509 results about "Dynamic learning" patented technology

The invention discloses a user behavior prediction system and method based on multi-modal data fusion, and particularly relates to the field of user behavior prediction, and the system comprises a multi-modal data collection module, a preprocessing and feature extraction module, a cross-modal fusion module, a user behavior prediction module, and a model optimization and feedback module. According to the system, multi-dimensional original data such as visual sense, auditory sense, text, physiological signals and environment context of a user are acquired in real time through a multi-modal data acquisition module; then, deep networks such as ResNet, VGGish and BERT are adopted to extract high-dimensional feature vectors of all modals, and contribution weights of features of different modals are dynamically learned through an attention mechanism; and finally, based on a time sequence model of Transform and LSTM, analyzing fusion features, and outputting probability distribution of future behavior intentions. And parameter joint optimization and continuous learning are realized through a multi-objective loss function and end-to-end back propagation.

The present application relates to the technical field of power plant optimization, and discloses a thermal power plant auxiliary power system optimized dispatching method and system considering a wind-solar-storage system, and a device and a storage medium. The method specifically comprises: collecting thermal power plant auxiliary power system data, and establishing an auxiliary power system multi-objective function on the basis of the thermal power plant auxiliary power system data and a wind-solar power generation cluster model in an auxiliary power system; introducing constraint penalties and constraint conditions to the auxiliary power system multi-objective function, and constructing a thermal power plant auxiliary power system optimized dispatching model; and processing the thermal power plant auxiliary power system optimized dispatching model by using a dynamic learning factor-based particle swarm algorithm to obtain an optimized dispatching result, and completing thermal power plant auxiliary power system optimized dispatching on the basis of the optimized dispatching result. According to the present application, the optimal interactive output among wind turbine units, photovoltaic units, energy storage units, and a generating set can be determined on the basis of the optimized dispatching result, auxiliary power system low-carbon optimized dispatching is implemented, and the problem in the prior art of lacking dispatching in which new energy and thermal power plant auxiliary loads are integrated for analysis is solved.

The invention belongs to the technical field of medical artificial intelligence, and discloses an artificial intelligence-based auxiliary diagnosis and treatment system, which comprises a multi-modal data acquisition module, a dynamic learning module, a diagnosis reasoning module, a privacy protection module, an interactive decision module and an early warning monitoring module, the output end of the multi-modal data acquisition module is connected with the input end of the privacy protection module, the output end of the privacy protection module is connected with the input end of the dynamic learning module, the output end of the dynamic learning module is connected with the input end of the diagnostic reasoning module, and the output end of the diagnostic reasoning module is connected with the input end of the interactive decision module. And the early warning monitoring module monitors abnormal data in real time and performs bidirectional interaction with the diagnosis reasoning module. According to the method, multi-source medical data are integrated, and high-precision real-time auxiliary diagnosis is realized by adopting a dynamic incremental learning and privacy encryption technology; the medical worker cooperation efficiency is improved through an interactive interface, the safety is guaranteed in combination with real-time monitoring and early warning, and the system can remarkably improve the diagnosis and treatment efficiency and accuracy.

The invention provides a natural language text data intelligent classification method and system based on deep learning, and relates to the technical field of natural language processing, and the method comprises the steps: 1, employing a context awareness mechanism to analyze the real semantics of a target vocabulary according to an antagonistic variant existing in a text, and obtaining a target vocabulary; in combination with a word meaning library and a pre-training process of a dynamic learning rate adjustment strategy, generating a candidate replacement vocabulary set with consistent semantics; and step 2, based on the candidate replacement vocabulary set, performing multi-dimensional semantic similarity calculation and emotional tendency discrimination, determining applicable vocabularies conforming to an original culture background through a context adaptation strategy, and generating a standardized text sequence. According to the method, through multi-dimensional semantic analysis, cultural context fusion, cross-granularity feature construction and dynamic parameter correction, the accuracy and adaptability of natural language text classification are realized.

The invention relates to the field of computer accessories, and discloses a computer accessory intelligent recommendation system and method based on user behavior analysis, and the method comprises the steps: collecting interaction data of a user in an e-commerce platform, and constructing a behavior feature data set of user preference and a current equipment state through a behavior semantic deconstruction algorithm and an equipment configuration recognition mechanism; performing multi-dimensional feature extraction on the behavior feature data set, constructing a user equipment ecological model based on an accessory dependence modeling method, and introducing a behavior context sensing mechanism to perform dynamic learning training on the model; judging whether the recommendation model reaches a stable state or not according to the change trend of the fitting dependence path in the model training process; based on the optimized equipment ecological model, adopting a heterogeneous relation fusion recommendation strategy; and generating a personalized accessory recommendation scheme according to a sorting result, and performing feedback verification on the scheme in combination with a preset recommendation rationality evaluation model. The method has the advantage of improving the precision and experience of accessory recommendation.

The invention belongs to the technical field of artificial intelligence education, and particularly relates to a student learning behavior prediction method based on artificial intelligence, and the method comprises the steps: obtaining student learning data and teacher teaching task data; constructing a student agent according to the student learning data, and constructing a teacher agent according to the teacher teaching task data; a dynamic teaching strategy generated by the teacher agent is input into the student agent, and the student agent predicts possible learning behavior change of the student based on a learned behavior mode in combination with a strategy-behavior causal model; the prediction result is linked with a teaching management mechanism, a dynamic learning file is generated, the teacher intelligent agent matches personalized learning resources through an intelligent recommendation algorithm according to the dynamic learning file, and the student intelligent agent receives feedback of students on the learning resources and performs targeted intervention actions in combination with feedback information. Therefore, the problems of insufficient causal reasoning ability, weak calibration ability, insufficient data fusion ability and the like in the prior art are solved.

The invention discloses an intelligent recognition system for urban and rural building styles and features based on multi-source data fusion. According to the invention, by fusing the emotion feedback and objective visual features of the user on the building, the system not only can accurately judge the style type of the building, but also can understand the subjective feeling difference caused by different styles. For example, when the Hui-style building is identified, the system can synchronously capture the worry emotion generated by the user on the'whitewashing and yerba ', so that the characteristics of the building can be displayed and the emotion demand of the tourist can be matched during tourism recommendation. The dynamic learning mechanism enables the system to continuously absorb fresh feedback of users in various regions and automatically correct cognitive deviation of an original model to regional characteristic buildings, for example, the detail distinguishing standard of southern Fujian red brick placing and Lingnan wok ear house is updated in time, the adaptive capacity to continuously changing urban and rural styles is kept, and in a cultural heritage protection scene, the system can be used for protecting cultural heritage. In combination with double dimensions of professional surveying and mapping data and public emotion evaluation, recent modern buildings with collective memory value can be positioned more accurately.

The invention belongs to the technical field of automatic driving vehicle identification, and discloses an automatic driving vehicle point cloud identification method and system based on a laser radar. According to the method, an automatic driving vehicle point cloud recognition model is built, modular dynamic edge convolution based on a feature sensitivity layer number selection strategy is provided in the model, local geometric information is better captured by dynamically learning a local geometric structure, and the extraction capability of geometric feature information is enhanced. In order to prevent the problem of insufficient information extraction caused by layer number simplification of modular dynamic edge convolution, a selective kernel attention mechanism is introduced, a feature fusion mode is adjusted, residual connection is added, more comprehensive statistical information is captured while original information is reserved, and the multi-scale feature capture capability is improved, so that the classification performance of the model is improved. Especially in the face of noise, sparse data and complex object shapes, high classification accuracy can still be kept, and a more accurate environment perception capability is provided for an automatic driving system.

The invention discloses a layered energy management method of a fuel cell hybrid tramcar with self-adaptive working condition perception. In the recognition layer, a sliding window mechanism is adopted to extract time domain and frequency domain features of load conditions, feature data are clustered based on a spectral clustering algorithm driven by a deep auto-encoder, a data set with category labels is obtained, and a deep dynamic learning vector quantization neural network classifier is trained; in the strategy layer, a double-delay depth deterministic strategy gradient reinforcement learning algorithm is adopted, a reward function is constructed, and lithium battery SOC fluctuation penalty term limit parameters in the reward function are adaptively adjusted according to the real-time load working condition category output by the recognition layer; training the reinforcement learning agent to obtain an optimal power distribution scheme between the multi-stack fuel cell power generation system and the lithium battery; and according to the performance degradation degrees of different fuel cell stacks, a distributed cooperative control strategy considering performance difference is adopted to distribute the output power of each stack, so that the coordinated control of the running state of the multi-stack fuel cell power generation system is realized.

The invention relates to the technical field of data governance, and discloses a data governance method based on an intelligent decision engine, which comprises the following steps: carrying out business semantic classification and marking on preliminarily processed real-time streaming data, and constructing a data portrait library; constructing a dynamic topological graph, learning an abnormal propagation rule based on a graph neural network, analyzing an influence range and establishing an influence grading mechanism; performing multi-dimensional quality evaluation on the data, and generating a dynamic data quality score and a grading strategy; constructing a data quality historical problem and reason case library, and generating a quality anomaly root cause judgment and influence quantification report by using a large language model agent; generating a candidate strategy set, and selecting an optimal governance strategy from the candidate strategy set by establishing a multi-objective optimization model; and performing compliance test and conflict identification on the optimal governance strategy by using a large language model agent, and dynamically adjusting the decision weight of a rule engine by using a reinforcement learning algorithm to realize a closed loop of data governance and dynamic learning.

The invention relates to the technical field of multi-view clustering, in particular to a multi-view clustering method and device, and can solve the problem that the overall effect of an existing method in a large-scale clustering task is limited due to the fact that the existing method has problems in the aspects of calculation efficiency, robustness and multi-view information integration to a certain extent. The method comprises the following steps: dynamically learning an anchor matrix and a projection matrix for each view, and constructing a bipartite graph to generate a similarity matrix; calculating a graph Laplacian matrix based on the similarity matrix of each view, and extracting spectrum embedding; the spectrums of multiple views are embedded and stacked into a third-order tensor, and cross-view shared information is extracted by using a low-rank tensor constraint; multi-view atlas embedding is aligned through a spectrum rotation technology, and a discrete clustering indication matrix is directly output.

A dynamic learning server-based logistics system includes a node-based logistics receptacle operative to receive a delivery item as part of a logistics transaction and also includes a backend server maintaining a management profile related to operation of the node-based logistics receptacle. The node-based logistics receptacle includes a plurality of monitored receptacle components, a wireless accessory sensor node having a plurality of sensors, and a bridge node operative to retrieve event information from the wireless accessory sensor node. The backend server receives the retrieved event information, compares the retrieved event information with the management profile, identifies a threshold change condition from the comparison, dynamically revises the management profile when the threshold change condition is identified, and transmits an adjustment message to the bridge node. The adjustment message is based upon the revised management profile, and the adjustment message initiates a timing change to operation of the bridge node.

The invention relates to the technical field of defect detection systems, and discloses a titanium alloy surface crack defect detection system based on deep learning. According to the system, a crack feature extraction module is used for collecting a titanium alloy surface image and extracting multi-scale crack features including crack trend distribution features and micro-crack density features; the multi-modal data fusion module is used for receiving the multi-scale crack features and performing space-time alignment on the multi-scale crack features and ultrasonic reflection wave features collected in real time to generate a fusion defect feature matrix; the dynamic learning engine module is used for constructing a crack propagation prediction model according to the historical change trend of the fusion defect feature matrix and outputting a dynamic defect response vector; the defect positioning module is used for mapping the dynamic defect response vector to a titanium alloy surface three-dimensional coordinate space to generate a defect position thermodynamic diagram; and the self-adaptive scanning control module is used for analyzing the defect confidence of each area in the defect position thermodynamic diagram and dynamically adjusting the scanning path and the focal length parameter of the industrial camera.

The invention discloses a Chinese herbal medicine intelligent drip irrigation regulation and control system based on end-cloud cooperation, and relates to the technical field of agricultural intelligent irrigation, and the system comprises an edge equipment end which is deployed in a Chinese herbal medicine planting area, comprises a multi-mode sensor module, a communication module and an irrigation execution mechanism, and is used for collecting soil humidity, illumination intensity and meteorological data in real time, and sending the collected data to a cloud server; and the drip irrigation valve is controlled through the irrigation executing mechanism. According to the Chinese herbal medicine intelligent drip irrigation regulation and control system based on end-cloud collaboration, through an end-cloud collaboration architecture and a hierarchical decision-making mechanism, the drip irrigation regulation and control accuracy and real-time performance in a Chinese herbal medicine planting environment are improved. A local AI inference engine of the edge equipment end is combined with a dynamic learning module, an optimization instruction can be automatically generated during network fluctuation, dependence on real-time communication of the cloud end is reduced, and continuity and reliability of irrigation operation in complex terrains are ensured.

The invention discloses an extreme weather photovoltaic power prediction method, system and device, and a medium. The method comprises the steps of obtaining related data of a power plant and performing first processing; constructing a first neural network to extract spatio-temporal features of the cloud picture, and realizing adaptive classification of extreme weather and non-extreme weather through a double-branch discriminator; guiding a conditional diffusion model to generate a non-extreme weather accurate cloud picture by taking the cloud picture spatial-temporal characteristics as constraint conditions; generating an extreme weather accurate cloud picture based on a cloud picture spatio-temporal feature guidance condition generative adversarial network; constructing a second neural network to capture global and local dynamic change characteristics of photovoltaic power and related meteorological data of the power plant; constructing a cross attention module to fuse the weather accurate cloud picture and the dynamic change features; and inputting the fusion features into a third neural network, and dynamically learning mapping from the fusion features to power. According to the invention, through a cloud picture generation framework and a multi-modal fusion mechanism, the problem of failure of a traditional prediction model in extreme weather is effectively solved.

The embodiment of the invention provides a multi-element deep learning correction method and device for numerical mode forecasting. The method is applied to the technical field of data processing, and comprises the following steps: marking observation points, and performing loss function construction on the positions of the observation points to obtain a model optimization target; carrying out iterative updating on model parameters, and carrying out training process optimization by adopting a dynamic learning rate adjustment strategy to obtain a forecast field correction model; and applying the prediction field correction model to target numerical mode prediction data acquired in real time, performing prediction effect evaluation, and performing standardization processing and anti-standardization processing to obtain a correction prediction result. According to the method, the information extraction capability of a deep learning visual algorithm model is utilized, fine deviation correction of data of a numerical mode forecast space grid by meteorological station observation data is completed, meanwhile, multiple types of meteorological elements are fused, the deep learning model is utilized to learn mutual influences and restrictions between related elements, and the prediction precision of the numerical mode forecast space grid is improved. And finer deviation correction is completed.

The invention discloses a multi-source heterogeneous learning path planning method based on personalized constraints, which solves the problems of single path, data staticization and the like in the prior art, and comprises the following steps: acquiring multi-source behavior data of a learner, carrying out feature modeling, and constructing a high-dimensional behavior portrait; according to the high-dimensional behavior portrait, utilizing a neural collaborative filtering algorithm to predict the interest and mastering probability of a learner to any knowledge point, and generating a preliminary learning path; carrying out dominant and implicit evaluation on the knowledge mastering state of the learner by adopting a cognitive diagnosis model, and carrying out dynamic correction on the preliminary learning path to obtain a corrected learning path; performing dimension reduction on the multi-source behavior data by adopting a principal component analysis algorithm to extract a core factor, and forming an evaluation basis for path optimization; and constructing a multi-objective path function model, and obtaining an optimal dynamic learning path by adopting a multi-objective optimization path algorithm in combination with the corrected learning path and the core factor. The method has high practical value and popularization value in the technical field of learning path planning.

The invention belongs to the technical field of agricultural information, and discloses a rape variety environmental adaptability evaluation system based on machine learning. The system is composed of a data acquisition standardization unit, a regional environment feature modeling unit, a variety phenotype and pedigree association unit, a feature screening and model training unit, a regional adaptability scoring and decision-making unit, a dynamic learning and evolution updating unit and an evaluation visualization and variety recommendation unit. By performing standardization processing on multi-source data, format and dimension differences are eliminated, and a high-quality data foundation is laid for subsequent analysis. Fusing environment and variety bilateral characteristics, and enabling the model to capture environment influence and variety heredity characteristics at the same time. Advanced methods such as ensemble learning and a time sequence neural network are adopted, and the complex relation is accurately mined. Compared with a traditional field test and the prior art, errors are greatly reduced, the adaptation degree of the variety in different areas is more accurately judged, a scientific basis is provided for planting recommendation, and the yield and quality of the oilseed rape are improved.

The invention discloses an online video content intelligent pushing method combined with a learning interest model. The method comprises the following steps: constructing a dynamic interest vector based on multi-source user behavior data, generating a user interest portrait vector set, and performing interest dimension clustering and weight distribution; generating a video content feature vector set according to a clustering result of the user interest portrait vector set; establishing a multi-dimensional association relationship between the user interest portrait vector set and the video content feature vector set, and outputting a user-video matching confidence matrix; converting the user-video matching confidence coefficient matrix into a push sequence based on a multi-objective optimization strategy and issuing the push sequence; and feedback behaviors of the user on the pushed video are collected in real time to realize closed-loop optimization. The method has the following advantages and effects: accurate perception and deep semantic matching of the dynamic learning interest of the user can be realized, and the accuracy, timeliness and user satisfaction of content distribution are remarkably improved, so that the learning efficiency and experience are optimized.

The invention discloses a lithium ion battery capacity inflection point prediction method and system based on a multi-parameter data fusion decision. The method comprises the following steps: collecting multi-parameter data of a lithium battery and preprocessing the multi-parameter data; constructing an inflection point prediction model based on deep learning, and extracting time sequence data characteristics of current, voltage and temperature; based on an attention-enhanced graph convolutional neural network AGCN, an attention mechanism is introduced into a graph convolutional neural network GCN to dynamically learn the association weight of a multi-parameter feature matrix, and multi-parameter data fusion features are obtained; dynamic decision making is carried out on the battery multi-parameter data fusion features, linear transformation is carried out on a dynamic decision making result to obtain a predicted value of an inflection point, and construction of an inflection point prediction model is completed; carrying out training optimization on the whole model, and predicting the residual cycle period of the battery to the inflection point; according to the method, inflection point high-precision prediction of any stage of the battery can be realized by depending on relatively short cycle period data.

The invention relates to the technical field of garbage recognition, and particularly discloses an intelligent garbage recognition system and method based on multi-modal fusion, a hardware sensing unit is composed of a three-dimensional vision module, a touch sensing module and a near infrared spectrum module, and point cloud data, a pressure distribution matrix and spectrum data of garbage are obtained respectively; and an edge computing unit is equipped to carry an NPU acceleration chip for data processing. And the software module realizes multi-modal feature fusion by using ResNet-50, LSTM (Long Short Term Memory), 1D-CNN (Convolutional Neural Network) and Transform Encoder, and is also provided with a multi-sensor timestamp synchronous controller. And the dynamic feature database starts online learning to update the material feature library when the garbage recognition confidence coefficient is lower than 85%. During feature fusion, a cross-modal feature mapping relation is established, the weight is adjusted through an attention mechanism, and dynamic learning adopts a model lightweight method based on knowledge distillation. And starting an arbitration mechanism to correct the classification result when different modal identification results conflict. According to the method, the advantages of different modal data are fully utilized, and the recognition capability in a complex environment is improved.

The invention belongs to the technical field of semantic understanding systems, and particularly relates to a semantic understanding system based on a large language model.The semantic understanding system is characterized in that firstly, a data preprocessing module is used for conducting cleaning, denoising and cross-modal conversion on input multi-modal data such as texts and images, and standardized data is generated; a semantic feature extraction module extracts general semantic features by using a pre-training model, adapts to field requirements through dynamic learning rate fine adjustment, and outputs scenarized semantic vectors; then, a dynamic semantic-knowledge bidirectional fusion module adjusts token weight according to a dynamic semantic weight algorithm, realizes real-time alignment of semantics and a knowledge graph by means of a knowledge entity association strength algorithm, and a knowledge enhancement fusion module further optimizes knowledge weight and dynamically updates association; then, the semantic reasoning module performs multi-round reasoning based on fusion information, and evaluates the result reliability in combination with a confidence coefficient algorithm; and finally, the output and optimization module generates a structured result, and iteratively optimizes parameters of each module according to feedback data to complete a semantic understanding processing flow.

The invention relates to a quay crane RTK positioning precision evaluation method and system and a storage medium, and the method comprises the steps: L1, when an unmanned container truck carries out the container loading and unloading operation under a quay crane, the quay crane RTK feeds back the positioning value of a vehicle according to a fixed frequency, and obtains the data information of the positioning value of the vehicle within a fixed time; l2, based on the data information of the positioning value of the vehicle within the fixed time, predicting the positioning value of the vehicle by adopting an improved ELMAN dynamic recurrent neural network prediction algorithm optimized based on a particle swarm optimization algorithm to obtain the data information of the predicted positioning value of the vehicle; and L3, based on the predicted data information of the positioning value of the vehicle, optimizing the positioning value of the vehicle by adopting a zebra optimization algorithm of a self-adaptive dynamic learning factor based on global search to obtain the optimized data information of the positioning value of the vehicle. The alignment success rate of the unmanned container truck can be increased, and harbor area operation can be prevented from being affected.

The invention provides an scRNA-seq data clustering method, system and device based on ZINB distribution and graph attention. The scRNA-seq data clustering system mainly comprises three core modules: a ZINB auto-encoder, which is used for modeling scRNA-seq data based on zero-expansion negative binomial distribution, generating robust potential representation through a denoising auto-encoder, and accurately capturing sparsity, excessive discreteness and shedding events of gene expression; the residual image attention auto-encoder is used for constructing a cell relation graph by using a Pearson correlation coefficient, dynamically learning a neighborhood weight in combination with a multi-head attention mechanism, retaining original features through residual connection, and relieving the excessive smoothness problem of image convolution; and a deep clustering model is self-optimized: target distribution and soft label distribution are minimized through KL divergence, and end-to-end joint optimization of embedded learning and clustering is realized.

The invention provides an elevator hall door lock fault detection method, device and system, and belongs to the technical field of elevator control. Comprising the following steps: S1, constructing an elevator hall door lock fault detection data set; s2, preprocessing the data and then extracting features; s3, constructing a fault detection model based on the features extracted in the step S2, and performing model training by using a cross entropy loss function to ensure that the model can accurately classify different fault states; s4, the model is trained based on dynamic learning rate adjustment and self-adaptive reweighting; and S5, deploying and using the model. Electrical signals and mechanical vibration signals of the elevator hall door lock are collected through the high-precision voltage and current sensor and the vibration sensor, fault detection and early warning operation are completed through the constructed fault detection model based on the deep neural network, adaptability to complex fault scenes is better, and detection accuracy and reliability are improved.

The invention relates to the technical field of electric power asset management, and discloses an electric power equipment full life cycle management system based on a digital twinning technology, and the system comprises a causal knowledge graph construction unit, a strategy rule learning unit, a decision option value quantification unit, a combined decision optimization unit, and a maintenance decision voucher management unit. The method comprises the following steps: constructing a knowledge graph containing a causal relationship among equipment, environment and management activities; carrying out anti-fact deduction based on the atlas to dynamically learn strategy rules; quantizing the value of the potential maintenance decision by adopting a physical option model; performing combination optimization under resource constraints of budget, spare parts and the like to generate an optimal decision combination; and finally, generating a standardized maintenance decision voucher for the decision in the combination. According to the method, decision-making flexibility and environment uncertainty can be quantified into specific economic values, future-oriented and globally optimal resource allocation is realized, and a decision-making process and a decision-making result are solidified into traceable and manageable digital assets.

The invention relates to the technical field of block chain technology and asset transfer, in particular to a multi-risk interception and artificial intelligence error correction method in RWA asset cross-chain transfer, comprising the following steps: step 1, cross-chain transfer initialization and asset mapping verification; step 2, multi-level dynamic risk interception; step 3, an artificial intelligence error correction mechanism; step 4, asset right confirmation and log auditing after cross-chain completion; according to the method, technical risks (such as contract vulnerabilities), data risks (such as information inconsistency) and behavior risks (such as abnormal transactions) in RWA asset cross-chain transfer are covered through a multi-level interception system of "pre-transaction-in-process-contract layer" in combination with AI dynamic learning ability, the risk identification rate is greatly improved compared with a traditional method, and the risk identification efficiency is greatly improved. An artificial intelligence error correction mechanism is used for automatically classifying risk types and generating strategies, so that the average error correction time is shortened, and the error correction success rate is improved.

The invention discloses a dynamic learning path planning method and system based on a learner portrait. The method comprises the steps of collecting learning behavior data, learning result data and background attribute data of a target scholar based on a plurality of data sources, and obtaining knowledge point mastery degree vector representation, learning style preference and cognitive ability level to construct a multi-dimensional dynamic portrait of the target scholar; generating an initial personalized learning path of the target scholar based on a preset learning target and the multi-dimensional dynamic portrait of the target scholar; learning process data of a target scholar for related learning resources is monitored in real time, a learning effect evaluation report is generated according to the learning process data, a multi-dimensional dynamic portrait is adjusted to trigger a path replanning mechanism to generate a brand new personalized learning path, and the brand new personalized learning path is pushed. The learning path and the current state of the learner are kept optimally matched all the time, and personalized static planning is upgraded to dynamic syndrome guidance.

The invention discloses an AI-based computer basic course personalized learning path recommendation method and system, and belongs to the technical field of AI-based data processing. According to the system, behavior data, cognitive data and course interaction data of a learner are acquired through a multi-dimensional data acquisition module, a computer basic course knowledge point association network is established in combination with a dynamic knowledge graph construction module, and a personalized learning path is generated by using an improved deep reinforcement learning algorithm. And the path is dynamically adjusted through the real-time feedback module. The core of the method is that a learner portrait is fused with space-time correlation features of a knowledge graph, a cognitive evaluation model is updated in real time through a Bayesian network, the problems that in a traditional recommendation method, paths are solidified, and the dynamic learning state of an individual is ignored are solved, more accurate personalized learning guidance is achieved, and the learning efficiency and effect of a computer basic course are improved.

The invention provides an atom pair distribution function calculation method based on a back propagation algorithm, and the method comprises the steps: building a micromapping relation between a material microstructure and experimental PDF data through constructing a physical calculation layer containing lattice parameters and atom displacement; defining a loss function by taking experimental data as a supervision signal, and synchronously optimizing tens of thousands of atomic positions and lattice parameters by utilizing gradient back propagation of the loss function covering the experimental data and model calculation result deviation; and a special optimization algorithm CrystalAdam and dynamic learning rate scheduling are combined, so that global efficient search of a high-dimensional parameter space is realized. Therefore, compared with a traditional modeling method, the method provided by the invention not only can remarkably improve the optimization efficiency, but also can provide finer local adjustment in a complex structure space, thereby achieving higher precision.