2115 results about "Eye movement" patented technology

The invention relates to the technical field of image processing, and discloses a display method and system for a vehicle-mounted LED display screen. The method comprises the following steps: acquiring ambient light intensity data, performing fusion processing to obtain a characteristic value, and predicting ambient light change; identifying a driving scene according to the road condition data, and analyzing the priority by combining the ambient light change data; arranging a display area in combination with the eye movement data, and determining display parameters; analyzing the multi-modal interaction data, determining an interaction strategy, and verifying to generate response data; adjusting display content according to the ambient light features, and outputting a display effect in combination with response data; and energy consumption is estimated by integrating various data, and control parameters are generated to adjust an LED driving circuit. Through intelligent analysis and processing of multi-dimensional data such as ambient light intensity, driving scene and eye movement tracking, dynamic optimization and energy-saving control of display content are realized, and the problem that display effect and energy efficiency are difficult to consider in the prior art is overcome.

The invention discloses an intelligent system and method for early screening of depression based on electroencephalogram-eye movement multi-modal data fusion. According to the system, in a virtual reality situation, emotion and cognitive response of a subject are stimulated through an elaborately-designed cognitive task, and brain electrical activity and sight behavior data are synchronously collected by adopting a wearable EEG device and a high-precision eye tracker. After signal preprocessing and feature extraction, deep fusion of multi-modal data is realized by using a weighted fusion formula or a graph neural network, the weight of each modal is automatically adjusted, interaction features between electroencephalogram and eye movement are extracted, and then the depression risk of a subject is accurately judged through a classifier. Experiments show that the method can effectively capture weak physiological abnormalities of mild depression patients, has the advantages of high sensitivity, high accuracy and real-time online screening, and meets the requirements of non-invasive, portable and intelligent clinical application.

The invention discloses a scene interactive AI rehabilitation evaluation training and health monitoring system, and relates to the technical field of rehabilitation medical treatment and artificial intelligence, a semantic perception module is used for collecting and recognizing voice input, facial expressions, action tracks and eye movement paths of a user in a training process, and extracting context parameters; the knowledge-driven training generation module is used for calling a rehabilitation knowledge graph constructed by a graph neural network based on context parameters and individual training history, and generating a multi-path training scheme; training a feedback regulation engine, collecting posture offset, physiological stress and emotion feedback, and dynamically adjusting task difficulty, rhythm and prompt mode based on a dual-channel reinforcement learning model; the prediction module fuses training and monitoring data, and predicts a network identification function degradation risk through degradation driving; the cloud edge fusion platform is used for realizing task quick response and graph strategy iterative updating; according to the invention, the individuation, self-adaption and intelligent prediction capabilities of rehabilitation training are improved, and the rehabilitation effect and the system practicability are obviously optimized.

The invention relates to the technical field of sleep aiding, in particular to a method and a system for extracting feature information of a sleep state monitoring model. The method comprises the following steps: carrying out multi-mode physiological signal acquisition on a detected object to obtain original sleep monitoring data; performing noise removal and signal enhancement processing on the original sleep monitoring data, and performing signal segmentation to obtain multi-channel sleep signal fragment data; performing feature extraction based on electroencephalogram, eye movement diagram, electromyogram, electrocardiogram and accelerometer signals on the multi-channel sleep signal fragment data to obtain multi-dimensional feature data; and performing ultrasonic feature and acoustic feature extraction on the multichannel sleep signal fragment data to generate acoustic feature data. According to the method, dynamic modeling and trend prediction are carried out on the conversion process of the sleep state, potential sleep disorder events can be accurately recognized, influence evaluation is carried out on the potential sleep disorder events through deep learning, and support is provided for personalized sleep diagnosis.

The invention relates to a remote education data processing system which comprises the following steps: under a remote teaching task, pre-defining a task intention and a data expectation point; a semantic timestamp and a task binding label are printed on each data fragment; mapping the collected confusion data including silence, eye movement drift and prediction into a unified learning semantic vector; a micro-expression + interactive behavior + time sequence decision path ternary modeling mode is introduced, and a potential cognitive intention corresponding to the feature combination is recognized; teaching context information is fused; constructing a cognitive state mapping model; reasoning a current cognitive state label from multi-modal sensing data; searching intervention track VS effect feedback data in a historical database; generating a predicted intervention behavior sequence by using a sequence modeling algorithm; a dynamic combination suggestion chain including light prompt, content reconstruction, personalized practice and tutoring invitation is adopted; and superposing the cognitive state sequences of all students into a group cognitive trajectory map.

The invention provides an emotion recognition method based on electroencephalogram eye movement multi-mode cross-attention feature fusion, and the method comprises the steps: firstly carrying out the preprocessing of an emotion recognition public data set, and building a corresponding training set and a test set; secondly, constructing an electroencephalogram eye movement multi-mode cross-attention feature fusion emotion recognition model; then carrying out model training and performance testing; finally, an electroencephalogram eye movement multi-mode online emotion recognition system is built, and the effectiveness of the method is verified. The method has the advantages that an electroencephalogram eye movement multi-mode cross-attention feature fusion emotion recognition model is designed, a dynamic graph convolutional network and an attention mechanism are flexibly applied, meanwhile, multi-dimensional data are used, the problem that single-mode emotion recognition information is insufficient is solved, and the recognition accuracy is improved; and an electroencephalogram eye movement multi-mode online emotion recognition system is built, so that the interactivity is enhanced. The average recognition accuracy of the method reaches 97.94% and is superior to that of an existing optimal method, and meanwhile the accuracy of online emotion recognition reaches 87.4%.

The invention relates to a construction method of a Ti-SA model for pilot situational awareness assessment, belongs to the technical field of aviation safety and situational awareness, and solves the technical problems that an existing perception model cannot assess the situational awareness ability of a pilot in real time, and selected data cannot adapt to a complex environment, so that the effectiveness is poor. Comprising the steps that S1, eye movement trace time sequence data and AOI fixation time sequence data in the simulated flight process of multiple pilots are acquired to serve as sample data, and the sample data and generated corresponding category labels form a training data set; wherein the category label is a level label of the scene awareness of the pilot; step S2, constructing a Ti-SA model used for pilot scene awareness assessment; and training the Ti-SA model by using the training data set until the trained Ti-SA model is obtained when the loss function converges. The method achieves the real-time evaluation of the situational awareness capability of the pilot through the Ti-SA model.

The invention relates to the technical field of artificial intelligence, and discloses an English teaching training system and method fusing semantic matching and cognitive assessment, and the method comprises the steps: synchronously capturing a text response, a voice intonation, an eye movement track, a facial micro-expression and a touch rhythm generated in a learning process; semantic deviation deconstruction and cognitive intention quantization processing are carried out on the learning interaction original sequence, and a bidirectional deep semantic matching network is adopted to carry out context alignment on student answers and target corpora; based on the word meaning divergence point set and the cognitive load multi-scale vector, extracting a nonlinear diffusion trajectory of a learning state by using a time gating multi-layer recursive trajectory evolution algorithm; the knowledge point nodes, the deviation type nodes and the emotion triggering nodes associated with the emotion instability candidate segments are fused to construct a local learning map; and forming an emotion cognition feedback result driven by learning interest based on the local learning map and the self-adaptive error correction intervention sequence. The method has the advantage of improving the learning interest of students.

The invention relates to the field of man-machine interaction training, virtual reality and intelligent evaluation, and particularly discloses a psychological exercise ability training method and system based on mixed reality and behavior analysis. The method comprises the steps that a multi-scene psychological training environment is constructed and presented in mixed reality equipment, action behavior data, eye movement path data and operation response time data of a pilot in the training process are collected, multi-modal original behavior characteristics are constructed, and then a psychological exercise ability label set is constructed; and realizing capability-task modeling through matching of a capability label and a task structure, generating a personalized training path and a capability difference atlas based on difference analysis with a preset capability standard, adjusting task parameter configuration data, dynamically updating a training scene, generating a capability evolution trajectory, and outputting a training analysis result. In this way, accurate ability modeling, task dynamic adaptation and visual tracking of the training effect can be achieved, and the individuation and intelligence level of psychological exercise ability training is remarkably improved.

The invention relates to a method for evaluating the situation awareness ability of a pilot based on a Ti-SA model. The Ti-SA model comprises an eye movement trace multi-dimensional attention enhancement module, an AOI multi-scale comprehensive feature module and a fusion module. The evaluation method comprises the steps of collecting eye movement trace time sequence data and flight training videos of a pilot at continuous moments in real time, and obtaining corresponding AOI fixation time sequence data based on the eye movement trace time sequence data, the flight training videos and a preset AOI subdivision area; preprocessing the eye movement trace time sequence data and the AOI fixation time sequence data; inputting the preprocessed eye movement trace time sequence data into an eye movement trace multi-dimensional attention enhancement module to obtain a category probability value corresponding to the eye movement trace time sequence data; inputting the preprocessed AOI fixation time sequence data into an AOI multi-scale comprehensive feature module to obtain a category probability value corresponding to an AOI fusion feature; and inputting the two category probability values into a fusion module to obtain a predicted situation awareness level.

The invention discloses a multi-modal fusion alertness assessment method and system based on cross attention, and the method comprises the steps: carrying out the preprocessing of an original electroencephalogram signal of a user, and extracting a time-frequency domain-space feature # imgabs0 #; using a depth separable convolution module and an LSTM module to extract an electroencephalogram modal time dependency relationship feature # imgabs2 from the time-frequency domain-space feature # imgabs1 # in sequence; the method comprises the following steps: preprocessing an original electro-oculogram signal of a user to extract an eye movement feature # imgabs3 #, and obtaining an eye movement modal internal relation feature # imgabs5 # from the eye movement feature # imgabs4 # by using an outer product module and a CNN module in sequence; and performing multi-modal fusion on the electroencephalogram modal time dependency relationship characteristic # imgabs6 # and the eye movement modal internal relationship characteristic # imgabs7 # based on cross attention to obtain a fusion characteristic # imgabs8 #, and performing regression to obtain an alertness prediction result # imgabs9 # of the user. According to the method, intra-modal feature extraction and multi-modal feature fusion are respectively carried out on different information sources, so that the robustness and generalization of the features are improved, and the accuracy of alertness assessment is improved.

The invention provides an online learning energy concentration degree evaluation method combined with formability evaluation, and belongs to the technical field of online education, and the method comprises the steps: capturing eye movement data of a learner in an online learning process, carrying out feature extraction and time sequence analysis, and combining formability evaluation data to construct a deep neural network energy concentration degree evaluation model. And performing real-time dynamic evaluation on the energy concentration state of the learner by using multi-dimensional indexes such as attention continuity, visual concentration degree and cognitive input degree. Through a sliding time window technology and a specially-designed evaluation equation, the score of the energy concentration degree of each learning stage can be accurately calculated, and finally the overall energy concentration level of the learner is comprehensively output, so that data support is provided for personalized learning. According to the method, learning energy concentration degree monitoring and analysis support is provided, and the problem that it is difficult to continuously, objectively and comprehensively monitor the attention state of a learner on the premise that learning activities are not affected in the prior art is solved.

The invention provides an autism assessment system and method based on multi-modal time sequence data fusion, and relates to the technical field of children autism spectrum disorder assessment calculation processing. The invention innovatively provides a dual time sequence alignment method based on dynamic time warping (DTW) and LSTM prediction, the problem of time sequence dislocation of cross-modal data (heart rate / eye movement / limb movement) caused by acquisition frequency difference is solved, millisecond-level synchronization precision is realized, the technical problem of cross-modal data time sequence dislocation in an existing autism assessment system is solved, and the accuracy of time sequence alignment of the cross-modal data in the autism assessment system is improved. The small-range time deviation caused by acquisition equipment delay or physiological response difference is eliminated, and the robustness and reliability of the autism evaluation system are improved.

Disclosed are system, method, and computer program product embodiments for determining a digital marker. An embodiment operates by determining one or more oculometric parameters associated with an eye of a user using a video stream. The video stream comprises at least one image of a face of the user. The embodiment then determines a pupillary response function of brightness or a gaze response function by performing a deconvolution process based on at least the one or more oculometric parameters. The pupillary response function of brightness or the gaze response function is indicative of a response of the eye of the user to an external factor. The embodiment determines one or more digital markers of the user based on the pupillary response function of brightness or the gaze response function. The one or more digital markers are indicative of a neurological condition or a mental health condition of the user.

The invention discloses a pilot cognitive load monitoring and early warning method based on electroencephalogram and eye movement data, and the method comprises the steps: 1) designing a pilot different cognitive load state induction experiment paradigm in a simulated flight task scene, and obtaining a multi-modal physiological signal and task performance of a testee during an experiment; wherein the multi-mode physiological signals comprise electroencephalogram signals and eye movement signals; 2) preprocessing the multi-modal physiological signals and task performance, extracting corresponding feature indexes, and fusing and selecting the feature indexes to construct an optimal feature subset; and 3) constructing a cognitive state classification and discrimination model based on the optimal feature subset so as to carry out monitoring and early warning on the cognitive load of the pilot. According to the invention, the cognitive load level of the pilot can be monitored and evaluated, especially the sudden change point of the overload state of the cognitive load is pre-warned, and the flight safety is improved.

The invention discloses a multi-mode electroencephalogram analysis model construction method and an online processing method and system. According to the method, multi-modal physiological signals such as electroencephalogram, electrocardio, skin electrical activity and eye movement are collected, and an integrated multi-modal signal collection device is used for preprocessing and feature extraction. A modal expert system based on an adaptive Transform architecture is adopted to replace a standard feed-forward network so as to enhance the feature processing capability. By means of the method, the features can be complemented under the condition of mode or data missing, the robustness, real-time performance and accuracy of the brain-computer interface technology in multi-mode data processing are improved, and the method is particularly suitable for the fields of emotion recognition, cognitive load monitoring, neural rehabilitation and the like.

The invention provides a model-based interaction method and system, wearable equipment and a storage medium, and belongs to the technical field of intelligent interaction.The method comprises the steps that in response to a received interaction instruction, voice data, a gesture image, eye movement data and an environment image are obtained based on the interaction instruction; extracting user intention features based on the voice data, the gesture image and the eye movement data, and determining scene type features based on the environment image; determining an interaction theme based on the interaction instruction, obtaining user historical interaction information associated with the interaction theme from a context memory database, and generating a context feature vector based on the user historical interaction information; and inputting the user intention feature, the scene type feature and the context feature vector into an intention recognition model based on quantum enhancement to obtain a user intention, and generating interaction response data based on the user intention. According to the invention, the accuracy of user intention recognition can be improved, and the intelligence of interaction is improved.

The invention belongs to the technical field of brain-computer interfaces, and particularly relates to a brain-computer interface driven emotion recognition and self-adaptive regulation and control system which comprises a multichannel nerve-peripheral coupling module, an emotion intensity probability mapping module and a closed-loop nerve regulation and control current module. The multi-channel nerve-peripheral coupling module is used for realizing overall quantification of central and peripheral emotional physiology; the emotion intensity probability mapping module is used for generating continuous emotion probabilities ranging from 0 to 1 through normalization and nonlinear mapping by utilizing emotion energy and combining eye movement fatigue and electroencephalogram entropy; and the closed-loop nerve regulation and control current module is used for dynamically adjusting the transcranial electrical stimulation intensity within the safety current upper limit according to the difference value between the emotion probability and the expected target. According to the invention, the recognition precision, the response speed and the use comfort are obviously improved.

The invention discloses a text-driven advertisement video generation method based on emotion feedback, relates to the technical field of digital advertisement and man-machine interaction, and aims to dynamically adjust advertisement content and improve individuation, interactivity and conversion rate by collecting emotion data such as facial expression, voice features and eye movement tracks of a user in real time. A text generation video model and a multi-mode emotion recognition technology are combined, and feature fusion and feedback processing are utilized, so that the advertisement video better conforms to the emotion of the user; multi-round feedback and self-adaptive optimization ensure that advertisements transmit core information and flexibly respond to user emotional states at the same time, attraction and participation degree are greatly improved, an innovative feature fusion algorithm further improves individuation and real-time response capability of generated videos, a brand new solution is provided for the field of digital advertisements, and the method is suitable for popularization and application. And the development of individuation and emotional interaction is promoted.

The invention belongs to the technical field of intelligent education, and particularly relates to a classroom attention detection method and system based on multi-modal data fusion. Aiming at the problems of high equipment cost, low multi-source data fusion efficiency, insufficient privacy protection and the like in the prior art, the invention provides the following solutions: collecting face, eye movement, posture, voice signals and heart rate variability data of a student through a sensor; multi-modal data synchronization is realized by adopting a time sequence alignment algorithm; respectively extracting a visual attention feature, a voiceprint matching feature and a physiological wake-up feature by using a lightweight deep learning model; constructing a multi-modal data fusion network, and dynamically adjusting a feature weight in combination with a classroom scene; attention anomaly detection is realized by adopting a hybrid model, and real-time early warning is output through edge computing equipment. The method has the beneficial effects that the hardware cost is greatly reduced while the detection precision is ensured, and the privacy of students is effectively protected; a dynamic weight distribution mechanism improves the adaptability of different teaching scenes.

The invention discloses an emotional disorder detection method based on a multi-modal heterogeneous graph convolutional neural network, and the method comprises the steps: collecting the electroencephalogram, expression pictures, eye movement data and gait video frame data of a subject, and aligning the data through an asynchronous time sequence cross-modal alignment and group horizontal alignment method. Thirdly, decomposing data of each mode by utilizing low-rank matrix decomposition of multi-mode characteristics, and performing alignment of different tasks with a nerve activation path; carrying out heterogeneous graph convolution channel path coding on each channel and embedding a modal; and finally carrying out lightweight cross-modal fusion on the obtained data stream. The invention further provides an emotional disorder detection device. According to the method provided by the invention, the recognition of the type of the emotional disorder is realized through the acquired multi-modal data including electroencephalogram, eye movement, micro-expression and gait information.

The invention discloses an automatic tracking system based on XR space, and relates to the technical field of XR space, the system comprises a sight tracking module, a scene pairing module, an interactive navigation module and a feedback adjustment module; the technical key points are as follows: establishing a preset instruction library, accurately mapping recognized eyeball poses to a specific analysis instruction in an XR space, and obtaining a fixation area and a movement direction in the XR space; according to the method, rough and fine virtual scene division is carried out based on eye movement data, the virtual scene where the user is located is judged in real time, the immersion and participation sense of the user are greatly improved, then an initial rotation value and guide time are set, a preliminary calculation model is dynamically adjusted according to user feedback, guide simulation is optimized, and it is ensured that the user obtains vivid experience.

The invention discloses a fast reading training control method based on eye movement tracking. The fast reading training control method comprises the steps of extracting coordinates of fixation points and retention time of the fixation points according to an eye movement trajectory curve, performing density clustering on the fixation points to obtain sight line distribution concentrated areas, calculating center coordinates and average retention time of each concentrated area, and generating a sight line distribution thermodynamic diagram; the reading efficiency score is calculated according to the reading speed and the review times of the reader, trend prediction is performed on a reading efficiency score sequence, the font size is dynamically adjusted according to a prediction result, if the predicted reading efficiency is in a descending trend, font amplification is triggered, and the font size adjustment amplitude is determined by the reading efficiency descending rate. Through real-time monitoring, intelligent analysis and self-adaptive adjustment, the effect and experience of quick reading training are remarkably improved, and an innovative solution is provided for improving the personalized reading ability.

The invention relates to the technical field of artificial intelligence, and provides an autism multi-source fusion diagnosis method, device and equipment and a storage medium, and the method comprises the steps: obtaining multi-modal data of a to-be-diagnosed object; a virtual reality device is worn on the head of the to-be-diagnosed object, the virtual reality device is used for playing an interaction task to the to-be-diagnosed object, and the multi-modal data is collected in the playing process of the interaction task; the multi-modal data comprises behavior data, electroencephalogram data, expression data and eye movement data; and inputting the multi-modal data into a pre-trained multi-modal diagnosis model, performing single-modal feature extraction on the multi-modal data by using the multi-modal diagnosis model, and performing feature fusion diagnosis on the extracted single-modal features to obtain an autism diagnosis result of the to-be-diagnosed object. Through feature fusion and analysis diagnosis of multi-modal data, feature complementation of different modals can be realized, and the accuracy and credibility of autism diagnosis results are improved.

A system for AI-based analysis of student behavior, consisting of: a variety of data collection devices, including cameras, microphones and wearable sensors; a central processing unit (CPU) that is communicatively connected to the data acquisition devices; and a behavioral analysis engine, wherein: (a) the cameras are configured to capture visual data indicating student behavior, including facial expressions, body language and eye movements, with the captured visual data being processed using computer vision techniques to detect certain emotional and engagement-related indicators such as stress, boredom, attention or excitement; (b) the microphones are configured to capture audio data, including speech patterns, tone of voice, volume and level of participation, the captured audio data being processed using natural language processing (NLP) techniques to assess the mood, engagement and verbal participation of students in classroom activities; (c) the wearable sensors are configured to collect biometric data, including heart rate, skin temperature and movement, with the biometric data being analyzed to detect physiological signs of emotional stress, fatigue or engagement; (d) The behavioral analysis engine is configured to process the captured visual, auditory, and biometric data in real time to create a behavioral profile of each student. It uses machine learning models to correlate certain patterns with academic performance, emotional well-being, and social engagement. (e) the system is also configured to create predictive models based on historical data to identify pupils at risk of declining performance, social isolation or emotional distress, and to send alerts to teachers, counselors or the administration to enable timely intervention to address the identified problems.

The invention relates to the field of electroencephalogram signal processing, and particularly discloses a multi-mode electroencephalogram characteristic cognitive ability evaluation system based on a neural network, comprising: an acquisition module used for synchronously acquiring whole brain signals and eye movement data during pilot simulation training; the data preprocessing module is used for primarily processing the received whole brain signals and the eye movement data, monitoring the primarily processed data in real time by utilizing a preset monitoring mechanism, and triggering to generate interaction test starting signals if preset symbolic fluctuation occurs in the whole brain signals or the eye movement data; and the test interaction sub-module is used for generating arithmetic test questions according to a preset rule after receiving the interaction test starting signal, and synchronously recording answer correlation parameters of the pilot. By adopting the technical scheme of the invention, the deep-level reaction mechanism of the brain under the stimulation of the special complex cognitive task of flight training can be fully considered, and the cognitive load change of the pilot in the simulated training can be comprehensively and accurately reflected.

The invention relates to the technical field of artificial intelligence and biomedical sensing, and discloses a cognitive disorder risk identification device and method integrating multi-modal physiological data, and the device comprises an EEG module, an eye movement module, an fNIRS module, an edge calculation module and an acceleration sensor; the EEG module collects EEG data, the eye movement module collects eye movement eye-tracing data, and the fNIRS module collects near infrared spectrum fNIRS data and inputs the data to the edge calculation module; the edge calculation module runs the multi-modal space-time attention fusion model to output a cognitive impairment risk assessment result by using the multi-modal space-time attention fusion model; the acceleration sensor operates based on an operation dynamic filtering algorithm of a motion accelerometer to inhibit signal drift caused by head motion. According to the invention, through the modularized head-mounted multi-mode edge device, the physiological data is collected and edge end processing and cognitive disorder recognition and screening are carried out in combination with the edge calculation module, so that early recognition and screening of neurodegenerative diseases can be conveniently and effectively carried out at low cost.

The invention discloses a pilot manipulation ability evaluation method and system based on multi-modal data and online learning driving. The method comprises the steps of determining an input parameter set, establishing a flight vision time sequence fusion network, and extracting each feature of the parameter set. And completing feature fusion of the multi-modal data through an attention mechanism and global aggregation. And constructing a pilot maneuvering ability evaluation architecture, and generating an offline pilot maneuvering ability evaluation model by designing a loss function based on a comprehensive scoring error and sub-scoring consistency constraint. Flight data and eye movement data streams generated in real time are fragmented and preprocessed by using a sliding window mechanism, a lightweight model based on pruning and parameter quantification technologies is designed for real-time scoring requirements, and feature expression ability in an offline pilot manipulation ability evaluation model is migrated to a lightweight online evaluation model. The method can be used for improving a pilot training scheme, optimizing the man-machine interface design of a cockpit, and improving the efficiency and safety of flight operation.

The invention discloses a dangerous driving critical state identification method, and relates to the technical field of intelligent driving safety. According to the method, multi-mode signals of eye movement, electrocardio, skin electricity, vehicle operation and the like are collected and converted into a unified phase field, and the synchronous coherence of the unified phase field is analyzed; the individual phase dynamics manifold of the driver is learned on line by using a Shenchang differential equation, and a system instability precursor is identified by detecting the behavior that a state point escapes from a steady state attractor; further, multi-dimensional indexes such as synchronous collapse and topological fracture are fused, collapse time is estimated in combination with a Lyapunov index, and an advanced early warning instruction is generated; and finally, based on the model predictive control and the personalized phase response curve, generating and executing targeted multi-mode phase reset intervention, and forming a sensing-early warning-intervention active safety closed loop. According to the invention, normal form transformation from post-event alarm to beforehand regulation and control is realized, and early warning advancement and intervention accuracy are improved.

The invention provides a multifunctional inspection device detection system based on a distribution network mobile operation terminal, and relates to the technical field of electric power detection, and the multifunctional inspection device detection system comprises a portable main terminal of a dynamic collaborative architecture, a wearable terminal for augmented reality interaction and a distributed heterogeneous sensing cluster, which form a closed-loop data chain through an electric power dedicated low-delay wireless communication network; the portable main terminal is integrated with a heterogeneous computing unit with adaptive computing power distribution, can perform real-time fusion analysis on multi-dimensional sensing data, and outputs a visual result containing fault location and confidence; the wearable terminal superposes fault information to a real scene in a three-dimensional marking form through a virtual-real fusion positioning technology, and supports eye movement and voice collaborative interaction; multi-module collaborative acquisition of the distributed heterogeneous sensing cluster is combined with a nanosecond timestamp synchronization mechanism, so that the limitation of traditional single parameter detection is broken through, rich and synchronous basic data is provided for subsequent analysis, and the comprehensiveness of routing inspection is greatly improved.