814results about "Data acquisition and logging" patented technology

The partition monitoring method for concrete dam operation key parts provided by the disclosure firstly utilizes the extracted monitoring data time-frequency vector to partition the concrete dam key parts, and on this basis, obtains time series measurement data of different types of monitoring instruments with high temporal and spatial correlation, so as to establish a graph structure. Then, the dependence of time dimension and variable dimension of multivariate time series data is captured, and the relationship between further learning and representation of graph attention network is provided. Furthermore, the final feature representation of time series measured data is obtained, and finally the anomaly score is calculated through the final feature representation to detect anomalies. The complementary mutual verification of multiple measuring points of monitoring instruments with various types is realized. The structural integrity and spatial distribution law of concrete dams are fully embodied.

Techniques are discussed herein for generating and evaluating autonomous vehicle control systems. In examples, a driving scenario is determined and log data, e.g., generated from user-supported vehicles travelling in an environment, may be used to determine instances in which vehicles experience the driving scenario. Based on the log data, an exposure metric, a severity metric, and / or a controllability metric may be determined. A safety goal can be determined from the metric(s) and vehicle control parameters are determined to comport with the safety goal. The vehicle control parameters may be used to control an autonomous vehicle in the environment.

The invention discloses a fire fighting system intelligent management method and device based on intelligent property management, and relates to the technical field of data management. The intelligent management method and device for the fire fighting system based on the intelligent property management comprises the following steps: S1, dynamically triggering and collecting a multi-source fire fighting data set through multiple protocols, and temporarily storing the multi-source fire fighting data set at the edge in a normalized manner; s2, constructing an event full-period window and a ternary association index of the event, the time sequence data and the original message; s3, performing double-track storage of the semantic analysis data and the original message, and constructing multi-dimensional index association; s4, reconstructing a visual playback alarm process through edge cloud increment synchronization and a multi-dimensional time sequence chain; and S5, performing transient analysis, protocol upgrading and responsibility evidence obtaining based on the cloud data collaborative link and the original message data set. According to the method, the complete acquisition of the fire-fighting data and the reproducibility of the event process are effectively improved, and the problem that in the prior art, only polling recording of single-point alarm is carried out, and an original message is discarded, so that the transient process and message-level evidence is lost is solved.

A first processing node of a cluster of processing nodes issues a first alert when first event data satisfies a trigger condition, and sends, to an alert data store external to the cluster, a first alert record of the first alert and suppression information based at least in part on the first alert. A second processing node of the cluster determines that second event data satisfies the trigger condition, obtains, from the alert data store, the suppression information indicating that an expiration time for suppressing the first alert is unexpired, and sends, to the alert data store, a second alert record of a second alert without issuing the second alert.

Disclosed in the present invention is a lightweight active online modeling method fusing mechanism and dynamic memory, which comprises the following steps: S1, data preprocessing and model initialization; S2, online data representativeness evaluation based on dynamic memory of process data; S3, online data informativeness evaluation based on dynamic memory of label data; S4, online monitoring of a mechanism state; S5, parallel hybrid active sampling policy; and S6, online modeling of a soft sensor model. The present invention separately designs memory policies for process data and label data and only reserves high-value data, thereby satisfying a lightweight hardware requirement; in addition, the present invention designs a time delay-based mechanism state online monitoring method to detect a real concept drift of the process, and further provides the parallel hybrid active sampling policy for detecting both a real concept drift and a false concept drift; in combination with dynamic memory and a kernel incremental regression method, the computation amount for model updating and the hardware overhead for edge deployment are reduced.

Methods and apparatuses for athletic training optimization are disclosed. In one example, a fitness level change is identified. In one example, a current training intensity is updated to reflect an updated user fitness level.

The present disclosure provides an in-vehicle monitoring and tracking system for rental vehicles that detects smoking events within a vehicle cabin and logs these events with associated location data. The system employs specialized detection algorithms to accurately identify various types of smoke while minimizing false positives. Additional functionality includes accident detection through rapid deceleration monitoring, and animal presence detection through acoustic sensing. When events are detected, the system can either store the information for later retrieval or transmit real-time notifications with precise location data to rental company servers, enabling prompt response to policy violations and facilitating vehicle recovery after accidents.

A method can include receiving input for a field scenario that comprises fluid dynamics and chemical phenomena, where the input defines substances amenable to speciation; executing a fluid dynamics simulator to perform timesteps for transport of the substances while calling a chemical phenomena framework that determines speciation of the substances; and generating results for the field scenario based on the executing.

The invention provides a multi-source heterogeneous data collection method and device, a storage medium and equipment. The method comprises the steps that a central scheduling module generates a data collection instruction according to a preset triggering condition; the central scheduling module awakens at least one RPA execution unit in the central scheduling module according to the data acquisition instruction, so that the awakened RPA execution unit calls the data acquisition module according to the data acquisition instruction, and sends the data acquisition instruction to the called data acquisition module; the data acquisition module generates at least one data calling signal according to the data acquisition instruction, and sends the data calling signal to at least one RPA acquisition unit in the data acquisition module, so that the RPA acquisition unit calls specific data from the processing platform subsystem according to the data calling signal; the data acquisition module sends the called data to the central scheduling module; and the central scheduling module integrates the called data, and sends the integrated data to the data storage module for storage according to a preset rule.

The invention relates to the technical field of information acquisition, and particularly discloses a cluster type textile machine information acquisition method and system based on a robot, and the method comprises the steps: receiving a time-containing signal uploaded by each module, and generating a signal matrix containing an identification label and a time label; identifying the signal matrix containing the identification label and the time label, and determining the anomaly degree of each textile machine; determining an information acquisition path of the robot based on the anomaly degree of each textile machine, and sending the information acquisition path to the robot; when the robot returns, reading collected data of the robot; the robot is additionally arranged between the data total end and the textile machine, the robot moves to the position close to the textile machine to carry out high-frequency data collection, the data total end carries out low-frequency collection on data of all the textile machines, and then the data collection process of the robot is adjusted, in this architecture, the collection frequency of the data total end can be very low, and the collection efficiency is high. And the resource consumption is greatly reduced.

Systems and methods are described, and an example system includes an AI enhanced multi-source health data integration logic that receives a first source electronic health record (EHR) data from a first EHR system and a second source EHR data from a second EHR system, and transforms, according to a knowledge representation schema, health-related information content of the first source EHR data and the second source EHR data to a first source transformed health data and second source transformed health data. The system includes a collaboration platform, configured to host a multi-source transformed health data database, including the transformed first source health data and the transformed second source health data, and hosts AI-enhanced, multiple level telecollaborative analyses by a plurality of participants of the multi-source transformed health data database, generating health management data.

Techniques for autoscaling a deduplication process include receiving a first plurality of event records in an event stream during a first period of time and determining real-time characteristics of the event stream based on the event records received during the first period of time. Based on the real-time characteristics of the event stream, a level of similarity for a deduplication process is selected to apply for future event records received in the event stream. The deduplication process may also be enabled or disabled based on the real-time characteristics. A second plurality of event records is received in the event stream during a second period of time. If deduplication is enabled, the second plurality of event records are deduplicated based on the level of similarity.

The invention relates to the technical field of data processing, in particular to a remote management system for experimental detection data of a traffic road section, which comprises a state acquisition and occupancy calculation module, a time sequence storage structure, a state acquisition and occupancy calculation module and a state acquisition and occupancy calculation module, the fluctuation characteristic gradient analysis module calculates the difference between the current occupancy rate and the historical occupancy rate to generate a fluctuation degree value, the sampling period dynamic decision module compares the fluctuation value with a threshold value to generate a target sampling interval, and the hardware servo and data packaging module adjusts the scanning frequency and generates a detection data packet. According to the method, a time sequence storage structure is constructed, the traffic flow fluctuation degree is quantified, the sampling interval is intelligently decided according to the fluctuation numerical value, the scanning frequency is adjusted in real time, the frequency is reduced to reduce redundancy when the traffic flow is stable, sampling is encrypted to capture features when fluctuation is violent, the data integrity is guaranteed, and the storage and transmission pressure is reduced. And efficient utilization of monitoring resources is realized.

System, devices, and methods facilitate determining positioning using distributed antenna system with service availability monitoring. Positioning methods include network based methods and handset assisted methods in addition to a monitoring system to report any service outage and possible location information loss. The system provides location information also at handset only level through application or apps and operating systems with online and off-line access to location database data. A combined monitoring system that monitors antenna output power for mobile coverage and service availability helps also in monitoring the availability of the localization system and dynamic update of lookup information. Monitoring system provides also asset tracking and service analytics features for the active or passive distributed antenna system.

An automated driving system one or more processors configured to recognize a situation around the vehicle from detection information detected by a recognition sensor and perform automated driving control based on a recognition result. The automated driving system execute a storing process of storing log data of the detection information in one or more storage devices while performing the automated driving control. In the storing process, the automated driving system specify a low reliability area around the vehicle where a reliability of the recognition result is low. When the low reliability area is specified, the automated driving system preferentially store the log data of target detection information that is the detection information related to the low reliability area.

According to one embodiment, a method, computer system, and computer program product for log compression is provided. The embodiment comprises receiving a log stream from an instance of an application executed within a container of a remote computational system via a network connection. The log stream comprises metadata. The metadata comprises an application identifier. The application identifier uniquely identifies the executable code of the application. The embodiment further comprises searching a compression dictionary library for a current compression dictionary using the application identifier. The application identifier uniquely identifies the current compression dictionary for the application. The embodiment further comprises sending the current compression dictionary to the remote computational system via the network connection.

Systems, methods, and non-transitory computer readable mediums are provided for testing and measuring the behavior or logic of one or more cloud services associated with a simulated autonomous vehicle (AV). For example, a system may be configured to receive log data of a simulated AV. In some examples, the log data may characterize one or more events that occurred during a simulation of the simulated AV. Additionally, the system may instantiate an AV bot based on the log data. Moreover, the system may generate, by the instantiated AV bot, a first set of messages. In some examples, the first set of messages may be based on the log data. Further, the system may provide the first set of messages to a cloud service.

A method for performing direct logging of sensor data in a vehicle network logging system (VNLS) installed on an autonomous vehicle (AV), the VNLS comprising a network switch connected between an onboard sensor of the AV and an onboard computer of the AV, the network switch comprising a logger for logging sensor data to a disk, is described and includes providing a converter node on the network switch; receiving, using the converter node, data generated by the onboard sensor; and converting, using the converter node, the received data to a data record suitable for logging by the logger to the disk.

A system for locating and tracking an object is provided. The system includes a measuring device configured to determine a property of a paving-related material, a locating device configured to determine a location of the measuring device, a tracking system configured to store tracking information associated with the measuring device and one or more properties determined by the measuring device, and a communications system configured to transfer, to a remote device, the location of the measuring device and the tracking information associated with the measuring device.

A method for biosignal data transformation can include: determining a set of biosignal data, determining a brain state representation (e.g., an embedding) based on the biosignal data, and determining a biomarker value based on the brain state representation. The method can optionally include training a model (e.g., training a model used to determine the brain state representation), and / or any other suitable steps. A system for biosignal data transformation can include a biosignal device and a computing system.

A maintenance system for a medical device includes a user device configured to communicate with the medical device and execute maintenance tasks on the medical device and communicate with a user via a user interface of the user device or the medical device. The maintenance tasks may be executed by either device or both devices, and communication with a user may occur via a user interface of either device or both devices. The system may also include a server configured to communicate with one or both of the user device and the medical device. The maintenance tasks may include an electrical safety check, a physical check, a performance check, and a functional check. Examples of functional checks include validation of a transient current detector and a thermal cutout.

In some implementations, a redundancy system may receive, from a log storage, a plurality of log files that were generated by a plurality of monitoring programs. The redundancy system may apply a machine learning model to the plurality of log files to detect one or more redundancies. The redundancy system may determine at least one monitoring program, in the plurality of monitoring programs, that is a candidate for removal based on the one or more redundancies. The redundancy system may transmit, to a ticket system, a command to open a ticket associated with the at least one monitoring program.

A computer implemented method of log compression comprises receiving a log stream from an instance of an application executed within a container of a remote computational system via a network connection 300. The log stream comprises metadata comprising an application identifier which uniquely identifies the executable code of the application. The method further comprises searching a compression dictionary library for a current compression dictionary using the application identifier wherein the application identifier uniquely identifies the current compression dictionary for the application 302. The method further comprises sending the current compression dictionary to the remote computational system via the network connection 304.

The invention relates to the technical field of data processing, and discloses a salt cavern gas storage brine discharging pipeline operation data processing method and system, and the method comprises the steps: periodically receiving a link detection data packet which is transmitted by a collection device and is provided with a transmission timestamp, and recording the arrival time of the link detection data packet; according to the sending timestamp and the arrival time, transmission characteristics of the link detection data packet are analyzed, and the transmission characteristics comprise transmission delay, jitter dispersion and out-of-order conditions; adaptively predicting a time offset pattern based on the transmission characteristics; generating a real-time calibration parameter according to the time migration mode; and receiving an actual data packet, and adjusting the timestamp of the actual data packet by using the real-time calibration parameter. According to the method, the limitation of traditional fixed compensation parameters is overcome, the time alignment precision of pressure and flow data flow is remarkably improved, leakage misinformation caused by time deviation is avoided, and the reliability and the operation and maintenance efficiency of a salt cavern gas storage brine discharging pipeline operation monitoring system are improved.

The invention system and method for making risk reduction protocol in oil and gas industry tanks based on risk management in the form of artificial intelligence use various detectors and cameras connected to the intelligent risk management system to determine the results by simulation and create related protocols for each special situation to reduce the risk effects in order to prevent personal and financial losses of the users in oil and gas industry tanks by considering all available signals as well as control structures and environmental conditions. Also the server is equipped with software related to machine learning and artificial intelligence in each case, can store the created information as new protocols in the database and send it to the central processor under similar conditions for proper use.

A battery information management system, comprises: a battery; and a server that establishes communication with the battery via a network. The battery includes: a storage unit configured to store maintenance information for maintenance of the battery, user information related to personal data of a user of the battery, and usage history information of the battery; an information communication unit configured to transmit the user information and the usage history information from the storage unit to the server; and a control unit configured to control the storage unit and the information communication unit.

According to embodiments of the disclosure, an example method herein may comprise: receiving, by a device, a model defining a configuration of synthetic monitoring operations to be performed by the synthetic agent; converting, by the device, the model into code to be utilized by the synthetic agent to perform the synthetic monitoring operations; transforming, by the device, results from a performance of the synthetic monitoring operations into transformed results in a common data format; and providing, by the device, the transformed results to a datastore.

Provided is a data logger. A data logger includes a sensor configured to sense a change in a gravitational acceleration of the data logger and a change in a rotation angle of the data logger, and generate a sensing signal, a communication module configured to receive configuration information including a sensing period and threshold values of the data logger from a portable reader, and a microprocessor unit configured to receive the sensing signal from the sensor, receive the sensing period and the threshold values from the communication module, generate sensing values corresponding to the sensing signal, compare the sensing values with the threshold values, determine whether an abnormal state occurs in the data logger according to a result of the comparison, generate and transmit to a server normal log information at each of the sensing period when the abnormal state does not occur in the data logger, and generate and transmit to the server abnormal log information whenever the abnormal state occurs regardless of the sensing period.

The embodiment of the present application discloses a motion control method, apparatus, related equipment and computer-readable storage medium. The method applied to a terminal includes: receiving an operation instruction initiated by a wearer of the terminal; in response to the operation instruction, decoding the video stream encoding information received from the first cloud node to obtain a motion posture key frame of a virtual object; the motion posture key frame of the virtual object is generated by the first cloud node based on motion model rendering; receiving a road surface recognition model sent by the first cloud node; recognizing the real-time road surface image taken based on the road surface recognition model to obtain corresponding road surface information; superimposing and displaying the road surface information and the motion posture key frame of the virtual object; and controlling the wearer of the terminal to follow the virtual object to move.

Various aspects involve explainable machine learning based on time-series transformation. For instance, a computing system accesses time-series data of a predictor variable associated with a target entity. The computing system generates a first set of transformed time-series data instances by applying a first family of transformations on the time-series data. Any non-negative linear combination of the first family of transformations forms an interpretable transformation of the time-series data. The computing system determines a risk indicator for the target entity indicating a level of risk associated with the target entity by inputting the first set of transformed time-series data instances into a machine learning model. The computing system transmits, to a remote computing device, a responsive message including the risk indicator. The risk indicator is usable for controlling access to one or more interactive computing environments by the target entity.