288 results about "Objective evaluation" patented technology

The invention discloses an intelligent evaluation method for real-time learning and teaching effect feedback, and the method comprises the steps: carrying out the preprocessing of collected multi-modal teaching scene data of a target classroom, and obtaining a multi-modal data stream which comprises the aligned target video data, target audio data and a timestamp; pre-training the visual basic model based on the ViT architecture by adopting a plurality of education scene data sets to obtain a first model, carrying out alignment fusion on the first model and a preset variational automatic encoder structure to obtain a preset target model, and carrying out dimension reduction processing on the extracted spatial-temporal features and feature vectors by utilizing the model to obtain target features; and determining a classroom atmosphere index, a student participation index and a teaching interaction effect index based on teaching behavior characteristics, student behavior characteristics and teaching interaction behavior characteristics identified from the target characteristics, and synchronously generating a real-time teaching quality evaluation report. According to the method, the classroom information can be accurately and comprehensively analyzed, and a real-time and objective evaluation result is synchronously generated.

A transformer-based student performance prediction and reasoning intervention is disclosed. The system comprises a data repository coupled to a transformer-based prediction module that processes student data through multi-head attention mechanisms to generate performance predictions and identify potential learning shortfalls. A reasoning-enhanced large language model algorithmically generates personalized corrective action plans by applying structured decomposition of learning challenges, multi-step reasoning, and hypothesis testing. An algorithmic prompt formulation system optimizes inputs using field-specific, level-specific, and shortfall-specific templates. The system implements a workflow including shortfall detection against educational thresholds, causal factor analysis, intervention generation, and adaptive refinement based on outcomes. This approach enables early identification of academic challenges and timely implementation of personalized interventions to improve student learning outcomes.

The invention relates to the technical field of fluid machinery design, and discloses a fluid machinery through-flow component intelligent design simulation system, which comprises a multi-physics field modeling module, which is used for constructing fluid mechanics, structural mechanics and heat conduction models based on preset boundary conditions and physical attributes; the intelligent optimization decision module is used for constructing and defining a through-flow performance optimization objective function; the cooperative calculation module is used for constructing a comprehensive performance evaluation model based on the candidate design parameter set; the virtual experiment feedback module is used for reconstructing a corresponding through-flow component structure model according to the multi-target evaluation result; and the scheduling control module is used for controlling algorithm path selection and computing resource allocation in the optimization process. According to the method, a method of combining multi-physics field coupling modeling and a dynamic feedback mechanism is adopted, and design parameters are continuously adjusted through simulation data fed back in real time, so that the optimization process can be dynamically updated according to actual operation conditions, and the optimization convergence speed and the overall performance are improved.

The invention discloses a low-carbon city subjective and objective mixed planning interval entropy weighting evaluation method, and relates to the technical field of city management and low-carbon evaluation, and the method comprises the steps: collecting and preprocessing index data in real time, building a user behavior prediction model according to the index data, and predicting the index data of a user; dynamically adjusting weight distribution of subjective and objective evaluation indexes based on the predicted index data, and constructing a comprehensive evaluation model to calculate a comprehensive score of each region in the city; and formulating optimization measures of each region according to the comprehensive score. A prediction model is established by using a support vector machine, an evaluation result is highly matched with real-time data through dynamic weight adjustment, the hysteresis problem of traditional static evaluation is avoided, and more accurate and flexible low-carbon city evaluation is realized through dynamic adjustment of weight distribution and construction of a comprehensive evaluation model. And the comprehensive score of the urban area is calculated by adopting a nonlinear transformation function, so that the marginal effect change of the evaluation index under different load conditions can be better reflected.

The invention provides a sound standardization evaluation system for multi-dimensional acoustic parameter and emotional scene dynamic analysis, and aims to realize structured and objective evaluation of voice performance. The system comprises an audio acquisition module, an audio processing module, a standard evaluation module and an integration processing module. The audio processing module performs noise reduction, pre-emphasis, sampling rate adjustment, framing and slicing operation on the original voice data; the standard evaluation module executes character and pronunciation accuracy analysis, acoustic basic skill feature evaluation or emotion and scene adaptation degree analysis based on multi-modal fusion according to an evaluation type set by a user; parameters including fundamental frequency, formant, sound intensity, fundamental frequency perturbation, amplitude perturbation and the like are extracted in evaluation, and classification prediction is completed in combination with a machine learning model; and finally, the integration processing module generates an evaluation report including a scoring result, question prompts and personalized training suggestions, and provides closed-loop feedback to support sound training and expression optimization.

The invention discloses a solid state disk performance fluctuation test method, device and equipment and a storage medium, and relates to the technical field of performance tests.A corresponding test action sequence is generated according to a performance test instruction, a plurality of test actions are organized into a standard test process capable of being repeatedly executed, and the performance fluctuation test efficiency is improved. The influence of the difference of manual test operation execution on the test result is eliminated, the performance fluctuation test result of the to-be-tested solid state disk is calculated based on the storage performance index of the to-be-tested solid state disk in the test action sequence execution process, and objective evaluation of the performance fluctuation of the solid state disk is realized on the basis of sufficient data support. The automation and standardization of the test are realized from the two aspects of the test process and the result evaluation, and the efficiency and accuracy of the test are improved.

The invention relates to the technical field of teaching quality evaluation, and particularly discloses a teaching quality comprehensive evaluation system and method based on a teaching sequence perspective, and the system comprises a data management module, a learning sequence analysis module, a teaching sequence analysis module, a teaching quality evaluation module, a feedback improvement module, and a decision support module. The data management module is used for collecting data generated in an education and teaching process, including learning sequence data of students and teaching sequence data of teachers, and performing arrangement and standardization processing on the collected data; the learning sequence analysis module is used for analyzing learning sequence data of the students through a data mining technology; according to the invention, by integrating the student learning sequence data and the teacher teaching sequence data and combining a data mining technology and a deep learning algorithm, key factors influencing the teaching quality are accurately identified, the teaching quality is objectively evaluated in a data-driven manner, the deviation of manual evaluation is avoided, and the teaching quality is accurately evaluated. And the transparency and fairness of teaching quality evaluation are enhanced.

The invention discloses a courseware content analysis method and device, equipment and a storage medium. The method comprises the following steps: acquiring a courseware picture; detecting each frame of courseware picture, and detecting a title and a text; inputting the detected title and text of each frame into a pre-trained character recognition model, and recognizing a title keyword set and a text keyword set of each frame of courseware picture; summarizing the title keyword set and the text keyword set of all frames of courseware pictures to obtain a courseware keyword set; obtaining a knowledge point set of a course corresponding to the courseware; obtaining a courseware integrity analysis result according to the matching-union ratio; and obtaining a courseware content analysis result according to the courseware integrity analysis result. According to the method, the teaching courseware content is analyzed and evaluated according to an objective specification standard, so that a scientific and objective evaluation conclusion is obtained.

The invention discloses a quantitative evaluation system and method for context retention capability of multi-round dialogues of a large language model, and relates to the technical field of natural language processing evaluation. In order to solve the problems of standard missing and subjective dependence of LLM in multi-round dialogue ability evaluation, the scheme adopted by the invention comprises a dynamic scene generation module for cooperatively generating multi-round dialogue streams containing marked information points based on a rule template and LLM; the interference injection module is used for inserting controllable interference into the dialogue stream; the three-layer evaluation module takes the dialogue stream with interference as input, quantifies basic memory decline, topic logic correlation degree and anti-interference robustness of LLM through a memory precision layer, a correlation depth layer and an interference resistance layer, and calculates an information attenuation rate, an attenuation index and an anti-interference coefficient; the attenuation rate analysis module is used for correspondingly generating a curve based on a calculation result and intuitively presenting the performance change trend of the LLM; and the visual report module is used for integrating the outputs to generate an interactive evaluation report. The method is used for objective evaluation of the LLM context understanding capability.

The invention discloses an intelligent evaluation system fusing behavior data and a scale, and the system comprises a data collection module which is used for collecting multi-dimensional behavior data; the data processing and feature extraction module is used for cleaning and preprocessing the data and extracting and calculating features; the analysis and evaluation module performs analysis and evaluation based on the behavior characteristics and scale scores; and the result output and explanation module is used for displaying the psychological state evaluation result generated previously to the user and explaining the evaluation result displayed by the user. According to the method, by collecting the multi-dimensional behavior data of the user in the answering process and combining the traditional psychological scale score, the limitation that the traditional psychological scale evaluation method excessively depends on self-reporting and lacks behavior data analysis and integration is overcome, more comprehensive, accurate and objective evaluation of the psychological state of the user is achieved, and powerful support is provided for the field of psychological health.

The invention relates to the field of intelligent city planning, and discloses a street space quality intelligent evaluation and image generation method and system based on deep learning, and the method specifically comprises the following steps: movably collecting a street space image, and recording the geographic position information; performing perspective correction and multi-view image segmentation on the acquired street image data, and performing spatial matching with geographical location information to obtain a fused image database; and performing semantic segmentation on the image in the fused image database based on a deep learning model, calculating objective and subjective visual perception indexes and facility function indexes of the image, and generating a street space quality evaluation result. The method solves the problems that in the prior art, evaluation dimensions are limited, and an automatic response generation mechanism is lacked, and has the advantages of being intelligent, efficient and comprehensive in subjective and objective evaluation.

The invention discloses a building low-carbon reconstruction evaluation method based on multi-objective optimization. The method comprises the following steps: acquiring real-time data; obtaining static data; based on the real-time data and the static data, a multi-target evaluation model covering the total carbon emission amount, the transformation cost, the energy-saving income and the investment payback period is constructed, and indoor comfort and project implementation constraint conditions are set for the multi-target evaluation model; an NSGA-II algorithm is adopted to carry out optimization solution on the multi-target evaluation model; and performing automatic screening on the Pareto optimal solution set generated by the NSGA-II algorithm by using an ideal point method to generate a unique recommended transformation scheme, wherein the recommended transformation scheme comprises an optimal low-carbon transformation strategy and various key performance indexes corresponding to the optimal low-carbon transformation strategy. According to the method, the problems of evaluation benchmark distortion, insufficient multi-target collaboration and strong subjectivity of a decision-making process of a current existing building low-carbon reconstruction evaluation method are solved.

The invention relates to an intelligent cabin man-machine interaction evaluation method, device and equipment and a medium, and the method comprises the steps: judging whether there is an evaluation demand, and the evaluation demand comprises to-be-tested environment building data and a user interaction requirement; if the evaluation requirement exists, building a virtual scene according to the to-be-tested environment building data, and collecting simulator state data, virtual environment data and multi-modal data of a user when the user performs man-machine interaction in the virtual scene based on the user interaction requirement; and obtaining a subjective evaluation result of the user, obtaining an objective evaluation result according to the simulator state data, the virtual environment data and the multi-modal data, and obtaining a man-machine interaction evaluation result according to the subjective evaluation result and the objective evaluation result. Therefore, the problems of poor flexibility, evaluation lagging, high iteration cost, high evaluation subjectivity and the like of an evaluation means based on a physical model machine are solved, the flexibility, the real-time performance and the objectivity of evaluation are improved, and the evaluation efficiency is improved.

The invention provides a preschool education teaching quality evaluation system based on data analysis, and relates to the technical field of preschool education and artificial intelligence combination, and the system comprises a data collection module which is used for collecting basic information, teaching behavior data and teaching effect information of teachers; the knowledge graph construction module is used for constructing a multi-dimensional knowledge graph based on the data collected by the data collection module; the teaching behavior analysis engine module is used for analyzing the relationship between the teaching behavior and the teaching effect of the teacher based on the data in the knowledge graph by utilizing a teaching behavior analysis model; the teaching behavior analysis model is constructed based on a graph convolutional neural network; and the teaching behavior analysis model takes the knowledge graph as input and outputs an evaluation result of teaching quality. According to the method, the teaching behavior knowledge graph for preschool education teachers is constructed, and the teaching behaviors, the teaching methods and the teaching effects of the teachers are comprehensively recorded and analyzed, so that the teaching quality of the teachers is objectively evaluated.

The invention relates to a chassis electric control system calibration platform and method based on hardware-in-the-loop, a chassis domain control H L rack comprises an electric control suspension H L rack, a brake H L rack and a steering H L rack, an upper computer has a subjective and objective evaluation corresponding consistency model of subjective evaluation items with respect to objective detection items, the upper computer enables the chassis domain control H L rack to operate, and the chassis domain control H L rack is connected with the steering H L rack. Obtaining data of rack detection items, taking the rack detection items as objective detection items, obtaining data of subjective evaluation items by combining the subjective and objective evaluation corresponding consistency model, screening out important items from calibration items corresponding to the objective detection items in the chassis electric control system based on the data of the subjective evaluation items, and taking the important items as calibration variables. And based on the value range of the important item and the subjective and objective evaluation corresponding consistency model, taking the objective that the subjective evaluation item reaches the optimal value as an optimization target, and obtaining data of all calibration items and the optimal value of the subjective evaluation item. According to the invention, multi-system interaction of the chassis is realized, and an actual vehicle using scene of a user is met.

The invention relates to an automatic parameter adjustment method and system for an automatic driving system, and the method comprises the steps: R1, model pre-training and preparation: carrying out the precondition and early-stage preparation of the overall automatic parameter adjustment, carrying out the first training of a parameter adjustment model based on a neural network, and completing the initialization, completing a reward and punishment function to formulate a subsequent objective evaluation score of the performance of the automatic driving system; and R2, online parameter adjustment: the online parameter adjustment is a main part of overall automatic parameter adjustment, objective work performance evaluation scoring based on a reward and punishment function is combined with manual scoring, and parameter setting is autonomously completed in a real vehicle operation test process of an automatic driving system. According to the method, the defects in the prior art can be overcome at a time, the scene adaptability and scene generalization of the parameter adjustment result of the automatic driving system are improved, and the problems of efficiency, parallelism and accuracy in the parameter adjustment process of the automatic driving system are solved.

The invention relates to an unsupervised field adaptive streetscape semantic segmentation method. The method comprises the following steps: establishing a PixEL unsupervised field adaptive network model; the method comprises the following steps: firstly, training a segmentation network Deeplabv2multi through a source domain data set to obtain a pre-trained segmentation model; perturbation is added to the features, obtained through a Deeplabv2multi encoder, of the image in a target domain, and a feature perturbation enhanced version of the image is obtained; performing consistency training on the strong enhancement version, the weak enhancement version and the feature disturbance version of the target domain image by using the segmentation model; in a target domain, energy scores of unmarked samples are calculated to evaluate the closeness degree of the unmarked samples and current training distribution, samples with higher energy scores are selected to generate pseudo labels, and the pseudo labels are utilized to further carry out optimization training on the model. Compared with an existing unsupervised field self-adaption method, the method has the advantages that a better semantic segmentation effect is achieved on a real reference data set through synthesis, the subjective vision and objective evaluation indexes are obviously improved, and the unsupervised field self-adaption problem in the street scene image semantic segmentation field is effectively solved.

The invention discloses a social contact big language model evaluation method, system and device and a storage medium. The method comprises the steps that according to a service scene, social contact big language model capacity is disassembled, and an objective evaluation data set is collected; for various questions in the objective evaluation data set, constructing prompts with different person-set backgrounds, and carrying out objective evaluation on the social contact big language model through the objective evaluation data set to obtain an objective evaluation result of the social contact big language model; performing a simulation session with the social big language model, and performing evaluation in the aspects of model emotion satisfaction degree, human setup consistency, answer coherence and style significance according to session output of the social big language model to obtain a subjective evaluation result of the social big language model; and outputting an evaluation report according to the objective evaluation result and the subjective evaluation result of the social big language model. The technical problems that in the prior art, evaluation content is excessively concentrated in the knowledge understanding aspect of the model, user experience is not concerned, and the evaluation content is not comprehensive are solved.

The embodiment of the invention provides a heterogeneous network scheduling method and device and a computer readable medium, and the scheme integrates objective evaluation based on index attributes and subjective evaluation based on artificial empowerment through a mode of combining objective evaluation and subjective evaluation, so that a comprehensive score serving as a network scheduling basis is improved, and the network scheduling efficiency is improved. According to the method, the effects of different types of heterogeneous networks for a certain specific target audio-visual service can be reflected more accurately, so that more accurate network scheduling is realized, the user experience is improved, meanwhile, dynamic switching of the networks can be realized, the terminal equipment can be ensured to be always connected with the optimal network in the whole process of executing the audio-visual service, and the user experience is improved. Therefore, the best transmission effect is obtained.

The invention provides a human factor intelligent situational awareness evaluation method and system based on a simulated training environment, and the method comprises the steps: displaying a situational awareness task of a preset dimension in the simulated training environment to a person wearing a virtual reality device at a preset visual angle, the dimensions of the situational awareness task comprise a perception dimension, an understanding task dimension, a prediction task dimension and a decision-making task dimension; receiving evaluation feedback information performed by the personnel according to the received situational awareness task of the preset dimension; and recording the evaluation feedback information, and comparing the evaluation feedback result with an evaluation rule in a preset evaluation system to obtain an evaluation result. According to the invention, the objective evaluation method for the situational awareness of the personnel in combination with the ecological data at the preset view angle can be provided.

The invention relates to the technical field of kayak motion evaluation, and discloses a kayak motion posture evaluation method based on vision. The method comprises the following steps: synchronously acquiring dynamic image data of an athlete in different exercise stages through a multi-angle image acquisition device, and constructing an original exercise posture data set; key frame extraction and preprocessing are carried out on the dynamic image data, and a multi-dimensional visual feature space is constructed based on the preprocessed data; constructing and training an attitude quality evaluation model by using the space, and extracting attitude evaluation indexes; establishing a corresponding relation between the kayak motion posture scoring grade and the evaluation index; obtaining real-time training image data, obtaining a dynamic self-adaptive attitude evaluation index by combining with the model, and further obtaining a real-time attitude scoring grade; and if the grade is lower than a preset standard value, generating a posture adjustment suggestion. The method can capture attitude information in multiple dimensions, forms objective evaluation, feeds back and adjusts the direction in real time, and is suitable for kayak exercise training scenes.

The invention relates to the technical field of talent evaluation and distribution, and discloses a talent evaluation recommendation method and system based on artificial intelligence, and the method comprises the following steps: S101, collecting the personnel data of candidates and the post data of posts; s102, preprocessing the personnel data of the candidates and the post data of the posts to obtain third feature data and fourth feature data; s103, constructing graph structure data according to the third feature data and the fourth feature data; step S104, constructing a matching degree prediction model according to the graph structure data, and outputting scores of edges of the graph structure data; and S105, generating a distribution scheme according to the improved genetic algorithm in combination with the matching degree prediction model. According to the invention, objective evaluation of talents is realized, and talent distribution is more reasonable.

The invention provides an NoC low-delay data transmission method based on a dynamic routing algorithm, relates to the technical field of data transmission, and aims to solve the technical problems that an existing NoC routing algorithm cannot adapt to a link dynamic load, is lack of congestion trend prejudgment, is slow in path search convergence and is insufficient in QoS differential scheduling. The method comprises the following steps: constructing a dynamic sensing module containing a double-branch LSTM time sequence prediction model, collecting states such as link bandwidth and queue length in real time, and predicting a congestion trend in 50-100ms in the future; constructing a multi-objective evaluation function taking delay as a core, and dynamically adjusting the weight according to the QoS level; solving an optimal path by adopting an improved ant colony algorithm which introduces a congestion penalty and pruning strategy; during transmission, dynamic reselection is triggered through a pre-calculation + fast matching mechanism; and iteratively optimizing the prediction model based on incremental learning. According to the method, congestion is avoided in advance, the path search efficiency and the transmission reliability are improved, the delay is remarkably reduced compared with a traditional algorithm, and the method is adaptive to delay sensitive scenes such as a high-performance processor and an AI chip.

The invention provides an analytic hierarchy process-based multi-target evaluation method for a module power supply radiator. The method comprises the following steps of: determining structure influence factors of the module power supply radiator according to an analytic hierarchy process; performing expert scoring on the structure influence factors, and determining a weight vector based on expert scoring; establishing a finite element simulation model of the module power supply comprising the radiator; based on the finite element simulation model and a thermosetting coupling analysis method, determining result data of the structure influence factors; determining the entropy weight of each structure influence factor according to the result data of the structure influence factors; based on the weight vector and the entropy weight of each structure influence factor, determining a comprehensive weight corresponding to each structure influence factor; and according to the comprehensive weight of each structure influence factor, comprehensive scores of the radiators of different specifications are calculated, and the radiator corresponding to the highest comprehensive score is the optimal radiator required by the module power supply. According to the scheme, different radiator structures are scored, and the reliability of the radiator structures is improved.

The invention relates to the field of intelligent transportation, in particular to a driving data acquisition and analysis system and method, and the system comprises a global multi-dimensional data acquisition module, an intelligent fusion perception processing module, a data collection and distribution module, and a backtracking analysis platform. The global multi-dimensional data acquisition module acquires data of an external environment of a vehicle, a state of the vehicle and an operation state of a driver in a running process of the vehicle; the intelligent fusion perception processing module carries out real-time fusion, structured processing and labeling on the data to form truth value perception and road label data; the data gathering and distributing module gathers the processed data, stores and manages the data, distributes the data to the subjective and objective evaluation module, and generates subjective and objective evaluation results through manual label dotting and algorithm rule matching; and the backtracking analysis platform is used for backtracking and evaluating vehicle violation or risk behaviors in combination with subjective and objective evaluation results, and generating an analysis report. Therefore, the problems of low sensing range, insufficient sensing precision, single evaluation function and the like in the prior art are solved.

The present invention discloses a method and device for optimizing drilling parameters, wherein the method comprises: constructing a training set and a validation set; training a machine learning model according to the training set to obtain a plurality of classification prediction models respectively established for each formation; using a plurality of classification prediction models to obtain the prediction accuracy of each classification prediction model; selecting a first classification prediction model from each classification prediction model according to the prediction accuracy of each classification prediction model and a preset performance evaluation index; obtaining a data group that can achieve drilling speed-up during drilling based on the first classification prediction model, and establishing a random forest regression model; constructing a dual-objective evaluation function using the importance of characteristic parameters of the input data of the random forest regression model as weights; and determining the value range of each characteristic parameter according to the dual-objective evaluation function. The present invention can optimize the value range of drilling parameters that can achieve drilling speed-up.

According to the pathological section image quality evaluation method and the computer program product provided by the invention, automatic and objective evaluation of pathological section quality is realized through the deep learning model, and the problems of low efficiency and low recognition rate of an existing manual evaluation system are effectively solved. The dynamic classification strategy can flexibly adapt to different clinical requirements, technical guarantee is provided for accuracy of pathological diagnosis, and the method has remarkable clinical application value.

The invention relates to an intelligent automobile multi-dimensional performance evaluation method and electronic equipment, and the method comprises the steps: defining multi-dimensional evaluation indexes of an intelligent automobile, including an evaluation criterion layer and a sub-criterion layer; determining the weight of each evaluation index by using an analytic hierarchy process; calculating the efficiency of each test model by using a data envelope analysis method; calculating a comprehensive evaluation score based on each evaluation index weight and the efficiency of each test model; and giving optimization suggestions for low-efficiency evaluation indexes of the model. Compared with the prior art, the method has the advantages of fusing subjective and objective evaluation, improving scientificity and credibility of evaluation results and the like.

A system is provided.SOLUTION: A system comprising: means for receiving and pre-processing lesson content; means for operating a generative AI model for automatically generating lesson plans and test questions based on the pre-processed content; means for transmitting the generated lesson plans and test questions to terminals of educational institutions; means for collecting lesson videos through cameras installed in classrooms; means for automatically generating objective evaluations of students; and means for transmitting the generated evaluation results to the terminals of the educational institutions.SELECTED DRAWING: Figure 1

The invention discloses a disabled training support evaluation system, and relates to the technical field of disabled training, and the system comprises a multi-dimensional evaluation module, a quantitative evaluation module, a qualitative evaluation module, and a visual report module. According to the system, a multi-dimensional comprehensive evaluation system including knowledge mastering, skill application, learning attitude and emotion development is constructed, the learning effect of the disabled students is comprehensively covered, and the multi-dimensional evaluation not only pays attention to the knowledge and skill levels of the students, but also deeply investigates the learning attitude and emotion development conditions of the students, so that the learning efficiency of the disabled students is improved. Therefore, one-sidedness possibly brought by single-dimension evaluation is avoided, meanwhile, the system adopts a qualitative and quantitative combined mode, interference of subjective judgment is reduced, objectivity and accuracy of evaluation results are ensured, the comprehensive and objective evaluation system provides more accurate and reliable feedback for trainees and trainees, and the training efficiency is improved. And the two parties can better understand the learning progress.