Abstract: The present disclosure discloses an industrial Internet of Things for intelligent control of filling production, including a user platform, a service platform, a management platform, a sensor network platform, and an object platform interacting in sequence, wherein the service platform is provided with a plurality of independent service sub-platforms; the management platform is provided with a general management platform and a plurality of independent management sub-platforms, the management sub-platforms interacting with the general management platform, and the management sub-platforms interacting with the corresponding service sub-platforms; and the sensor network platform is provided with a general sensor network platform and a plurality of independent sensor network sub-platforms, the sensor network sub-platforms interacting with the general sensor network platform, and the general sensor network platform interacting with the general management platform.

Abstract: The disclosure provides a method for managing a cleaning route in a smart city, an Internet of Things system and storage medium. The method includes: obtaining target information of a target area within a preset time period based on an object platform; sending the target information to a management platform through a sensor network platform; determining a cleaning route of the target area by processing the target information of the target area based on the management platform, including: determining an estimated amount of fallen leaves of each section of road in the target area; determining an estimated falling range of fallen leaves of each section of road in the target area; determining a cleaning difficulty evaluation value of each section of road based on the estimated amount of fallen leaves and the estimated falling range; and determining a cleaning route based on the cleaning difficulty evaluation value.

Abstract: The embodiments of the present disclosure provide a method for optimizing performance indicators of smart gas, a smart gas Internet of Things (IoT) system, and a medium. The method may comprise obtaining at least one user need through the smart gas user platform based on the smart gas service platform; determining at least one optimization objective based on the at least one user need; determining a constraint that a target proportioning feature satisfies according to the at least one optimization objective; generating at least one candidate proportioning feature according to the constraint; performing at least one round of iterative processing on the at least one candidate proportioning feature, and determining the candidate proportioning feature and an evaluation value after each round of iteration until the evaluation value satisfies the constraint; and determining the candidate proportioning feature with the evaluation value satisfying the constraint as the target proportioning feature.

Abstract: The present disclosure provides a method for evaluating a pipeline cleaning solution based on smart gas and an Internet of Things system. The method comprises: obtaining an operation feature of at least one gas pipeline and inspection data of the at least one gas pipeline; determining an area to be cleaned and an area feature of the area to be cleaned; generating at least one candidate pipeline cleaning solution based on the area feature of the area to be cleaned; processing each of the at least one candidate pipeline cleaning solution and the area feature through a pipeline cleaning effect prediction model to determine a pipeline cleaning effect of each of the at least one candidate pipeline cleaning solution; determining a pipeline cleaning solution based on the pipeline cleaning effect; and evaluating a pipeline cleaning effect of the pipeline cleaning solution based on an implementation of the pipeline cleaning solution.

Abstract: The present disclosure provides a method for cleaning communal facilities in a smart city based on an Internet of Things.

Abstract: Systems and methods are provided for determining faces and bodies of people in an image by adaptively scaling images and by iteratively using a deep neural network for inferencing. A camera captures an image including faces and bodies of people. A face/body determiner determines faces and bodies of people appearing in the image by resizing the image into a predetermined pixel dimension as input to the deep neural network. A region cropper determines a crop region associated with a low level of confidence in detecting faces and bodies that are too small to determine with an acceptable level of confidence. The region cropper resizes the crop region into the predetermined pixel dimension as input to the deep neural network. The face and body determiner determines other faces and bodies appearing in the resized crop region. An aggregator aggregates locations of the determined faces and bodies in the image.

Abstract: The present disclosure discloses a method for determining an abnormal device in a process of measuring energy of natural gas based on Internet of Things (IOT). The method may include obtaining a natural gas detection parameter detected by at least one detection device via a sense network platform in response to a query request; determining first energy data and second energy data by processing the natural gas detection parameter; determining whether the abnormal device exists by comparing the first energy data and the second energy data; in response to determining that the abnormal device exists, for each detection device, determining a probability that the detection device is abnormal based on related information of the detection device, the first energy data, and the second energy data; and determining the abnormal device based on the probability that the detection device is abnormal.

Abstract: The present disclosure discloses an industrial Internet of things with a sensor network platform in a front sub platform type, comprising: a user platform, a service platform, a management platform, a sensor network platform, and an object platform which are interacted sequentially. The sensor network platform adopts front sub platform layout, which can process data for different target objects, and then summarize data, so as to reduce the data processing capacity of entire sensor network platform. The management platform is arranged independently to form multiple independent data processing channels of the same or different. The gateway on each communication network shares part of the calculation for the management platform, effectively reducing the calculation pressure of the management platform, and ensuring that the data is transmitted according to a specific path or processed by a specific server, so as to ensure the safety and independence of the data.

Abstract: Provided are a system and a method for impact testing and monitoring of a high-energy flexible net. The system includes a vertical impact testing unit, a slope impact testing unit, an impact simulation unit, and an impact monitoring unit. The vertical impact testing unit includes a vertically positioned gravity wall. The slope impact testing unit includes a wall slope positioned perpendicularly to a second side of the gravity wall. A first side of the gravity wall and a slope surface of the wall slope are securely provided with a flexible net, respectively. The impact simulation unit includes an impact assembly and a lifting assembly. The impact monitoring unit is configured to monitor a deformation result and an internal force change result of the flexible net.

Abstract: The disclosure provides an Internet of Things system and a method for managing a people flow of a public place in a smart city. The method may comprise obtaining pedestrian distribution information in a preset area during a current time period via network from a storage device; determining, by processing the pedestrian distribution information through an area location prediction model, at least one area location in the preset area for a future time period, a population flow load of the area location being greater than a first threshold, wherein the area location prediction model includes a graph neural network model, a graph input into the graph neural network model includes at least two nodes and at least one edge; generating, based on the area location, prompt information; and feedbacking the prompt information to a user terminal of a user platform through a service platform via the network.

Abstract: Example embodiments of the present disclosure provide a method and a smart gas Internet of Things (IOT) system for automatic control of an industrial gas flow. The method may include: obtaining a water valve adjustment history record of an industrial user who uses a hot water supply device; determining a target temperature data table for the industrial user based on the water valve adjustment history record; obtaining current water valve data of the hot water supply device in response to determining that the industrial user uses the hot water supply device; and determining a gas flow parameter based on the current water valve data and the target temperature data table. The smart gas IoT system may include a smart gas user platform, a smart gas service platform, a smart gas device management platform, a smart gas sensing network platform, and a smart gas object platform.

Abstract: The present disclosure discloses an industrial Internet of Things (IoT) of integrating centralized platforms and rear sub-platforms, the industrial IoT includes a user platform, a service platform, a management platform, a sensor network platform, and an object platform interacting in sequence, both the service platform and the sensor network platform are arranged using a centralized platform arrangement and the management platform is arranged using a rear sub-platform arrangement, the production line manufacturing equipment and the production line accessory supply equipment collect accessory data of corresponding equipment through the equipment data collectors and add the index item information through the management platform. Therefore, the corresponding index item information is classified and processed by sub-platforms of corresponding and different management platforms, so as to ensure the transmission and classification of data.

Abstract: A method and an Internet of Things (IoT) system for monitoring smart gas pipeline network construction are provided.

Abstract: Disclosed is a method and a system for safety management of a gas pipe well based on a smart gas IoT. The method may be executed by a processor and include: obtaining an environmental information sequence of the gas pipe well and an in-well monitoring data sequence by controlling a monitoring component according to a monitoring parameter based on the smart gas IoT, and storing the environmental information sequence and the in-well monitoring data sequence in a storage unit; determining an in-well risk value; adjusting the monitoring parameter in response to determining that the in-well risk value exceeds a risk threshold and updating the in-well risk value; determining an impact degree of a gas component; determining a pipe well inspection program and generating a corresponding pipe well inspection instruction to be sent to an inspection terminal; and adjusting the monitoring parameter and clearing expired data in the storage unit.

Abstract: A method and an Internet of Things system for determining a gas compensation scheme based on smart gas are provided. The method includes: determining, based on a planned gas supply quantity of a gas supplier, a predicted gas supply quantity for a future preset time period; predicting a gas demand quantity for the future preset time period based on a gas usage quantity of a historical user; predicting a gas supply deviation rate of the gas supplier based on a gas pipeline design map, weather information for the future preset time period, and the gas demand quantity; determining a gas supply quantity for the future preset time period based on the predicted gas supply quantity and the gas supply deviation rate; determining a gas gap for the future preset time period based on the gas supply quantity and the gas demand quantity; and determining the gas compensation scheme.

Abstract: Disclosed is monitoring and early warning method for gas gate station and IoT system thereof. The method comprises: obtaining a downstream user feature and historical warning data corresponding to the gas gate station; determining, based on the downstream user feature and the historical warning data and in combination with operation data of the gas gate station, an associated node related to the gas gate station in a preset time period; obtaining monitoring data of the associated node; determining a pipeline network monitoring feature based on pipeline network monitoring data; determining ideal operation data of the gas gate station at a future time through a prediction model, the gate station operation data, and the pipeline network monitoring feature; and issuing an early warning notification in response to a difference between gate station monitoring data and the ideal operation data exceeding a threshold.

Abstract: Systems and methods are provided for determining faces and bodies of people in an image by adaptively scaling images and by iteratively using a deep neural network for inferencing. A camera captures an image including faces and bodies of people. A face/body determiner determines faces and bodies of people appearing in the image by resizing the image into a predetermined pixel dimension as input to the deep neural network. A region cropper determines a crop region associated with a low level of confidence in detecting faces and bodies that are too small to determine with an acceptable level of confidence. The region cropper resizes the crop region into the predetermined pixel dimension as input to the deep neural network. The face and body determiner determines other faces and bodies appearing in the resized crop region. An aggregator aggregates locations of the determined faces and bodies in the image.

Abstract: Systems and methods are provided for determining faces and bodies of people in an image by adaptively scaling images and by iteratively using a deep neural network for inferencing. A camera captures an image including faces and bodies of people. A face/body determiner determines faces and bodies of people appearing in the image by resizing the image into a predetermined pixel dimension as input to the deep neural network. A region cropper determines a crop region associated with a low level of confidence in detecting faces and bodies that are too small to determine with an acceptable level of confidence. The region cropper resizes the crop region into the predetermined pixel dimension as input to the deep neural network. The face and body determiner determines other faces and bodies appearing in the resized crop region. An aggregator aggregates locations of the determined faces and bodies in the image.

Abstract: Systems and methods are provided for determining faces and bodies of people in an image by adaptively scaling images and by iteratively using a deep neural network for inferencing. A camera captures an image including faces and bodies of people. A face/body determiner determines faces and bodies of people appearing in the image by resizing the image into a predetermined pixel dimension as input to the deep neural network. A region cropper determines a crop region associated with a low level of confidence in detecting faces and bodies that are too small to determine with an acceptable level of confidence. The region cropper resizes the crop region into the predetermined pixel dimension as input to the deep neural network. The face and body determiner determines other faces and bodies appearing in the resized crop region. An aggregator aggregates locations of the determined faces and bodies in the image.