System and method for telecom package optimization based on rapid analysis of adjacent mass data
A technology for fast analysis and massive data, applied in data processing applications, electrical digital data processing, special data processing applications, etc., to achieve the effects of reducing running time, fast analysis, and improving computing speed
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Embodiment 2
[0104](1) User input related parameters
[0105] Step (201): According to the target user group, the user selects 100 representative target users from the customer information database.
[0106] Step (202): Set the desired package including call duration less than 100 minutes, text message range less than 50, data not included, and package tariff standard between 10 yuan and 20 yuan.
[0107] Step (203): set the time length as the data of the past 2 years.
[0108] Step (204): Set the reference data size to 10,000 user records.
[0109] Step (205): set the population size to 50, and the maximum number of iterations to 10,000 generations.
[0110] (2) Automatic package optimization by computer
[0111] Step (206): extract the original target data of the user records of the target user group in the last 2 years.
[0112] Step (207): Extract feature vectors for each user from the original target data (user's monthly average call time, user's average number of SMS messages per...
specific Embodiment 3
[0138] (1) User input related parameters
[0139] Step (301): The user selects 100 representative target users from the customer information database according to the target user group.
[0140] Step (302): Set the expected package to include 30 to 50 text messages, 5M to 10M traffic, excluding calls, and the package fee to be between 10 and 20 yuan.
[0141] Step (303): set the time length as the data of the last 3 years.
[0142] Step (304): Set the reference data size as 100,000 user records.
[0143] Step (305): set the population size to 100, and the maximum number of iterations to 3000 generations.
[0144] (2) Automatic package optimization by computer
[0145] Step (306): extract the original target data of the user records of the target user group in the last 3 years.
[0146] Step (307): Extract feature vectors for each user from the original target data (the average number of SMS messages per month, the average monthly Internet traffic of users, the monthly cons...
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