1170 results about "Via device" patented technology

Some examples of the disclosure are directed to systems and methods for scrolling a user interface element. A user interface element including a plurality of selectable options scrollable along a first axis can be presented in an environment by a device. In some examples, the device detects an input corresponding to a request to scroll the user interface element. In accordance with a determination that the input satisfies one or more first criteria, including a criterion that is satisfied when the input includes movement of the device that corresponds to movement of a head of a user that exceeds a threshold amount of movement over a period of time, the device scrolls the user interface element in a first mode of operation. In accordance with a determination that the input satisfies one or more second criteria, the device scrolls the user interface element in a second mode of operation.

Provided are a method, a system, and a storage medium for intelligent diagnosis of a device failure based on an IIoT. The method includes: obtaining device-related data of an abnormal device from a total sensor database; predicting failure warning information of the abnormal device and a confidence level corresponding to the failure warning information based on the device-related data; determining a failure processing parameter based on the failure warning information and the confidence level, and generating a failure processing instruction; and sending the failure processing instruction via the device sensor network platform to a maintenance personnel terminal of the device perception and control platform or to a failure shooting device, and controlling the failure shooting device to perform failure shooting.

Needle-guiding systems and methods can facilitate establishing vascular access with a nonmagnetic needle. For example, a method of a needle-guiding system can include detecting and registering registration marks about a patient by patient-facing cameras of an alternative reality (“AR”) device, the AR device thereby establishing its location and orientation relative to a target area of the patient. The method can also include anchoring ultrasound images of the target area as viewed through a display screen of the AR device, the ultrasound images anchored about the patient relative to either an instant or previous location and orientation of the ultrasound probe; and providing an instant virtual needle trajectory of the needle as viewed through the display screen of the AR device, thereby indicating to a clinician whether the needle is properly oriented for establishing vascular access to a target vessel of the ultrasound images.

Systems and methods are described for dynamic execution of artificial intelligence (“AI”) agents. A server can receive, from a client device, an input associated with an AI agent. Based on a manifest file or user profile, the server can identify a management policy that applies to the AI agent. The server then dynamically configures access to the agent objects based on applying the management policy. The management policy is applied to a device status of the client device, a user profile of a user of the client device, and / or a network configuration of the client device. The server then executes a modified workflow based on the dynamically configured access, wherein the modified workflow bypasses or changes operation of at least one of the agent objects. Based on the modified workflow, the server transmits an output to the client device.

The invention provides a semiconductor equipment data acquisition method and a semiconductor equipment data acquisition system supporting standardized communication transformation, which are used for realizing data acquisition and process control on different types of semiconductor equipment, formulating corresponding communication rules for equipment conforming to a communication standard, and converting an equipment communication protocol by a cluster system so as to match a standard interface. For equipment which does not completely meet the standard but can realize communication through programming, the trunking system directly acquires required data and converts the required data to meet the standard interface specification. For equipment without communication capability, data is acquired through an equipment interface by adopting an image recognition technology, data input and function operation are realized through simulating keyboard and mouse operation, and finally, protocol conversion from data to a standard interface is completed by a cluster equipment control system. And the equipment data is converted into a message data packet conforming to a standard semiconductor equipment communication interface, so that docking with a semiconductor equipment data analysis and process control system is realized, and the production efficiency and quality of the semiconductor equipment are improved.

The invention relates to a control method based on a cloud PC server and a cloud PC management platform. The method comprises the following steps: starting the cloud PC management platform, and establishing and keeping link between a computing blade and the cloud PC management platform through a host blade management module; a user logs in a preset host through an access layer of the cloud PC management platform, and then remotely accesses the preset host by establishing an audio and video transmission channel and responding to a keyboard and mouse event; every preset time interval, the cloud PC management platform detects the working state of the cloud server through the equipment management module and updates the database; and if the user has an upgrading demand, accessing the computing blade through the host blade management module to execute upgrading. By configuring a special audio and video channel, the audio and video experience of a user is improved, and meanwhile, the stability of the cloud server is improved by detecting the working state of the cloud server in combination with the equipment management module and upgrading the computing blade by the host blade management module.

The invention discloses an industrial internet equipment fault prediction method and system based on digital twinning, and belongs to the field of industrial internet. Operation data and data volume processing data of scene equipment related to industrial internet-of-things abnormal behaviors are acquired for equipment fault primary prediction; and finally, performing final prediction on the fault equipment based on the operation data of the primary prediction fault equipment and the equipment fault primary prediction, and finally displaying fault equipment information obtained by final prediction on the digital twinborn model, and reminding maintenance personnel to maintain the equipment. According to the method, the abnormal behavior before the fault occurs is accurately judged based on the digital twinborn model, and the equipment causing the abnormal behavior is searched through the parameter correlation degree and the time-space correlation between the equipment and the behavior in the abnormal behavior analysis process, so that the searching efficiency of the fault equipment is improved, and the fault diagnosis accuracy is improved. And the security of the industrial internet scene is further improved.

The invention, which relates to the technical field of coal mine transportation scheduling, discloses a coal mine intelligent transportation scheduling optimization system comprising a map construction module, a data acquisition module, a communication module, a monitoring module, an intelligent scheduling module, an emergency processing module and a man-machine interaction module. The system has the advantages that the operation states of coal mine collection equipment and transportation equipment are monitored in real time through the equipment monitoring unit and the environment monitoring unit, the environment state of a mining area is comprehensively monitored, equipment abnormity and environment risks can be found and early warned in time, safety accidents are prevented, and the system is safe and reliable. The service life prediction unit can predict the residual service life of the equipment by monitoring and analyzing the operation time and the accumulated workload of the equipment, and gives out early warning when the equipment is close to the end point of the service life, so that potential safety hazards caused by equipment aging are avoided, and the safety of coal mine transportation is improved.

A system and method for inferring device types. A method includes selecting a device type inference model from among a plurality of device type inference models based on a manufacturer of a device, wherein each device type inference model corresponds to a respective manufacturer and is trained using training data of devices manufactured by the respective manufacturer, wherein each device type inference model is trained to output a device type prediction; and determining an inferred device type for the device, wherein determining the inferred device type for the device further comprises applying the selected device type inference model to a plurality of features, wherein the plurality of features is extracted from device activity data indicating ports used by the device and at least one volume of traffic communicated via each port used by the device.

Systems and methods are described for dynamic execution of artificial intelligence (“AI”) agents. A server can receive, from a client device, an input associated with an AI agent. Based on a manifest file or user profile, the server can identify a management policy that applies to the AI agent. The server then dynamically configures access to the agent objects based on applying the management policy. The management policy is applied to a device status of the client device, a user profile of a user of the client device, and / or a network configuration of the client device. The server then executes a modified workflow based on the dynamically configured access, wherein the modified workflow bypasses or changes operation of at least one of the agent objects. Based on the modified workflow, the server transmits an output to the client device.

A method including transmitting, by a device to a storage server, a request to receive encrypted content and one or more encrypted keys stored in association with the encrypted content; receiving, by the device from the storage server, the encrypted content and the one or more encrypted keys; receiving, by the device, a master string of alphanumeric characters; determining, by the device, a master key based at least in part on the master string; decrypting, by the device based at least in part on utilizing the master key, an encrypted assigned private key associated with the device to determine an assigned private key, the encrypted assigned private key being included in the one or more encrypted keys; and decrypting, by the device, the encrypted content based at least in part on utilizing the assigned private key is disclosed. Various other aspects are contemplated.

Needle-guiding systems and methods can facilitate establishing vascular access with a nonmagnetic needle. For example, a method of a needle-guiding system can include detecting and registering registration marks about a patient by patient-facing cameras of an alternative reality ("AR") device, the AR device thereby establishing its location and orientation relative to a target area of the patient. The method can also include anchoring ultrasound images of the target area as viewed through a display screen of the AR device, the ultrasound images anchored about the patient relative to either an instant or previous location and orientation of the ultrasound probe; and providing an instant virtual needle trajectory of the needle as viewed through the display screen of the AR device, thereby indicating to a clinician whether the needle is properly oriented for establishing vascular access to a target vessel of the ultrasound images.

A method includes rendering on displays of a plurality of extended reality (XR) devices an XR environment. The XR environment includes a first user avatar authorized to interact with an XR application of the plurality of XR applications and a second user avatar unauthorized to interact with the XR application. The method includes receiving, by sensors of an XR device, sensor data indicative of a user intent to interact with the XR application by the second user avatar, generating, based on the sensor data, an estimation of an unauthorized interaction with the XR application by the second user avatar, executing, based on the estimation of the unauthorized interaction, a data obfuscation of information associated with an authorized interaction with the XR application by the first user avatar, and re-rendering on the displays of the plurality of XR devices the XR application in accordance with the executed data obfuscation.

A private identity system performs on-device embedding and homomorphic tokenization (HT) to map plaintext inputs to non-invertible HT tokens. During enrollment, HT tokens are stored and a centroid can be computed for each user. During prediction, a new HT token shortlists nearest centroids, followed by 1:1 distance verification against stored embeddings. On success, the system returns a UUID bound to the user. Because passkeys can be shared via device or account keychains, the UUID binds the ceremony to the same enrolled individual without exposing biometrics.

The invention relates to the technical field of machine learning, and discloses a user recharging prediction method and device, equipment and a storage medium, and the method comprises the steps: associating behavior data of multiple platforms of a user through an equipment fingerprint algorithm, obtaining a user multi-dimensional feature set, determining a clustering number based on an elbow rule, and obtaining a user recharging prediction result; and performing clustering analysis on the user multi-dimensional feature set according to the clustering number, generating a user value grouping label, and inputting the user value grouping label into a random forest model to obtain recharging prediction results of different user groups. According to the method, multi-platform user behavior information is comprehensively integrated through an equipment fingerprint algorithm, data islands are broken, user value grouping labels are generated through elbow rule clustering, then the user value grouping labels are input into a random forest model to predict a recharging result, user basic features are considered, value grouping information is integrated, feature dimensions are enriched, and the recharging efficiency is improved. And users with different values can be described more accurately, so that the accuracy of recharging prediction of user groups with different values is improved.

The invention relates to the technical field of digital control, in particular to a digital control system and method. The method comprises the following steps: acquiring an industrial equipment object and constructing a simulation model based on the industrial equipment object so as to simulate the running state of the equipment and acquire corresponding simulation data; analyzing the transient current surge phenomenon of the equipment according to the simulation data, and further analyzing the multi-dimensional interactive aging trend of the equipment, so as to estimate the attenuation condition of the dynamic precision of the equipment; estimating the equipment fault risk through the equipment dynamic precision attenuation data, and estimating the quality attenuation state of the industrial product according to the fault risk; the system transmits fault risk and product quality attenuation data to a digital control system, carries out equipment abnormity early warning through the system, and adjusts equipment parameters according to an early warning result. According to the invention, the industrial equipment is digitally controlled, so that the operation efficiency of the industrial equipment is higher.

The invention, which relates to the processing and manufacturing field, discloses an intelligent processing and manufacturing management system comprising a data acquisition module, a data transmission and processing module, a production planning and scheduling module, an equipment management module, a quality management module, an inventory management module and a personnel management module. And the equipment management module is used for carrying out full-life-cycle management on the processed and manufactured equipment, monitoring the running state of the equipment in real time, and predicting the fault occurrence probability of the equipment through analysis of the running data of the equipment. According to the management system for intelligent processing and manufacturing, refined and intelligent management of the whole life cycle of equipment from access to scrapping can be realized, the reliability, the available duration and the operation efficiency of the equipment can be effectively improved, common fault models can be identified through the constructed diagnosis model, and the reliability of the equipment can be improved. And faults caused by multiple factors can be accurately positioned and analyzed.

The invention relates to the technical field of equipment configuration management, in particular to a visual equipment element configuration system which comprises an operation trend analysis module, a configuration parameter screening module, a parameter conflict detection module, a parameter dynamic adjustment module and a configuration result analysis module. According to the method, through dynamic analysis of the extraction state values of the operation parameters of the equipment elements, accurate identification and induction of the operation change trend of the equipment are realized, clear capture of dynamic change information is ensured, and through screening of the operation parameters in the target range and elimination of configuration combinations which do not accord with the range, the pertinence and accuracy of screening are improved, and the configuration coordination is enhanced; parameter conflict value grading and influence range analysis are combined, the conflict degree and influence thereof are clearly defined, a conflict value range and a logic relation are optimized, configuration flexibility and matching performance are improved, and it is ensured that an optimization result effectively adapts to actual requirements through logic consistency analysis, support degree evaluation and matching performance verification; and the configuration efficiency, the accuracy and the intelligent level are obviously improved.

The invention provides an equipment monitoring method and system based on AI and industrial Internet of Things, equipment and a medium, and belongs to the technical field of equipment operation monitoring, and the method comprises the steps: obtaining equipment monitoring operation; triggering an equipment monitoring process, and obtaining a monitoring key data set according to the equipment monitoring process; querying a preset key data analysis rule table according to the monitoring key data to obtain equipment data analysis parameters of the monitoring key data; querying a preset monitoring task sequence to obtain a monitoring process label; calling an equipment running state analysis process according to the monitoring process label; analyzing the monitoring equipment through the equipment running state analysis process to obtain monitoring target data; and analyzing the monitoring key data according to the monitoring target data to obtain a target equipment monitoring result. According to the invention, accurate monitoring of the operation state of the industrial Internet of Things equipment is realized. Abnormal conditions and key information in equipment operation can be captured in time, and the efficiency and reliability of monitoring work are improved.

A system and method for vulnerability detection. A method includes: tokenizing device attribute data for a device into at least one set of first tokens, wherein each of the first tokens is formatted according to a token schema; creating at least one device attribute string, each device attribute string including one of the first tokens; matching each of the at least one device attribute string to combinations of device attributes stored in a vulnerabilities database in order to identify at least one matching combination of device attributes for the device, wherein the vulnerabilities database stores mappings between combinations of device attributes and vulnerabilities, wherein each combination of device attributes in the vulnerabilities database includes second tokens formatted according to the token schema; detecting at least one vulnerability of the device based on the at least one matching combination of device attributes and the mappings in the vulnerabilities database.

The invention provides an equipment predictive maintenance method based on deep reinforcement learning, and relates to the technical field of equipment monitoring, and the method comprises the steps: obtaining a replacement reason and a timestamp of each part, forming a work data set according to historical work data, forming a task set in which each part participates, building a fault probability judgment model according to the task set, and carrying out the maintenance of the fault probability judgment model. Setting a reward formula, obtaining a replacement guidance model, obtaining a replacement score of each part according to the installation time of the currently equipped part, obtaining a breakage coefficient according to the next task, obtaining a age limit coefficient through the service life of the equipment, forming a comprehensive replacement coefficient of each part, and constructing a correction threshold value; according to the method, the execution capacity of the crane for future tasks is judged according to the synergistic effect of different breakage conditions among the parts, and accurate predictive maintenance of the parts of the crane is achieved.

Systems and methods receive, by an electrical device coupled to an elevator car of an elevator and via device component(s) electrically coupled to the electrical device, analog signal(s), wherein the electrical device comprises processor(s), and wherein the analog signal(s) comprise a measurement derived from one or more elevator activities. The analog signal(s) are converted, via the electrical device, into digital data and preprocessing is performed thereon via the processor(s) of the electrical device. The preprocessed data is wirelessly transmitted, via a peripheral device of the electrical device, to remote server(s) of a cloud computing environment, the preprocessed data being transmitted, at least in part, for data aggregation and to facilitate access by user(s) for elevator asset management, wherein the remote server(s) are configured to perform additional processing of the preprocessed data as part of the data aggregation.

The invention provides an intelligent equipment operation and maintenance method and system based on an industry large model technology, equipment and a medium, and belongs to the technical field of intelligent equipment operation and maintenance. Inputting the equipment operation data into a multi-agent cooperation engine, and analyzing time domain, frequency domain and working condition parameters; the diagnosis agent generates diagnosis result information in combination with the diagnosis intelligent knowledge graph; the diagnosis result information is converted into a work order text, and the work order text is synchronized to a monitoring system; after fault processing is completed, the processing process and result are fed back to the monitoring system and supplemented to the diagnosis intelligent knowledge graph based on a supplement mechanism, and knowledge iteration updating is achieved. A multi-dimensional data fusion and analysis method is adopted. Information of different types of data is utilized, and the accuracy and reliability of fault diagnosis and operation and maintenance decision are improved through a multi-dimensional analysis perspective.

The invention provides an environment-based perception enhancement method and system, electronic equipment and a storage medium, and relates to the field of AR (Augmented Reality). The method comprises the steps that N environment images are acquired through a camera of AR equipment, an environment image set is obtained, scene classification is performed based on the environment images in the environment image set, and a preliminary scene type is obtained; performing optimization processing on the preliminary scene type to obtain a target scene type, and matching a corresponding application scene template from a preset scene template library according to the scene type; determining a to-be-collected environment parameter type according to the application scene template, and collecting environment data through a sensor corresponding to the to-be-collected environment parameter type to obtain target environment data; generating AR equipment adjustment parameters according to the target environment data; and adjusting a display parameter of the AR equipment according to the AR equipment adjustment parameter. According to the technical scheme, the accuracy of environment-based perception enhancement is improved. Through the scheme, the problem that targeted display optimization is difficult to carry out according to specific scene characteristics in the prior art is solved.

The invention relates to the technical field of medical apparatuses and instruments, in particular to a medical apparatus and instrument state management system based on the Internet of Things technology, which can remotely monitor and manage medical equipment and medical consumables in the whole period. Through the combination of the equipment sensor, the Internet of Things platform and the mobile terminal application, medical equipment management personnel can know information such as the operation state, the energy consumption condition, the maintenance period and the operation time of the medical equipment in real time, meanwhile, preventive maintenance can be carried out, related faults can be handled in time, and the quality and efficiency of medical services are improved. By means of intelligent remote monitoring and management, information islands are eliminated, efficiency is improved, data unification and sharing among all systems and departments are achieved, and the purpose of conducting real-time remote monitoring and management on medical instruments in the whole period is achieved.

The invention discloses an AR-based intelligent switch cabinet fault diagnosis method and device and electronic equipment, and relates to the field of AR. The method comprises the following steps: acquiring a switch cabinet image collected by AR equipment; based on the switch cabinet image, identifying the internal structure of the switch cabinet by using a preset three-dimensional model identification library to obtain spatial position information of each component of the switch cabinet; constructing a virtual fault diagnosis environment of the switch cabinet according to the spatial position information of each component; acquiring a temperature distribution matrix of each component, and generating a thermodynamic cloud chart of each component according to the temperature distribution matrix; determining a problem part according to the thermodynamic cloud picture, and superposing the problem part to the virtual fault diagnosis environment to obtain a target fault diagnosis environment; and displaying the target fault diagnosis environment to the user through the AR equipment. According to the technical scheme provided by the invention, the internal environment and the problem parts of the switch cabinet are displayed to the user through the AR equipment, and the fault diagnosis efficiency is improved.

The invention provides an interactive film watching control method and electronic equipment, and belongs to the technical field of artificial intelligence. According to the method, a user enters a movie and television virtual environment through AR equipment or VR equipment, and a visual simulation effect and an auditory simulation effect are adjusted to generate the best immersion feeling; and then collecting the user behavior and / or the user selection, and adjusting the film and television content according to the user behavior and / or the user selection, so that the film and television content can be dynamically adjusted according to the user behavior and / or the user selection, an interactive film watching experience is provided, and the immersion and participation sense of the user can be improved. And the movie and television content is analyzed through the AI, so that the user can be helped to better understand story backgrounds, role development, plot turning and the like in the movie and television content, and the film watching experience of the user can be enhanced. And according to the real-time feedback data of the user, the environment parameters of the film and television virtual environment and / or the behaviors of the non-player roles are dynamically adjusted, so that more personalized and adaptive film watching experience can be provided.

The invention discloses an NDIR sensor self-calibration method, which comprises the following steps of: firstly, starting and initializing equipment, starting the equipment, initializing each component, checking and storing data, then carrying out automatic zero calibration operation, switching a measurement light path to a zero gas chamber when a preset triggering condition is met, acquiring light intensity of double light paths, calculating a zero drift value, storing, and recovering a normal light path. Then data acquisition and transmission are carried out, original measurement and environment data of a sensor are acquired, packaged according to a protocol after being preprocessed and sent to a remote calibration server through a communication module, then remote model calibration is carried out, the server receives the data and inputs a calibration model to calculate a calibration result and generate a report, and finally model updating and optimization are carried out. And the server periodically searches new data, retrains the model when conditions are met, optimizes and pushes update, and the sensor system downloads and tests a new model. According to the invention, automatic zero calibration and application model calibration are realized through equipment structure improvement, and the accuracy of monitoring data and the monitoring precision are improved.

Systems and methods are provided herein for only including portions of a user's environment that have been approved by a user in a video conference while excluding portions that have not been approved. This may be accomplished by a device receiving a policy identifying one or more approved objects of a scene of a video stream. The device may then generate a filtered video stream by only including portions of the scene that comprise the one or more objects that were approved by the policy in the filtered video stream. The filtered video stream may be combined with other video streams to generate a video conference that is transmitted and / or stored by one or more devices participating in the video conference.