42 results about "Exam score" patented technology

The invention relates to a learning condition analysis method and system based on a large language model, a terminal and a medium, and relates to the field of education system technology, the method comprises the following steps: collecting learning data of students, the learning data comprising structured learning data and unstructured learning data, the structured learning data comprising examination scores and homework completion conditions, and the unstructured learning data comprising examination scores and homework completion conditions; the unstructured learning data comprises classroom behavior data, learning process data, student self-evaluation and mutual evaluation data and teacher teaching records; preprocessing the learning data to generate standardized data which can be used for analysis; input interaction information is received, analysis demand parameters are generated, and the interaction information comprises analysis demands of teachers or feedback information of students; based on a large language model, semantic understanding and deep analysis are carried out on the standardized data and the analysis demand parameters, and a learning condition analysis result is obtained; and displaying the study condition analysis result in a visual mode. The application has the effects of improving the teaching quality and meeting the actual teaching demand integrating degree.

The invention discloses a multi-modal data-driven teaching evaluation report automatic generation and intelligent analysis method. According to the method, multi-modal data is collected from various education information systems, and examination scores of students, classroom interaction, learning behaviors and feedback data of teachers are covered. The data are standardized, cleaned, preprocessed and the like, and abnormal values, missing values and unstructured data are processed. An evaluation model is constructed based on a multilayer neural network, a teaching evaluation report is generated by using an adaptive weight scoring system, and personalized teaching improvement suggestions are generated through a probability model. Meanwhile, the model is optimized by utilizing historical data and an incremental learning method, and finally, the method performance is measured through prediction accuracy, mean absolute error and comprehensive evaluation indexes, so that a comprehensive, accurate and intelligent teaching evaluation method is provided for the education field.

The invention discloses a control system and method for an intelligent test of an industrial-grade still kettle. The control system comprises a teaching management module, an analog control module and an early warning correction module, relates to the technical field of test of still kettles, and solves the technical problem that a lot of time needs to be spent or test scores cannot be accurately obtained due to the fact that the score applied to an examination can only be scored by a teacher or the score of an examinee can only be judged by a still kettle control system according to a final answer. According to the method, the simulation state of the still kettle and the simulation operation of the examinee are obtained, the simulation operation of the examinee and the simulation state of the still kettle are respectively graded with the grading steps of the corresponding questions in the database, and each simulation operation of the examinee is automatically graded, so that a large amount of manpower and material resources can be saved; and the score is made according to the simulation operation of each step of the examinee and the actual simulation state of the reaction kettle, so that the actual level of the student can be better met.

The invention belongs to the field of resource recommendation, and particularly relates to a learning resource recommendation method, system and device and a storage medium, the learning time of a user is divided into a fragmented time range and a long-time learning time range, the learning ability of the user is calculated, and according to the learning ability and the association degree of each chapter in a knowledge graph, the learning resource is recommended. When the current time is in the fragmented learning time range, a recommendation emergency value is obtained according to the current learning urgency degree and the knowledge gap risk degree, when the recommendation emergency value is smaller than a preset threshold value, a chapter with a maximum recommendation strategy is matched in the knowledge graph for recommendation, and if the recommendation emergency value is larger than the preset threshold value, the recommendation strategy is matched with the chapter with the maximum recommendation strategy. And if yes, optimizing and recommending chapters corresponding to the urgently needed learning resource type for learning. According to the method and the device, the corresponding learning resource recommendation is carried out in consideration of the time fragments of user learning, and the content which is urgently needed to be learned at present is also considered during recommendation to carry out preferential learning, so that the examination score is improved.

The invention discloses a teaching evaluation system based on artificial intelligence and big data, relates to the field of education evaluation, solves the problems that existing teaching evaluation depends on artificial questionnaire survey or examination scores, and is high in subjectivity and single in data dimensionality, and comprises a data processing module, a basic analysis module, an expansion analysis module and a comprehensive evaluation module, the data processing module is used for processing the average score of each culture course test of the class, sending the score data obtained by processing to the basic analysis module, and sending the expansion data obtained by processing to the expansion analysis module; the basic analysis module is used for analyzing the score level and stability of a class, the expansion analysis module is used for analyzing the expansion ability of students, and the result output module is used for generating comprehensive teaching evaluation for a class owner of the class, and comprehensive evaluation of the teaching condition is realized based on artificial intelligence and big data.

The invention discloses an intelligent management system based on data analysis, and belongs to the field of learning management systems, and the system comprises a weak subject analysis module which is used for extracting each subject score of a target student from a historical examination score database; the assistance matching module is in communication connection with the weak subject analysis module and is used for establishing a voluntary application one-to-one assistance relationship for the students with the weak subjects; the wrong question path generation module is used for analyzing the examination wrong questions of the target student in recent three months, counting knowledge point weights and constructing a dynamic learning path; the risk early warning module is used for detecting students whose scores decline in a cliff mode and starting three-level risk early warning through behavior and state analysis of campus monitoring; and the notification execution engine is used for triggering teacher appointment conversation, parent communication, seat adjustment and learning resource pushing operation according to the analysis result. According to the invention, quantitative evaluation can be carried out on dynamic change of subject strength, and then intervention necessity is automatically judged.

The invention provides an entrepreneurship practical training platform optimization method. The entrepreneurship practical training platform optimization method comprises the steps of a, collecting student data, including learning behavior data, social interaction data, practical training project data and examination score data of students, and performing data cleaning and preprocessing, noise data removal and missing data filling, and b, based on historical learning data and personal characteristics of the students, carrying out data processing on the data, so as to obtain the entrepreneurship practical training platform. And constructing a student learning model by using a machine learning algorithm, wherein the learning model can dynamically predict the learning progress, the course demand and the personalized path recommendation of the student. According to the entrepreneurship practical training platform optimization method, by introducing a multi-dimensional data acquisition and processing technology, the problem of inaccurate learning path recommendation caused by incomplete data or poor quality in a traditional platform can be effectively solved. And through data cleaning and preprocessing, a noise data detection and missing data filling method is adopted to ensure the quality of the data, so that the accuracy of a recommendation algorithm is ensured.

The invention discloses a modular examination score analysis and improvement method based on big data, and the method is characterized in that the method comprises the following steps: 1, carrying out the modular classification of examination questions according to the standards of question types, knowledge points, difficulty and the like, and building a corresponding database; 2, collecting error question data of the students, and inputting the error question data into a database; the invention relates to the technical field of modular score analysis and improvement methods, and relates to a modular examination score analysis and improvement method based on big data, so that students can obtain personalized learning suggestions, learning efficiency and scores can be improved, and the students can have better learning experience. Teachers and educational institutions can make more targeted teaching decisions by using the analysis results, the teaching quality is improved, and the accuracy and correlation of the analysis results can be improved along with time by adjusting the regression coefficient of the model and updating the model in real time.

A method (100) for generating an ultrasound exam score, comprising: (i) receiving (104) a temporal sequence of ultra-sound image data for each of a plurality of different lungs zones; (ii) identifying (106) using a feature identification algorithm, for each zone, one or more features of the lung(s); (iii) determining (108), for each identified feature, a confidence score; (iv) determining (110), using a composite score algorithm, a composite score for each zone indicative of a disease or condition severity or a diagnosis; (v) determining (112) a lung score using a lung score algorithm, indicative of a disease or condition severity or a diagnosis for the lung(s); and (vi) providing (114), via a user interface, one or more of the lung score, the composite score for one or more of the plurality of different zones, and the confidence score for one or more of the identified one or more features.

The invention discloses an intelligent identification method and device for paper marking scores and a computer readable storage medium. The intelligent identification method for the paper marking score comprises the steps of obtaining a to-be-identified answer carrier image; wherein the to-be-identified answer carrier image comprises at least one test question score area; determining a target scoring column corresponding to at least one test question score region in the to-be-identified answer carrier image by using the template image; determining a score corresponding to each test question score region according to the target scoring column; and calculating a total score corresponding to the to-be-identified answer carrier image according to the score corresponding to each test question score region. In this way, the recognition accuracy of the score of the scoring column is improved.

The invention discloses a sports test management system based on artificial intelligence, and relates to the technical field of test management, the sports test management system comprises a data acquisition unit, a data processing unit and an application service unit, the data acquisition unit is used for acquiring data during a test, performing real-time video stream analysis by adopting a YOLOv8 algorithm after the acquisition is completed, and transmitting the data to the application service unit; and during acquisition, actions of examinees are captured through a camera, and target detection and attitude tracking are completed in combination with a machine vision technology. The dynamic state of an examination room is detected in real time through full-process intelligent invigilation and a YOLOv8 algorithm, target tracking and identity verification under the scene that multiple persons are examined at the same time are supported, the manual invigilation cost is reduced, automatic scoring and feedback are achieved, scoring is decomposed through actions, the take-off angle, the air posture and the landing distance are captured through YOLOv8, the score is calculated in combination with a physical model, and the intelligent invigilation effect is achieved. And generating examination scores in real time.

This invention discloses an AI-based physical education exam scoring method, belonging to the field of exam scoring methods, including the following steps: (1) activating the cameras deployed in the exam room to collect motion images and facial expression images of the examinee during pull-ups; (2) analyzing the collected facial expression images using AI image recognition technology to divide the exam process into three stages; (3) for each stage, analyzing the examinee's motion images using AI technology to determine whether each pull-up motion is a qualified motion; (4) counting the number of qualified motions in each stage and scoring them separately, adding the scores of the three stages to obtain the examinee's final exam score. This invention can divide the exam into three stages based on facial expression recognition, perform differentiated analysis on the motion characteristics of each stage, better fit the examinee's physical fitness change patterns, and make the evaluation results more targeted.

The invention discloses a course goal achievement degree analysis and evaluation method based on artificial intelligence, and relates to the technical field of course evaluation, and the method comprises the steps: obtaining the performance score of a student in each stage based on a stage goal, and obtaining the examination score of the student during course examination based on the course goal; performing mean value calculation on the performance scores of the same student to obtain a performance mean value; carrying out discreteness analysis on the expression mean value to obtain a discrete coefficient; filtering the students based on the discrete coefficient to obtain a screened student list; based on the screened student list, inputting the performance scores and the examination scores of the students on the list into a built-in evaluation model to obtain course achievement degree data; evaluating and analyzing the course achievement degree data to obtain an evaluation report; and outputting the performance score, the examination score, the course achievement data and the evaluation report of the student based on a built-in course goal achievement report output format. The method has the effect of improving the accuracy of the evaluation result.

To enable provision of support to students and instructors in implementing effective learning methods.SOLUTION: A test support device is provided, comprising a target grade acquisition unit for acquiring target grades for target schools of test takers, and a task determination unit for determining learning tasks to be presented to the test takers based on utilization grates of the test takers based on test results of the test takers and the target grades.SELECTED DRAWING: Figure 1

This invention discloses a blockchain-based distributed examination method and system, comprising multiple modules including a management server, a teacher management unit, a student management unit, an examination business management unit, and a smart terminal. The teacher management unit allocates personal accounts and initial keys to teachers. These accounts contain teachers' personal information such as email addresses and mobile phone numbers. When a teacher accesses the grades section, the blockchain network receives a request and sends a dynamic key to the teacher's email address or mobile phone. The dynamic key is valid for 5 minutes. The teacher enters the dynamic key to log in to the grades section, and then the examination business management unit publishes the examination results. Students can check their results via their smart terminals. By adding a dynamic key to the grades section of the teacher management unit, and associating the dynamic key with the teacher's personal terminal, the security of the examination system is enhanced, preventing students from logging into the teacher management unit and tampering with the results.

1. Name of the product in this design: Unmanned Aerial Vehicle Training and Examination Scoring System Equipment. 2. Purpose of this design: For use in a training and scoring system for unmanned aerial vehicles (UAVs). 3. The key design feature of this product is its shape. 4. The image or photograph that best illustrates the design's key points: a 3D model.

The invention relates to the field of artificial intelligence, in particular to an AI-based teaching quality evaluation system. A traditional teaching quality evaluation mode often depends on subjective judgment of teachers and examination scores of students, and the teaching effect cannot be comprehensively and objectively evaluated. The AI-based teaching quality evaluation system provided by the invention adopts a multi-aspect evaluation model to evaluate students, enables evaluation results to be more objective and more comprehensive, and comprises a data collection module, a data preprocessing module, a learning condition analysis model, a first student evaluation model, a second student evaluation model and an evaluation integration module, and visualizing the interface. According to the system, a deep learning model is used to define the relationship among the attendance condition of the student, the classroom test score and the homework completion condition, so that the classroom performance of the student is further reflected, and the evaluation report of the student is more accurate and more objective.

The invention discloses a student score analysis method and system based on a multi-knowledge graph. The method comprises the following steps: acquiring examination answer data and student parameter data of a to-be-analyzed student; screening out a plurality of associated subjects according to the examination answer data and the student parameter data; inputting the examination answer data into a knowledge graph model corresponding to each associated subject to obtain a plurality of model analysis results; and determining an examination score analysis result of the to-be-analyzed student according to the plurality of model analysis results and a preset advantage and disadvantage association rule between subjects. Visibly, the knowledge graph models of multiple subjects can be fully utilized to realize student score analysis, so that more effective and deep student score analysis results are obtained, and a more accurate data basis is provided for subsequent teaching.

1. Name of the product in this design: Graphical User Interface for Robot Disassembly and Assembly Training Platform for Electronic Equipment. 2. Purpose of this design: An electronic device. 3. The key design features of this product are the graphical interface on the screen of the electronic device. 4. The image or photograph that best illustrates the design's key points: the front view. 5. Human-computer interaction method of graphical user interface: This graphical user interface is applied to computers. Using the main view as the main interface, clicking the "Exam" button on the left side of the main interface will take you to interface state 1, where you can select the exam subject; clicking the "Assembly Exam" button in interface state 1 will take you to interface state 2, where you can perform an assembly exam on the robot; clicking the "End Exam" button in the upper right corner of interface state 2, or when the "Exam Remaining Time" in the upper left corner of interface state 2 is "00:00:00", will take you to interface state 3, which displays the exam score and exam completion status. In each view, the specific text content can be replaced, and the number of texts does not limit the number of characters that can be replaced.

The invention relates to the technical field of AI vision, and provides a football examination system based on AI vision, an image acquisition module is used for acquiring image information in an examination process, a three-dimensional coordinate module is connected with the image acquisition module to establish a three-dimensional coordinate system, and three-dimensional coordinates of each point in an image are calculated; the recognition module is connected with the three-dimensional coordinate module and calculates the motion trails of the examinee and the football, the judgment module is connected with the recognition module and judges foul and goal of the examinee according to preset rules, the analysis module is connected with the judgment module and calculates examination scores according to the judgment result of the judgment module and generates examinee training suggestions, and the data management module is connected with the analysis module. Storing the examination data, and generating an examinee score report and an examinee training suggestion report; through the recognition module, the judgment module and the analysis module, the examination efficiency is improved, the examination process is more scientific and normative, examinees can clearly know own examination performance and defects, and a clear direction is provided for subsequent training.

The invention provides a psychological assessment method and system for senior high school students based on artificial intelligence, and the method comprises the steps: enabling a testee to wear an intelligent bracelet in daily learning life, obtaining the daily physiological data of the testee through the bracelet, and obtaining the ranking of all examination scores of the testee from a school; performing data statistics, feature extraction and analysis on the acquired physiological data and score ranking data to obtain physiological and score feature data of the testee; the physiological and score feature data are imported into a high school student psychological assessment model to speculate a psychological assessment result of the subject, a psychological health score is generated, and the high school student psychological assessment model is obtained by training an artificial intelligence model through machine learning and deep learning and is updated through reinforcement learning. According to the method, the internal relation and rule among the physiological indexes, the academic performance and the psychological health state can be revealed, and the algorithm and accurate speculation of the psychological health state of the senior high school students are realized.

The present invention relates to the technical field of performance data analysis, and specifically to a student test performance intelligent analysis system. The present invention obtains the independent learning performance trend of each student and the overall learning performance trend of all students in each test; analyzes the difference characteristics between the learning performance trends, and obtains the change correlation between the independent learning performance trend and the overall learning performance trend of each student in the current test; then obtains the performance influencing factor of each student in the current test; then adjusts the current original performance data of each student to obtain the current adjusted performance data of each student; clusters all students according to the changes in the current adjusted performance data and the adjacent previous original performance data to obtain student clustering clusters; and analyzes the test performance. The present invention improves the accuracy of the clustering effect and the effectiveness of the performance analysis by adaptively adjusting the test performance of students.

The invention provides a teaching data management system and method based on cloud computing, and belongs to the field of data management. The problem of complex data management is solved; the data collection module is used for acquiring the number of students and examination scores to form student information; obtaining teaching data; the data processing module is used for averaging examination scores and teaching data in combination with the number of students; obtaining a correlation coefficient according to the obtained mean value in combination with the examination score; according to the correlation coefficient, calculating a total influence value of the teaching data on the student examination scores; the data prediction module is used for performing regression analysis on the examination scores and the total influence value, constructing a score prediction model, predicting student scores, comparing predicted scores with the examination scores, and providing teaching plans for the students with the scores lower than the predicted scores; according to the method, the data is acquired and predicted, and the predicted data is analyzed and judged, so that the data management difficulty is reduced.

The invention relates to the technical field of teaching quality evaluation, and discloses an English teaching quality evaluation method based on a knowledge graph, and the method comprises the steps: collecting the voice, text, examination score and interactive behavior data of a student; extracting phoneme accuracy, intonation curves and fluency indexes from the voice data, performing subject-predicate-object structure recognition and logical relationship labeling on the text data, and processing score and interaction data to form a preprocessed data set; establishing a knowledge graph containing vocabularies, grammar, semantics and communication strategy elements according to the preprocessed data set, and generating an element evolution trajectory; mapping the data to knowledge graph identification semantic isolated points and logic interruption points to form a teaching quality factor group; and correcting the factor group, generating a change process, generating an early warning level according to the trend and the element redundancy, and finally outputting an evaluation result. According to the invention, comprehensive, dynamic and traceable evaluation of English teaching quality is realized through the knowledge graph.

The invention provides a departure intelligent evaluation system and method for medical student training, and relates to the field of medical training system management, and the system comprises a data collection module, an index evaluation module, a result generation module, and a data storage module. The data acquisition module is used for collecting training data of medical students in a training period, and the training data comprises teaching activity data, attendance data, skill assessment data, medical record writing data, patient satisfaction survey data, theoretical examination results and teaching teacher evaluation data; the index evaluation module is used for evaluating the training data according to preset evaluation indexes of various training data to obtain evaluation results; the result generation module is used for generating a departure index qualification condition table according to the evaluation result; and the data storage module is used for storing a departure index qualification condition table and training data. According to the technical scheme, the problem that a conventional evaluation system needs to consume a large amount of time to arrange and analyze evaluation data is solved.

The invention belongs to the field of intelligent query, and particularly relates to an examination score and admission destination query and analysis method and system.The method comprises the steps that firstly, a query demand instruction is analyzed through a clustering algorithm, and a query cluster representing a query intention is generated; based on the initialized query concurrence tree, intelligently mapping query clusters to corresponding regional concurrence nodes according to examination number mapping or regional information, and establishing rapid indexes of examination numbers and nodes; analyzing a query intention condition field in the instruction, generating an effective query condition chain and evaluating query concurrency difficulty; and dynamically generating a query demand feedback information cluster by combining the real-time load state of each data storage node according to a comparison result of the concurrence difficulty and a preset threshold value. According to the method, intelligent routing of the query request and accurate scheduling of load awareness are realized, the processing pressure of a core node is effectively relieved, system overload and crash in a high-concurrency scene are avoided, and the throughput and service reliability of a score query system are remarkably improved.

The application discloses a kind of based on blockchain examination management method, system, storage medium and equipment, belong to blockchain technical field.Method includes: blockchain triggers smart contract according to the examination subject issued by examination system to issue digital assets, each examination subject corresponds to a kind of digital assets;According to the examination subject of each reading side will be read by the paper, enough digital assets of corresponding kind are transferred to the on-chain address of each reading side;Examination system sends the transfer request carrying the reading score of each examinee side to blockchain according to the digital identity of reading side;Blockchain transfers corresponding kind and corresponding amount of digital assets from the on-chain address of reading side to the on-chain address of examinee side;Examination system statistics the amount of digital assets of each kind in the on-chain address of examinee side, obtains the examination result of corresponding examination subject;Ensure that the result statistics is carried out on chain, so that examination result is authentic and credible, and check score review is open and transparent.

This invention relates to the field of teaching management technology, specifically to an anti-cheating system based on big data monitoring. The system includes a video monitoring module and a wristband. The video monitoring module is evenly distributed around the sports field and is equipped with a Bluetooth communication module. The wristband includes a housing and a strap movably connected to the housing. The housing contains a data processing module that transmits signals to the Bluetooth communication module. The housing also contains a one-way tightening unit for the strap, which prevents cheating by proxy. A reset unit is also included to release the one-way tightening of the tightening unit. This reset unit transmits a stop-exam signal to the data processing module when the one-way tightening is released. By improving the timing method in middle- and long-distance running physical education exams, only a small number of invigilators are needed. The system uses video monitoring combined with automatic single-shot continuous timing to record exam scores, preventing cheating such as proxy testing.

The invention discloses a middle school examination admission prediction visual analysis method for optimizing a random forest based on a genetic algorithm, and belongs to the technical field of visual analysis. The method comprises the steps of firstly obtaining related simulation filling data in a Chinese school examination data set; performing automatic hyper-parameter tuning on the random forest model by using a genetic algorithm; predicting a student admission result by using the trained model; a model interpretation view is constructed, a student prediction result view is constructed, and multi-dimensional data such as examination scores, volunteer filling and admission prediction are presented in an interactive table form; and finally, constructing a student list. The method can effectively solve the problems that a traditional prediction model is low in parameter tuning efficiency, insufficient in interpretation and one-sided in visual analysis, and provides data-driven scientific decision support for student personalized voluntary guidance, school teaching quality analysis and educational resource balanced configuration through multi-view collaborative interaction and dynamic prediction feedback.

This invention provides a differentiated learning content search method based on knowledge state assessment, belonging to the field of intelligent education technology. The method includes: analyzing learning resources based on a pre-set knowledge graph, labeling related knowledge point nodes and difficulty levels, and constructing a three-dimensional virtual coordinate space to complete resource mapping; obtaining users' historical exam scores and browsing records to generate a knowledge state profile and personalized search vectors; parsing search keywords to generate constraints and initial search points, performing vector-oriented retrieval, and capturing suitable resources through Euclidean distance thresholds and constraints; generating personalized tags based on the captured vectors; calculating recommendation scores by integrating difficulty matching degree and interaction intensity, and outputting tagged results after sorting. This invention achieves end-to-end personalization from resource retrieval and sorting to presentation, providing differentiated learning content based on learners' actual ability levels, effectively solving the problems of homogeneous results and lack of adaptability in traditional search systems.