresponding target network are updated.The application uses the above method, improves the experience utilization rate of intelligent agent, improves the performance of reinforcement learning, and has wide application potential in many fields.

The present invention discloses a space-frequency fused low-light image enhancement method and system, which relate to the field of image enhancement technology. The acquired original low-light image is preprocessed and then input into a trained image enhancement model to output a high-resolution image corresponding to the original low-light image. The training process of the image enhancement model is as follows: the original low-light image is acquired and preprocessed to construct a training data set, and the low-light image in the training data set is sequentially input into a shared weight encoder component, a denoising decoding component and a color correction decoding component to output fusion features; after the fusion features are convolved, an RGB image is obtained using a pixel shuffle operation; the loss of the obtained RGB image and a clean image corresponding to the original low-light image is calculated to update the model parameters of the image enhancement model; the low-light image enhancement method and system are helpful to extract the texture details and denoising capability of the image.

The present application relates to a kind of small sample disease classification methods based on association learning, comprising: first, build a small sample asphalt disease data set containing multiple asphalt disease types;Second, using the principle of association learning, develop a set of deep learning model and its training strategy suitable for small sample disease classification;Then, by setting "precision-speed" comprehensive evaluation index and a variety of damage simulation mode, evaluate the comprehensive performance and robustness of the model;Finally, the model is further adjusted and optimized, and deployed to road disease inspection equipment, realize the accurate classification of road disease.Compared with the prior art, the present application aims to solve the problem that traditional deep learning method excessively depends on training data size, alleviate the deficiency of existing method under small sample condition, improve the classification accuracy and system robustness, and provide a new solution for road disease detection and classification.

The invention discloses a Double DQN-based rare association rule mining method, which belongs to the technical field of data mining, and comprises the following steps of: obtaining a data set of data to be mined, and preprocessing the data set; constructing a rare association rule mining model based on a Double DQN algorithm, and taking the preprocessed data set as an environment of the rare association rule mining model; generating a state variable according to environment initialization; calculating a state change value according to the state variable; the item with the highest state change value is selected to be changed to obtain a new state value, and an association rule is generated; a reward value is generated based on the association rule. According to the method, the Double DQN is applied to mining of the rare association rule, so that the mining effect and efficiency of the high rare association rule are improved.

The invention provides an AI model production method and system based on meta universe data tagging, and the method comprises the following steps: collecting and arranging a data set, and storing the data set on a block chain; selecting an AI model framework according to the type of the data set, and training by using the data set to obtain an initial AI model; and deploying the initial AI model to a meta-universe platform for testing and optimization to obtain an AI model. According to the method, the quality, the safety and the credibility of the data can be improved by utilizing the data marking and the meta-universe platform, an efficient, high-performance and high-adaptability AI model is produced, and an intelligent solution is provided for various application scenes.