126 results about "Assessment evaluation" patented technology

A method for evaluating textual output of one or more machine-learned language models is presented. The method includes receiving, from a user of a client device, a first prompt for input to one or more machine-learned language models, providing the first prompt to the one or more models for execution, and receiving a set of generated responses to the first prompt from the one or more models. The method further includes generating a user interface (UI) on the client device displaying the first prompt and generated responses as a table user interface element. The method applies a selected evaluation function to the generated response to evaluate the response with respect to an evaluation objective and identifies words that influence the evaluation. The method generates one or more UI elements on the UI to display the results of the evaluation for the generated responses.

The invention discloses a fall risk real-time assessment system and method based on big data, and relates to the field of risk assessment, and the system comprises a data collection and preprocessing module, a big data processing and integration module, a feature extraction and selection module, a fall risk assessment module and a real-time monitoring and early warning module which are sequentially connected and cooperatively work. Wherein the data acquisition and preprocessing module is responsible for acquiring data from a plurality of sources and converting the data into digital signals; the big data processing and integration module is responsible for secure transmission, convergence and preprocessing of data; the feature extraction and selection module is responsible for extracting features related to the falling risk from the preprocessed data; the tumble risk assessment module is responsible for real-time assessment of tumble risks based on the extracted features; and the real-time monitoring and early warning module is responsible for triggering an early warning signal according to the falling risk assessment result. According to the invention, accurate and real-time evaluation of the falling risk, accurate and timely output of the evaluation result and rapid response of the early warning signal are realized.

The invention discloses a power market user load prediction method, system and device and a medium, and relates to the technical field of power market user load prediction, and the method comprises the steps: collecting and preprocessing power market user load data, and dividing the preprocessed data into a training set and an evaluation set; and constructing a user load prediction model based on LSTM and dual Q network optimization, and inputting the training set to carry out single-step prediction. And evaluating the power market user load prediction model based on the evaluation set, and after the evaluation is passed, inputting future prediction data to the user load prediction model to obtain a predicted power market user load. According to the method, the external variables influencing the power load can be more comprehensively captured, so that the accuracy and the reliability of the prediction model are improved. And an LSTM network is combined with a double Q network model, so that the recognition capability of the model on the long-term trend is enhanced, and the accuracy of a prediction result and the generalization capability of the model are improved.

Embodiments of the disclosed technologies are capable of evaluating content recommendations. The embodiments describe creating a prompt using a search query and a content recommendation output by a machine learning model in response to the search query. The embodiments further describe causing a LLM to generate an evaluation of the content recommendation and the search query using the prompt. The evaluation includes a relevance score of the content recommendation and the search query. The embodiments further describe training the machine learning model to generate an updated content recommendation in response to the search query. The training includes using the relevance score of the content recommendation and the search query.

The invention relates to the technical field of training simulation, and discloses a VR-based gas safety emergency training system, which comprises a three-dimensional scene construction module, an interactive data acquisition module, an interactive data processing module, an examination data analysis module and an examination evaluation module, entity drilling is replaced, the same scene can be repeatedly trained for infinite times, and the training cost is remarkably reduced; the VR technology provides multi-sensory stimulation, the intuitive cognition of students on danger is enhanced, the safety awareness and the emergency response capability are effectively improved, and no danger exists; by collecting operation data of students, the system automatically calculates key coefficients such as equipment operation precision, process compliance, disposal timeliness and path planning rationality, and scientific quantification and accurate scoring of training effects are achieved; and automatic grading evaluation is carried out according to a grading result, and a customized remedy module is triggered for students with insufficient abilities, so that a'training-assessment-intervention 'closed loop is formed, and the overall training efficiency is improved.

The invention provides a multi-modal large model question and answer evaluation system based on a unified platform and benchmark. The system comprises a visual question and answer evaluation platform, a question and answer benchmark based on a unified capability framework and an evaluation strategy based on a large language model. The visual question and answer evaluation platform is a unified platform which is based on concurrency control and supports multi-modal data; the systematic evaluation of the model performance is realized through a standardized data structure and task design according to the homemade question and answer benchmark; the evaluation strategy scores the answers based on multiple independent scores and a semantic comparison mechanism. According to the method, efficient task scheduling and stable performance expression are realized, and the requirements of a high-load evaluation scene can be met; systematic evaluation of different model performances is realized, and all core capabilities required by questions and answers of owners are covered. And the answers are scored based on multiple independent scoring and a semantic comparison mechanism, so that the scoring accuracy and consistency are improved, and the limitation of a traditional evaluation method in processing complex answers is overcome.

Methods and apparatus are provided for automated analysis and verification of plumbing code compliance in architectural design plans using artificial intelligence (AI). The system analyzes design plans, such as floor plans or architectural drawings, to identify, classify, and evaluate the placement, type, and number of plumbing fixtures, including water closets, sinks, and showers. The AI engine assesses these fixtures against applicable building and plumbing codes, evaluating spatial relationships, clearances, and pipe configurations. Where non-compliance is detected, the system offers automated suggestions for design revisions and optimizations, providing code adherence and functional efficiency before construction begins. The apparatus includes interactive tools for real-time simulation and collaboration, enabling designers, architects, and code compliance officers to make informed adjustments to the design.

The invention discloses an employee ability growth navigation and resource recommendation system and method, and relates to the technical field of employee growth navigation and resource recommendation. Comprising a learning map construction module, a multi-modal ability evaluation module, a dynamic navigation engine, a resource recommendation module, a digital twinborn training module and a user interaction module. A learning map construction module; the multi-modal fusion realizes' voice-text-image-equipment 'four-dimensional evaluation, the evaluation dimension is expanded from traditional 3 items to 8 items, and the evaluation accuracy of the cross-department cooperation capability and the fault decision-making capability is improved; the assessment weight is dynamically adjusted through reinforcement learning, the technical trend matching degree is improved, the assessment period is shortened to the hour level from the quarterly level, and the talent inventory efficiency is improved; a composite fault scene is dynamically generated, practical training pertinence is improved, and the processing success rate of employees in a multi-equipment linkage failure scene is improved.

The invention provides a digital beauty teaching system based on artificial intelligence, and the system comprises a learning condition collection and analysis module which is used for collecting learning condition data, constructing a student file, and carrying out the calculation to obtain a learning condition comprehensive index; the course planning and recommendation module is used for performing course recommendation, learning planning, course dynamic adjustment and cross-domain course recommendation; the intelligent tutoring and question answering module is used for constructing an error type database and an operation diagnosis model and carrying out diagnosis analysis and intelligent question answering on real-time operation; the learning condition evaluation module is used for constructing an AI scoring model and carrying out evaluation on real-time homework; and the visualization and adjustment module visually displays the learning condition evaluation result and dynamically adjusts the homework improvement suggestions and the recommended courses. The system can quantify the overall learning participation degree and expression level of students, accurately analyze the learning condition, accurately recommend courses, intelligently diagnose common errors, generate improvement suggestions, and score works, so that the scoring result is more accurate, and efficient proceeding of beauty teaching is promoted.

The invention discloses a method and device for improving the fine tuning effect of a large model based on an iterative data enhancement strategy. The method comprises the following steps: manually generating a batch of seed data with different distributions according to requirements; enhancing artificially generated seed data based on a rule or a large model; checking the quality of the enhanced data; finely adjusting the model by using the checked data, and reasoning training data by using the trained model; performing comprehensive evaluation on the reasoning result in various aspects; whether the evaluated indexes meet requirements or not is judged; if the error sample does not meet the requirement, enhancing the error sample again, and iterating repeatedly; and if the evaluation result meets the requirement, ending the whole process. According to the method, the threshold of fine adjustment of the large model under the small data set is remarkably reduced, and the model is excellent in performance in a data scarce scene by generating the targeted synthetic data.

The invention provides a reserve land development potential grading evaluation method and system based on pattern spot data, and relates to the technical field of reserve land development management, and the method comprises the steps: carrying out the pattern spot data mining of reserve land, and obtaining an original pattern spot data set with pattern spot attribute information; performing multi-level classification and layering according to a spatial planning hierarchical relationship, and extracting a potential constructable base range; performing pattern spot spatial topology processing, extracting pattern spot geometric feature parameters, and performing pattern spot scale division based on a preset area threshold value; establishing a pattern spot potential evaluation index system, and performing pattern spot development potential category division; and executing the refined extraction of the available space to form a visual board for the reserve land. The technical problems that in the prior art, the development potential of the reserve land is evaluated by depending on manual judgment and a rule-based simplified evaluation model, the evaluation precision is insufficient, the development potential of the land cannot be comprehensively reflected, and the scientificity and rationality of a development decision are further influenced are solved.

The invention discloses a massage effect evaluation method and system, and the method comprises the following steps: S1, data collection: obtaining a plurality of physiological parameters; s2, data preprocessing: carrying out filtering, normalization and feature extraction processing on the physiological parameters; s3, effect evaluation: inputting the data after feature extraction into an effect evaluation model, and adaptively outputting an evaluation result by the effect evaluation model according to the massage manipulation; and S4, optimization iteration: optimizing the effect evaluation model by adopting a gradient descent method so as to improve the evaluation effect. The massage effect is evaluated by fusing the EMG feature, the skin temperature feature, the pressure distribution feature, the HRV feature and other parameter data, and the evaluation effect is more accurate.

The invention discloses a method for constructing an effect evaluation model based on meteorological early warning service quality, and relates to the technical field of meteorological early warning service quality evaluation, and the main scheme is as follows: calculating a structural dimension index of a precipitation field by analyzing forecast precipitation intensity data and observation precipitation intensity data; performing grade evaluation on the structural dimension index through a grade division model, and analyzing forecast occurrence time data and actual occurrence time data to obtain a timeliness score; a public influence degree score is obtained through the early warning level data, the actual occurrence position data and the received data of the mobile phone users in the urban area and the remote area, the structural dimension comprehensive score, the timeliness comprehensive score and the public influence degree score are comprehensively analyzed, and a comprehensive quality score is obtained; and summarizing the assessed contents into a comprehensive assessment report, thereby solving the problems of single assessment mode, lack of comprehensive assessment and poor assessment authenticity in the prior art.

The invention belongs to the technical field of pilot ability assessment, and particularly discloses a pilot ability assessment method based on dynamic time warping and hierarchical clustering, which comprises the following steps of: acquiring eye movement data of a tested pilot in a flight simulation task process, preprocessing the eye movement data, extracting behavior indexes of each stage of a flight task, and calculating a pilot ability assessment result; generating a fixation area number sequence according to the fixation point position; in the selection target evaluation stage, a dynamic time warping algorithm is used for carrying out nonlinear alignment on the gaze sequences of all the pilots, and an eye movement difference degree matrix between the pilots is generated; based on the difference degree matrix, adopting a hierarchical clustering algorithm to group the pilots; and outputting an ability evaluation result of the pilot according to the distribution of the pilot in the difference degree matrix and the deviation information of the pilot and the teacher watching sequence. According to the method, structured comparison and capability grade evaluation of complex cognitive behaviors can be realized, and an evaluation result has relatively high objectivity and interpretability.

The invention provides a crane health assessment method and system. The method comprises the following steps: acquiring data of a crane in a working state; performing health assessment based on the collected data and a health assessment model; and obtaining quantized data based on an evaluation result, and performing maintenance guidance based on the quantized data. According to the system, the use condition of the crane can be monitored in real time, the states of main structural parts and key parts of the crane can be analyzed and evaluated, the health condition of the crane can be evaluated, quantitative data guidance is provided for equipment maintenance, and production and maintenance departments can predict the equipment condition in advance, so that maintenance or part plans can be arranged in a targeted manner.

The invention provides an employee work assessment recommendation method and system based on a collaborative filtering algorithm, and relates to the technical field of employee work assessment, and the method comprises the steps: extracting a task dependency relationship and time consumption data from a project management system, and constructing an employee task dependency graph; secondly, collecting and analyzing work interaction information containing document operation and task marking features, and identifying task switching critical points to generate quantitative indexes; constructing a similar employee group and extracting an efficient working mode by means of a collaborative filtering algorithm after establishing an evaluation data set through space-time alignment of the two; by taking the task dependency graph as an environment state and the efficient mode as a reference, generating a task sequence optimization scheme by using a reinforcement learning model; according to the scheme, the staff group composition is adjusted according to the scheme execution effect, the optimization model is fed back, finally, a comprehensive evaluation result is generated based on the scheme and actual task sequence matching degree and the quantitative index stability, the task sequence optimization and physiological state adjustment ability can be accurately evaluated, and the work assessment scientificity and the task execution efficiency are improved.

The present invention relates to a dynamic evaluation model and implementation method for the value of data elements, which belongs to the field of data assets, including quality evaluation: evaluating the quality of data elements; value evaluation: evaluating the value generated by data elements in the market in actual applications; risk evaluation: evaluating the risks that may be generated by data during use; comprehensive evaluation: combining the evaluation results of the data element quality evaluation module, the data element market application evaluation module and the data element value risk evaluation module, performing a correlation analysis of the quality, market application and risk of the data elements through a neural network, assigning corresponding weights to them, and using a weighted average formula to obtain a comprehensive evaluation result. The present invention can comprehensively and dynamically reflect the value status of data elements, ensuring the comprehensiveness and accuracy of the evaluation results.

The invention discloses a newborn congenital heart disease screening method based on tandem mass spectrometry, and the method comprises the following steps: 1, collecting heel blood tandem mass spectrometry data of newborns subjected to echocardiography examination after birth, and preprocessing the data; 2, performing feature extraction on the data collected and preprocessed in the step 1, and dividing the data into a training set and a test set according to the extracted features; and 3, constructing a model by using a sklearn packet in Python, training the model on the training set divided in the step 2, evaluating the model on the test set, inputting data to be screened into the model, and screening data of the congenital heart through the model, wherein the evaluation index is the area under the ROC curve. According to the newborn congenital heart disease screening method based on tandem mass spectrometry, ventricular septal defect with the maximum proportion in congenital heart disease is focused on, and good economic cost benefits are achieved.

Systems and methods for improving query creation and AQM are provided. A user interface is provided that allows an analyst to generate and test queries. After the analyst provides a query, one or more subsets of previous call center interactions are selected to test the provided query using methods such as a large language model or string vectorization. Once the subsets of interactions are selected, an LLM is used to evaluate the query against the subset. The evaluations may indicate interactions which were positive and which interactions were negative and may include quotes from the interactions that support the evaluations. The evaluations are presented to the analyst for review, and the analyst may revise the query in view of the evaluations. Once the analyst is satisfied with the performance of the query, the query may be used by the LLM to perform AQM for new interactions in the call center.

The invention discloses an intelligent networked automobile safety situation assessment method based on multi-source information fusion. The method comprises the following steps: 1) acquiring training parameters of a speed prediction model; 2) training a neural network by using the speed prediction model training parameters, and constructing a speed prediction model; 3) predicting the speed of the vehicle at the next moment by using the speed prediction model; 4) acquiring driving safety information, and processing the safety information to obtain a safety situation assessment parameter; and 5) processing the safety situation assessment parameter to obtain a current safety situation assessment value so as to determine the driving safety state of the vehicle. According to the method, the self-vehicle speed prediction model and the driving safety situation evaluation model are constructed, and the importance of the safety situation value on the driving safety of the intelligent networked vehicle is clarified by taking the prediction of the self-vehicle speed as a link, so that theoretical and technical support is provided for realizing the safe driving technology of the intelligent networked vehicle.

The application belongs to the technical field of large model security, and specifically discloses a large model threat sample library construction method, which comprises the following steps: step S1, embedding malicious instructions or malicious data into prompt words to construct threat samples; step S2, inputting the threat samples into a target large model as prompt words; step S3, using an evaluation large model to evaluate the output results of the target large model; and step S4, if the evaluation result is harmful content, adding the threat sample into a threat sample library, otherwise, discarding the threat sample. The application solves the problem of the lack of a threat sample library for security testing of an existing large model, and automatically tests and evaluates the threat samples during the construction of the sample library, so that the threat samples can be stored in the library only after the evaluation, thereby effectively improving the construction efficiency of the threat sample library. Meanwhile, the constructed threat sample library can support the security testing of the target large model, and can be used to improve the security of the target large model.

The application provides a crane health assessment method and system, the method comprising collecting data of a working state of the crane; performing health assessment based on the collected data and a health assessment model; obtaining quantitative data based on a result of the assessment and performing maintenance guidance based on the quantitative data. The application can monitor the use of the crane in real time, analyze and assess the state of main structural parts and key components of the crane, assess the health of the crane, provide quantitative data guidance for equipment maintenance, and realize early prediction of the state of the equipment by the production and maintenance departments, so as to arrange maintenance or component plans in a targeted manner.

The embodiment of the invention provides a reliability evaluation method based on mechanical equipment component-level mechanism analysis. The method comprises the following steps: analyzing an equipment failure mode; key components are decomposed; evaluating the failure rate of the component; determining a multiplier factor; and summarizing equipment failure rates. According to the embodiment of the invention, dependence on equipment experiment data is eliminated, the influence of environmental conditions on the parts is determined by issuing the operation environment faced by the equipment to the part level, the reliability of the mechanical equipment is evaluated under the condition that the experiment data is incomplete and insufficient, the evaluation precision is improved, the early failure is early warned, the maintenance cost is reduced, and the reliability of the mechanical equipment is improved. By establishing a'material-component-system 'multi-scale model, the modeling barrier between a micromechanism and macroscopic performance of a traditional method is broken through, the component failure rate is expressed in a multiplier factor mode, and quantitative calculation of the component failure rate is achieved.

The invention discloses a hierarchical penetration test effect evaluation method and device, and the method comprises the steps: constructing a hierarchical penetration test effect evaluation index set according to an evaluation object, and the hierarchical penetration test effect evaluation index set comprises a penetration test performance evaluation index subset and a social influence effect evaluation index subset; processing the hierarchical penetration test effect evaluation index set to obtain a penetration test performance evaluation result and a social influence effect evaluation result; and integrating the penetration test performance evaluation result and the social influence effect evaluation result to obtain a hierarchical penetration test effect evaluation result. According to the method, the final result of the network penetration test is taken as an index division basis, different penetration test means, penetration test modes and penetration test tools are adopted for performing penetration test effect difference analysis aiming at the same penetration test target, and auxiliary formulation of a penetration test task scheme is facilitated.

The invention belongs to the technical field of air pollution assessment, and relates to an urban air pollutant change amplitude assessment and ranking method and system, and the method comprises the steps: 1) obtaining the concentrations of a plurality of air pollutants of a plurality of cities in different time dimensions, and carrying out the standardization processing of the concentrations; 2) performing multi-dimensional time sequence analysis on the air pollutant concentration to obtain a multi-dimensional time sequence analysis result; 3) calculating a comprehensive score of each city according to the air pollutant concentration and the multi-dimensional time sequence analysis result; 4) calculating a dynamic evaluation score of each city and dynamically ranking the plurality of cities according to the dynamic evaluation scores; and 5) evaluating the change amplitude of the pollutant concentration of each city, and evaluating the plurality of cities according to the multi-dimensional time sequence analysis result and the change amplitude to obtain the risk level of each city. Evaluation is carried out based on the multi-dimensional time sequence analysis result and the dynamic weight, and the evaluation result is comprehensive and accurate.

The invention relates to an underground mine mining work evaluation method, device and system and a medium, and belongs to the technical field of mine field management.The method comprises the steps that positive indexes and negative indexes of a carry-scraper driver are obtained; wherein the positive indexes comprise the total underground working time length of a driver, the mineral mining quantity in each working stope and the moving distance of driving a carry-scraper; the negative indexes comprise work violation times; determining the positive influence degree on the mining work according to the positive index, and determining the negative influence degree on the mining work according to the negative index; and generating a mining work evaluation result of the carry-scraper driver according to the positive influence degree and the negative influence degree. The mining work of the driver is evaluated from multiple dimensions, the evaluation mode is more reasonable, and the evaluation result can more accurately reflect the mining work completion condition of the driver.

The invention relates to a nurse standardized training examination evaluation system and method based on big data. The system comprises a training module used for collecting training data in a nurse standardized training process and evaluating the training data to obtain a nurse standardized training evaluation coefficient Q1; the assessment module is used for collecting assessment data in the nurse standardized assessment process and evaluating the assessment data to obtain a nurse standardized assessment evaluation coefficient Q2; and the analysis and calculation module is used for calculating a nurse standardized training examination evaluation result Q according to the nurse standardized training evaluation coefficient Q1 and the nurse standardized examination evaluation coefficient Q2. According to the method and the device, the technical effects of performing standardized statistical calculation on various training examination items and performing comprehensive evaluation are realized, so that the technical problems that nurse training examination is not comprehensive and standardized statistics cannot be performed in the prior art are solved.

The invention relates to the technical field of rehabilitation systems, in particular to a game type rehabilitation platform for children, which comprises a user registration and login module for a user to perform account registration and login operation; the rehabilitation evaluation module is used for comprehensively testing rehabilitation conditions of children, including data comparison before and after rehabilitation, and providing user data and reference standards for the training module; the rehabilitation training module is used for establishing a virtual environment and providing rehabilitation training for children in combination with virtual equipment; and the data visualization module is used for displaying the training data and the evaluation result in the forms of charts and reports. According to the whole technical scheme, through a personalized rehabilitation scheme, the beneficial effects of improving user motivation and participation degree, real-time feedback and guidance, data-driven rehabilitation monitoring and optimization, user feedback and technical improvement and the like are achieved, the rehabilitation effect and experience of children can be improved, and more comprehensive and personalized rehabilitation services are provided.

One embodiment proposes a brainwave-based cognitive function evaluation method. The brainwave-based cognitive function evaluation method includes deriving a predicted response intended by an evaluation subject based on evaluation data including brainwaves of the evaluation subject for a specific task, generating a response accuracy including voice clarity, whether an answer matches, and answer meaning accuracy based on the predicted response and a preset correct answer for the specific task, and evaluating a cognitive function of the evaluation subject based on the response accuracy.