Improved systems, methods, and architectures to enhance decision making in Smart Cities and Smart Regions. A system includes an index structure including a first hierarchical data structure including a first hierarchical score based on a plurality of first-level elements, each of the plurality of first-level elements having a respective weighting, and a second hierarchical data structure including a plurality of second hierarchical scores based on a plurality of second-level elements, each of the plurality of second-level elements having a respective weighting, such that the first hierarchical score is based on the plurality of second hierarchical scores through an index factor; and a computer-implemented regional monitor engine to manage local access to a plurality of external data sources to coordinate writes to the index structure.

Improved systems, methods, and architectures to enhance decision making in Smart Cities and Smart Regions. A system includes an index structure including a first hierarchical data structure including a first hierarchical score based on a plurality of first-level elements, each of the plurality of first-level elements having a respective weighting, and a second hierarchical data structure including a plurality of second hierarchical scores based on a plurality of second-level elements, each of the plurality of second-level elements having a respective weighting, such that the first hierarchical score is based on the plurality of second hierarchical scores through an index factor; and a computer-implemented regional monitor engine to manage local access to a plurality of external data sources to coordinate writes to the index structure.

The present disclosure relates to a method for verifying the production process of field devices, including a step of accessing a service platform on which data from field devices, including identification data, the respective type of field device, configuration data, containing application-specific data, environment information of the field devices or parameter data, data relating to the production date of a respective field device and repair or troubleshooting cases of the field devices are stored. The method also includes steps of detecting anomalies by statistically evaluating the repair or troubleshooting cases stored on service platform and creating a notification in the event of a detected anomaly, supplying the data of the field devices and the notifications to a machine learning or prognosis system, and evaluating the data of the field devices and the notifications by means of the machine learning or prognosis system for forecasting series errors of the field devices.

The present disclosure relates to a method for verifying the production process of field devices, including a step of accessing a service platform on which data from field devices, including identification data, the respective type of field device, configuration data, containing application-specific data, environment information of the field devices or parameter data, data relating to the production date of a respective field device and repair or troubleshooting cases of the field devices are stored. The method also includes steps of detecting anomalies by statistically evaluating the repair or troubleshooting cases stored on service platform and creating a notification in the event of a detected anomaly, supplying the data of the field devices and the notifications to a machine learning or prognosis system, and evaluating the data of the field devices and the notifications by means of the machine learning or prognosis system for forecasting series errors of the field devices.

The disclosure relates to a system and method of configuring and validating multi-vendor and multi-region Internet-of-Things (IoT) devices using reinforcement learning. In some embodiments, the method includes generating a matching table for each of a plurality of IoT sensors based on a plurality of sensor attributes extracted from a product data associated with an IoT sensor; acquiring an identification information and operational information associated with the IoT sensor and a set of neighboring IoT sensors for each of the plurality of IoT sensors; identifying an appropriate set of IoT sensors from the plurality of IoT sensors, based on a user requirement, the matching table, the identification information and the operational information, using a Reinforcement Learning (RL) model; and dynamically configuring each of the appropriate set of IoT sensors based on a vendor type.

The disclosure relates to a system and method of configuring and validating multi-vendor and multi-region Internet-of-Things (IoT) devices using reinforcement learning. In some embodiments, the method includes generating a matching table for each of a plurality of IoT sensors based on a plurality of sensor attributes extracted from a product data associated with an IoT sensor; acquiring an identification information and operational information associated with the IoT sensor and a set of neighboring IoT sensors for each of the plurality of IoT sensors; identifying an appropriate set of IoT sensors from the plurality of IoT sensors, based on a user requirement, the matching table, the identification information and the operational information, using a Reinforcement Learning (RL) model; and dynamically configuring each of the appropriate set of IoT sensors based on a vendor type.

A delivery path generation system (20), which generates a delivery path for a delivery terminal to deliver packages to a plurality of destinations, includes destination information acquisition means (11), estimation means (12), and delivery path generation means (13). The destination information acquisition means (11) acquires destination information at least including information based on the behavior of consignees at the respective destinations. The estimation means (12) estimates whether or not the consignee is present based on the destination information. The delivery path generation means (13) generates a delivery path based on the estimation.

A smart pipe segment for use in construction of a pipeline. The smart pipe segment includes a pipe body and a sensing nerve network that is associated with the pipe body and is configured to monitor a condition of the pipe segment in real-time. The sensing nerve network comprises optical nerves, electrical nerves or a combination thereof.

According to one embodiment, a condition monitoring device includes a processor. The processor is configured to acquire a time-series signal about a condition of a monitor target from a first sensor, acquire operation timing information indicating start of operation of the monitor target, detect a first operation segment signal from the time-series signal based on the operation timing information, detect a second operation segment signal from the first operation segment signal based on a waveform feature of the first operation segment signal, and determine the condition of the monitor target based on the second operation segment signal.

According to one embodiment, a condition monitoring device includes a processor. The processor is configured to acquire a time-series signal about a condition of a monitor target from a first sensor, acquire operation timing information indicating start of operation of the monitor target, detect a first operation segment signal from the time-series signal based on the operation timing information, detect a second operation segment signal from the first operation segment signal based on a waveform feature of the first operation segment signal, and determine the condition of the monitor target based on the second operation segment signal.