A method for installing a gas pipeline compensator of smart gas, implemented by a processor of a smart gas device management platform of an Internet of Things system for installing a gas pipeline compensator of smart gas, wherein the Internet of Things system includes a smart gas user platform, a smart gas service platform, the smart gas device management platform, a smart gas sensor network platform, and a smart gas object platform, the smart gas device management platform includes a smart gas indoor device management sub-platform, a smart gas pipeline network device management sub-platform, and a smart gas data center:
- the smart gas object platform is configured as a terminal device;
- the smart gas sensor network platform is configured to upload data collected by a sensor device to the smart gas device management platform;
- the smart gas device management platform includes the processor and a storage medium;
- the method comprising:
- obtaining a pipeline feature and an estimated operation feature of a target pipeline from the terminal device of the smart gas object platform, and uploading the pipeline feature and the estimated operation feature to the smart gas data center of the smart gas device management platform;
- generating an estimated stretching and contracting feature of the target pipeline based on the pipeline feature and the estimated operation feature;
- wherein the estimated stretching and contracting feature includes a first estimated stretching and contracting amount sequence, and
- the generating the estimated stretching and contracting feature of the target pipeline based on the pipeline feature and the estimated operation feature includes:
- obtaining a first gas temperature sequence and a first environmental temperature sequence of a preset scenario;
- determining a first temperature sequence of the target pipeline based on the first gas temperature sequence, the first environmental temperature sequence, and a medium feature of the target pipeline, wherein the medium feature includes an internal medium feature and an external medium feature of the target pipeline;
- determining the first estimated stretching and contracting amount sequence of the target pipeline through a temperature sequence model based on the pipeline feature, the estimated operation feature, and the first temperature sequence,
- wherein the first temperature sequence and the first estimated stretching and contracting amount sequence correspond to the preset scenario;
- the temperature sequence model is a deep neural network;
- first training samples of the temperature sequence model are data used for performing simulation experiments, including a sample first gas temperature sequence, a sample first environmental temperature sequence, and a sample medium feature of the target pipeline;
- first training labels of the temperature sequence model are actual first temperature sequences at multiple time points obtained through monitoring in the simulation experiment data;
- the smart gas device management platform inputs the first training samples into an initial temperature sequence model to obtain an initial first temperature sequence, constructs a loss function based on the initial first temperature sequence and the first training labels, and obtains a trained temperature sequence model by updating parameters of the initial temperature sequence model based on the loss function:
- generating an installation parameter of a compensator based on the estimated stretching and contracting feature, wherein the installation parameter at least includes a device parameter of the compensator and a pre-stretching degree of the compensator;
- transmitting the installation parameter to the smart gas user platform;
- and
- pre-stretching the compensator based on the pre-stretching degree of the compensator, to install the compensator at both ends of the target pipeline while maintaining stretched state.