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.

A computing system is configured to analyze both measurement data and indicator data as big data aggregated in measurement database and indicator database by deep learning for each lot of a part or for each lot of a finished product and a part pre-associated with each other, and also for each consolidation target between bases subordinate to the same start point corresponding to identification information that specifies a business user of the computing system. Analysis target layers by the deep learning are a three-layer serial hierarchical structure containing a production condition layer and an environment condition layer as a start point for analysis of a part layer, or a four-layer serial hierarchical structure containing a part layer, a production condition layer, and an environment condition layer as a start point for analysis of a finished product layer.

A computing system is configured to analyze both measurement data and indicator data as big data aggregated in measurement database and indicator database by deep learning for each lot of a part or for each lot of a finished product and a part pre-associated with each other, and also for each consolidation target between bases subordinate to the same start point corresponding to identification information that specifies a business user of the computing system. Analysis target layers by the deep learning are a three-layer serial hierarchical structure containing a production condition layer and an environment condition layer as a start point for analysis of a part layer, or a four-layer serial hierarchical structure containing a part layer, a production condition layer, and an environment condition layer as a start point for analysis of a finished product layer.

Embodiments include a system, method, and computer program product that enable secure access to cameras in smart buildings. Some embodiments control outbound video from an environment such as a local network through an intelligent on-event video pushing mechanism. The local intelligent on-event video pushing mechanism hides the IP address of a source video camera, transcodes the video to a reduced size for wide area distribution, and pushes video to a recipient upon an event triggered received within the local environment (e.g., the local network.) Embodiments enable a remote video client on the far-side of the local network firewall to view the video streams of cameras on the near-side of the local network firewall when an event or trigger occurs.

Disclosed is a method for evaluating cooking quality that includes: in response to receiving evaluation information of a food sent by a terminal, retrieving an assessment result corresponding to the food, the assessment result being an assessment result of the cooking quality of a cooking appliance; and determining an evaluation result of the cooking quality of the cooking appliance based on the assessment result, the evaluation information, and a preset evaluation rule. Also disclosed is a device for evaluating cooking quality. With this disclosure, both the assessment result and the evaluation information are weighted in computing the evaluation result of the cooking quality of the cooking appliance. Such an evaluation result not only reflects users' subjective evaluation of the cooking quality, but it also reflects an objective and tenable evaluation of the cooking quality, making the evaluation of the cooking quality more reasonable.

Disclosed is a method for evaluating cooking quality that includes: in response to receiving evaluation information of a food sent by a terminal, retrieving an assessment result corresponding to the food, the assessment result being an assessment result of the cooking quality of a cooking appliance; and determining an evaluation result of the cooking quality of the cooking appliance based on the assessment result, the evaluation information, and a preset evaluation rule. Also disclosed is a device for evaluating cooking quality. With this disclosure, both the assessment result and the evaluation information are weighted in computing the evaluation result of the cooking quality of the cooking appliance. Such an evaluation result not only reflects users' subjective evaluation of the cooking quality, but it also reflects an objective and tenable evaluation of the cooking quality, making the evaluation of the cooking quality more reasonable.

A method and system remotely controlling a cleaning cycle of a household appliance using transmission of wireless signals is provided. Signals are transmitted between a mobile dispensing device arranged in the household appliance and an electronic device. The method determines the strength of a signal received by the mobile dispensing device. The determined strength is compared to a threshold value. If the determined strength is above the threshold value transmission of wireless signals from the mobile dispensing device to the electronic device is enabled. If the determined strength is below the threshold value transmission of signals from the mobile dispensing device to the electronic device is disabled. When transmission of signals is enabled, a status wireless signal comprising status information about the cleaning cycle is sent to the electronic device.

Embodiments include a system, method, and computer program product that enable secure access to cameras in smart buildings. Some embodiments control outbound video from an environment such as a local network through an intelligent on-event video pushing mechanism. The local intelligent on-event video pushing mechanism hides the IP address of a source video camera, transcodes the video to a reduced size for wide area distribution, and pushes video to a recipient upon an event triggered received within the local environment (e.g., the local network.) Embodiments enable a remote video client on the far-side of the local network firewall to view the video streams of cameras on the near-side of the local network firewall when an event or trigger occurs.

A computer-implemented method for determining and mitigating open space health risk factors comprising processors configured for partitioning an open space area into sections based on Internet-of-Things (IoT) devices being present in each of the one or more sections, determining a risk factor score based on sensor data gathered from the one or more IoT devices, determining one or more events occurred in one or more of the sections based on the sensor data, updating the risk factor score for the one or more sections based on the one or more events, and responsive to updating the risk factor score, generating one or more action items to mitigate the events and reduce the risk factor score. The risk factor score includes a time weight based on the events that reduces the risk factor score over time.

A computer-implemented method for determining and mitigating open space health risk factors comprising processors configured for partitioning an open space area into sections based on Internet-of-Things (IoT) devices being present in each of the one or more sections, determining a risk factor score based on sensor data gathered from the one or more IoT devices, determining one or more events occurred in one or more of the sections based on the sensor data, updating the risk factor score for the one or more sections based on the one or more events, and responsive to updating the risk factor score, generating one or more action items to mitigate the events and reduce the risk factor score. The risk factor score includes a time weight based on the events that reduces the risk factor score over time.