10 results about "Usability assessment" patented technology

The embodiment of the invention provides a mobile terminal service availability inspection method based on system state matrix driving, which belongs to the technical field of physics, and specifically comprises the following steps: constructing a system service state matrix; detecting a structure dimension, a behavior dimension, a signal dimension, an abnormal dimension and a user perception dimension by using a multi-modal probe system, mapping detection results into corresponding discrete state values, and filling and updating a system service state matrix according to the discrete state values; dynamically deciding a subsequent inspection path and a detection priority by combining a real-time environment state and a user behavior mode of the mobile terminal on the basis of each dimension state value filled in the system service state matrix and a predefined association rule; and controlling the multi-mode probe system to execute a corresponding detection action according to the inspection path, and generating an availability evaluation result of the target system service based on the final state of the system service state matrix. Through the scheme disclosed by the invention, the inspection efficiency, accuracy and adaptability are improved.

This invention discloses a method and system for hierarchical monitoring of the entire chain of distributed applications, relating to the field of computer system operation and maintenance monitoring technology. The method includes: constructing a full-stack dependency topology configuration model containing three component types: the application itself, infrastructure components, and related systems; sequentially executing first-level monitoring of the target application itself, second-level monitoring of infrastructure components, and third-level monitoring of related systems; summarizing the monitoring results at each level and performing collaborative diagnosis; determining multi-dimensional indicators based on a differentiated set of key indicators and mapping component status levels; triggering a full-chain veto based on key dependency tags and key indicator anomalies; dynamically correcting the component score coefficient bidirectionally based on the status duration; calculating a weighted comprehensive score and mapping it to a health level; and executing hierarchical alarms based on the health level and the veto trigger situation. This invention achieves end-to-end availability assessment and accurate fault location across the entire chain of distributed systems.

The application discloses a kind of based on layered feature fusion's no reference image usability evaluation method.There is following step: first, respectively extracting the low-level picture quality feature vector of the image to be evaluated, middle layer structure feature vector and high-level semantic feature vector;Subsequently, respectively to feature vector is normalized and splicing;Then, total feature vector is input to the regression network of integrated layered attention fusion ware, the network passes through multi-head self-attention mechanism dynamically learns the contribution weight of each level feature to the final availability score, and carries out regression prediction, outputs the availability evaluation score of image.The application realizes the fundamental change of evaluation target from "human visual perception quality" to "machine task availability" by systematically fusing multidimensional features from picture quality to semantics, and the evaluation result is strongly related to the performance of high-level visual tasks such as object detection and pose estimation, and is suitable for image screening and quality control in scenarios such as autonomous driving and intelligent monitoring.

This invention discloses an integrated air cannon equipment status monitoring system and method, belonging to the field of intelligent equipment monitoring technology. The invention utilizes sensors to collect operating data of the integrated air cannon; constructs a jetting demand assessment model, using different operating data to calculate blockage signal components, load change components, time factor components, and historical pattern components as inputs to the jetting demand assessment model, weighted and fused to obtain a demand index, calculates the theoretically optimal number of groups for the integrated air cannon, optimizes the theoretically optimal number of groups by judging the real-time pressure of the air tank, and constructs a time-constrained dynamic calculation interval; constructs an air source availability assessment model to calculate the maximum number of concurrent actions, and dynamically schedules the air cannon according to the maximum number of concurrent actions; optimizes and adjusts the weights of the jetting demand assessment model; and sets up a Chinese and English operating interface to construct an intelligent integrated air cannon operation panel.

The application relates to the field of industrial planning, and provides an enterprise data monitoring method based on multi-modal feature fusion and a corresponding product. The method comprises the following steps: acquiring multi-modal monitoring data of an enterprise; performing feature extraction on the multi-modal monitoring data to obtain an initial feature set corresponding to different modes; based on the initial feature set of each mode, evaluating the availability of the initial feature set in a current monitoring task, and screening out core modes participating in subsequent fusion according to the evaluation; based on a learned inter-modal correlation relationship, fusing the initial feature set corresponding to the core modes to generate a unified multi-modal fusion feature representation; performing enterprise state analysis based on the multi-modal fusion feature representation to output a monitoring result; and according to feedback information of the monitoring result, adjusting the basis for evaluating the availability of the core modes participating in subsequent fusion, and / or adjusting the inter-modal correlation relationship for fusing the initial feature set corresponding to the core modes.

The invention relates to the technical field of medical robots, in particular to a surgical robot availability data analysis method, which is characterized by comprising the following steps: S1, acquiring multi-source data in the front stage, the middle stage and the rear stage of a surgery; s2, timestamp calibration and synchronization are carried out on the collected multi-source data, and time sequence alignment of the multi-modal data is achieved; s3, performing preprocessing and feature extraction on the synchronized data to obtain a structured time sequence feature vector; s4, key operations are detected and marked based on the operation logs, the video frames and the robot sensor events, and time sequence segments of the operation process are divided; s5, inputting the structured feature vector into a multi-modal fusion model for joint modeling, and outputting an availability index; and S6, generating usability evaluation reports before, during, after and overall operations according to the usability indexes, and visually displaying the usability evaluation reports. According to the multi-source data fusion and intelligent analysis, objective evaluation of the operation process of the surgical robot is realized, and the system performance and the operation safety are improved.

This application discloses a method and electronic device for determining server availability scheme combinations, relating to the field of computer technology. The method includes: constructing a server availability assessment model using availability parameters of each server component in a target server; then constructing constraints based on several availability schemes corresponding to the target server, cost data corresponding to each scheme, the availability assessment model, and preset availability indicators; and using a preset pruning optimization algorithm to select a target availability scheme combination that satisfies the target constraints and preset cost rules from several availability scheme combinations based on the partial order relationship between availability schemes. The target availability scheme combination includes component parameter indicators of each target server component corresponding to each target availability scheme. Therefore, the partial order relationship between schemes can improve the selection efficiency of the pruning optimization algorithm, thereby quickly selecting a server availability scheme combination that meets the requirements from several server availability schemes.

The invention discloses an equipment system availability evaluation method and system, and relates to the field of complex system availability evaluation, and the method comprises the steps: converting a task of a target equipment system into a ternary coupling system structure according to a preset system structure frame; defining an element set in the target equipment system, wherein the element set comprises a task activity set, a communication service set and a personnel role set; extracting a mapping relation among the element sets from the ternary coupling system structure; converting the element set and the mapping relation into a ternary coupling relation; constructing a ternary coupling matrix based on the ternary coupling relationship and the ternary coupling system structure, and calculating a ternary coupling degree according to the ternary coupling matrix; according to the method, the accuracy and authenticity of availability evaluation are remarkably improved, and evaluation deviation caused by neglecting personnel coupling is avoided.

The invention discloses a non-reference image availability evaluation method based on hierarchical feature fusion. Comprising the following steps: firstly, respectively extracting a low-layer image quality feature vector, a middle-layer structure feature vector and a high-layer semantic feature vector of a to-be-evaluated image; thirdly, performing normalization processing and splicing on the feature vectors respectively; and then, inputting the total feature vector into a regression network of an integrated hierarchical attention fuser, dynamically learning the contribution weight of each level feature to a final availability score by the network through a multi-head self-attention mechanism, performing regression prediction, and outputting an availability evaluation score of the image. According to the method, multi-dimensional features from image quality to semantics are systematically fused, so that the fundamental transformation of an evaluation target from human visual perception quality to machine task availability is realized, and an evaluation result is strongly related to high-level visual task performance such as target detection and attitude estimation; the method is suitable for image screening and quality control of scenes such as automatic driving and intelligent monitoring.

The invention provides an availability test evaluation method of a brain-computer interface system, an edge computing device and a medium, and the method comprises the steps: obtaining the EEG data of a subject based on a first SSVEP normal form set in the brain-computer interface system; inputting the parameters of the first SSVEP normal form and the EEG data into an evaluation network, and generating an availability label corresponding to the first SSVEP normal form; wherein the evaluation network is obtained by training an EEG data sample, a parameter sample and a label sample, the EEG data sample is EEG data corresponding to a second SSVEP normal form of the brain-computer interface system, the parameter sample is a parameter of the second SSVEP normal form, the label sample is an availability label of the second SSVEP normal form, and the evaluation network is determined by the EEG data and an adaptability score of a subject to the second SSVEP normal form. According to the technical scheme, the accuracy of availability evaluation of the brain-computer interface system can be improved.