Unsupervised algorithm-based card raising number detection method and system
A number detection and unsupervised technology, applied in the field of maintenance card identification, can solve the problems of low identification accuracy of maintenance card numbers and lack of self-adaptive ability, and achieve the effect of improving the accuracy rate
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Embodiment 1
[0046] The embodiment of the present invention provides a method for detecting card numbers based on an unsupervised algorithm, the method comprising:
[0047] S101: Collect log data of the operator's power channel login.
[0048] Collect the log data of the operator's telechannel login, including but not limited to the user name phonenumber (phone number), login time logindate, login result loginresult, login IP, IMEI (International Mobile Equipment Identity, International Mobile Equipment Identity) of the login device, etc., among which , for the operator's channel system, the user name generally refers to the mobile phone number. In the embodiment of the present invention, the user name and user both refer to the user's mobile phone number. The IMEI is the serial number of the mobile phone and the "serial number" of the mobile phone, which is used to identify mobile communication devices such as each independent mobile phone in the mobile phone network, and is equivalent to...
Embodiment 2
[0087] with the present invention figure 1 Corresponding to the illustrated embodiment, the embodiment of the present invention also provides a maintenance card number detection system based on an unsupervised algorithm.
[0088] figure 2 A schematic structural diagram of a card number detection system based on an unsupervised algorithm provided by the embodiment of the present invention, as shown in figure 2 As shown, the device includes:
[0089] The collection module 201 is used to collect log data for logging in the operator's electric channel;
[0090] The acquisition module 202 is configured to acquire the user's login behavior features from the login log data, and use the user's login behavior features as a first feature set, and use the high-dimensional statistical features corresponding to the user's login behavior features as a second feature gather;
[0091] The identification module 203 is used to identify each abnormal group corresponding to the first featur...
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