Abstract: The embodiment of the present disclosure provides a method and an Internet of Things (IoT) system for safety monitoring and early warning of a smart gas pipeline network. The method includes: obtaining basic monitoring information; identifying abnormal information in the basic monitoring information; determining at least one abnormal information distribution map; determining a change feature of the abnormal information along a direction of the pipeline network; determining a monitoring point whose change feature meets a preset change condition and an abnormal information type of the monitoring point as at least one key monitoring point and a monitoring content of the at least one key monitoring point; determining an inspection plan and a performance allocation plan; adjusting a preset monitoring frequency of distributed monitoring components located at different key monitoring points and at areas surrounding the different key monitoring points; and generating an alarm message.

Abstract: Embodiments of the present disclosure disclose a method for managing personnel safety at a smart gas pipeline network station based on Internet of Things, wherein the method is executed by a smart gas safety management platform of a system for managing personnel safety at a smart gas pipeline network station based on Internet of Things, comprising: obtaining dispatching data of a gas station; determining a reference personnel frequency of a target region, based on the dispatching data; and in response to a first difference not meeting a first preset condition, giving a first warning, and triggering a risk monitoring; wherein the risk monitoring includes: obtaining monitoring data based on a preset monitoring parameter; determining a risk degree of the target region based on the monitoring data; and in response to the risk degree meeting a second preset condition, giving a second warning.

Abstract: The present disclosure provides methods and systems for assessing a pipeline failure based on a smart gas Internet of Things (IoT). The method is implemented by a smart gas safety management platform of an IoT system for smart gas pipeline network safety management, and the method includes obtaining at least one first failure risk in a gas pipeline and a downstream user feature, generating a plurality of candidate gas processing schemes based on the at least one first failure risk, and determining at least one second failure risk based on the at least one first failure risk and at least one of the candidate gas processing schemes.

Abstract: A method and a smart gas Internet of things (IoT) system for metering anti-interference of a gas ultrasonic meter. The IoT system including a smart gas user platform, a smart gas service platform, a smart gas sensing network platform, a smart gas object platform, and a smart gas device management platform. The method may be performed by the smart gas device management platform. The method may include: transmitting at least two sound waves of different frequencies at at least two time-dividing points and receiving at least two echo signals by the gas ultrasonic meter. The at least two time-dividing points may be determined at least based on time-dividing point correlation data; determining, based on the at least two echo signals, a gas flow difference; determining, based on the gas flow difference, whether a noise interference exists; and in response to a determination that the noise interference exists, adjusting a gas metering strategy.

Abstract: The embodiment of the present disclosure provides a method for determining a smart gas inspection plan and an Internet of Things system, the method is implemented based on a smart gas pipeline network safety management platform, including: obtaining an area to be inspected; determining one or more downstream users based on the area to be inspected, and obtaining gas consumption data of each of the one or more downstream users; determining, based on the gas consumption data of the each of the one or more downstream users, peak-valley features of gas consumption at the future time; and determining, based on the peak-valley features of gas consumption at the future time, an inspection time of the area to be inspected.

Abstract: The present disclosure provides a method for frost heave prevention treatment of a smart gas pipeline and an Internet of Things system. The method includes: determining gas pressure change data of a target point based on gas transmission data of the target point and gas pipeline data of the target point; predicting temperature change data of the target point through a temperature model based on the gas pressure change data of the target point; and predicting, based on the temperature change data of the target point, the gas pipeline data of the target point, and the gas pressure change data of the target point, and in combination with environmental data of the target point, frost heave degree data of the target point.

Abstract: The present invention discloses a Bluetooth mesh network system having wake-up management mechanism that includes a control node and low power nodes. The control node broadcasts a wake-up parameter setting packet at broadcast time spots based on a broadcast period. Each of the low power nodes receives the wake-up parameter setting packet to be operated in a wake-up management mode to configure a start time spot, a time length and a period interval length of a wake-up period that is synchronous among the low power nodes accordingly. The control node further transmits data corresponding to the low power nodes in the wake-up period and the low power nodes is operated in a wake-up state within the wake-up period to receive the data from the control node and is operated in a sleep state outside of the wake-up period.

Abstract: According to implementations of the subject matter described herein, there is provided a solution for protein structure prediction. In this solution, a constraint set for a target protein is obtained, the constraint set comprising constraints for structural properties of the target protein. Feature information is extracted from the constraints respectively, and weights corresponding to the constraints are determined respectively based on the feature information of the constraints. Each weight indicates a degree of influence of the corresponding constraint in prediction of a structure of the target protein. The structure of the target protein is predicted based on the constraints in the constraint set and the weights. According to the solution, through the pre-processing on the constraints for use, it is possible to solve potential conflicts in the constraint set and eliminate constraint redundancy. This enables accurate prediction of the structure of the target protein.

Abstract: According to implementations of the present disclosure, a solution is proposed for protein structure prediction. In this solution, from a fragment library for a target protein, a plurality of fragments is determined for each of a plurality of residue positions of the target protein. Each fragment comprises a plurality of amino acid residues. Then, a feature representation of structures of the plurality of fragments is generated for the each residue position. Next, a prediction of at least one of a structure and a structural property of the target protein is determined based on the respective feature representations generated for the plurality of residue positions. In this way, the solution can leverage structural information of fragment libraries to complement and complete information used in protein structure prediction, and the accuracy of protein structure prediction is thus improved.

Abstract: This application provides an augmented reality method, to dynamically associate an orientation of a virtual object with an orientation of a real object. The method in this application includes: obtaining a target image shot by a camera and first location information of a first object in the target image; obtaining second location information of a second object in a three-dimensional coordinate system and third location information of a third object in the three-dimensional coordinate system; obtaining a pose variation of the first object relative to the second object based on the first location information and the second location information; transforming the third location information based on the pose variation, to obtain fourth location information of the third object in the three-dimensional coordinate system; and rendering the third object in the target image based on the fourth location information, to obtain a new target image.

Abstract: A battery management circuit and method for a mobile device is disclosed. The battery management circuit includes a power supply input configured to receive power from an external power source and a system supply output. A battery supply terminal is configured to be coupled to a battery having a series arrangement of two or more battery cells. A battery charging circuit includes a buck converter having a buck converter input coupled to the power supply input and a buck converter output coupled to the system supply output. A battery isolation circuit and a switched capacitor DC-DC converter are arranged between the system supply output and the battery supply terminal. The switched capacitor DC-DC converter steps up the buck converter output voltage by a step-up factor during battery charging and steps down the battery voltage during battery discharge.

Abstract: The present invention discloses a Bluetooth mesh network system having a communication range extending mechanism that includes a plurality of nodes and a provisional node. The provisional node is configured to transmit a connection setting packet to the nodes to control the nodes to establish a coded PHY connection between each two of the nodes such that a packet error rate is smaller than a threshold value when the nodes perform communication when a physical distance between each two of the nodes is larger than a maximum non-coded-PHY communication distance, in which when the nodes establish a non-coded-PHY connection and the physical distance is larger than the maximum non-coded-PHY communication distance, the packet error rate is larger than the threshold value.

Abstract: The present invention provides a high-content carotenoid compound from Adonis amurensis. The content of the total carotenoid of the carotenoid compound is higher than 95%. The content of all-trans (3S, 3?S)-carotenoid is higher than 80%. The carotenoid crystals have a high purity, and can be used in multiple forms in the fields of a dietary supplement of a human being, a food additive, a feed additive and a cosmetic product. In addition, the present invention also provides a method for manufacturing the compound.

Abstract: A video generation method, may be applied to the field of image processing and video generation in the field of artificial intelligence. The method includes: receiving a video generation instruction, and obtaining text information and image information in response to the video generation instruction, where the text information includes one or more keywords, and the image information includes N images; obtaining, based on the one or more keywords, an image feature that is in each of the N images and that corresponds to the one or more keywords; and inputting the one or more keywords and image features of the N images into a target generator network to generate a target video, where the target video includes M images, and the M images are images that are generated based on the image features of the N images and that correspond to the one or more keywords.

Abstract: This application provides a method and apparatus for updating an object recognition model in the field of artificial intelligence. In the technical solution provided in this application, a target image and first voice information of a user are obtained. The first voice information indicates a first category of a target object in the target image. A feature library of a first object recognition model is updated based on the target image and the first voice information. The updated first object recognition model includes a feature of the target object and a first label indicating the first category, and the feature of the target object corresponds to the first label. A recognition rate of an object recognition model can be improved more easily according to the technical solution provided in this application.

Abstract: The present invention discloses a Bluetooth mesh network system having unprovisioned communication mechanism that includes a delivering node and a receiving node. The delivering node generates a beacon signal having identification information that is not a key and performs broadcast communication. The receiving node receives the beacon signal and performs identification and processing under a network communication protocol to execute a command corresponding to the beacon signal.

Abstract: A switched capacitor (SC) power stage includes a first stage circuit with a set of switches coupled in series, a first flying capacitor coupled to a first node between a first and second switch and to a second node between a fifth and sixth switch, a second flying capacitor coupled to a third node between the second and a third switch and to a fourth node between a seventh and eighth switch, and a third flying capacitor coupled to a fifth node between a third and fourth switch and a second terminal coupled to the second node. A control circuit establishes a first configuration of the switches to precharge the first, second, and third flying capacitors to a first voltage, and a second configuration of the switches to precharge the first and second flying capacitors to a second voltage while the third flying capacitor remains charged at the first voltage.

Abstract: A switched capacitor (SC) power stage includes a first stage circuit with a set of switches coupled in series, a first flying capacitor coupled to a first node between a first and second switch and to a second node between a fifth and sixth switch, a second flying capacitor coupled to a third node between the second and a third switch and to a fourth node between a seventh and eighth switch, and a third flying capacitor coupled to a fifth node between a third and fourth switch and a second terminal coupled to the second node. A control circuit establishes a first configuration of the switches to precharge the first, second, and third flying capacitors to a first voltage, and a second configuration of the switches to precharge the first and second flying capacitors to a second voltage while the third flying capacitor remains charged at the first voltage.

Abstract: A battery management circuit and method for a mobile device is disclosed. The battery management circuit includes a power supply input configured to receive power from an external power source and a system supply output. A battery supply terminal is configured to be coupled to a battery having a series arrangement of two or more battery cells. A battery charging circuit includes a buck converter having a buck converter input coupled to the power supply input and a buck converter output coupled to the system supply output. A battery isolation circuit and a switched capacitor DC-DC converter are arranged between the system supply output and the battery supply terminal. The switched capacitor DC-DC converter steps up the buck converter output voltage by a step-up factor during battery charging and steps down the battery voltage during battery discharge.

Abstract: An instance segmentation method and apparatus are provided. A to-be-trained segmentation network performs the following processing on each instance group that is in a sample original image and that is of pixels of a marked instance, where each instance group includes at least one marked instance, and the processing includes: predicting at least two different first basic feature maps and a first attention feature map corresponding to each first basic feature map; performing weighted processing on the at least two first basic feature maps and pixel values of respective first attention feature maps corresponding to the at least two first basic feature maps, to obtain a first feature fusion map; and training the to-be-trained segmentation network based on the first feature fusion map and the sample original image. A segmentation model can precisely determine pixels of an instance in an original image.