A waste management system comprises a waste bin storing waste, wherein the waste bin comprises a smart waste bin sensor device installed on the waste bin of a waste bin owner. The smart waste bin sensor device comprises a set of sensors that sends and receives signals through a wireless network to a cloud server. The set of sensors implements, operates, detects, measures, and monitors environmental conditions inside or outside the waste bin. A waste and litter sensor detects, measures, and monitors a waste type, a waste volume, a litter type, a litter level, a biohazardous waste type, and a biohazardous waste level. A pathogen biosensor detects, measures, and monitors a pathogen type and a biosafety level. The pathogen biosensor comprises a sterilizer to kill pathogens. A waste bin mobile application and a waste collection facility application functionality enable a user to monitor waste in the waste bin.

The embodiments of the present disclosure provide a management method and a management Internet of Things system for a maintenance personnel of smart gas. The management method is implemented based on a smart gas safety management platform of a management Internet of Things system for maintenance personnel of smart gas. The method may include the following operations. Maintenance personnel information of at least one maintenance personnel may be obtained. The maintenance personnel information may include first positioning information of each of the at least one maintenance personnel. Second positioning information corresponding to at least one maintenance task may be obtained. A candidate maintenance task may be determined based on the first positioning information and the second positioning information. The candidate maintenance task may be sent to a user terminal of the maintenance personnel.

The embodiments of the present disclosure provide a management method and a management Internet of Things system for a maintenance personnel of smart gas. The management method is implemented based on a smart gas safety management platform of a management Internet of Things system for maintenance personnel of smart gas. The method may include the following operations. Maintenance personnel information of at least one maintenance personnel may be obtained. The maintenance personnel information may include first positioning information of each of the at least one maintenance personnel. Second positioning information corresponding to at least one maintenance task may be obtained. A candidate maintenance task may be determined based on the first positioning information and the second positioning information. The candidate maintenance task may be sent to a user terminal of the maintenance personnel.

The embodiments of the present disclosure provide a method for predicting gas transmission loss of smart gas, implemented by a smart gas equipment management platform of an Internet of Things (IoT) system for predicting gas transmission loss of smart gas, comprising: obtaining gas flow data, gas pressure data, and ambient temperature data of a plurality of time points respectively based on gas metering devices, pressure detection devices, and temperature monitoring devices at a plurality of positions of a gas pipeline network; predicting a gas metering error based on the ambient temperature data; determining whether gas loss is abnormal loss based on the gas flow data, the gas pressure data, and the gas metering error; and in response to a determination that the gas loss is the abnormal loss, sending a warning notice.

The embodiments of the present disclosure provide a method for predicting gas transmission loss of smart gas, implemented by a smart gas equipment management platform of an Internet of Things (IoT) system for predicting gas transmission loss of smart gas, comprising: obtaining gas flow data, gas pressure data, and ambient temperature data of a plurality of time points respectively based on gas metering devices, pressure detection devices, and temperature monitoring devices at a plurality of positions of a gas pipeline network; predicting a gas metering error based on the ambient temperature data; determining whether gas loss is abnormal loss based on the gas flow data, the gas pressure data, and the gas metering error; and in response to a determination that the gas loss is the abnormal loss, sending a warning notice.

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 provides a method for smart gas pipeline life prediction based on safety. The method includes: obtaining the operation information of the target gas pipeline section within the first time period; determining, based on operation information, the first performance parameter of the target gas pipeline section of at least one moment within the first time period, the first performance parameter including at least the transport performance of the target gas pipeline section within the first time period; determining, based on the first performance parameter of at least one moment, the first performance parameter sequence of the target gas pipeline section within the first time period, the first performance parameter sequence being the sequence obtained by arranging the first performance parameters of at least one moment in chronological order; and determining, based on the first performance parameter sequence, the remaining life of the target gas pipeline section.

The present disclosure provides a method for smart gas pipeline life prediction based on safety. The method includes: obtaining the operation information of the target gas pipeline section within the first time period; determining, based on operation information, the first performance parameter of the target gas pipeline section of at least one moment within the first time period, the first performance parameter including at least the transport performance of the target gas pipeline section within the first time period; determining, based on the first performance parameter of at least one moment, the first performance parameter sequence of the target gas pipeline section within the first time period, the first performance parameter sequence being the sequence obtained by arranging the first performance parameters of at least one moment in chronological order; and determining, based on the first performance parameter sequence, the remaining life of the target gas pipeline section.

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.