20 results about "Standard score" patented technology

The invention discloses an intelligent auxiliary method, a medium and equipment for constructional engineering title review. According to the method, key feature information of unstructured text data in a declaration material is extracted by using a natural language processing technology, image data features are analyzed by using a ResNet-50 deep learning model, and a comprehensive quantitative scoring mechanism of a construction engineering performance material is established. And integrating education background, working experience, scientific research achievements and multi-modal performance data, constructing a constructional engineering title review rule base, setting a standard score threshold based on historical declaration data, and generating a title simulation review score and a review prediction result. And finally, aiming at the model, designing a feature weight analysis layer, and automatically outputting improvement suggestions of the declaration material defects according to the simulation review score after being associated with the associated review rule base. According to the method, automatic feature extraction and quantitative evaluation of the unstructured material are realized, the problems of low manual auditing efficiency and fuzzy standard are solved, and the objectivity of title evaluation is improved through the rule base and the evaluation prediction model.

The invention relates to the technical field of knowledge base generation, and particularly discloses an education knowledge base content generation method and system based on AI big data. The method comprises the following steps: comparing user answer content with answer information to obtain a matching degree, calibrating a positive sample and a negative sample to train an evaluation model, and determining a reference standard score according to a quantitative relationship between the positive sample and the negative sample; and screening the plurality of candidate knowledge contents based on the reference standard score and the tolerance threshold to calibrate the candidate knowledge contents as stable knowledge contents or to-be-corrected knowledge contents, and outputting the stable knowledge contents or the to-be-corrected knowledge contents. According to the method, the evaluation system based on the real answer data of the user and the evaluation standard reflecting the actual application effect of the knowledge content are constructed, and the evaluation standard is automatically calibrated according to data distribution, so that objective screening and continuous iteration of the knowledge content are realized, and the accuracy and reliability of the knowledge base content are improved.

The application belongs to the field of evaluation assistance, and particularly relates to an interactive teaching evaluation assistance system and device based on multi-modal fusion. The system collects multi-dimensional information through a portrait module, combines multi-modal feature extraction and a knowledge graph algorithm to construct a user training action portrait library. A strategy recommendation module generates an optimal guidance scheme from a portrait library, real-time information and a teaching guidance strategy library through a correction scheme matching model. An auxiliary adjustment module collects training action images in real time, uses a space-time feature extraction model and an evaluation algorithm to obtain a continuous correction action sequence and a standard score after guidance. A traceability feedback module combines an action standard knowledge base, obtains a horizontal and vertical space-time change chain of non-standard actions through a forward reasoning model, and feeds back to the instructor to adjust the scheme in real time until the action meets the standard. The application realizes precise evaluation and personalized scheme matching of multi-modal data fusion, and effectively improves the scientificity and interactivity of evaluation assistance.

The invention provides a dynamic optimization method, system and device for a network security guarantee strategy and a medium, and relates to the technical field of network security, and the method comprises the steps: collecting and preprocessing the security operation data of an enterprise, extracting multi-dimensional monitoring indexes, carrying out the standardization and standardization score smoothing of each index, and obtaining a corresponding index smoothing standard score; setting a weight for each index, weighting the index smoothing standard score corresponding to each index according to the corresponding weight to obtain an index comprehensive factor, matching the index comprehensive factor with a set threshold, and if the matching is successful, triggering a risk review mechanism; and substituting the comprehensive factor into a preset strategy adjustment function, and generating a dynamic optimization scheme of the current network security guarantee strategy in combination with a strategy mapping table. According to the method, the matching degree of a network security protection strategy and a real-time risk situation can be improved, a risk-driven forward feedback mechanism is constructed, and the overall security toughness is enhanced.

This invention discloses a risk assessment method and system for large language model (LLM) vulnerabilities, relating to the fields of artificial intelligence security and network security technology. The method includes the following steps: semantic-driven vulnerability modeling, constructing a large language model vulnerability feature identification model from three dimensions: attack mechanism, model behavior, and propagation impact; constructing a scoring system, introducing three key indicators—semantic manipulation complexity, model behavior impact, and attack propagation potential—onto the CVSS standard scoring mechanism; designing a scoring transfer judgment mechanism to calculate the score, obtaining the CVSS basic score, constructing an AI-extended risk factor for score transfer, the AI-extended risk factor being calculated from the quantified values ​​of the three key indicators and their corresponding weights, and using the AI-extended risk factor and the CVSS basic score to establish a comprehensive scoring function to obtain the comprehensive score. This invention achieves quantitative assessment and automated scoring of LLM vulnerabilities by extending new risk indicators based on CVSS.

This invention discloses a method and system for intelligent evaluation of blasting effects, relating to the field of information processing technology. The method includes: acquiring the pre-blast step profile line and the corresponding post-blast pile outline of the target object from a server; extracting post-blast pile change feature values; normalizing the post-blast pile change feature values ​​to obtain standard score values; constructing a mapping space between the standard score values ​​and engineering indicators to obtain engineering indicator evaluation scores; weighting the engineering indicator evaluation scores to obtain a comprehensive score; and determining the blast pile morphology level based on the comprehensive score and a preset grading threshold. This invention improves the adaptability and domain transferability of intelligent blasting effect evaluation by extracting and normalizing blast pile change features; it enhances standardization by using a dimensional projection matrix to map low-dimensional morphological features to high-dimensional engineering indicators; and it improves adaptability by introducing a multi-indicator matrix evaluation method and a quality grading method, allowing for flexible configuration of evaluation preferences according to actual needs.

The application belongs to the technical field of underwater unmanned cluster evaluation grading, and discloses a method for underwater unmanned cluster intelligence evaluation grading and related devices; wherein the method for underwater unmanned cluster intelligence evaluation grading comprises the following steps: based on the function type of the underwater unmanned cluster to be evaluated and graded, determining the evaluation task type and obtaining the environmental parameter statistics, task parameter statistics and technical feature statistics; based on the standard scores of the obtained environmental difficulty index and the standard scores of the task effect index, calculating the obtained external weight; multiplying the obtained external weight with the technical feature original score to obtain the improved technical feature score; based on the obtained improved technical feature score, performing evaluation grading to obtain the underwater unmanned cluster intelligence evaluation grading result. The technical scheme disclosed by the application can solve the problems of subjective evaluation, generalization application, fuzzy definition and solidification classification existing in the prior art.

The invention provides a person and post matching quantitative evaluation method based on a competency model. According to the method, a unified competency evaluation model containing 15 secondary indexes in total in three dimensions of core ability, general ability and professional ability is constructed, post weights are calculated by adopting an analytic hierarchy process to generate standard scores, and comprehensive employee scores are generated in combination with 360-degree evaluation data and performance adjustment coefficients. And through the cosine similarity and the Euclidean distance, a man-post matching coefficient is calculated, and accurate diagnosis and personalized development recommendation are realized. The problems that in the prior art, post evaluation and employee evaluation data standards are heterogeneous, matching results are fuzzified, and system linkage is poor are solved.

The invention provides a copy number variation degree analysis method and device and a storage medium, and relates to the technical field of bioinformatics analysis. The method comprises the following steps: determining a copy number baseline of each chromosome region through a reference sample set; according to the copy number baseline of each chromosome region, determining a Z standard score of the corresponding window-level chromosome region, and summing the Z standard scores of which the absolute values are greater than a set threshold value to obtain a whole genome cumulative score; determining threshold values of normal samples and abnormal samples in the whole genome cumulative fraction by using an ROC curve method; after the to-be-detected sample is calculated according to the method to obtain the whole genome cumulative fraction, the result is judged according to the whole genome cumulative fraction threshold value, the risk that the analysis result deviates from reality can be reduced, and the reliability of the method in clinical application can be improved.

The application relates to the technical field of knowledge base generation, and particularly discloses an education knowledge base content generation method and system based on AI big data. The method comprises the following steps: comparing user answer content with answer information to obtain a matching degree, calibrating positive samples and negative samples to train an evaluation model, and determining a reference standard score according to the quantity relationship between the positive samples and the negative samples; based on the reference standard score and a tolerance threshold, a plurality of candidate knowledge contents are screened to calibrate them as stable knowledge contents or to-be-corrected knowledge contents, and are output. By constructing an evaluation system based on user real answer data and an evaluation standard reflecting the actual application effect of the knowledge content, and automatically calibrating the evaluation standard according to the data distribution, objective screening and continuous iteration of the knowledge content are realized, and the accuracy and reliability of the knowledge base content are improved.

This invention provides a sensor data fusion method and apparatus. The method includes: acquiring multiple indicator measurement data collected by multiple sensors in real time for each monitoring indicator; obtaining standard scores for each sensor based on the indicator measurement data at the current time; determining instantaneous weights corresponding to the standard scores of each sensor based on the mapping relationship between the standard scores and instantaneous weights; obtaining fusion weights based on the instantaneous weights and the cumulative reliability scores of each sensor at the current time; estimating indicator prediction data for the current time based on the indicator fusion data obtained by each sensor at the previous time using an adaptive weighted Kalman filter algorithm; constructing a measurement noise model for the current time based on the fusion weights to obtain the Kalman gains; and fusing the indicator prediction data and the indicator measurement data at the current time to obtain indicator fusion data. This invention can improve the accuracy of data fusion.

This invention discloses a method and system for quantitative diagnosis of website GEO readiness. The method includes the following steps: S1: collecting raw indicator data for multiple evaluation dimensions; S2: mapping the raw indicator data of each dimension to standard score intervals; S3: calculating the subjective weight vector and objective weight vector of each dimension to obtain the adaptive weight vector of each dimension; S4: constructing a directed acyclic graph G of causal dependencies between each dimension and calculating the dependency decay factor of each dimension; S5: weighted summation to obtain a comprehensive score, and classifying each dimension into different priorities. This invention provides multi-dimensional quantitative diagnosis of the readiness of a website in generative AI search engines, overcoming the shortcomings of existing technologies such as single evaluation dimensions, linear scoring methods, assumptions of independence between dimensions, fixed weights, and lack of predictive ability.

A computing system retrieves historical hole-by-hole data for a plurality of holes of a plurality of golf tournaments for a plurality of players. The historical hole-by-hole data includes a code number for each hole and a standard score associated with each hole. The computing system clusters the plurality of holes into a plurality of clusters of hole types. The computing system generates a score per hole type metric for the hole-by-hole data for each player. The computing system adjusts the score per hole type metric based on a strength field metric associated with each tournament. The strength field metric represents a strength of the field of players in a given tournament. The computing system generates a ranking of the plurality of players based on the adjusted score per hole type metric.

The application relates to a method, device, equipment and medium for evaluating an air quality prediction model, comprising the following steps: acquiring prediction data and measured data output by each air quality prediction model, wherein the prediction data comprises pollutant concentration prediction values and air quality index prediction values, and the measured data comprises pollutant concentration actual values and air quality index actual values; determining a primary pollutant according to the measured data; calculating the values of each model with respect to each first index in a first evaluation index system according to the concentration prediction values and the actual values of the primary pollutant; calculating the values of each model with respect to each second index in a second evaluation index system according to the air quality index prediction values and the actual values; calculating the standard scores of each model with respect to each first index; performing importance evaluation on the first evaluation index system according to the values of the first index and the second index, so as to obtain the weights of each first index; and performing weighted summation on the standard scores and the weights of the first index, so as to obtain the final scores of each model.

This application provides a clinical skills assessment method, device, storage medium, and program product, including: acquiring clinical skills operation videos; the clinical skills operation videos include multimodal data; extracting target modality data from the multimodal data; evaluating the multimodal data using a first assessment model according to assessment standard data to obtain a first assessment result; evaluating the target modality data using a second assessment model to obtain a second assessment result; fusing the first assessment result and the second assessment result to obtain a target assessment result, including multiple target assessment items and their corresponding target action execution results; matching the multiple target assessment items with multiple standard assessment items; and determining the target score and total score based on the target action execution results corresponding to the multiple target assessment items and the standard score values ​​corresponding to the matched standard assessment items. The technical solution provided by this application improves the efficiency and accuracy of clinical skills assessment.

One of the technical solutions of the present application is to provide a golf swing evaluation method based on video, which is characterized by including a key frame extraction network and a swing action comparison and analysis module. Another technical solution of the present application is to provide a golf swing evaluation system realized based on the above golf swing evaluation method, which is characterized by being divided into a presentation layer, a communication layer, a service layer and a data layer, and including a user management function module, a video management function module and an AI swing action comparison and analysis function module. The present application only needs a smart phone, compares and analyzes the swing action according to the swing key events, can more quickly and accurately locate the problem of the action, and can view the difference between the swing action and the professional player from multiple angles through 3D reconstruction of the skeleton model. At the same time, quantitative data analysis can be provided, such as the angle of some joints, and the distance between the skeletal points can be used as a standard score.

The invention relates to the technical field of teaching quality evaluation, and particularly discloses a teaching quality comprehensive evaluation system and method based on data analysis, and the method comprises the following steps: obtaining comments issued by students on a social network platform, extracting a target comment, and determining a first proportion; labels of the target comments are obtained based on a pre-constructed label output model, and the proportion of the number of the target comments with the positive labels to the total number of the target comments is counted and recorded as a second proportion; obtaining an answer result of the teaching evaluation questionnaire, obtaining a questionnaire score based on the answer result, obtaining a median B of the questionnaire score, and correcting the median B based on the first proportion and the second proportion to obtain a standard score F of the teacher; and evaluating the teaching quality of the teacher according to the standard score. The teaching quality evaluation accuracy is improved.

The present disclosure provides an abnormal communication number identification method, device, electronic equipment and readable medium, wherein the abnormal communication number identification method comprises: performing clustering processing on feature index data of any suspected abnormal number output by a trained deep learning model through an improved clustering algorithm; determining the weight of each item index in the feature index data after clustering processing; determining the standard score of each item index in the feature index data; calculating the abnormality score of each item index according to the weight and the standard score; and determining that the suspected abnormal number is an abnormal number according to the abnormality score. Through the embodiments of the present disclosure, the accuracy and reliability of abnormal number identification are improved.

The invention discloses a kettle fault prediction method and system, and the method comprises the steps: determining a first-stage standard score and a second-stage standard score according to the message data of each node in a first-stage Mesh node list, the message data of each node in a second-stage Mesh node list, and the actual value of a sensor; for each sensor in the kettle, if the first-level standard score is greater than a first preset score threshold value or the second-level standard score is greater than a second preset score threshold value, predicting that the sensor breaks down; the second preset score threshold value is greater than the first preset score threshold value. According to the method, the Bluetooth Mesh network topology and the lightweight LSTM prediction model are combined, and the core problems of resource limitation, unreliable data and untimely prediction in kettle fault prediction are solved.

The present application relates to the technical field of detection device fault detection, in particular to a detection device fault detection method and system, which comprises calculating the mean value and standard deviation of the information entropy value of the healthy detection device subset, and then calculating the standard score corresponding to the information entropy value of each detection device in the current sliding window based on the mean value and standard deviation; taking the absolute value of the standard score corresponding to each detection device to obtain the abnormality coefficient of each detection device, and implementing graded early warning on each detection device according to the abnormality coefficient. The present application takes the dynamically screened healthy detection device group as the calculation reference, significantly improves the diagnostic robustness in multiple fault concurrent scenarios, prevents mutual interference between faulty detection devices, avoids missed or mistaken judgments, and ensures the continuous monitoring capability of the system under the condition of partial component failure.