27 results about "User experience evaluation" patented technology

A method for evaluating and optimizing user experience in human-computer interaction includes: acquiring a multimodal biological signal of a user during human-computer interaction, and preprocessing the multimodal biological signal to extract a fixation position and index parameters of multiple dimensions; standardizing the index parameters, and performing, based on each index parameter and a weight for each index parameter, weighted average to calculate a user experience evaluation score of each dimension; acquiring a weight for the user experience evaluation score, and performing weighted average to calculate an overall user experience evaluation result; visually presenting, based on the overall user experience evaluation result, user experience situation data of the user for a current fixation position; and acquiring a correction parameter of the user, and optimizing, based on the correction parameter, the weights using a machine learning optimization algorithm.

The invention provides a multi-user VR video streaming media rate-based adaptive adjustment method and system, and relates to the technical field of VR, comprising the following steps: constructing a double-layer architecture of an edge layer and a user layer, carrying out video space-time segmentation and multi-quality coding on the edge layer, and realizing video adaptive transmission based on user motion information; a forward and reverse weighted user experience evaluation model is adopted to carry out optimal allocation on system resources; a quality balanced user subset is constructed in combination with time dimension equilibrium constraint, and resource dynamic optimization is carried out; the multi-dimensional resource verification model is used to realize video quality grade optimization distribution; and generating a panoramic frame through integrity verification, time sequence synchronization and spherical projection reconstruction. According to the invention, the service quality balance of multi-user VR video transmission is improved, the system resource overhead is reduced, and the user watching experience is enhanced.

A data transmission method and a communication apparatus are provided. The method includes: A network device sends first information to a terminal. The terminal receives the first information, and determines an evaluation mode of user experience based on the first information. The terminal sends second information to the network device. The network device receives the second information, and communicates with the terminal based on the second information. The first information is used to configure the evaluation mode of user experience, the evaluation mode of user experience is one of a plurality of evaluation modes of user experience, the evaluation mode of user experience is used to evaluate impact of network transmission on user experience, and the second information includes information about the impact of the network transmission on user experience.

The invention discloses a cabin function index evaluation mechanism construction method fusing user emotional tendency correction, and the main design concept is that the emotional tendency in user evaluation is corrected; and establishing a multi-level evaluation strategy with a unified benchmark for user emotion tendency correction, product function dimension empowerment and user experience evaluation index empowerment, and realizing fine management of user evaluation data. Specifically, the method abandons the idea of optimization from an equipment side, and finds another way from a user side and fully considers individual differences of users: 1, screening high-quality evaluation individuals and evaluation data thereof from a large sample, and endowing the high-quality evaluation individuals and the evaluation data with a relatively high weight, so that the professional and credible evaluation result is ensured; secondly, non-objective user evaluation data with obvious emotional tendency are compensated through an emotional correction means, and evaluation scores are pulled back to a datum line in a weight compensation setting mode; the objectivity of the evaluation data of the intelligent cabin of the vehicle is effectively improved in a low-cost mode.

The invention discloses a multi-modal physiological signal fused user experience emotion objective evaluation method, and relates to the technical field of human-computer interaction and user experience evaluation, and the method comprises the following steps: S1, constructing a VR emotion induction experiment environment, synchronously collecting an electroencephalogram signal, an electrocardiosignal and a skin electric signal based on an interaction task of a user and a product, and carrying out the interaction of the user and the product; and recording subjective evaluation data synchronized with the interaction event. According to the method, a complete standardized experiment system is formed by constructing a VR emotion inducing environment and synchronously collecting multi-modal physiological signals and combining standardized target emotion type definition, multi-dimensional task profile extraction and standardized signal collection parameter setting; therefore, the standardization and repeatability of the whole process from experimental design to data acquisition are ensured, the problems of large individual difference, inconsistent experimental conditions and difficulty in result reproduction in the traditional subjective evaluation method are effectively solved, and a solid foundation is laid for obtaining high-quality comparable physiological data.

The invention relates to the technical field of Internet of Vehicles big data analysis, and provides a new energy vehicle user experience evaluation method and device based on Internet of Vehicles data, and the method comprises the steps: determining the full-power nominal endurance and the full-power actual endurance of a target vehicle based on the Internet of Vehicles data, and determining the endurance index score of the target vehicle; based on the Internet of Vehicles data, determining a target battery recession value of the target vehicle, and determining a battery recession index score of the target vehicle; based on the Internet of Vehicles data, determining target hundred-kilometer fast charging time consumption of the target vehicle, and determining a charging time index score of the target vehicle; and generating a user experience score of the target vehicle according to a weighted result of the endurance index score, the battery recession index score and the charging time index score. According to the technical scheme provided by one or more embodiments, the vehicle performance and the user experience can be objectively and comprehensively evaluated in multiple dimensions in a long time span.

The invention provides a user experience evaluation method and system based on multidimensional network time delay characteristics, and belongs to the field of network performance monitoring. The method comprises the following steps: receiving an original traffic data packet from a network, then receiving an original access request of a client, and decomposing the original traffic data packet into three groups of traffic, including a DNS analysis stream, a network transmission stream and an application layer request stream; calculating a DNS response time based on the DNS resolution stream; calculating network connection time and retransmission delay based on the network transport stream; calculating server response time and data transmission time based on the application layer request stream; obtaining a multi-dimensional time index; an experience evaluation model is constructed, index parameters are set according to business scenes, a comprehensive score is calculated, and then evaluation results of user experience are graded and visually displayed. According to the method, flow analysis is accurately carried out, delay sources are subdivided, and the bottleneck positioning error is smaller than 5%; the network performance problem is accurately positioned; and a client does not need to be transformed, and the compatibility is good.

The present invention relates to the technical field of user experience evaluation, and specifically discloses a user experience evaluation method and system based on multiple databases. The present invention establishes access links to multiple target databases corresponding to multiple online shopping malls; classifies and analyzes the multiple target databases to determine the user's frequent purchase categories; according to the frequent purchase categories, selects multiple corresponding target frequently purchased goods from the multiple target databases; obtains user evaluation information of the user for the multiple target frequently purchased goods, and obtains service record data of the multiple target frequently purchased goods; conducts an experience evaluation comparison of the multiple online shopping malls. It can classify and analyze multiple target databases to determine the user's frequent purchase categories, and then select multiple corresponding target frequently purchased goods, obtain multiple service record data and multiple service record data, and conduct an automatic experience evaluation comparison of multiple online shopping malls, so as to provide more objective and accurate comparison suggestions for the user's online shopping.

The invention relates to the technical field of natural language processing, and discloses an intelligent customer service optimization method and system based on reinforcement learning, and the method comprises the steps: obtaining a current dialogue between a user and an intelligent customer service and user experience, evaluating the problem difficulty degree of the current dialogue, and obtaining the problem difficulty degree of the current dialogue according to the problem difficulty degree of the current dialogue and a preset difficulty degree threshold value; dividing the current dialogue into simple questions and difficult questions, and constructing a reward mechanism based on user experience; for a simple problem, a service track is generated through intelligent customer service autonomous decision processing, and a decision model is updated by adopting a reinforcement learning strategy according to the service track and a reward mechanism; and processing the difficult problem by using the artificial customer service, recording the decision making process of the artificial customer service as an expert demonstration track, and updating the decision making model by adopting a mixed strategy combining imitation learning and reinforcement learning according to the expert demonstration track and a reward mechanism. A solution of manual customer service intervention is used as a demonstration track, so that the intelligent customer service performs imitation learning.

The invention discloses a car purchase user experience evaluation method and system. The method comprises the steps of collecting social media data about car purchase user experience; constructing a user experience evaluation questionnaire according to the social media data; and taking the survey result of the user experience evaluation questionnaire as market feedback data. According to the invention, the user experience evaluation questionnaire is constructed based on social media data, objective and real vehicle use experience of the vehicle purchase user is mined based on media big data, and comprehensive and objective evaluation items are obtained through data analysis. The defects that in a traditional questionnaire, the sample size is small, representativeness is insufficient, evaluation item setting is difficult to cover all dimensions, evaluation items are lack of objectivity and the like are overcome, and therefore more accurate market feedback and improvement directions are provided for vehicle enterprises and dealers.

The present application relates to the technical field of network transmission congestion, and particularly relates to a network data packet capturing and analyzing method and system for mobile terminals. The method divides discrete transmission batches based on historical interval characteristics, determines an observation reference rate by time window sampling combined with quantile statistics, and constructs an ideal reference transmission curve. The method determines a time domain lag barycenter parameter by analyzing the cumulative lag distribution in the time dimension, eliminates false congestion interference, and extracts a load congestion index. The method dynamically updates the reference delay baseline through the load congestion index to judge the congestion state and implement a hierarchical early warning mechanism. The present application is based on time domain barycenter analysis of lag distribution characteristics, distinguishes between head lags caused by protocol establishment overhead and real congestion lags caused by bandwidth limitations, significantly reduces false congestion false positive rates, realizes adaptive and accurate monitoring of network congestion states, and improves the accuracy of user experience evaluation.

The application discloses a kind of based on comprehensive data analysis's software user experience evaluation method and system, it is related to human-computer interaction technical field, this method is by embedding behavior perception engine in software operation interface, collect the use frequency of user in task path, jump operation frequency, process standard step number and access user total number, and further unified normalization processing is carried out in background server, constructs normalized behavior dataset, calculates path deviation joint index PDI.In based on path deviation joint index PDI joint consideration path selection information entropy and path jump rate mean two dimensions, whether the use of user path exists concentrated deviation or process jump behavior can be effectively identified, and then whether there is forced guidance, process hidden shortcut in system path design is judged.Compared with the evaluation mode of traditional only relying on click heat map or jump rate, the present method realizes higher dimensional path behavior modeling and deviation identification ability.

The invention relates to the technical field of network transmission congestion, in particular to a mobile terminal-oriented network data packet capture analysis method and system. According to the method, discrete transmission batches are divided based on historical interval characteristics, an observation reference rate is determined through time sequence window sampling in combination with quantile statistics, and an ideal reference transmission curve is constructed; time-domain lag gravity center parameters are determined by combining time dimension analysis accumulated lag distribution, and pseudo congestion interference is eliminated to extract load congestion indexes; and dynamically updating a reference delay baseline through a load congestion index to judge a congestion state and carry out a grading early warning mechanism. According to the method, lag distribution characteristics are analyzed based on the time domain gravity center, head lag caused by protocol establishment overhead and real congestion lag caused by bandwidth limitation are distinguished, the false congestion false alarm rate is remarkably reduced, the network congestion state is adaptively and accurately monitored, and the accuracy of user experience evaluation is improved.

The application provides a system, platform and method for data integration based on real-time data flow, the system comprising a collector configured to generate a real-time data flow in response to a first data source and collect the real-time data flow; wherein the first data source is one of a plurality of data sources, and the real-time data flow is generated based on user operation; a data flow processor configured to calculate a real-time score of the first data source based on real-time data of each evaluation index extracted from the real-time data flow; obtain a historical score calculated based on historical data flow of a second data source, and calculate a real-time user experience evaluation score based on the real-time score and the historical score; and a data flow outputter configured to output the user experience evaluation score in different output formats according to different output positions. The application can be used to trigger the calculation and analysis of the user experience evaluation score immediately after submitting feedback or performing business operations, thereby improving response speed and user experience.

The invention provides a system, platform and method for data integration based on real-time data streams, and the system comprises a collector which is configured to respond to a first data source to generate a real-time data stream and collect the real-time data stream; wherein the first data source is one of a plurality of data sources, and the real-time data stream is generated based on user operation; a data stream processor configured to calculate a real-time score of the first data source based on real-time data of each evaluation index extracted from the real-time data stream; obtaining a historical score obtained by calculating the historical data stream of the second data source, and calculating a real-time user experience evaluation score based on the real-time score and the historical score; and the data stream output device is configured to output the user experience evaluation scores in different output formats according to different output positions. The method and the device can be used for immediately triggering calculation and analysis of the user experience evaluation score after operations such as feedback submission or business handling, and can improve the response speed and the user experience.

The invention discloses a system user experience evaluation method and device, a storage medium and electronic equipment, and relates to the technical field of operating systems, and the method comprises the steps: obtaining application scene data of a system in an actual scene, and obtaining an abnormal type monitored for a subsystem in the system; carrying out weight adjustment according to the exception type by adopting a user experience scoring model to obtain a multi-dimensional weight coefficient; according to the application scene data and the multi-dimensional weight coefficient, user experience influence evaluation is carried out, and a multi-dimensional experience influence score is obtained; and performing calculation according to the multi-dimensional experience influence score to obtain a user experience score of the system in the actual scene. According to the method, the user experience of the system in an actual scene under the system abnormality can be accurately evaluated, and the improvement effect of system optimization on the user experience is improved.

The embodiment of the invention provides an evaluation method of an intelligent cockpit model and a construction method of a cockpit index evaluation system. The evaluation method of the intelligent cockpit model comprises the steps of determining a to-be-tested intelligent cockpit model in an intelligent cockpit system of a vehicle; determining an evaluation strategy of the to-be-tested intelligent cabin model based on a preset use scene associated with the to-be-tested intelligent cabin model; according to the evaluation strategy, evaluating the to-be-tested intelligent cabin model to obtain an evaluation result; calling a target cabin index evaluation system, and evaluating the model performance of the to-be-tested intelligent cabin model from a plurality of evaluation dimensions according to the evaluation result to obtain a performance evaluation result of the to-be-tested intelligent cabin model, the evaluation dimensions at least comprise a model application evaluation dimension, a system application evaluation dimension, a scene application evaluation dimension and a user experience evaluation dimension. The technical problem of low evaluation accuracy of the intelligent cockpit model in the prior art is solved.

The invention discloses a product parameter optimization method and system based on user experience, and relates to the technical field of artificial intelligence. The method comprises the following steps: acquiring user experience evaluation data for a plurality of product samples; on the basis of the user experience evaluation data, a variance fluctuation uncertainty multi-attribute weighting strategy is adopted, weights of multiple evaluation dimensions are calculated, and a comprehensive user experience evaluation value of each product sample is determined; training a radial basis function (RBF) neural network prediction model by taking the product parameter combination as an input feature and integrating the user experience evaluation value as a target variable, and optimizing parameters of the RBF neural network by adopting an improved sparrow search algorithm ISSA; and utilizing the trained RBF neural network prediction model to determine an optimal product parameter combination which maximizes the comprehensive user experience evaluation value.

The application provides a user experience rate evaluation method and optimization method, device, electronic equipment and computer readable storage medium, and relates to the technical field of communication quality evaluation. The evaluation method comprises: collecting measurement report MR data of a target area in a preset time period, wherein the MR data comprises field information of a plurality of users; performing N-dimensional gridding on the MR data of the target area to obtain field information of each N-dimensional grid, wherein N comprises one of 2, 3 and 4; and evaluating the user experience rate of each N-dimensional grid based on the field information of each N-dimensional grid. At least the problems of the related art, such as inaccurate division of network performance data, poor geographical presentation effect of network performance data and low evaluation accuracy of user experience rate, are solved. The application is suitable for user experience evaluation and optimization scenarios.

The invention discloses a user experience management method and related equipment, and relates to the technical field of experience management, and the method comprises the steps: obtaining user behavior data generated in the interaction process of a user and a preset interaction interface; according to the user behavior data and a preset strenuosity index calculation model, generating a comprehensive strenuosity index, the strenuosity index calculation model processing the user behavior data based on at least two mutually independent calculation dimensions; and performing user experience evaluation according to the comprehensive labor consumption index. According to the method and the system, an enterprise can accurately position specific experience links and dimensions which cause strenuous user experience, accurate data support is provided for implementing targeted optimization measures, and finally the enterprise is assisted to more effectively improve user experience and loyalty.

The invention discloses a software user experience evaluation method and system based on comprehensive data analysis, and relates to the technical field of man-machine interaction. The method comprises the steps that a behavior perception engine is embedded in a software operation interface, and the number of use times, the number of jump operation times, the number of flow standard steps and the total number of access users of a user in a task path are collected; and further performing unified normalization processing on the background server, constructing a normalized behavior data set, and calculating a path deviation joint index PDI. On the basis of a path deviation joint index PDI, two dimensions of a path selection information entropy and a path jump rate mean value are considered in a joint manner, so that whether concentrated deviation or process jump behaviors exist in user path use can be effectively identified, and then whether problems of forced guidance, process hiding shortcut and the like exist in system path design can be judged. Compared with a traditional evaluation mode which only depends on clicking a thermodynamic diagram or a jump rate, the method has the advantage that higher-dimension path behavior modeling and deviation recognition capability are realized.

The invention relates to the technical field of smart home product design evaluation, and discloses a product design user experience evaluation method based on a smart home scene. The method comprises the following steps: constructing a virtual scene prototype and placing the virtual scene prototype into a design scheme model; executing a behavior script through a virtual user, and recording an interaction event to form original track data; performing behavior slicing and intention labeling processing on the original data to generate a structured experience event sequence with a user intention label; calling a multi-dimensional experience gauge to analyze the sequence, and extracting quantitative indexes; inputting the indexes into an experience fusion decision engine, and automatically generating a comprehensive evaluation conclusion; and driving the design model to carry out parameterized adjustment and iterative optimization based on the conclusion. According to the method, a closed loop from automatic analysis of user behavior semantics to automatic design optimization is realized, and the depth of evaluation analysis and the efficiency of design iteration are improved.

The invention discloses a map availability evaluation method and system based on a multi-dimensional quantitative model, and belongs to the technical field of map making and user experience evaluation. The method comprises the following steps: firstly, constructing a VR immersion environment according to the mapping size and purpose of a map, and collecting multi-modal cognitive behavior data and subjective evaluation data of a subject; based on the multi-modal cognitive behavior data, statistical calculation is carried out on quantitative scores of three dimensions of element distinction degree, element readability and retrieval efficiency; and finally, through a hierarchical entropy weight and expert calibration fusion weighting mechanism, performing weighted fusion on the quantitative score of each dimension and the experience scale data to obtain a comprehensive availability evaluation result. According to the method, the natural reading scene of the map can be truly restored, multi-dimensional quantitative evaluation is realized, the evaluation efficiency and accuracy are improved, and a scientific basis is provided for map optimization.

The embodiment of the invention discloses a user experience measurement method and device, belongs to the technical field of user experience evaluation, and is used for solving the problem of low accuracy of user experience measurement. Comprising the steps that monitoring data related to user experience are obtained, the monitoring data are coded, a behavior journey graph of a first-line seat interacting with a user is obtained, the monitoring data comprise one or more of user interview data, user observation data and operation log data, and the behavior journey graph comprises behavior contacts; according to a pre-constructed alternative index library of user experience measurement, experience indexes related to user experience are screened out, and the experience indexes comprise multiple dimensions and sub-features of the dimensions; mapping the behavior contact and the experience index to obtain a key index data set corresponding to the user; and carrying out hierarchical weighted fusion processing on the key index data set through a user experience measurement quantitative model to obtain an experience measurement score of the user.

The invention provides a user experience evaluation method and system, and the method comprises the steps: obtaining the interaction data of an interaction process between a user and an AI product, analyzing the interaction emotion of the user in the interaction process based on the interaction data, and determining an evaluation result of the experience of the user on the AI product according to the interaction emotion. The method can effectively fit the actual user application scene and the actual user feeling, and relatively effectively covers the complex personalized characteristics of the user. And the effectiveness and the reliability of evaluation can also be improved.

The present invention relates to the field of big data processing, and discloses a user experience comprehensive evaluation system and method based on big data, wherein the comprehensive evaluation system comprises: a project information module, a personnel information module, an experience information module and an analysis module; the basic project information comprises positioning information of a survey product and basic project information; the positioning information and basic project information of each survey product are input through a product positioning module; the basic information of experience personnel is collected through a personnel information module; the basic information of the experience personnel and the positioning information and time information of each survey product are analyzed through an analysis module, and the survey product is sent to the experience personnel according to the analysis result; the experience information of using the same survey product is then analyzed to obtain a comprehensive evaluation result of the survey product; thereby, the survey product and the experience personnel are highly matched, and the accuracy of the comprehensive evaluation result of the survey product is improved.

The application relates to the technical field of smart home product design evaluation, and discloses a product design user experience evaluation method based on a smart home scene. The method comprises the following steps: constructing a virtual scene prototype and placing a design scheme model; recording interaction events to form original track data by executing a behavior script through a virtual user; performing behavior slicing and intention labeling processing on the original data to generate a structured experience event sequence with user intention labels; calling a multi-dimensional experience measurement rule to analyze the sequence, and extracting quantitative indexes; inputting the indexes into an experience fusion decision engine to automatically generate a comprehensive evaluation conclusion; and driving a design model to perform parameterized adjustment and iterative optimization based on the conclusion. The method realizes a closed loop from automatic analysis of user behavior semantics to automatic optimization of design, and improves the depth of evaluation and analysis and the efficiency of design iteration.