Abstract: A method for traceability calibration of calibration device of rock chiseling specific power tester includes static calibration and dynamic calibration. Static calibration includes: placing impact indicator sensor of calibration device on static calibration stage; installing standard weight holder on adapter head of impact indicator sensor; adding a standard weight to standard weight holder several times; and calculating static coefficient k. Dynamic calibration includes: placing impact indicator sensor on dynamic calibration stage; resetting dynamic calibration coefficients a and b of calibration device; recording standard impact energy W0 of dynamic standard hammer and measured indication value W of impact indicator sensor to obtain standard deviation S=W?W0; and calculating dynamic coefficients a and b. Rock chiseling specific power magnitude is effectively traced to equal mass standard of standard weights. A new traceability method and system for specific power magnitude is constructed.

Abstract: A method for intelligently distributing messages across multiple devices and associated chat applications is disclosed. In one embodiment, such a method includes registering multiple devices and associated chat applications to receive intelligent distribution of messages. The method monitors, using an agent, a message copied from a first device of the multiple devices to a clipboard. The method automatically analyzes content of the message to determine which other devices and associated chat applications of the plurality are eligible to receive the message. The message is then distributed to other devices and associated chat applications that are deemed eligible to receive the message. In certain embodiments, the message originates from a first chat application of a first type and the message is distributed to a second chat application of a second type that is different from the first type. A corresponding system and computer program product are also disclosed.

Abstract: Computer-implemented methods for performing shutdown of a computing system using custom shutdown intervals are provided. Aspects include receiving, by the computing system, a command to shutdown the computing system and issuing a first command to shutdown a first subsystem of the computing system. Aspects also include determining, after a first interval since issuing the first command, that the first subsystem has not shutdown and issuing a second command to shutdown the first subsystem of the computing system, wherein the first interval is obtained from a trained machine learning model.

Abstract: A display panel is provided. The display panel includes a base substrate and a plurality of display units disposed on the base substrate. The display unit includes a signal line, a light-emitting device and a drive unit. The light-emitting device is disposed in a flexible display region of the base substrate, the drive unit is disposed in a pixel circuit region of the base substrate, and the signal line is connected with the light-emitting device and the drive unit.

Abstract: The present disclosure relates to systems and methods for cross-layer device fingerprinting. A method is provided for efficiently fingerprinting networked devices, comprising, monitoring network communications of a plurality of electronic devices, extracting cross-layer features for each of the plurality of electronic devices, compiling the cross-layer features of each of the plurality of electronic devices into a device fingerprinting database, monitoring a network communication of an electronic device, extracting the cross-layer features from the network communication of the electronic device, classifying the electronic device into a device category using the cross-layer features and the device fingerprinting database, and performing a downstream network operation on the electronic device based on the device category.

Abstract: A dataset is determined to include skewed data and based on determining that the dataset includes skewed data, one or more sparse indexes for the skewed data of the dataset are built. A sparse index of the one or more sparse indexes is for a range of data of the skewed data, and the building the sparse index includes indicating one pointer for a selected record of the range of data and indicating another pointer for another selected record of the range of data. The sparse index is provided to be used in a query of the dataset.

Abstract: This application discloses a device authentication method and system, and an apparatus, and relates to the field of communication technologies, to perform authentication on a terminal device. The system includes a first analyzer, a second analyzer, and a network device. The first analyzer sends a first authentication model to the second analyzer. The network device sends a transmission feature of a first terminal device to the second analyzer. The second analyzer performs authentication on the first terminal device based on the transmission feature of the first terminal and the first authentication model.

Abstract: The technology of this disclosure relates to a method, an apparatus, a device, a storage medium, and a system for determining a collection period. The technology relates to the field of machine learning technologies. In this disclosure, a machine learning model is obtained by using a training dataset collected based on candidate collection periods of X features, and a candidate collection period of each of the X features is determined as a target collection period of each feature based on the obtained machine learning model and a first condition. Because the target collection period of each feature is greater than a minimum collection period of each feature, subsequently, if data is collected based on the target collection period of each feature, an amount of the collected data is reduced, to reduce collection load of a device and memory occupied by sampled data, thereby reducing time redundancy.

Abstract: A method for implementing a generic routing encapsulation (GRE) tunnel, an access point (AP), and a network system. The method includes establishing a Control And Provisioning of Wireless Access Points (CAPWAP) tunnel with an access controller (AC), receiving addresses of at least two gateways, establishing at least two GRE tunnels coupled to the gateways respectively according to the addresses of the at least two gateways, and selecting one of the at least two GRE tunnels to transmit data.

Abstract: This application provides a method, an apparatus, and a system for determining data flow information. The method includes: obtaining flow parameters of a plurality of data flows within a first time period; obtaining at least one data flow group according to the flow parameters of the plurality of data flows and a flow parameter rule of at least one preset access mode, where a relationship between data flows in each data flow group satisfies a flow parameter rule of one preset access mode; and determining group parameters of each data flow group.

Abstract: A device identification method, apparatus, and system are provided. A management device or a collection device first determines a network traffic feature of a to-be-identified device based on a first dataset. The first dataset includes a plurality of pieces of first data, and each piece of first data includes a data amount of a data packet that is of the to-be-identified device and that is collected within one first periodicity. Then, the management device or the collection device determines a device type of the to-be-identified device based on a device identification model and the network traffic feature of the to-be-identified device.

Abstract: A method, an apparatus, a system, a device, and a storage medium for implementing terminal verification are provided. In this method, a transmission feature of a terminal is reconstructed to verify the terminal. For example, if a difference between the transmission feature obtained through reconstruction and the transmission feature of the terminal is relatively large, it indicates that the transmission feature of the terminal is abnormal, and the terminal is an abnormal terminal. In this case, it is determined that the verification of the terminal fails.

Abstract: This application provides a data collection method and a device. A data collection device determines, based on a threshold corresponding to a target indicator, a collection interval for to-be-collected data of the target indicator from a plurality of available collection intervals; and the data collection device collects the data of the target indicator based on the collection interval.

Abstract: In an embodiment, a data collection method includes: collecting, by a data collection device, performance indicator data of a target device based on a collection periodicity; detecting change amplitude of the collected performance indicator data that is in a change detection window, where the change detection window includes multiple collection periodicities; and when it is detected that change amplitude of the performance indicator data that is in the change detection window is greater than or equal to a change detection threshold, sending the performance indicator data that is in the change detection window to a data analysis device.

Abstract: A method, an apparatus, and a device for obtaining an artificial intelligence model, and a storage medium are provided. A client receives a first artificial intelligence AI model sent by a service end (303). The first AI model includes a plurality of neurons. The client determines, from the plurality of neurons, a target neuron participating in a current round of training, where the current round of training is a non-first round of training, and a quantity of target neurons is less than a total quantity of the plurality of neurons (304). The client trains the target neuron based on local data (305). The client returns parameter data corresponding to the target neuron to the service end (306). The parameter data corresponding to the target neuron is used by the service end to obtain a converged target AI model.

Abstract: This application provides network device capacity expansion methods and devices. One method includes: obtaining network performance data of a target network device, determining a state of the target network device based on the network performance data of the target network device, and determining, based on the state of the target network device, whether to perform capacity expansion for the target network device. In the foregoing technical solution, whether a capacity expansion operation needs to be performed on a network device can be determined in a timely manner based on a state of the network device.

Abstract: A network quality determining method includes, when experience quality represented by a customer experience index of a target user within a first time period is lower than experience quality represented by a customer experience index threshold, determining, using a first parameter value that is of a network key performance indicator (KPI) parameter of the target user within a second time period and that is collected by a collection device in a carrier network, a second parameter value of the network KPI parameter of the target user within the second time period in a home network, and when network quality represented by the second parameter value of the network KPI parameter is lower than network quality represented by a first threshold corresponding to the network KPI parameter in the home network, determining that quality of the home network is poor.

Abstract: A method and an apparatus for updating an application identification model, and a storage medium are provided. A client device may determine a plurality of training samples based on identification results of a plurality of pieces of data traffic, and train an application identification model using the training samples. Then, the client device may upload model data of the trained application identification model to a server, and the server performs joint update based on the model data uploaded by a plurality of client devices. Then, the client device may obtain a jointly updated application identification model based on jointly updated model data delivered by the server.

Abstract: A network device includes: a network interface configured to receive target data, a first processor configured to determine feature information of the target data, a second processor configured to perform preprocessing on the feature information, and a third processor configured to perform inference on a preprocessing result. In addition, the second processor is further configured to perform policy analysis based on an inference result.

Abstract: In certain embodiments, a forwarding device receives at least one service flow. The forwarding device obtains service information of the at least one service flow, where the service information of the service flow includes identification information of a network object to which the service flow belongs and M key performance indicators KPIs of the service flow. M is an integer greater than o, and the network object includes one or more devices. The forwarding device sends detection information to a first device, where the detection information includes the service information of the at least one service flow or a feature set obtained based on the service information of the at least one service flow. The detection information is used to detect whether the network object is in a faulty state.