196 results about "Learning analytics" patented technology

The invention provides an industrial control network security service security guarantee system based on behavior analysis, which belongs to the technical field of industrial control network security, and comprises a multi-source data fusion acquisition module, a dynamic behavior modeling engine, a federal learning analysis cluster, an attack chain prediction module, a self-adaptive protection strategy executor and a model evolution feedback ring, wherein the multi-source data fusion acquisition module synchronously acquires industrial control network flow (including OPC UA / Modbus / DNP3 protocol analysis), equipment operation logs, user operation behavior fingerprints and physical interface state data, and the physical interface state data comprises electrical characteristic fluctuation monitoring of USB / network interfaces. According to the scheme, through multi-technology fusion and closed-loop design, the problems of static performance, single-dimension analysis defects and response lag of a traditional industrial control security scheme are effectively solved, a comprehensive protection system with dynamic modeling, intelligent decision making, privacy protection and continuous optimization is constructed, and the security and service reliability of an industrial control network are remarkably improved.

The invention provides an unmanned aerial vehicle autonomous flight path planning and image acquisition method and system, and relates to the technical field of unmanned aerial vehicle inspection. Firstly, through intelligent three-dimensional grid division and dynamic route planning, the coverage rate and efficiency of feature region monitoring are remarkably improved; secondly, due to the integration of real-time meteorological data and historical feature data, the pertinence and adaptability of acquisition are enhanced, and high-quality images can be obtained in a complex and changeable environment; through combination of real-time processing and ground deep learning analysis, the accuracy and efficiency of feature extraction are greatly improved; and on the basis of a self-adaptive supplementary sampling mechanism of the region of interest, quick response and accurate positioning of the suspected feature region are realized, and reliable data support is provided for subsequent detailed analysis and decision making. The method is not only suitable for identification tasks of various preset features, but also has good expansibility and flexibility, and can be adjusted and optimized according to different application requirements.

The invention belongs to the technical field of power systems, and particularly relates to an audit data early warning method and system, and the method comprises the steps: collecting the node voltage and current of a power supply network and the charging and discharging efficiency data of an energy storage system in real time through a distributed sensor network, and generating an original data flow; carrying out localized cleaning and standardized format conversion through an edge computing node; generating a dynamic risk assessment result and a scheduling scheme based on fuzzy logic reasoning and digital twinborn simulation; and analyzing and predicting deviation through meta-learning, and realizing closed-loop optimization by adopting a model distillation technology. The system comprises a distributed sensor network, an edge computing node, a multi-source data fusion module and the like. According to the method, the anomaly detection real-time performance, the risk assessment accuracy and the energy storage scheduling economy of the power system are improved.

The invention discloses an automatic identification method and system for a wafer internal defect image of a 3D stacked chip, and belongs to the technical field of semiconductor manufacturing and detection. According to the method, optical, X-ray and ultrasonic image data are synchronously acquired based on a multi-modal imaging technology, imaging parameters are dynamically adjusted to adapt to different wafer levels and material characteristics, multi-modal features are extracted in combination with layered filtering and denoising, multi-resolution registration and a self-supervised deep learning method, and micron-sized defects are positioned by using an attention mechanism. And further constructing a defect-process parameter correlation model through reinforcement learning, generating a closed-loop process optimization instruction, and transmitting the closed-loop process optimization instruction to an execution system. The system comprises a multi-modal imaging module, a noise suppression module, a deep learning analysis module and a process optimization module, and supports edge computing deployment. According to the invention, the internal defect detection efficiency and precision of the multi-layer stacked chip are significantly improved, real-time closed-loop control of detection-analysis is realized, the wafer manufacturing quality risk is reduced, and the method is suitable for intelligent defect detection of an advanced packaging production line.

The invention provides a traditional Chinese medicine physique dynamic change identification system. The system comprises a data acquisition module, a feature extraction module, a deep learning analysis module, a physique analysis and prediction module and a diagnosis report generation module. The data acquisition module is used for acquiring tongue image video streams, pulse condition waveforms and body surface infrared thermal imaging data and recording environment temperature and humidity, user behavior logs and medication information. The feature extraction module is used for extracting various feature data. And inputting the multi-dimensional data set into a deep learning analysis module, and calculating the transformation relation probability of the traditional Chinese medicine constitution types. The physique analysis and prediction module judges the current physique type based on the probability data and predicts the physique evolution trend in combination with historical data, and finally the diagnosis report generation module outputs a diagnosis report containing physique evolution analysis and conditioning suggestions. According to the method, multi-dimensional physiological, environment and behavior data can be integrated, dynamic identification of traditional Chinese medicine physique is realized, the accuracy of physique analysis is improved, and a personalized health management scheme is provided.

The invention relates to the technical field of geological disaster monitoring, and discloses a debris flow monitoring and early warning system based on Internet of Things, comprising a multi-source data acquisition module used for acquiring environmental data related to debris flow; the data transmission module is used for transmitting the environment data to a data center or a cloud end from a collection point; the data preprocessing module is used for carrying out abnormal value cleaning, time synchronization and normalization processing on the environment data; the deep learning analysis module is used for analyzing the preprocessed data so as to predict the occurrence risk of debris flow; and the dynamic early warning and publishing module is used for generating a multi-stage early warning signal according to an output result of the deep learning analysis module and publishing early warning information to a user in multiple modes. The accuracy, the real-time performance and the adaptability of the debris flow early warning system are improved through multi-source data fusion, edge calculation and a dynamic learning mechanism so as to cope with debris flow disasters in a complex geological environment.

The invention discloses an intelligent personalized learning path recommendation system, and relates to the technical field of learning systems, and the system comprises a multi-source data collection module which is used for synchronizing behavior data of students in a cross-platform learning scene in real time; the cognitive feature analysis engine comprises a style recognition sub-engine and a demand prediction sub-engine to predict the potential learning demand intensity of students for unmastered knowledge points and generate a priority list comprising knowledge gaps; the personalized path generator generates a three-dimensional path plan; the proportion of guided questioning, example demonstration and autonomous exploration is automatically configured according to knowledge difficulty; the dynamic self-adaptive adjustment unit is used for designing a reward function including short-term progress speed and long-term ability growth potential by taking real-time performance of students as a state space and taking path adjustment action as a decision space based on a reinforcement learning framework; and a double-loop feedback mechanism is realized. According to the method, the dimension limitation of traditional learning analysis is broken through, the difficulty of stiffness of a static course template is broken through, and meanwhile, the semantic gap of subject cognition is broken through.

The invention relates to the technical field of electrochemical testing, in particular to an electrochemical impedance spectroscopy testing system based on multi-model combination, which comprises a data acquisition unit, a deep learning analysis unit, a frequency control unit and a feedback regulation unit. The deep learning analysis unit adopts a convolutional neural network and a long-short-term memory network to construct a model, after training, features are extracted, trends are analyzed, and dynamic frequency priority ranking is carried out, the frequency control unit drives the electrochemical workstation to adjust the frequency according to a weight generation strategy, and the feedback adjustment unit calculates predicted and actually measured impedance errors. When the threshold value is exceeded, parameter correction is carried out in each unit, the test frequency is intelligently optimized according to the dynamic change of the electrochemical process, key information is accurately captured, resource waste is avoided, the test efficiency and accuracy are improved, the stability and reliability of the system are ensured through a feedback regulation mechanism, and the research and analysis capability of the electrochemical system is effectively improved.

The invention discloses an old people cognitive health monitoring system based on virtual reality, and relates to the related technical field of virtual health monitoring technologies. Comprising a multi-modal sensing acquisition module, a virtual reality environment generation module, a cognitive nerve feedback module, a real-time feedback module, a deep learning analysis module, a personalized training plan generation module, a data storage and management module, a remote medical platform and a virtual assistant and coach module. The system can help the elderly to improve the work memory ability through a special work memory training task and a cognitive nerve feedback module, and the elderly can be more efficient during daily life activities by improving the work memory and the attention basic cognitive function aiming at the common attention distraction problem of the elderly, so that the life quality is improved, and the elderly can be helped to take care of the elderly. The cognitive neural feedback module not only pays attention to a cognitive function, but also helps the elderly to better manage emotions by adjusting an emotional network, promotes social interaction, and reduces aloneness and anxiety.

The invention relates to the technical field of medical data integration, in particular to a block chain and AI-based medical whole industry data asset integration system and method. Comprising a data acquisition layer, a block chain right confirmation layer, a federal learning analysis layer, a data asset transaction platform and a supply chain traceability optimization module. The data acquisition layer comprises an edge computing node, a data cleaning engine and a desensitization algorithm are built in the edge computing node, and the edge computing node is used for carrying out localized cleaning and desensitization processing on hospital HIS system data, medicine RFID data, medicine research and development experiment data and medical image data; the block chain right confirmation layer constructs a medical data asset account book based on an alliance chain architecture, the asset account book comprises a data hash record, a contributor identity identifier and a use authorization log, and an integrated intelligent contract module is used for dynamic right management; according to the invention, medical data islands can be broken, and safe integration and efficient utilization of cross-domain data are realized.

The invention discloses a fiber bragg grating strain measurement method and system based on deep learning, designs a double-grating structure and a deep learning analysis module, and separates temperature and strain components in grating wavelengths in real time. The system comprises a fiber grating sensor module, a data acquisition module, a deep learning analysis module and a display module, high-precision separation and real-time compensation of strain and temperature are realized through high-precision data acquisition and deep learning model processing, and the system is widely applied to the field of health monitoring of engineering structures such as bridges and tunnels.

The invention belongs to the technical field of intelligent traffic and regional economic management, and discloses an intelligent road derivative economic service platform system and method based on big data and a GIS technology. The system comprises a multi-source real-time data fusion and adaptive analysis module, a multi-level GIS intelligent analysis module, a big data intelligent prediction and self-learning analysis module, a personalized intelligent recommendation and feedback learning module and an intelligent visualization and augmented reality interaction module. Through multi-source real-time data acquisition and self-adaptive data processing, the system completes preprocessing such as data cleaning and standardization by utilizing an ETL (Extract-Transform-Load) technology. The GIS module realizes multi-level analysis and visualization of geographic space data, and performs dynamic prediction of traffic flow and economic activities in combination with a machine learning model. And the decision support module generates a multi-objective optimization scheme according to the prediction analysis result, and provides scientific traffic management and economic development suggestions. The system can be widely applied to highways, national and provincial roads, rural highways and other scenes.

The invention discloses a microcomputer synchronous automatic closing analysis system, which relates to the technical field of power system automation and comprises a data acquisition module, a data processing and storage module, a machine learning analysis module, a closing control module and a maintenance decision module. Electrical quantity data and equipment state data are monitored in real time through the data acquisition module, and the machine learning analysis module is utilized to perform modeling analysis on key parts and the overall operation state of the system, so that health assessment and fault prediction of the key parts are realized; when a fault possibly influencing closing safety or a health score of a key component is predicted to be lower than a set threshold value, the closing control module can adjust a closing control strategy to ensure the safety and reliability of closing operation, and the intelligent closing control mechanism remarkably improves the operation safety and reliability of the microcomputer synchronous automatic closing system.

The invention relates to the field of teaching monitoring and recognition, and particularly discloses a practical training monitoring system based on machine vision and deep learning, and the system comprises a data collection module which comprises an image collection unit, a sound collection unit, and a multi-type sensor unit, and is used for synchronously obtaining the image data, sound data, and environment parameter data of a practical training scene; the data processing module is used for preprocessing the multi-modal data acquired by the data acquisition module, realizing multi-modal data fusion through feature alignment and a space-time association algorithm, and generating structured practical training data; the deep learning analysis module adopts an improved convolutional neural network model and is used for performing real-time analysis on the fused structured practical training data and identifying personnel actions, equipment states and environment changes in a practical training scene; by adopting the technical scheme of the invention, the defects of dimension limitation of a single sensor and environment robustness of traditional image processing can be broken through, and accurate pre-judgment of practical training safety risks and objective evaluation of learning effects are realized.

The invention discloses a camera monitoring system based on wireless communication and a communication protocol optimization method, and relates to the technical field of communication optimization, the camera monitoring system is composed of a monitoring camera group, a wireless communication network, an edge computing unit, a cloud data center and a user interaction terminal; the monitoring camera group simulates human eye vision, adjusts a visual angle and a resolution ratio according to needs, and collects data; a wireless communication network is combined with quantum encryption and a traditional technology, and a communication mode is intelligently selected; the edge calculation unit processes data with low power consumption by using a neuromorphic chip; the cloud data center fuses the block chain and federal learning analysis data; the user interaction terminal has holographic projection and emotion perception functions and provides personalized services. According to the invention, the modules are collaboratively innovated, the pertinence of collected data is strong, the communication is efficient and safe, the processing is fast and accurate, and the interaction experience is good; the system has the functions of intelligent analysis, energy-saving management and the like, the intelligence and reliability of the system are improved, and the system has wide application prospects and market value.

The invention relates to the technical field of computers, and discloses a quality traceability method and system for an electronic device, and the method comprises the steps: collecting and preprocessing the data of each production link in real time; fusing and constructing a digital twinborn model; analyzing quality risks and abnormities by using deep learning; a non-tampering traceability chain is constructed through a block chain technology; and real-time monitoring and early warning are provided. The system comprises a data acquisition module, an edge module, a central platform module, a deep learning analysis module, a block chain traceability module and a visual early warning module. According to the scheme, closed-loop acquisition and intelligent analysis of data in the whole production link are realized, the authenticity of traceability data is ensured, and the quality management and traceability capabilities are improved.

Generating performance metrics and recommendations to improve an unlearned model includes executing an unlearning algorithm to expunge the influence on a machine learning model of a selected sample of the machine learning model's training dataset. Executing the unlearning algorithm creates an unlearned model. Performance metrics are generated by a metrics generator for the unlearned model and the machine learning model. Based on the performance metrics, an unlearning analysis is generated by a comparator comparing the performances of the unlearned model and machine learning model. A recommender, based on the unlearning analysis, generates a recommendation recommending a revision to the unlearned model in response to detecting a deviation of more than a predetermined threshold of one or more of the performance metrics of the unlearned model from one or more of the performance metrics of the machine learning model. An evaluator generates an unlearning evaluation of the unlearned model.

The invention discloses a refrigerator door body vibration induction detection control system, and relates to the technical field of refrigerator detection, the system comprises the following components: a vibration data acquisition subsystem, a data processing and analysis subsystem, a machine learning analysis module, a fault early warning and maintenance prompt subsystem and a data storage and management subsystem; through the high-precision piezoelectric vibration sensor and an advanced signal processing algorithm, the vibration state of the refrigerator door body can be monitored in real time, the system can accurately capture the key information of the vibration amplitude, frequency, phase and waveform of the door body, and the vibration data is deeply analyzed by using a machine learning algorithm, so that the accuracy of the vibration state of the refrigerator door body is improved. The door body fault early warning and accurate positioning function is achieved, the problems that due to the door body fault, the sealing performance of the refrigerator is reduced, and energy consumption is increased are effectively solved, the service life of the refrigerator is prolonged, and meanwhile the maintenance cost and time cost of a user are reduced.

The invention relates to the technical field of numerically-controlled machine tool diagnosis, and discloses a numerically-controlled machine tool fault diagnosis system based on machine learning. The system comprises a multi-source sensing data acquisition module for acquiring multi-dimensional sensing data such as vibration spectrum, spindle current waveform, temperature distribution, servo motor encoder feedback and the like; the operation feature coding module receives the multi-dimensional sensing data, extracts time domain statistical features and frequency domain energy distribution features, and generates a multi-source feature coding result; the incremental learning analysis module dynamically updates the feature weight through an incremental learning algorithm, and constructs an incremental training data set; the genetic optimization module optimizes the network structure and hyper-parameter configuration of the fault diagnosis model according to the incremental training data set, and generates optimized network structure parameters; and the integrated diagnosis decision module receives the current operation state data and the optimized network structure parameters, fuses diagnosis results of a plurality of base classifiers through an integrated learning algorithm, and outputs fault type classification signals.

The invention discloses a resin tank pressure resistance test method and system based on deep learning, and relates to the technical field of data processing. The method comprises the following steps: performing significant feature analysis based on a surface image of a target resin tank, determining a plurality of abnormal areas, performing defect identification, and constructing a defect distribution diagram according to an identification result; based on equipment attributes, setting retrieval constraint conditions to retrieve voltage withstanding test records of similar products, obtaining a sample data set, performing supervision training on the deep learning operator by using the sample data set, and constructing a voltage withstanding performance predictor; environment parameters of the target resin tank in a preset time zone are collected, the defect distribution diagram and the environment parameters are input into the pressure resistance predictor, and the predicted maximum pressure bearing value is output to serve as a pressure resistance test result. The technical problem that in the prior art, a resin tank pressure resistance test result is not accurate enough is solved, defect distribution and environmental parameters are analyzed in combination with deep learning, and the technical effect of improving pressure resistance prediction accuracy is achieved.

The invention belongs to the technical field of online education, and particularly relates to an online education interaction system based on a distributed algorithm. The online education interaction system comprises a user management module, a course management module, a learning content delivery module, an interaction and communication module, a homework and examination module, a distributed calculation and storage module, a data analysis and report module and a system monitoring and maintenance module. High availability and stability can be realized through a distributed database and load balancing, resource transmission is accelerated by using a content distribution network, smooth video live broadcast and video-on-demand are ensured, discussion areas and instant messages are supported, teacher-student interaction is enhanced, and an automatic scoring system and a learning analysis tool improve teaching efficiency and feedback quality. Real-time monitoring and log management of the system ensure stable operation, large-scale user requirements are met, and modular design facilitates expansion and maintenance.

The invention discloses a logistics freight vehicle transportation supervision system and method based on the Internet of Things, and relates to the technical field of vehicle transportation supervision, the supervision system is constructed based on multi-source data fusion and hierarchical anomaly detection, and the problems of false data and signal interference which are difficult to identify in traditional supervision can be effectively solved; through multi-level comparison and block chain evidence storage, supervision blind areas caused by manual tampering or equipment faults are greatly reduced; meanwhile, the self-learning analysis module dynamically recognizes repeated or regular pseudo data, and by iteratively updating a threshold value and improving an early warning level, false alarms can be remarkably reduced, and accurate positioning of potential anomalies can be enhanced; the real-time performance and traceability of freight vehicle transportation supervision are improved, and the method has significant improvement value for the traditional mode which depends on a single data source and lacks a rapid verification mechanism.

An AI development platform operates by: facilitating the development of a training dataset; providing access to auto machine learning tools to facilitate the development of an AI model; providing access to machine learning analysis tools to facilitate the evaluation of the AI model, wherein the machine learning analysis tools include an AI moral insight model that generates predicted moral score data associated with the AI model, wherein the AI moral insight model is trained based on at least one-hundred thousand sets of training data, wherein the training data includes reviewed media content and moral score data associated with moral indices, wherein the moral indices includes two or more of: a degree of immorality / morality, a degree of betrayal / loyalty, a degree of degradation / sanctity, a degree of harm / care a degree of cheating / fairness and a degree of subversion / authority; or providing access to a version control repository for storing versions of the training dataset and the AI model.

The invention discloses a teaching evaluation method and system based on artificial intelligence, and relates to the technical field of teaching evaluation, and the method comprises the steps: constructing a student learning analysis time series data set based on an LMS learning management system integration platform; constructing a dynamic student learning behavior evaluation model by using an unsupervised learning algorithm, and generating a student personalized learning portrait; based on the personalized learning portrait of the student, combining the historical score data of the student and the learning progress of the student, utilizing a deep neural network optimization model to realize dynamic real-time prediction and self-adaptive adjustment of the learning state of the student, and generating a continuously updated student learning state evaluation map; and based on the continuously updated student learning state evaluation map, analyzing the relationship between the student learning progress and the teacher teaching effect, dynamically adjusting the student learning strategy and the teaching plan, and generating an artificial intelligence teaching evaluation scheme. The method has the beneficial effects that personalized and scientific teaching evaluation and optimization are realized, and the teaching effect and the learning achievement of students are improved.

A system of a telecommunications network that uses IP Multimedia Subsystem (IMS) network elements to screen inbound calls. A Media Resource Function (MRF) intercepts an inbound call and uses various techniques to screen the call. The MRF redirects the call to an interactive voice response (IVR) system, which prompts the caller to state the caller's name, records the caller's response, and relays it to the called subscriber, who can then decide to accept or reject the call. The system can present a random number challenge to the caller. The system can use machine learning analytics and a large language model (LLM) to analyze the response's contents, accepting the call only if it relates to a topic allowed by the subscriber, perform audio analysis to determine whether the caller sounds robotic, or detect whether the response contains other known robocall markers.

The invention discloses an intelligent guidance system and method for predicting ship navigation disturbance release based on machine learning. The intelligent guidance system comprises a data acquisition module, a machine learning analysis module and a ship navigation prediction feedback module. A channel water pollution prediction model is constructed through a machine learning algorithm, the concentration of pollutants in overlying water of a channel after a ship passes is predicted in real time, and an optimal route is recommended after water environment protection and economic benefits are comprehensively considered, so that the ship is guided to sail. The problem that endogenous pollution release caused by ship navigation disturbance is difficult to predict and control is solved, ecological protection is considered while ship transportation is developed, the method has the advantages of automation, high efficiency, accuracy, flexibility and the like, and technical support is provided for controlling water environment risks in a navigation channel.

The invention relates to the technical field of intelligent data analysis, in particular to an artificial intelligence big data processing method and system for intelligent traffic, and the method comprises the following steps: collecting real-time traffic data, removing wrong or incomplete records, classifying and marking frequently accessed data, and generating cleaning and classification results. According to the method, data are collected and cleaned in real time, errors or incomplete records are removed, and the data accuracy is ensured. And then the data is intelligently classified, frequent and low-frequency access data are distinguished, and the data access efficiency is improved. And then analyzing the data by using machine learning, and constructing an access frequency model to provide a scientific basis for optimization of a data storage position. Besides, by dynamically adjusting the positions of the data among different storage levels, the distribution of storage resources is optimized, the overall efficiency of the data processing system is improved, the operation cost is reduced, and the practicability is high.

A substation disaster prevention digital twinning system relates to the technical field of power grid disaster prevention and comprises a multi-mode sensing layer, a dynamic twinning modeling layer, a credible federal learning analysis layer and a disaster linkage control layer. The multi-modal sensing layer collects equipment, environment and personnel behavior data in real time through various sensors; the dynamic twin modeling layer constructs a high-precision static model by using technologies such as laser radar scanning, and realizes dynamic updating through a local model refreshing mechanism; the credible federated learning analysis layer fuses multi-source data, and adopts a layered federated learning framework to accurately predict equipment faults; the disaster linkage control layer generates an emergency plan based on a knowledge graph, and realizes automatic response in a disaster scene through cross-system linkage; the method has the advantages of omni-directional multi-mode sensing, high-precision dynamic twin modeling, credible federated learning analysis, intelligent linkage control and the like, the disaster prevention and reduction capability of the transformer substation can be effectively improved, and safe and stable operation of a power system is guaranteed.

There is a need for solutions that classification solutions in hierarchical prediction domains. This need can be addressed by, for example, performing one or more online machine learning, co-occurrence analysis machine learning, structured fusion machine learning, and unstructured fusion machine learning. In one example, structured predictions inputs are processed in accordance with an online machine learning analysis to generate structurally hierarchical predictions and in accordance with a co-occurrence analysis machine learning analysis to generate structurally non-hierarchical predictions. Then, the structurally hierarchical predictions and the structurally non-hierarchical predictions in accordance with processed by a structured fusion model to generate structure-based predictions. Afterward, the structure-based predictions and non-structure-based predictions are processed in accordance with an unstructured fusion model to generate one or more unstructured-fused predictions.

The invention discloses a biodegradable mulching film degradation period prediction system and method based on machine learning, and belongs to the technical field of biodegrading.The system comprises an information acquisition module used for acquiring soil property data and mulching film data of preset soil, building a database based on soil properties, distinguishing the soil properties and determining the mulching film degradation period of the preset soil; the LSTM network data acquisition module is used for acquiring the position of a region where preset soil is located; and the LSTM network data acquisition module responds to the soil property data acquired by the information acquisition module and is used for acquiring characteristic data of soil properties of a region where preset soil is located and distinguishing the acquired soil property characteristic data. Compared with the prior art, the degradation change of the mulching film under various property data conditions can be better obtained by utilizing a machine deep learning analysis technology and analyzing and dividing soil data, so that the change of related data can be more accurately predicted in a future time period, and the degradation trend of the mulching film can be predicted.