166 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 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 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 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 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 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 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.

Systems and methods for real-time monitoring and control of well operations at a well site use machine learning (ML) based analytics at the well site. The systems and methods perform ML-based analytics on data from the well site via an edge device directly at the well site to detect operations that fall outside expected norms and automatically respond to such abnormal operations. The edge device can issue alerts regarding the abnormal operations and take predefined steps to reduce potential damage resulting from such abnormal operations. The edge device can also anticipate failures and a time to failure by performing ML-based analytics on operations data from the well site using normal operations data. This can help decrease downtime and minimize lost productivity and cost as well as reduce health and safety risks for field personnel.

The present invention provides a method and system for autonomous trajectory planning and image acquisition of unmanned aerial vehicles, which relates to the field of unmanned aerial vehicle inspection technology. First, through intelligent three-dimensional grid division and dynamic route planning, the coverage and efficiency of feature area monitoring are significantly improved; secondly, the integration of real-time meteorological data and historical feature data enhances the pertinence and adaptability of acquisition, which helps to obtain high-quality images in complex and changeable environments; then, the combination of real-time processing and ground deep learning analysis greatly improves the accuracy and efficiency of feature extraction; based on the adaptive supplementary sampling mechanism of key focus areas, rapid response and precise positioning of suspected feature areas are achieved, providing reliable data support for subsequent detailed analysis and decision-making. The present invention is not only suitable for various recognition tasks of preset features, but also has good scalability and flexibility, and can be adjusted and optimized according to different application requirements.

The invention provides an interpretable automatic machine learning analysis method for designing extrusion casting process parameters, and belongs to the field of extrusion casting process parameter design. The method comprises the following steps: constructing squeeze casting process data, and then establishing a machine learning prediction model of squeeze casting process parameters based on an automatic machine learning framework; and on the basis, an interpretable artificial intelligence algorithm is used for carrying out interpretable analysis on the extrusion casting process parameter prediction decision. According to the design method, an advanced automatic machine learning framework and an interpretable artificial intelligence algorithm are combined, so that the extrusion casting process parameter design process is more transparent and reliable. Meanwhile, the method is combined with an existing squeeze casting process data set, implicit relations between material components and process parameters are mined through high-order knowledge dimensions of a machine learning algorithm, and a new thought is provided for modeling optimization design of materials and process parameters of the squeeze casting process.

The invention is suitable for the technical field of laser spot regulation and control, and provides a laser spot regulation and control system with a self-adaptive feedback mechanism and a regulation and control method thereof, and the system comprises a workpiece detection module, a model generation module, a learning analysis module, a working condition collection module, an optimization adjustment module and a hardware execution module. The workpiece detection module is used for collecting workpiece space data and workpiece material components; the model generation module is used for generating a digital twinborn body according to the workpiece space data and the workpiece material components; the learning analysis module is used for analyzing the digital twins and generating or optimizing process parameters, working paths and to-be-processed data in cooperation with the self-learning processing library; the working condition collecting module is used for collecting actual machining data and external environment data in the workpiece machining process. According to the device, the problem of insufficient laser spot regulation and control precision is solved, and the effects of reducing deviation and improving the machining precision are achieved.

The invention discloses an intelligent early warning method and system for frozen shrimp transportation based on deep learning, and belongs to the technical field of cold-chain logistics and intelligent monitoring. According to the method, a multi-dimensional data analysis model is constructed by using a deep learning technology, and intelligent monitoring and anomaly prediction of a transportation environment are realized. The system is composed of a data acquisition module, a deep learning analysis module and an early warning decision module, can automatically identify potential risks (such as temperature abnormity and equipment faults) in the transportation process, and timely notifies related personnel through a multi-level early warning mechanism (such as short messages, mails and system popup windows). Through self-learning and optimization of the deep learning model, the accuracy and the real-time performance of early warning are improved, the loss risk of frozen shrimps in the transportation process is effectively reduced, the product quality is guaranteed, meanwhile, the intelligent level of cold-chain logistics is improved, and remarkable economic benefits and application values are achieved.

The invention discloses a medical image intelligent monitoring device for brain diseases, which comprises a device control unit for acquiring learning data, a central control unit connected with the device control unit and used for receiving data of the device control unit, generating a control instruction of the device and analyzing the state of the device, comprising an environment control module, a storage module, a safety verification module, a patient condition optimization module and a learning analysis module, the detection alarm unit is connected with the central control unit and is used for receiving the equipment parameters of the central control unit, monitoring the running state of the equipment under the condition of a patient in real time and carrying out fault analysis; the communication control unit is electrically connected with the equipment control unit, the central control unit and the detection early warning unit, is used for data transmission communication among the units, and comprises a wireless module and a communication module; the control assembly comprises a screen display part and a control handle part connected with the screen display part.

The invention relates to the technical field of electrical equipment maintenance, and discloses an upper limb exoskeleton control method, system and equipment for assisting replacement of an insulator. The method comprises the following steps: acquiring image data of an insulator and a field environment, and acquiring motion vector information according to the image data based on a preset optical flow method; analyzing the motion vector information based on a deep learning algorithm to obtain motion state information of the insulator; based on a dynamic fusion algorithm, according to the motion state information of the insulator and the position information of the upper limb exoskeleton, obtaining adjustment motion parameters of the upper limb exoskeleton; and issuing a control instruction to the upper limb exoskeleton according to the adjusted motion parameters. Dynamic changes of the insulator and the surrounding environment are monitored through the optical flow method, the insulator in motion is accurately recognized and analyzed in combination with deep learning analysis, the motion trail of the upper limb exoskeleton can be adjusted in time, it is ensured that the upper limb exoskeleton stably and accurately tracks and operates the insulator, the physical burden of operators is relieved, and the working efficiency is improved. And the operation safety and efficiency are improved.

The invention discloses an optical domain reflection and machine learning fusion device for optical fiber network fault positioning, and relates to the technical field of optical fiber network fault positioning. According to the invention, the fine reflection characteristics of the optical fiber network are captured by using the synergistic effect of the optical signal transmitting unit and the receiving unit; the signal processing module and the data acquisition unit ensure the data quality through efficient filtering and feature extraction; the machine learning analysis module automatically identifies and classifies fault modes based on a convolutional neural network model, and overcomes the limitation of a traditional method; the fault positioning unit combines algorithm output to calculate accurate position coordinates; seamless coordination between modules is realized through a control interface, and the response efficiency of the system is improved; the display output module provides a visual result, which is convenient for operators to monitor in real time; the power supply control unit optimizes energy management, ensures stable operation of the device and saves energy in idle time, thereby remarkably improving the accuracy, efficiency and reliability of fault positioning, and reducing the maintenance cost.