| CPC F17D 5/06 (2013.01) [F17D 5/005 (2013.01)] | 14 Claims |
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1. A method for assessing a loss of a maintenance medium of a smart gas pipeline network, implemented based on a smart gas safety management platform of an Internet of Things (IOT) system for assessing a loss of a maintenance medium of a smart gas pipeline network, the loT system further comprising a smart gas user platform, a smart gas service platform, a smart gas sensor network platform, and a smart gas object platform; wherein the smart gas user platform is configured as a terminal device, the smart gas safety management platform includes a smart gas emergency maintenance management sub-platform and a smart gas data center, the smart gas data center is configured as storage equipment, the smart gas object platform includes a smart gas equipment object sub-platform and a smart gas maintenance engineering object sub-platform, the smart gas equipment object sub-platform is configured as various types of gas equipment and monitoring equipment, the gas equipment includes gas pipeline network, valve control equipment, and gas storage tanks, the monitoring equipment includes gas flowmeters, pressure sensors, and temperature sensors, the smart gas maintenance engineering object sub-platform at least includes hand-held terminals of maintenance persons and maintenance equipment, the method comprising:
determining a degree of a maintenance impact based on maintenance data;
wherein the maintenance data refers to data related to a maintenance pipeline, the maintenance data includes a maintenance time point, a maintenance type, a maintenance process, and information of a maintenance person; and the degree of the maintenance impact refers to a relevant indicator reflecting a degree of impact caused by maintenance on gas supply, the degree of the maintenance impact includes time consumption for each maintenance process, a gas supply restoration duration, and a degree of maintenance shutdown and decompression;
determining a target supply for a maintenance pipeline branch in a target time period based on the degree of the maintenance impact and historical supply data;
determining a target demand for the maintenance pipeline branch in the target time period based on historical usage data;
determining a target loss in the target time period based on the target supply and the target demand; and
determining a replenishment parameter of a gas loss based on the target loss;
wherein the replenishment parameter includes a gas replenishment time period and a gas replenishment amount corresponding to the gas replenishment time period, and the determining the replenishment parameter of the gas loss based on the target loss includes:
in response to a determination that the target loss in the target time period is greater than a difference threshold, determining the target time period as the gas replenishment time period;
determining the gas replenishment amount based on the target loss corresponding to the gas replenishment time period and the difference threshold; and
calling backup gas from a gas storage station based on the gas replenishment amount;
wherein the target time period includes a maintenance time period and a gas supply restoration time period, and the determining a target supply for a maintenance pipeline branch in a target time period based on the degree of the maintenance impact and historical supply data includes:
determining the maintenance time period based on the degree of the maintenance impact;
determining a first loss feature based on historical supply data of a historical maintenance time period; wherein first loss feature refers to a feature related to gas supply during the maintenance time period, and the first loss feature includes a gas allocation proportion sequence, and a degree of stability of the gas supply during the maintenance time period;
determining a supply sequence of the maintenance time period based on the first loss feature;
predicting the gas supply restoration duration through a prediction model based on the maintenance data, a pipeline network design map, and reference gas delivery information, wherein the prediction model is a machine learning model; wherein
the prediction model includes a feature extraction layer and a time prediction layer; the feature extraction layer includes a CNN model, an input of the feature extraction layer includes the pipeline network design map, and an output of the feature extraction layer includes pipeline network distribution features; the time prediction layer includes a neural network model, an input of the time prediction layer includes the pipeline network distribution features, the maintenance data, and the reference gas delivery information, and an output of the time prediction layer includes the gas supply restoration duration;
the feature extraction layer and the time prediction layer are obtained through joint training based on training samples and labels, the training samples include sample pipeline network design map of the gas pipeline network, sample maintenance data, and sample reference gas delivery information, the labels are adjusted historical gas supply restoration durations corresponding to the training samples; and
the joint training includes: inputting the sample pipeline network design map into an initial feature extraction layer to obtain a sample pipeline network distribution feature output by the initial feature extraction layer; inputting the sample pipeline network distribution feature, the sample maintenance data, and the sample reference gas delivery information into an initial time prediction layer to obtain sample gas supply restoration duration output by the initial time prediction layer; constructing a loss function base on the labels and the sample gas supply restoration duration; updating parameters of the initial feature extraction layer and the initial time prediction layer synchronously; and obtaining the feature extraction layer and the time prediction layer;
determining the gas supply restoration time period based on the gas supply restoration duration;
determining a second loss feature based on the gas supply restoration duration; wherein the second loss feature refers to a feature related gas supply during the gas supply restoration time period; and
determining a supply sequence of the gas supply restoration time period based on the second loss feature.
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