US 11,870,937 B2
Methods for smart gas call center feedback management and Internet of things (IoT) systems thereof
Zehua Shao, Chengdu (CN); Haitang Xiang, Chengdu (CN); and Junyan Zhou, Chengdu (CN)
Assigned to CHENGDU QINCHUAN IOT TECHNOLOGY CO., LTD., Chengdu (CN)
Filed by CHENGDU QINCHUAN IOT TECHNOLOGY CO., LTD., Sichuan (CN)
Filed on Apr. 16, 2023, as Appl. No. 18/301,250.
Claims priority of application No. 202310220626.2 (CN), filed on Mar. 9, 2023.
Prior Publication US 2023/0254409 A1, Aug. 10, 2023
Int. Cl. H04M 3/523 (2006.01); G06Q 50/06 (2012.01); H04M 3/22 (2006.01)
CPC H04M 3/5233 (2013.01) [G06Q 50/06 (2013.01); H04M 3/2218 (2013.01); H04M 2203/408 (2013.01)] 8 Claims
OG exemplary drawing
 
1. A method for smart gas call center feedback management, wherein the method is implemented based on a smart gas management platform, the method comprising:
receiving a call message of a target customer through a call center, and a content of the call message being related to a gas business;
converting the call message to a text message;
determining a service category corresponding to the text message; and
determining, based on the service category, the feedback mode; wherein the determining, based on the service category, the feedback mode comprises:
determining, based on one or more of a service category of a message of other customers, a feedback mode of the other customers, and a service category of a message of the target customer, the feedback mode of the target customer through a preset algorithm; wherein the preset algorithm comprises:
establishing a training data set, a sample of the training data set including the service category of the message of the other customers and the feedback mode of the other customers; finding N counts of messages of the customers with the shortest distance to the message of the target customer in the training data set, and if among the N counts of messages, the messages in a feedback mode A are the most, then the feedback mode of the target customer is A; wherein the distance is related to a service category similarity, a real-time pipeline network situation, a customer positioning similarity, and an emergency similarity of the call message, the emergency similarity being predicted using a prediction model, the prediction model being a machine learning model including a first embedding layer, a second embedding layer, and an emergency similarity prediction layer; wherein
an input of the first embedding layer includes a text message of the target customer and a service category of the target customer, and an output includes a target customer feature;
an input of the second embedding layer includes a text message of the other customers and a service category of the other customers, and an output includes other customer features; and
an input of the emergency similarity prediction layer includes the target customer feature, the other customer features, a real-time pipeline network situation, a customer positioning of the target customer, and customer positionings of the other customers, and an output includes an emergency similarity;
in response to the feedback mode being manual feedback, determining a target operator through the call center to feed back a call of the target customer; and
in response to the feedback mode being automatic feedback, determining a feedback content through the call center and sending the feedback content to the target customer.