14 results about "Visitor behavior" patented technology

The invention provides a text travel scene adaptive illumination dynamic regulation and control method and system based on personal perception, and relates to the technical field of scene perception, and the method comprises the steps: capturing visitor multi-dimensional behavior data and scene environment state data in real time through a distributed sensor network; constructing a body perception model fusing the visitor spatial position, the moving speed, the staying duration and the physiological wake-up degree, performing space-time correlation coding on the multi-dimensional behavior data, and generating a body perception feature vector; establishing a dynamic affinity matrix based on the feature vector and scene semantic information, and mapping the dynamic affinity matrix to a spatial topological structure of lighting equipment to form a regional lighting coupling map; according to the map node weight distribution and visitor perception state clustering result, generating a partition cooperative illumination regulation and control instruction through a multi-objective optimization algorithm; and carrying out online evolution on the model by utilizing the regulated and controlled visitor behavior track and physiological index change. According to the invention, accurate matching of the illumination environment and the visitor immersion state can be realized, and the experience quality of a text travel scene is improved.

The invention discloses a museum audience behavior analysis system integrating positioning and digital twinning. The system comprises an audience real-time positioning module, an exhibition hall collection digital twinning module, an audience behavior analysis module and a behavior feedback adaptation module. The audience real-time positioning module adopts a multi-source positioning data fusion algorithm to obtain audience three-dimensional position coordinates and motion parameters; the exhibition hall collection digital twinning module reconstructs an exhibition hall collection three-dimensional model based on multi-modal data, and an integrated positioning data interface maps the positions and tracks of audiences in the twinning model in real time; the audience behavior analysis module extracts audience behavior features, establishes association between the features and exhibit positions and exhibition area attributes in the twin model through a behavior-scene association algorithm, and outputs a behavior analysis result; and the behavior feedback adaptation module dynamically adjusts the display strategy according to the analysis result. According to the system, deep fusion of positioning and digital twinning is realized, the accuracy of audience behavior analysis is improved, and support is provided for exhibition hall operation optimization and visiting experience upgrading.

The application discloses a kind of campus security control systems of fusing security monitoring and visitor management, it is related to campus security control technical field, when visitor enters school, the identity information, school time and expected stay area of it are collected, visitor label is constructed;Visitor behavior reference model is matched based on historical behavior data, path trajectory set and behavior characteristic vector are generated;Real-time call monitoring image extracts current behavior characteristics, and reference characteristics are carried out similarity matching, and behavior deviation is calculated;Visitor current position and reference trajectory offset section are combined to carry out spatial analysis, and trigger abnormal behavior alarm instruction;And execute regional early warning, abnormal trajectory upload and high-risk label marking;Feedback data is used for the dynamic update and incremental learning of behavior model simultaneously;The method realizes the whole process dynamic monitoring and intelligent discrimination of visitor behavior, and improves the real-time performance and scalability of campus security system.

The application relates to the technical field of video analysis, and discloses a linkage alarm method, device and equipment based on video analysis and a storage medium, the method comprising the following steps: performing behavior semantic identification on a video frame collected by a doorbell to obtain a visitor behavior mode, and encoding the video frame and the visitor behavior mode to obtain a semantic vector; constructing a three-dimensional semantic map in front of the door according to the video frame and tracking a visitor to obtain visitor trajectory data; decoding the semantic vector and the visitor trajectory data to obtain a behavior analysis result; selecting a linkage strategy from a preset action space and generating an alarm instruction according to the behavior analysis result and the visitor trajectory data, and executing the alarm instruction through the doorbell; the application realizes end-to-end real-time response, meets the strict requirements of an actual doorbell application scene on response speed, and further improves the accuracy of doorbell video analysis and linkage alarm.

We provide the system. [Solution] A sensing means for collecting visitor behavior and audio information, A communication means for transmitting the aforementioned behavior and voice information to a computer device, The aforementioned computing device includes an analysis means for analyzing the behavior and voice information and detecting abnormal events, A warning generation means that generates warning information and sends a notification based on the analysis results, Information sharing means for sharing the aforementioned warning information with other information processing devices in the region, A user warning means that displays visual and audible warnings on the user's terminal in real time, A system that includes this.

The application discloses a museum immersive exhibition optimization method based on cross-domain digital twinning. The method carries out three-dimensional modeling on entity exhibits, generates exhibit digital twinning bodies in combination with geometric shape, material and photoacoustic calibration data; collects geographical, architectural, historical and social and cultural data associated with the exhibits, establishes a unified space-time coordinate and semantic system, and forms a cross-domain context data set; inputs the structured information of the exhibit digital twinning bodies and the context data set into a multi-source fusion engine, constructs a cross-domain semantic space-time knowledge graph and generates a cross-domain coupling graph, and realizes bidirectional constraint of the entity environment and the virtual scene; under a given exhibition theme and immersion performance index, a context rendering parameter set is generated to guide virtual reality rendering; context parameters are dynamically adjusted according to visitor behavior and physiological feedback, and adaptive optimization of the exhibition is realized. The application improves the intelligent interaction and immersion experience of virtual and real exhibitions.

The invention discloses a campus safety control system integrating security monitoring and visitor management, and relates to the technical field of campus safety control, identity information, school entering time and a predicted staying area of visitors are collected when the visitors enter the school, and visitor labels are constructed; matching a visitor behavior reference model based on the historical behavior data, and generating a path track set and a behavior feature vector; calling a monitoring image in real time to extract a current behavior feature, performing similarity matching on the current behavior feature and a reference feature, and calculating a behavior deviation degree; carrying out spatial analysis in combination with the current position of the visitor and the reference trajectory offset section, and triggering an abnormal behavior alarm instruction; carrying out area early warning, abnormal track uploading and high-risk label marking; meanwhile, the feedback data is used for dynamic updating and incremental learning of the behavior model; according to the method, the whole-process dynamic monitoring and intelligent judgment of visitor behaviors are realized, and the real-time performance and expandability of a campus security and protection system are improved.

The application relates to the field of visual technology, in particular to a visual doorbell based on face recognition, which comprises a doorbell body, the front of the doorbell body is provided with a camera and a doorbell button, the doorbell body is provided with a doorbell system, the doorbell system comprises a face collection module, the face collection module is arranged on an outdoor unit and is used for capturing a visitor's face image in real time, a local recognition engine, a built-in pre-stored face database, feature extraction and identity matching of the collected image, a dynamic response unit, a differentiated response strategy is triggered according to a recognition result, when the recognition result is that the visitor is an authorized user, the dynamic response unit automatically unlocks the access control and pushes personalized welcome information to an indoor terminal, when the recognition result is that the visitor is an unauthorized person, the dynamic response unit starts real-time alarm and records a visitor behavior video, a cloud collaborative module, data synchronization with the local recognition engine, remote update of the face database and the recognition algorithm are supported, the visual doorbell based on face recognition automatically enhances the call volume and the interface display brightness for the old users, and a voice assistant is activated to guide the children.

The application provides a scenic spot tourist behavior detection clustering method, system, device and storage medium, the method comprises the following steps: collecting pictures with geographical information based on a scenic spot; identifying the behavior of tourists in the pictures based on the positional relationship between the skeleton points, obtaining a behavior label, and establishing a mapping relationship between the behavior label and the geographical information of the picture; gridizing an electronic map of the scenic spot, and collecting the number of times of various behavior labels appearing in each grid; when the number of behavior labels in the grid meets the preset threshold value corresponding to the behavior label, the grid obtains the behavior label and generates an interest point on the electronic map; based on the matching of the historical behavior label of the tourist and the interest point in the electronic map, the interest point meeting the matching is provided to the tourist. The application can identify the behavior of tourists in the scenic spot, add interest points in various places of the scenic spot according to the behavior label of the tourist, optimize the scenic spot map, and also enhance the data-guided experience of the scenic spot.

The invention discloses a self-adaptive exhibition display method and system based on machine learning, and electronic equipment, and relates to the field of Internet of Things and artificial intelligence, and the method comprises the following steps: 1, collecting behavior data of field audiences in real time through a sensor group disposed in an exhibition space, the behavior data comprises movement tracks of audiences, residence time in front of an exhibit and audience sight line direction data acquired through an image sensor. According to the invention, audience behaviors and emotions are perceived in a non-inductive manner through a multi-modal sensor, comprehensive audience state description is generated in real time by using a deep learning model fusing CNN and LSTM, digital content, environment light and personalized commentary generated in real time are dynamically regulated and controlled, accurate one-to-one intelligent guide is provided for individuals, interactive cooperation experience is triggered for groups, and the audience experience is improved. The system realizes macroscopic pre-configuration through continuous self-optimization of feedback data and prediction of hotspots and association relationships based on historical data, so that the immersion, interactivity and individuation level of exhibition are remarkably improved.

A behavior detection platform may obtain video data of an environment including a guest and may determine a set of normal guest behaviors associated with the environment. The platform may analyze, using a first machine learning model, the video data to identify features of the guest and analyze, using a second machine learning model, the video data and the features to determine an actual guest behavior of the guest. The platform may predict, using the second machine learning model, a predicted guest behavior of the guest based on the actual guest behavior. The behavior detection platform may determine that at least one of the actual guest behavior or the predicted guest behavior is not included in the set of normal guest behaviors. The platform may cause assistance to be provided to the guest based on determining that the actual guest behavior or the predicted guest behavior are not included.

Improved bot detection systems and methods are disclosed. A page fingerprinting algorithm can be used to categorize web pages. The categorization of web pages enables improved insights in profiling the way a human interacts with a website as opposed to a bot. Learning the patterns of humans and bots for a given web page category (or navigation across categories), a heuristic ruleset and / or machine learning system can differentiate between humans and bots. In this way, a human website visitor can be distinguished from a bot in an improved manner. The teachings hereof include systems and methods for deriving page fingerprints from makeup language files, for categorizing pages based on their structure and associated fingerprints, as well as heuristics and machine learning techniques to characterize website visitor behavior and detect bots, based on such web page categorization and fingerprinting.

We provide the system. [Solution] A means of acquiring data to collect visitor behavior information, A generation means that analyzes the aforementioned behavioral information and generates activity routes and recommended information optimized for the visitor, Information presentation means that provides the aforementioned recommended information to visitors in real time, A visualization means that presents visual information using augmented reality technology, A home navigation system that suggests optimized activity routes based on the interests and schedules of residents within the home, A system that includes this.

This invention discloses a method, system, and device for predicting the state of physical artworks using digital twin mapping, relating to the field of data processing technology. The method includes: constructing a digital twin based on exhibition hall architectural information and exhibit display data; synchronously collecting visitor behavior data, environmental parameters, and non-contact response data using multi-source sensing modules; performing coupling analysis on these data through a dynamic coupling model; outputting a collaborative perturbation coupling factor and injecting it into the digital twin; simulating the material degradation path caused by the collaborative effect of human-induced perturbation; and predicting the risk of artwork degradation. This invention solves the technical problem that existing artwork protection mainly relies on static environmental monitoring, which cannot quantify the dynamic collaborative degradation impact of visitor interaction on artwork materials. It achieves the technical effect of accurately predicting the material degradation path caused by the collaborative effect of multiple factors involving humans, objects, and the environment by dynamically coupling visitor behavior data, environmental parameters, and artwork response data.