23 results about "Web analytics" patented technology

The invention discloses an equipment health state monitoring method and system based on the Internet of Things. According to the method, data in a multi-source sensor is obtained and preprocessed, a real-time operation data set of equipment is obtained, and a health quantification deviation value and a health state trend are determined through the real-time operation data set. And when the health state is abnormal, recording a starting point coordinate of an abnormal event, marking an abnormal event triggering timestamp, and analyzing the dependency relationship between the equipment by utilizing graph network modeling. Furthermore, the potential risk probability is analyzed through the long-short-term memory network, real-time monitoring, fault propagation prediction and risk assessment of the equipment health state are achieved, and the operation reliability and the maintenance efficiency of the industrial equipment are improved.

The invention relates to the technical field of image processing, and discloses a woolen sweater intelligent production monitoring method and system based on the Internet of Things. The method comprises the following steps: acquiring a wool fiber three-dimensional point cloud through multispectral imaging and a space stereo matching algorithm, and analyzing the twist and crimpness of yarns through a gradient convolutional network to obtain a quality feature set; a high-speed acquisition system is adopted to obtain a knitting needle motion sequence, and a fabric structure map is obtained through a line enhancement algorithm. And constructing knit fault prediction based on the spatial-temporal characteristic network and the memory model, and obtaining a density uniformity index. And inputting the quality feature set and the density index into a fusion network, and performing multi-layer attention processing to obtain a quality evaluation model. And constructing an early warning system by using the depth map network, and generating a process optimization scheme. According to the method, comprehensive perception, dynamic early warning and intelligent optimization of the production process are realized, and the problems of incomplete data acquisition, inflexible parameter control, untimely quality early warning and the like in the prior art are solved.

The invention provides a personalized image content generation and optimization method and system fusing generative AI, and relates to the technical field of AI. The personalized image content generation and optimization method comprises the steps of analyzing a historical interaction track and a current intention expression of a target user based on a deep perception network, and constructing a multi-dimensional behavior portrait; user groups with similar generation preferences are identified by using a group feature recursive quantization technology, and a group wisdom feature map is constructed; performing multi-level mapping analysis on the user features and the group wisdom feature map, anchoring the group affiliation relationship of the target user and concretizing the guide features; constructing a multi-level guide vector according to current intention expression and guide features, performing accurate mapping on hidden space expression of an image generation model, quantizing deviation in real time and triggering intelligent calibration; and reconstructing a group distribution structure based on the evaluation data of the target user to realize dynamic evolution of the features. According to the invention, personalized demands of users can be accurately grasped, and the accuracy of image generation and the satisfaction degree of the users are improved.

Techniques are described herein for selecting a template for efficient generation of dashboards for the template that derive insights into website performance. A client can provide search criteria, and, in response, the client can view and select a template that matches the context in which the client would like insights into the website data. The client can interact with the selected template to populate dashboards for the selected template. An identification of a metric can be detected for a placeholder field for a dashboard, and a specification of event type(s) to be used in populating the metric can be obtained. The dashboard can be loaded in memory, and user session data can be processed to detect events in the user session data. Metrics can be generated in the dashboard using the events in the user session data. The metric in the dashboard can be displayed at the client device.

A method to incentivizing health related activity based on social media presence is described. The method includes providing a digital communication network for members of a population to communicate with each other; and analyzing, by the digital communication network, the communications in the digital communication network to identify a user who has influence over other members. Then, the identified user is targeted for at least one intervention. The methods may also include providing digital communications of health-related activity based on incentives. A member of the population may be informed about incentives for sharing their experience. The system determines whether the member shares their experience, and, if they do, the system provides the incentive to the member.

Various embodiments of the present technology generally relate to systems and methods for providing an analytics engine. In an aspect, a method may be provided that includes receiving, by an analytics engine, a user query from a client device via a virtual assistant application hosted by a web-based application, where the web-based application includes various user interface (UI) elements, each of which is assigned to a respective event tag. Responsive to receiving the user query, the analytics engine may determine a first event tag based on the user query, where the first event tag is associated with a first UI element on the web-based application. The analytics engine may tag the user query with the first event tag to associate the user query with the first UI element and transmit a first report including the first event tag to a web analytics system associated with the web-based application.

The present invention relates to the technical field of news hotspot monitoring, and provides a news hotspot monitoring method and system based on social network analysis, comprising: calculating the correlation between different news events, using the correlation between two news events as the weight of an edge, establishing a relationship network diagram, and calculating a metric for each news event to select hot news events, and obtaining effective hot news events by calculating the synergistic benefit weight results of the selected hot news events; sequentially removing the selected hot news events from the relationship network diagram according to the ranking of the synergistic benefit weight results to obtain a new relationship network diagram, and reselecting hot news events based on the new relationship network diagram to obtain differences from the first selected hot news events, and selecting the hot news event that causes the greatest difference as a key event. This helps to gain a deeper understanding of the connection and influence between hot news events and other news events in news reports.

A method for semantic perception editing of objects in a three-dimensional scene includes steps as follows: input point cloud data of a three-dimensional scene is analyzed by using a three-dimensional instance segmentation network, and a current position and a bounding box of a target object are determined based on an editing instruction. A set of semantic relationship rules between the multiple object instances from a historical editing case database is learned by using a Bayesian dynamic updating mechanism, and dynamic semantic constraints are dynamically generated. Editing operations are converted into a standardized mathematical representation by using a unified parametric framework, and the verified editing operations are optimized based on a comprehensive loss minimizing function to obtain optimized editing parameters. The three-dimensional scene is transformed based on the optimized editing parameters to obtain the edited three-dimensional scene.

The invention provides a social network user personality detection method based on multi-view feature fusion, and the method comprises the following specific steps: firstly, extracting semantic features of a posted text released by a user based on a pre-training language model, and carrying out the sentiment analysis of the posted text in combination with an external sentiment knowledge base, so as to form initial posted text embedding; thirdly, constructing a poster-personality trait heterogeneous graph, setting node interaction of three levels, and extracting multi-level interaction features by using a graph attention mechanism; furthermore, a multi-instance gating loop unit is introduced to model a user post sequence, time sequence features under different personality traits dimensions are captured, and information sharing among personality traits is promoted through an interactive attention layer. And finally, fusing the disordered interaction features and the sequence features, predicting personality characteristics of the user in multiple dimensions, and forming a comprehensive personality portrait. According to the method, the personality characteristics of the user can be comprehensively described, and the method is suitable for scenes such as personalized recommendation, user portrait construction, social network analysis and psychological health auxiliary diagnosis.

The application discloses a cultural relic data storage and access authorization management method based on a block chain, relates to the technical field of database storage, and comprises the following steps: based on a cultural relic block alliance chain member table, evaluating access permissions of each member of the cultural relic block alliance chain, constructing an authorized link network of the cultural relic block alliance chain, analyzing relative target cultural relic access actions of each alliance chain member in a historical access log, obtaining a cultural relic node access behavior knowledge graph in the authorized link network, training a cultural relic access path reasonable behavior model, obtaining a cultural relic block alliance chain authorized link network compliance access path vector, constructing a cultural relic access path abnormal behavior model, marking an authorized link network abnormal access path in a real-time access log, and updating the access permissions of each member of the cultural relic block alliance chain.The cultural relic data access security and operation and maintenance efficiency are improved.

The application discloses a kind of based on the equipment health state monitoring method and system of Internet of Things.The method is by obtaining the data in multiple source sensors and pre-processing, obtains the real-time running dataset of equipment, determines health quantitative deviation value and health state trend by real-time running dataset.When health state is abnormal, the starting point coordinate of abnormal event is recorded and marked abnormal event trigger timestamp, and the dependence relationship between devices is analyzed using graph network modeling.Furthermore, the potential risk probability is analyzed by long short-term memory network.The application realizes real-time monitoring of equipment health state, fault propagation prediction and risk assessment, and improves the operation reliability and maintenance efficiency of industrial equipment.

The application discloses a kind of social group member dynamic management method and system, it is related to social network analysis field, including, the online interaction data and offline co-occurrence data of social group member are acquired, and are washed, the online interaction data and offline co-occurrence data after washing are converted into structured data set;Structured data set is imported into graph database to obtain initial weight, and dynamic attenuation weight is calculated, and basic relationship network is constructed;The edge weight matrix in basic relationship network is converted into quantum bit entanglement state, quantum bit entanglement state is measured to form quantum probability amplitude, quantum entanglement determination threshold is set, and quantum relationship thermodynamic diagram is generated by Grover algorithm.The application realizes the accurate management and risk prevention and control of social group member relationship by the technical scheme that quantum computation is combined with dynamic entropy analysis, the edge weight matrix of basic relationship network is converted into quantum bit entanglement state, and quantum relationship thermodynamic diagram is generated using quantum measurement and Grover algorithm.

The application provides a personalized image content generation and optimization method and system based on fusion generative AI, relates to the technical field of AI, and comprises the following steps: analyzing the historical interaction track and current intention expression of a target user based on a deep perception network, and constructing a multi-dimensional behavior portrait; identifying a user group with similar generation preferences by using group feature recursive quantization technology, and constructing a group wisdom feature map; performing multi-level mapping analysis on the user features and the group wisdom feature map, anchoring the group affiliation of the target user and visualizing the guide features; constructing a multi-level guide vector according to the current intention expression and the guide features, accurately mapping the hidden space representation of an image generation model, quantifying the deviation in real time, and triggering intelligent calibration; and reconstructing the group distribution structure based on the evaluation data of the target user, and realizing dynamic evolution of the features. The application can accurately grasp the personalized needs of users, and improve the accuracy of image generation and user satisfaction.

The application belongs to the field of guided topic network analysis, and particularly relates to a social network group discovery method based on user preferences, comprising: obtaining relevant information from a social network platform; calculating the interaction degree and reputation degree of users; defining a random walk strategy of IBT2vec algorithm, and representing the user topic network as a feature vector matrix; using RoBERTa to obtain the long-term and short-term preferences of users, fusing the same with the initial feature vector to form an enhanced user node representation; compensating for the network space features through meta-transfer learning to alleviate the data sparsity problem; classifying the user features through a spectral clustering method, and obtaining key influential user groups with similar interests and behaviors after full connection layer and normalization processing. The application not only considers the similarity between users, but also comprehensively considers the influence of users, so that the group discovery is more accurate and practical, and reliable support is provided for applications such as public opinion monitoring, information dissemination and marketing promotion.

A system and method for detecting interactive network of automated accounts, the interactive network of automated accounts comprising a plurality of automated accounts posting to a social media channel, the system comprising: an ingestion engine operated by a computational device for connecting to the social media channel and receiving a plurality of social media postings from a plurality of posting entities; a bot model operated by a computational device for determining whether at least one posting entity is a suspected bot; and a computer network for communication between said computational devices.

The invention relates to the field of account qualitative analysis, in particular to a social media account qualitative analysis method. The method comprises the following steps: performing multi-dimensional feature extraction on a social media account, constructing comprehensive feature representations of the social media account, and performing fusion calculation on a plurality of comprehensive feature representations; step 2, based on a Double DQN reinforcement learning method, adaptively optimizing the weight of the comprehensive feature of the social media account; step 3, deep time sequence feature learning is realized through an LSTM-GRU hybrid network architecture, a time sequence is predicted, abnormal fusion data is detected through a variational auto-encoder, and network parameters are optimized; 4, performing deep social network analysis on the social media account, performing node representation learning and influence propagation modeling through a GraphSAGE method, and verifying propagation prediction data; and 5, evaluating the propagation prediction data through a multi-dimensional evaluation system to obtain a qualitative analysis result of the social media account.

The invention relates to the technical field of network public opinion data monitoring, and relates to a data security online intelligent monitoring management system. Structured analysis of copywriting, image and audio data is carried out on the network public opinion video to generate a copywriting emotion polarity score, an image sensitivity index and an audio feature abnormal value, and a comprehensive malicious index is generated through dynamic weighted fusion, so that multi-dimensional features of video contents are fully integrated; the accuracy and comprehensiveness of malicious content identification are effectively improved; an information propagation topology network is constructed based on user interaction behavior data, the diffusion speed and the content similarity of all propagation nodes are analyzed, abnormal propagation nodes are dynamically identified, the propagation path and the diffusion trend of malicious propagation content can be clearly mastered, a key basis is provided for subsequent early warning response and accurate strike, and the method is suitable for popularization and application. And a flow limiting strategy and an account number banning operation are triggered in real time, so that the timeliness and effectiveness of network public opinion video security monitoring management are effectively improved.

A method to incentivizing health related activity based on social media presence is described. The method includes providing a digital communication network for members of a population to communicate with each other; and analyzing, by the digital communication network, the communications in the digital communication network to identify a user who has influence over other members. Then, the identified user is targeted for at least one intervention. The methods may also include providing digital communications of health-related activity based on incentives. A member of the population may be informed about incentives for sharing their experience. The system determines whether the member shares their experience, and, if they do, the system provides the incentive to the member.

The invention provides a DDQN-based social network viewpoint polarization and divergence intervention improvement method, and belongs to the field of social network analysis. The method specifically comprises the following steps: initializing an initial viewpoint of a social network, simulating interactive updating between users by adopting a Friedkin-Johnsen model, and calculating an initial divergence value and a polarization value of the social network after an iteration number moment reaches a stable state; designing an intervention agent, selecting some users, modifying viewpoints of the users, and calculating a divergence value and a polarization value after intervention after a stable state is reached through iteration for several times; a DDQN model is adopted, a state vector of a user is input, and an optimal intervention strategy is learned by taking maximization of a divergence value and a polarization value of the social network as a target; and training the model to achieve an optimal intervention strategy. According to the method, polarization regulation and control with low interaction cost are realized through modular design, different network topologies can be flexibly adapted, and technical reference is provided for dynamic intervention of social network opinions.

The present invention relates to the technical field of online public opinion data monitoring, and relates to an online intelligent monitoring and management system for data security. The present invention generates a text sentiment polarity score, an image sensitivity index, and an audio feature outlier value through the structured analysis of text, image, and audio data of online public opinion videos, and generates a comprehensive malicious index through dynamic weighted fusion, fully integrating the multi-dimensional features of video content, effectively improving the accuracy and comprehensiveness of malicious content recognition; constructs an information dissemination topology network based on user interaction behavior data, analyzes the diffusion speed and content similarity of all dissemination nodes, dynamically identifies abnormal dissemination nodes, can clearly grasp the dissemination path and diffusion trend of malicious dissemination content, provides a key basis for subsequent early warning response and precise strike, and triggers a traffic limiting strategy and an account banning operation in real time, effectively improving the timeliness and effectiveness of online public opinion video security monitoring and management.

The invention relates to the technical field of social network analysis, and discloses a social network user influence analysis method and system, and the method comprises the steps: collecting the original data, related to the user influence, of each user from a social network in real time; key features related to user influence are extracted and calculated from the original data of each user, wherein the key features comprise user self-influence, user relation breadth, user authority and time dynamic characteristics; at least according to the user self-influence, the user relationship breadth and the user authority of each user, utilizing an improved PageRank algorithm to carry out iterative calculation to obtain the user influence of each user; and according to the time dynamic characteristics, introducing a time attenuation factor, and adjusting the user influence of each user. According to the method, the multi-dimensional indexes are introduced, key features are extracted, and the depth and the breadth of quantitative analysis of the influence of the user are enriched; a traditional PageRank algorithm is optimized to calculate the user influence, dynamic adjustment of the user influence is carried out in combination with a time decay factor, and finally comprehensive and accurate evaluation of the user influence is achieved.

The invention discloses a service system security assessment method, and relates to the technical field of service system security assessment, and the assessment method comprises the following specific steps: S100, data collection and integration: collecting social network data of employees in a service system through an internal monitoring tool of the service system, collecting external social network information through a web crawler, and carrying out data collection and integration; according to the method, social network data of internal employees and external social network data are integrated, potential security risk points can be accurately identified by deeply analyzing social network relationships of the internal employees of a service system, and specifically, the method utilizes a social network analysis tool to meticulously sort contact networks among the employees, so that the security risk points are accurately identified. According to the method, employee nodes which are in abnormal frequent contact with external suspicious personnel and risk areas in which compact small groups are possibly formed are effectively found, and the refined analysis not only enhances the accuracy of risk identification, but also provides powerful data support for the formulation of a safety management strategy.

The application belongs to the field of social network analysis, and particularly relates to an early guiding type topic detection method based on emotion accumulation and dynamic graph representation, comprising: obtaining topic data generated before and after a topic outbreak, including basic information of a user publishing a comment in the topic, comment information and a friend relationship; establishing a relationship weight matrix between users according to theme distribution similarity of historical comment publishing of the user and emotion influence of the user; converting the basic information of the user into a vector representation; inputting the vector representation of each user under the topic and the relationship weight matrix between the users into a time coding-based graph attention network to determine whether the current topic belongs to a guiding type topic. The application can effectively identify guiding type topics, and is crucial for timely controlling further evolution of guiding type topics such as rumors and correctly guiding network public opinion.