81 results about "Questionnaire data" patented technology

The invention discloses a depression risk screening optimization method based on large and small model linkage. The method comprises the following steps: selecting depression related indexes, obtaining interviewee questionnaire data, and preprocessing the data to obtain a scale source database; a depression risk prediction model is constructed, and PHQ-9 measurement results are compared for model training and verification; training a dialogue strategy module of a semantic analysis enhanced fine-tuning training large language model, constructing answer mapping through dynamic question generation and dialogue flow control, and converting a natural language of a user into standardized data required by a small model; training a man-machine interaction reinforcement learning model, generating a depression risk screening result based on a small model, inviting a user to carry out recognition degree evaluation, and dividing feedback into two types of recognition and question; for different feedbacks, strengthening or correcting the current interaction strategy and prediction logic, and storing the audited data as high-quality data to a training database by the system for subsequent large model fine tuning; and outputting a result and performing result interpretation and suggestion by using the semantic analysis reinforced fine-tuning large language model. Hierarchical early screening of depression risks is carried out based on large and small model linkage.

The invention discloses a risk assessment method and system based on a Bayesian network and an evidence theory, and relates to the technical field of risk intelligent assessment, and the method comprises the steps: determining an assessment dimension and an assessment index of a high-investment low-income risk of an educational institution in MOOC learning according to a human-cargo-field model and an industry report; constructing a MOOC learning risk assessment index system according to the assessment dimensions and the assessment indexes; constructing a Bayesian network structure according to the MOOC learning risk assessment index system; obtaining questionnaire data and expert opinions according to the MOOC learning risk assessment index system, and determining Bayesian network parameters based on the questionnaire data and the expert opinions; and inputting the Bayesian network parameters into the Bayesian network structure to obtain an assessment result of the high-investment low-income risk, the assessment result including a risk prediction result, a key risk factor and a sensitivity analysis result. According to the method, the high-investment and low-income risk in MOOC learning can be accurately evaluated.

The invention relates to a method for identifying multiple categories of mental disorders, and aims to realize accurate classification and rapid diagnosis of mental disorders through collection and analysis of multi-modal data. The method comprises the following steps of collecting multi-modal data of a patient, wherein the multi-modal data comprises physiological data, behavior data, medical image data and psychological questionnaire data; filtering, de-noising, standardizing and other pre-processing are carried out on the collected data, key features of each mode are extracted, and feature vectors are formed; generating a uniform feature vector by weighting and fusing the feature vectors of different modals; and training and classifying the feature vectors based on a deep learning model (MLP), and outputting the confidence of each mental disorder category. And finally, generating a diagnosis report according to a classification result, prompting high-risk and medium-risk categories, and providing treatment suggestions. According to the method, the limitation of a single data mode can be overcome, multi-dimensional information is comprehensively integrated, and the accuracy and efficiency of diagnosis are remarkably improved.

The invention relates to the technical field of tourism evaluation feedback, in particular to an intelligent elderly tourism product comment feedback method and system. According to the scheme, firstly, a tourism product (namely a target product) needing to be commented by a user is determined through a tourist terminal and then fed back to a cloud server, and the cloud server generates questionnaire data corresponding to the target product and sends the questionnaire data to the tourist terminal; the tourist terminal generates a questionnaire page based on the questionnaire data, obtains actual touch data corresponding to a touch action performed by the user on the questionnaire page, judges whether the user has a false touch action on the questionnaire page or not, generates a correction page if the false touch action occurs, and performs correction on the correction page; and the correction touch data corresponding to the click operation performed by the user on the correction page is obtained, and the comprehensive feedback score value of the user for the target product is subsequently generated based on the actual touch data and the correction touch data, so that more accurate score data of the user for the tourism product is obtained.

The invention discloses a method and system for optimizing a child preoperative anxiety relieving scheme based on an intelligent agent, and relates to the technical field related to medical health, and the method comprises the steps: executing the questionnaire data collection of a child guardian, and building a child questionnaire data set; calling the combined sensor group to collect physiological data of the child, and establishing a time sequence physiological data set; anxiety grade prediction is carried out, and anxiety grade correction is carried out through context awareness and age compensation; performing alleviation strategy optimization according to the anxiety grade correction result and the children preference data; and generating a mitigation scheme optimization result according to the mitigation strategy optimization result. The technical problems that in the prior art, the child preoperative anxiety relies on subjective experience, accuracy is insufficient, personalized relieving strategies cannot be provided according to different medical scenes and individual differences, and consequently the child preoperative anxiety relieving efficiency and effect are poor are solved. The technical effects of improving the efficiency and effect of relieving the preoperative anxiety of the children and achieving personalized services are achieved.

A recording medium stores a program for causing a computer to execute processing including: generating rules that indicates an answer pattern to questions included in questions, based on questionnaire data that indicates answers of respondents to each of the questions in a conducted questionnaire survey; specifying a respondent who has provided a correct answer that is same as an answer pattern to a question indicated in a rule, for each of the rules, based on the questionnaire data; and determining a first rule selection pattern, based on the number of questions included in selected rules, from among rule selection patterns that satisfy a first constraint condition that each of the respondents is specified as the respondent who has provided the correct answer by any one of the single or the selected rules, among rule selection patterns generated by selecting the single or the rules from among the rules.

The invention discloses a processing method, system and device for intelligent questionnaire survey and a medium, and the method specifically comprises the steps: obtaining questionnaire data and evaluation parameter setting inputted by a user, calling a main model to carry out universal dimension inspection on a questionnaire, and obtaining a preliminary analysis result; according to a preliminary analysis result, distributing different types of problems to corresponding expert models for deep analysis; in combination with culture feature data inquired in real time, a culture suitability score is generated to supplement an analysis result of the expert model; based on the analysis results of the plurality of expert models, performing weighted fusion processing on the score of each dimension by using a weight coefficient dynamically adjusted through a reinforcement learning mechanism to obtain a comprehensive evaluation score; the comprehensive evaluation score is returned to the user interface, feedback data are collected after the user completes interaction, and the feedback data are used for iteratively updating model parameters and setting evaluation parameters. According to the invention, efficient and accurate examination of questionnaire contents is realized.

The invention provides an adaptive linkage method for a network security insurance questionnaire and a risk assessment model, and the method comprises the steps: building a network security questionnaire based on a GIPDRR model, transmitting the network security questionnaire to an enterprise, filling the network security questionnaire, and recovering the network security questionnaire to obtain corresponding questionnaire data; collecting threat intelligence data from a threat intelligence library, and preprocessing the questionnaire data in combination with the threat intelligence data; key features are extracted from the preprocessed data, all the extracted and selected features are integrated into a unified feature vector, and the feature vector serves as input of a risk rating module; and constructing a deep learning model based on Transform, performing network security condition assessment by integrating the feature vector and the threat intelligence updated in real time to obtain a corresponding risk score, and generating a risk assessment report. According to the method, the network security condition of the enterprise can be accurately evaluated, and the risk score for each dimension is provided.

The invention relates to the technical field of artificial intelligence, in particular to an outbound quality inspection method based on artificial intelligence, which comprises the following steps: S1, data acquisition: acquiring a satisfaction outbound recording file, questionnaire data and predefined quality inspection rule data; step S2, data preprocessing: the sound recording file is transferred into structured text data; s3, cue word construction: loading predefined quality inspection rules and questionnaire data, and constructing large model system cue words according to the quality inspection rules and the questionnaire data; s4, normative quality inspection execution: calling a big language model based on the big model system cue word, executing access process normative quality inspection on the structured text data, and generating a quality inspection result; and S5, outputting a quality inspection report: generating and outputting a detailed quality inspection report of the quality inspection result, wherein the detailed quality inspection report is displayed in a structured data form.

An information processing device according to one aspect of the present invention performs intervention using a negative incentive in a program, the information processing device comprising: an acquisition unit that acquires questionnaire data including a question allowing the measurement of the current bias of a user; a first determination unit that determines the degree of the current bias of the user on the basis of the questionnaire data; a second determination unit that determines the length of a term, a period in which a reward for the incentive is determined, on the basis of the current bias; and a calculation unit that calculates, on the basis of the term, an initial grant incentive granted at the start of the program.

The invention relates to the technical field of artificial intelligence, in particular to a user satisfaction analysis method based on an artificial intelligence technology, comprising the following steps: S1, loading data: loading the data including original outbound voice data and corresponding questionnaire data; s2, data preprocessing: converting the outbound voice data into a text, segmenting the converted text into a plurality of question and answer contexts based on the questionnaire data, performing error correction on the converted text based on the questionnaire data, and segmenting the outbound voice data into a plurality of corresponding voice segments; s3, performing satisfaction analysis: performing intelligent analysis on the segmented question and answer context and voice segments to obtain a satisfaction analysis result; and S4, outputting a satisfaction analysis result. The technical scheme of the invention has the advantages of no need of a large amount of manual analysis, high processing efficiency and low analysis cost, and is optimized based on the combination of speech recognition, natural language processing, large language model and multi-modal fusion model technologies and the combination of subdivided technical scenes and business characteristics.

The invention discloses a data analysis method and system based on questionnaire investigation, and particularly relates to the technical field of data analysis, and the method comprises the following steps: S1, collecting and preprocessing questionnaire data, and obtaining original questionnaire data; s2, questionnaire feature engineering: extracting core features from the standardized questionnaire data set based on a joint screening mechanism of semantic similarity and information gain, and obtaining low-dimensional feature vectors through a dimension reduction algorithm; s3, subject mining and user group division are carried out on the low-dimensional feature vectors; and S4, outputting a result based on the analysis model, and generating a targeted questionnaire analysis report which comprises topic distribution, group difference and potential demand association conclusions. According to the data analysis method and system based on questionnaire investigation, through a multi-strategy fusion preprocessing mechanism, a combined screening feature engineering method, a fusion modeling analysis strategy and a self-adaptive model optimization module, the accuracy and efficiency of questionnaire data analysis are improved.

To provide a dashboard system capable of easily referring to information of each company about human capital management including health management.SOLUTION: A dashboard system 10 uses a plurality of answer sentence data acquired from questionnaire data of health management degree questionnaire data or the like to execute topic estimation processing for each theme, calculates a topic distribution of each answer sentence data, uses an answer sentence vector of answer sentence data having higher topic values to calculate and store the topic vector in sub theme information storage means 55, calculates similarity between a company sentence vector of a plurality of company sentence data created from company document data of integrated report data or the like and the topic vector of each sub theme to be a relevant score by similarity calculation means 39, and uses the relevant score to display the company sentence data on a screen by output means 41.SELECTED DRAWING: Figure 1

The invention relates to a data detection method and device and a storage medium, and aims to perform anomaly detection on questionnaire data. The method comprises the following steps: acquiring target questionnaire data, wherein the target questionnaire data comprises target sub-satisfaction and target total satisfaction; inputting the target sub-satisfaction of the target questionnaire data into a target classification model to obtain a target classification result corresponding to the target questionnaire data, the target classification result being used for representing a classification probability corresponding to the predicted total satisfaction of the target questionnaire data; determining a first group corresponding to the target classification result and a second group corresponding to the target total satisfaction according to a preset grouping rule; and determining an anomaly detection result of the target questionnaire data according to the first group and the second group.

The invention provides a visual-physiological multi-mode pilot workload detection method and device. The method comprises the following steps: acquiring facial image data, physiological sequence data, subjective questionnaire data and objective task performance data of a pilot in a flight simulation environment; and carrying out standardization processing and comprehensive analysis on subjective and objective data through an entropy weight method, and calculating the comprehensive workload level of the pilot. An image encoder is adopted to perform feature extraction on face image data, an LSTM time sequence encoder is combined to extract time sequence features, multi-modal feature fusion and deep learning training are performed through a Transform model, a workload detection model is constructed, workloads of pilots under different task difficulties can be accurately predicted, and the workload detection efficiency is improved. And the accuracy and the real-time performance of pilot workload detection are improved. The instantaneous workload change of the pilot can be effectively captured, and the limitation of a single data source in the prior art is solved.

The invention discloses a computer-aided face visiting method and system based on a Web technology and a deep neural network, and the method comprises the steps: an accessor fills in a questionnaire during face visiting, and uploads the questionnaire data to a cloud end in real time; performing basic verification, abnormal value detection and logical relationship check on the text data in sequence to obtain qualified text data; performing verification according to the attribute of the audio, marking abnormal data in the verification process, and giving a prompt to an accessor; performing voice separation on the verified audio, and counting the number of sound sources; comparing the number of the sound sources with a preset number, and marking whether the data is valid or not; recognizing human faces and counting the number of human faces in the image data, then performing similarity judgment on human face features, and reserving the image data meeting set conditions; and checking the positioning data, and marking whether the data is valid or not. By automatically and intelligently checking the questionnaire information, the efficiency and accuracy of questionnaire processing and statistics are improved, and the data quality is optimized.

Questionnaire completion systems and methodologies for a data platform. The data platform receives from a consumer an unstructured questionnaire to be completed based on structured database objects, semi-structured database objects, and unstructured database objects stored on the data platform by a provider. The data platform generates a secured completion of the unstructured questionnaire based on a questionnaire completion model and the unstructured questionnaire. The data platform determines a confidence score for the completion and in response to determining the confidence score does not exceed a threshold value, the data platform generates a structured query based on the unstructured questionnaire and a structured query model, and generates the secured completion based on querying the structured database objects using the structured query. The data platform applies a security function to the secured completion to generate a completion of the unstructured questionnaire and provides the completion to the consumer.

An object of a system according to an embodiment is to extract useful information from a large amount of questionnaire data and propose an appropriate measure.SOLUTION: A system includes a questionnaire data collection part, a preprocessing part, a classification analysis part, a comment extraction part, and an improvement plan proposal part. The questionnaire data collection part collects questionnaire data. The preprocessing unit preprocesses the questionnaire data collected by the questionnaire data collection unit. The classification analysis unit classifies and analyzes the questionnaire data preprocessed by the preprocessing unit. The comment extraction unit extracts a useful comment from the data classified and analyzed by the classification analysis unit. The improvement plan proposal unit proposes an improvement plan based on the comment extracted by the comment extraction unit.SELECTED DRAWING: Figure 1

This specification provides a multimodal emotion recognition index construction system and method, wherein the method includes: collecting first questionnaire data and first voice data of a user to be identified; preprocessing the first questionnaire data and the first voice data to obtain observed variables in the first questionnaire data and the first voice data; inputting the observed variables of the user to be identified into a trained emotion recognition model, wherein the emotion recognition model outputs values ​​corresponding to different emotion types for the user to be identified based on the correlation between the observed variables and different emotion types; and determining the true emotion type of the user to be identified based on the values ​​of the different emotion types. The method of this specification can improve the accuracy of prediction.

This invention discloses a graph mining-based questionnaire analysis method, system, electronic device, and medium, relating to the field of data processing. The method includes: acquiring questionnaire data to be analyzed; establishing a questionnaire matrix based on the questionnaire data; cleaning the questionnaire matrix to obtain a scoring matrix; calculating the similarity between different attribute scores based on the scoring matrix to establish a core attribute adjacency matrix; using attributes in the core attribute adjacency matrix as nodes in the network to establish a core attribute network; performing graph mining on the core attribute network to obtain a minimal core attribute network; and analyzing the influence of different core attributes based on the minimal core attribute network to obtain the questionnaire analysis results. This invention can reduce the influence of extreme data in questionnaires and improve the interpretability of the data in questionnaires.

The invention discloses a civil aviation emergency management current situation analysis and improvement method. The method comprises the following steps: acquiring civil aviation emergency management questionnaire data; preprocessing the questionnaire data to obtain preprocessed data; performing document category division on the preprocessed data, and performing document clustering and visualization on the divided documents to form a plurality of categories; extracting document keywords from different types of documents, constructing a co-occurrence network, calculating network indexes, performing feature analysis, and identifying structural features of the network; and calculating node indexes according to the identified structural features of the network, sorting node importance based on the node indexes, and identifying key nodes. The civil aviation emergency management capability is improved.

Systems and methods that integrate structural biology, artificial intelligence, and psychology / psychiatry to explore the physical boundaries of self-consciousness are described. Parallels between protein interactions and psychological self-structures are drawn to analyze and visualize patterns in self-report questionnaire data. Variation in self-report response patterns across individuals can be leveraged to map the topology of self-consciousness boundary, akin to genetic linkage mapping. It is thus possible to conduct whole psyche analysis and leverage variations in the response patterns across individuals to solve the physical topology of the self-boundary.

An apparatus (100) for determining a pelvic floor dysfunction condition of a patient is provided. The apparatus (100) comprises a processor (105). The processor (105) is configured to receive patient data. The patient data comprises at least one of questionnaire data and behavioural data of the patient. The processor (105) is also configured to provide, using a machine learning algorithm (115), a determination of one or more pelvic floor dysfunction conditions of the patient based on received patient data.

The embodiment of the invention provides an anomaly detection method and system. The method comprises the steps that the electronic equipment responds to user operation and sends an NPS questionnaire to an anomaly detection platform; the anomaly detection platform obtains first data of an electronic device corresponding to the NPS questionnaire when the user source sound of the NPS questionnaire is fuzzy and unclear; the exception detection platform determines an exception field of the electronic equipment according to the first data, and determines exception data of the electronic equipment in the exception field; and the anomaly detection platform obtains second data of the electronic equipment, and determines anomaly detection data of the electronic equipment according to the second data and the anomaly data in the anomaly field. Therefore, the anomaly detection platform can determine the anomaly of the electronic equipment when the user source sound of the NPS questionnaire is fuzzy and unclear, so that the anomaly detection capability of the electronic equipment is improved, the problems that the NPS questionnaire with fuzzy and unclear user source sound is difficult to analyze and the equipment anomaly cannot be identified are solved, and the resource waste of NPS questionnaire data is avoided.

Questionnaire completion systems and methodologies for a data platform. The data platform receives from a consumer an unstructured questionnaire to be completed based on structured database objects, semi-structured database objects, and unstructured database objects stored on the data platform by a provider. The data platform generates a secured completion of the unstructured questionnaire based on a questionnaire completion model and the unstructured questionnaire. The data platform determines a confidence score for the completion and in response to determining the confidence score does not exceed a threshold value, the data platform generates a structured query based on the unstructured questionnaire and a structured query model, and generates the secured completion based on querying the structured database objects using the structured query. The data platform applies a security function to the secured completion to generate a completion of the unstructured questionnaire and provides the completion to the consumer.

The invention provides a psychological questionnaire data analysis method and system based on a hidden variable model, and belongs to the technical field of data analysis. The method comprises the steps that a first questionnaire data set is acquired, the first questionnaire data set comprises a plurality of effective filling results about a preset psychological questionnaire, and the preset psychological questionnaire comprises a plurality of psychological evaluation question items and a plurality of grade score options corresponding to each psychological evaluation question item; performing standardization processing on the first questionnaire data set to obtain a second questionnaire data set; taking the plurality of psychological evaluation items as a plurality of observation variables and constructing an undirected graph, and separating the undirected graph according to the second questionnaire data set and a first preset transformation independent noise condition to form all single factor sets; and according to the second questionnaire data set and a second preset transformation independent noise condition, determining a causal direction among all hidden variables corresponding to all the single factor sets to form a causal structure diagram. The accuracy of identifying the causal structure between the psychological hidden variables can be improved.

The invention discloses an EV user psychological portrait identification guiding method and device based on hidden variable identification, and the method comprises the steps: obtaining the subjective evaluation data of a user through a Like seven-point scale, obtaining the four psychological hidden variables: perception information dependence, user friendliness, perception risk, and response enthusiasm through exploratory factor analysis, and carrying out the recognition of the psychological portrait of the EV user. Constructing a psychological hidden variable model; the reliability and validity of the model are tested through the measurement model, and a stable psychological hidden variable model is obtained through optimization; constructing a structural equation model, and obtaining an effective structural equation model through maximum likelihood estimation and fitting test; user questionnaire data are aggregated, hidden variable scores are calculated, score grades are divided through a quantile method and summed, five user response types are determined according to the total score, and a differentiation incentive strategy is matched. The method can improve the power grid demand response efficiency, assists the stable operation of the power system, and can be applied to a load aggregator system and other scenes.

The invention discloses a questionnaire index system construction method, a questionnaire index system construction device, equipment, a medium and a product, and relates to the field of questionnaire indexes. According to the questionnaire result, processing each option in each multi-choice question by adopting an improved multiple dichotomy to obtain a sparse matrix corresponding to each multi-choice question; performing data inspection on the codes corresponding to the options in the single-choice questions and the sparse matrixes corresponding to the multiple-choice questions, and correcting or eliminating the questions according to an inspection result; inputting the questions into a random forest model to obtain an initial index system; and R-type clustering is performed on the initial index system, and the index system of the target questionnaire is constructed according to the clustering result, so that human prejudice interference can be avoided, complex interaction among questionnaire data can be captured, the efficiency can be improved, the interpretability can be enhanced, and the situation of over-fitting or information loss can be avoided in the face of high-dimensional questionnaire data.

Various methods and processes, apparatuses / systems, and media for questionnaire data digitization and reconciliation are disclosed. A processor generates an autonomous program for continuously monitoring shared mailbox for unread emails having questionnaire data containing a plurality of line items filled out by a client; converts, by utilizing an OCR tool, the questionnaire data containing the plurality of line items into a machine-readable format data; reads, by utilizing an automated reconciliation tool, the machine-readable format data for each line item; compares, by utilizing the automated reconciliation tool, data for each line item against a corresponding predefined guidance data; identifies, based on comparing, missing response data, negative response data, and insufficient response data corresponding to the questionnaire data filled out by the client by applying predefined rules; and automatically reconciles the missing response data, negative response data, and insufficient response data.