WO2017186090A1 - Communication number processing method and apparatus - Google Patents

Abstract

Disclosed are a communication number processing method and apparatus. The method comprises: acquiring, from a communication service device, a bill of a pre-set number of communication numbers within a first pre-set time; parsing the bill to obtain the type of communication information comprised in the bill, and extracting at least one type of communication information about each communication number in the bill and combining same to form a preprocessing bill; parsing the at least one type of communication information about each communication number in the preprocessing bill to obtain a feature possessed by a corresponding type of communication information about each communication number in the preprocessing bill; and extracting, from the communication numbers comprised in the preprocessing bill, a target communication number matching a pre-set feature. By means of the present application, the speed and accuracy of number recognition can be improved.

Description

Communication number processing method and device

The present application claims the priority of the Chinese Patent Application, filed on Apr. 25, 2016, which is hereby incorporated by reference.

Technical field

The present application relates to data processing technologies in the field of communication technologies, and in particular, to a communication number processing method and apparatus.

Background technique

Telecommunications fraud refers to the criminal act of criminals making false information, setting up scams, conducting remote and contactless fraud on the victim, and inducing the victim to make money or transfer money to criminals through telephone, internet and SMS. The rise of mobile Internet, telecommunications fraud crimes are increasingly rampant, data shows that the amount of telecommunications fraud is growing at an exponential rate every year. In 2015, the public security organs of the country established a total of 590,000 telecom fraud cases, an increase of 32.5% year-on-year, causing economic losses. 22.2 billion yuan; and behind each case, it may be a family broken by fraud.

In order to curb telecom fraud and prevent users from being scammed by fraudulent phone calls, the prior art collects the tag information of the user by using the application software (app) on the mobile phone. If a certain number is found to be simultaneously marked as a fraudulent number by multiple users, it is considered The number is a fraudulent number and alerts the user who is talking to the fraudulent number to be vigilant to avoid being scammed.

However, on the one hand, the prior art needs to collect user tag information. However, in reality, the probability that the user marks the number is relatively low, and many users often do not need to mark the type of the number when a strange call is received, and the prior art needs After collecting enough user tags, the number can be considered as a fraudulent number. Therefore, the prior art fraudulent number is recognized slowly and inefficiently. On the other hand, in the prior art, the user marks the number is subjective behavior, many When the user receives some harassing calls, such as advertisements and other malicious calls, the harassment number is often marked as a fraudulent number. Therefore, the prior art scam number identification accuracy is low.

Summary of the invention

In view of this, the embodiment of the present application is expected to provide a communication number processing method and apparatus, which can provide The speed and accuracy of high number identification.

In order to achieve the above object, the technical solution of the present application is implemented as follows:

In a first aspect, an embodiment of the present application provides a communication number processing method, where the method includes:

Acquiring a preset number of communication numbers in the first preset time from the communication service device;

Parsing the CDR to obtain the type of the communication information included in the CDR, extracting at least one type of communication information of each communication number in the CDR, and combining to form a pre-processed CDR;

Parsing at least one type of communication information of each communication number in the pre-processed bill, and obtaining characteristics of corresponding types of communication information of each communication number in the pre-processed bill;

Extracting a target communication number that matches the preset feature from the communication number included in the pre-processed bill.

Optionally, the parsing the CDR obtains the type of the communication information included in the CDR, extracts at least one type of communication information of each communication number in the CDR, and combines to form a pre-processed CDR ,include:

Parsing the CDR to obtain at least one of the following types of communication information included in the CDR: a communication initiation number, a communication response number corresponding to the communication initiation number, a communication start time, and a communication duration;

Extracting at least one type of communication information associated with each communication initiation number in the CDR to form a communication record of each communication initiation number;

The extracted communication records of the respective communication initiation numbers are combined to form the pre-processed bill.

Optionally, the parsing the at least one type of communication information of each communication number in the pre-processed bill, and obtaining the characteristics of the corresponding type of communication information of each communication number in the pre-processed bill, including:

Calculating, respectively, an edit distance of each communication initiation number and a yellow page number in the pre-processed bill, wherein the edit distance indicates an operation number of the yellow page number becoming a communication initiation number;

Obtaining a similarity between each communication initiation number and a yellow page number in the preprocessed bill based on the edit distance;

Extracting, from the communication number included in the pre-processed CDR, a target communication number that matches the preset feature, including:

Extracting, from each communication initiation number included in the pre-processed CDR, a communication initiation number whose degree of similarity with the yellow page number is greater than a first threshold;

Or based on the phase of the yellow page number in each communication initiation number included in the pre-processed CDR The ordering of the similarity extracts the first proportion of the communication initiation number with the highest similarity.

Optionally, the parsing the at least one type of communication information of each communication number in the pre-processed bill, and obtaining the characteristics of the corresponding type of communication information of each communication number in the pre-processed bill, including:

Extracting a communication start time of each communication number in the pre-processed bill as a communication initiation number;

Calculating the number of communications of each communication initiation number in the pre-processed bill in a unit time;

Extracting, from the communication number included in the pre-processed CDR, a target communication number that matches the preset feature, including:

Extracting, from each communication initiation number included in the pre-processed CDR, a communication initiation number whose communication time is greater than a second threshold in a unit time;

Alternatively, based on the order of the number of communication times of each communication initiation number included in the pre-processed CDR in a unit time, the communication initiation number of the second ratio with the highest number of communication times is extracted.

Optionally, the parsing the at least one type of communication information of each communication number in the pre-processed bill, and obtaining the characteristics of the corresponding type of communication information of each communication number in the pre-processed bill, including:

Extracting a communication duration of each communication number in the pre-processed bill as a communication initiation number;

Calculating an average communication duration of each communication initiation number in the pre-processed bill;

Extracting, from the communication number included in the pre-processed CDR, a target communication number that matches the preset feature, including:

Extracting, from each communication initiation number included in the pre-processed CDR, a communication initiation number whose average communication duration is greater than a third threshold;

Alternatively, based on the ordering of the average communication durations of the communication initiation numbers included in the pre-processed CDR, the communication initiation number of the third ratio having the highest average communication duration is extracted.

Optionally, the parsing the at least one type of communication information of each communication number in the pre-processed bill, and obtaining the characteristics of the corresponding type of communication information of each communication number in the pre-processed bill, including:

Obtaining a attribution of a communication response number corresponding to each communication number in the pre-processed bill as a communication initiation number;

Calculating, by the number of different attributions of the communication response number corresponding to each communication initiation number in the pre-processed bill;

Extracting a target pass signal matching the preset feature from the communication number included in the pre-processed bill Code, including:

Extracting, from each communication initiation number included in the pre-processed CDR, a communication initiation number whose number of different attributions of the corresponding communication response number is greater than a fourth threshold;

Or, based on the order of the number of different attributions of the communication response numbers corresponding to the communication initiation numbers included in the pre-processed CDR, extracting the fourth proportion of communication with the highest number of different attributions of the corresponding communication response number Initiate the number.

Optionally, extracting, from the communication number included in the pre-processed CDR, a target communication number that matches the preset feature, including:

The machine learning model is used to analyze the characteristics of the corresponding type of communication information of each communication number in the pre-processed bill, and the target communication number matching the preset feature is extracted from the communication number included in the pre-processed bill.

Optionally, the method further includes:

Receiving feedback information of the user side for the target communication number, determining whether the target communication number is a security number;

Determining an error rate of the machine learning model based on the number of target communication numbers in the identified target communication number that are fed back to the security number by the user side;

When the error rate of the machine learning model is greater than the fifth threshold, the machine learning model is retrained based on the communication record of the security number in the pre-processed bill.

Optionally, the machine learning model is retrained based on the communication record of the security number in the pre-processed bill, including:

Parsing at least one type of communication information of the communication record of the security number in the pre-processed bill, and obtaining characteristics of at least one type of communication information of the security number;

Updating a threshold used by the machine learning model to identify the target communication number based on characteristics of at least one type of communication information of the security number.

Optionally, after the target communication number that matches the preset feature is extracted from the communication number included in the pre-processed CDR, the method further includes:

Determining a degree of matching between a feature of the corresponding type of communication information of the target communication number and a preset feature;

Determining a risk level of the target communication number according to a matching degree of a feature of the corresponding type communication information of the target communication number and a preset feature;

Transmitting the communication behavior of the target communication number based on the danger level of the target communication number Should be handled.

Optionally, when determining that the risk level of the target communication number is low risk, responding to the communication behavior of the target communication number, including: performing danger to a user having a communication response number of the communication record with the target communication number Reminder; wherein the danger reminder includes a voice reminder and/or a text reminder;

Or determining, when the risk level of the target communication number is high risk, responding to the communication behavior of the target communication number, comprising: performing an immediate danger reminder to a user of the communication response number that is communicating with the target communication number; Or, directly intercept the ongoing communication with the target communication number.

Alternatively, the real-time level of response processing is positively correlated with the level of danger.

In a second aspect, the embodiment of the present application provides a communication number processing apparatus, where the apparatus includes:

An obtaining module, configured to acquire, from the communication service device, a CDR of a preset number of communication numbers in a first preset time;

a pre-processing module, configured to parse the CDR to obtain a type of communication information included in the CDR, extract at least one type of communication information of each communication number in the CDR, and combine to form a pre-processed CDR ;

a parsing module, configured to parse at least one type of communication information of each communication number in the pre-processed bill, and obtain a feature of the corresponding type of communication information of each communication number in the pre-processed bill;

And an extracting module, configured to extract, from the communication number included in the pre-processed bill, a target communication number that matches the preset feature.

Optionally, the pre-processing module is specifically configured to:

Parsing the CDR to obtain at least one of the following types of communication information included in the CDR: a communication initiation number, a communication response number corresponding to the communication initiation number, a communication start time, and a communication duration;

Extracting at least one type of communication information associated with each communication initiation number in the CDR to form a communication record of each communication initiation number;

The extracted communication records of the respective communication initiation numbers are combined to form the pre-processed bill.

Optionally, the parsing module is specifically configured to: separately calculate an edit distance of each communication initiation number and a yellow page number in the preprocessed bill; and obtain, according to the edit distance, each communication initiation in the preprocessed bill The similarity between the number and the yellow page number, wherein the edit distance indicates the number of operations of the yellow page number becoming the communication initiation number;

The extracting module is specifically configured to: extract, from each communication initiation number included in the pre-processed CDR, a communication initiation number whose degree of similarity with the yellow page number is greater than a first threshold; or, based on the pre-processing words The ranking of the similarity of the yellow page numbers among the communication initiation numbers included in the single is extracted, and the communication initiation number of the first ratio with the highest similarity is extracted.

Optionally, the parsing module is specifically configured to: extract a communication start time of each communication number in the pre-processed bill as a communication initiation number; and calculate each communication initiation number in the pre-processed bill in a unit time Number of communications;

The extracting module is specifically configured to: extract, from each communication initiation number included in the pre-processed CDR, a communication initiation number whose communication time is greater than a second threshold in a unit time; or, based on the pre-processed CDR The number of communication times of each communication initiation number in the unit time is sorted, and the communication ratio number of the second ratio with the highest communication number is extracted.

Optionally, the parsing module is specifically configured to: extract a communication duration of each communication number in the pre-processed bill as a communication initiation number; and calculate an average communication duration of each communication initiation number in the pre-processed bill;

The extracting module is specifically configured to: extract, from each communication initiation number included in the pre-processed CDR, a communication initiation number whose average communication duration is greater than a third threshold; or, based on each communication included in the pre-processed CDR The order of the average communication duration of the originating number is extracted, and the third ratio of the communication originating number with the highest average communication duration is extracted.

Optionally, the parsing module is specifically configured to: acquire a attribution of a communication response number corresponding to each communication number in the pre-processed bill as a communication initiation number; and calculate each communication initiation number in the pre-processed bill The number of different attributions of the corresponding communication response number;

The extracting module is specifically configured to: extract, from each communication initiation number included in the pre-processed CDR, a communication initiation number whose number of different attributions of the corresponding communication response number is greater than a fourth threshold; or, based on the The order of the number of different attributions of the communication response number corresponding to each communication initiation number included in the pre-processing CDR is extracted, and the communication initiation number of the fourth ratio having the highest number of different attributions of the corresponding communication response number is extracted.

Optionally, the extracting module is specifically configured to: analyze, by using a machine learning model, a feature of the corresponding type of communication information of each communication number in the pre-processed bill, from the communication number included in the pre-processed bill Extract the target communication number that matches the preset feature.

Optionally, the device further includes:

a training module, configured to receive feedback information of the user side for the target communication number, and determine the target Whether the communication number is a security number; determining an error rate of the machine learning model based on the number of the target communication numbers that are fed back to the security number by the user side in the identified target communication number; the error rate of the machine learning model is greater than the fifth At the threshold, the machine learning model is retrained based on the communication record of the security number in the pre-processed bill.

Optionally, the training module is configured to: parse at least one type of communication information of the communication record of the security number in the pre-processed bill, and obtain at least one type of communication information of the security number. Having a feature; updating a threshold used by the machine learning model to identify the target communication number based on characteristics of at least one type of communication information of the security number.

Optionally, the device further includes:

a response module, configured to determine a degree of matching between a feature of the corresponding type of communication information of the target communication number and a preset feature; and a matching degree between a feature of the corresponding type of communication information of the target communication number and a preset feature Determining a risk level of the target communication number; and responding to the communication behavior of the target communication number based on the risk level of the target communication number.

Compared with the prior art, it is required to collect the marking information of the user, and the embodiment of the present application obtains the characteristics of the corresponding type of communication information of each communication number by parsing the CDR of the preset number of communication numbers in the first preset time, and based on each The corresponding type of communication information of the communication number has characteristics for extracting the target communication number matching the preset feature from each communication number. On the one hand, the communication number CDR is objective data maintained by the operator, and can be truly and completely reflected. For all the communication records of the user in a certain time interval, the embodiment of the present application uses the communication number CDR as the processing basis, which can improve the accuracy of the number identification. On the other hand, since the generation and maintenance process of the CDR generally does not require each user. The direct participation of the operator is responsible for the speed and efficiency of the communication number CDRs. Therefore, the embodiment of the present application can improve the speed and accuracy of the number identification.

DRAWINGS

FIG. 1 is a schematic diagram of an optional application scenario of a method for processing a communication number in an embodiment of the present application;

2 is an optional schematic flowchart of a method for processing a communication number according to Embodiment 1 of the present application;

FIG. 3 is an optional schematic flowchart of a method for processing a communication number according to Embodiment 2 of the present application;

4 is an optional schematic flowchart of a method for processing a communication number in Embodiment 3 of the present application;

FIG. 5 is an optional schematic flowchart of a method for processing a communication number according to Embodiment 4 of the present application;

FIG. 6 is an optional schematic flowchart of a method for processing a communication number according to Embodiment 5 of the present application;

FIG. 7 is an optional schematic flowchart of a method for processing a communication number according to Embodiment 6 of the present application;

FIG. 8 is an optional schematic flowchart of a method for processing a communication number according to Embodiment 7 of the present application;

FIG. 9 is an optional schematic flowchart of a method for processing a communication number according to Embodiment 8 of the present application;

10 is an optional schematic flowchart of a method for processing a communication number in Embodiment 9 of the present application;

FIG. 11 is an optional schematic diagram of a user application running on a user equipment in a state of receiving a user indication according to an embodiment of the present disclosure;

FIG. 11b is an optional schematic diagram of a user application running on a user equipment in a text reminding state according to an embodiment of the present application;

FIG. 12 is an optional structural diagram of a communication number processing apparatus according to an embodiment of the present application;

FIG. 13 is another schematic structural diagram of a communication number processing apparatus according to an embodiment of the present application;

FIG. 14 is still another schematic structural diagram of a communication number processing apparatus according to an embodiment of the present application.

detailed description

The present application will be further described in detail below with reference to the accompanying drawings and embodiments. It is understood that the specific embodiments described herein are merely illustrative of the application and are not intended to be limiting.

The embodiment of the present application describes a communication number processing method. Referring to FIG. 1 , an optional application scenario of the communication number processing method in the embodiment of the present application, the user equipment 11 , the user equipment 12 , the user equipment 13 , and the network device 14 . (such as carrier gateway or enterprise gateway), communication service device 15, application background server 16 respectively access communication network (such as wireless network or wired network), communication service device 15 such as business support system (BSS, Business Support System) / An operation support system (OSS), or a telecommunication switch; the communication service device 15 is configured to provide a bill for a communication number; the network device 14 is configured to provide service support for each user equipment accessing the communication network; 16 is used to provide service support for the application; here, corresponding to the background server 16 of the application, the client of the application installed on the user equipment is also used to provide service support for the application; the application may be a communication application, for example: Tencent Mobile phone housekeeper, WeChat, Tencent mailbox, etc. Of course, applications are not limited to communication applications. The application device does not specifically limit this; in the above scenario, the number of user equipments is at least one, and each user equipment is associated with at least one different communication number, for example, the user equipment 11 shown in FIG. 1 is associated with at least one communication. The number A, the user equipment 12 is associated with at least one communication number B, and the user equipment 13 is associated with at least one communication number C. The communication number A, the communication number B and the communication number C are different from each other. Applicable to the above scenario, the communication number that satisfies the preset condition is identified from the plurality of communication numbers.

The embodiment of the present application further describes a communication number processing apparatus, which can be used to execute the embodiment of the present application. Communication number processing method; the communication number processing device can be implemented in various manners, for example, in a user device such as a smart phone, a fixed telephone, a tablet computer, a notebook computer, a wearable device (such as smart glasses, a smart watch, etc.) All components of the device, or all components of the device are implemented in a network device such as an enterprise gateway or a carrier gateway, or the components in the device are implemented in a coupled manner on the user device side or the network side, or the communication number processing device It can also be a client application or a background server of the user application. For example, when the user application is a Tencent mobile phone housekeeper, the corresponding communication number processing device can be a client or a background server of the Tencent mobile phone housekeeper.

Based on the application scenario and the communication number processing device described above, the following specific embodiments are proposed.

Embodiment 1

The embodiment provides a communication number processing method, which can be applied to a scenario in which a communication number that satisfies a preset condition needs to be identified from multiple communication numbers, for example, identification of a network-wide number in a communication network, or The identification of the communication number to be identified, or the identification of the communication number for communicating with the current user; the type of communication service includes but is not limited to any one of the following service types or combinations: voice call; short message; flash message; Data services (such as WeChat), this application is not limited to this.

Based on the foregoing communication number processing apparatus, referring to FIG. 2, the communication number processing method provided in this embodiment includes the following steps:

Step 201: Acquire, from the communication service device, a bill of a preset number of communication numbers in a first preset time.

The communication service device may include a telecommunication support system device, such as a BSS/OSS, or a telecommunication switch; the first preset time may be flexibly set by the user or the operator according to actual conditions such as actual service requirements; the communication number is not limited to the mobile phone number and fixed. a number or the like; the communication number may include, for example, all communication numbers in the communication network, or a communication number to be identified indicated by the user, or a communication number to be called with the current user; wherein the communication number indicated by the user, such as the user The communication number to be identified specified in the application running on the user equipment (such as the Tencent mobile phone housekeeper), or the user sends an indication message carrying the communication number to be identified to the operator server.

The implementation manner of obtaining the CDR of the preset number of communication numbers in the first preset time from the communication service device may be at least one of the following manners:

1) acquiring, from the communication service device, the CDRs of all the communication numbers in the communication network in the first preset time;

2) obtaining, according to the communication number to be identified indicated by the current user, a bill of the communication number to be identified in the first preset time from the communication service device;

3) When detecting the communication number of the current user, the communication service device acquires the CDR of the communication number of the current user in the first preset time;

4) When it is determined that the communication number of the current user is a strange communication number, the communication service device obtains the CDR of the strange communication number within the first preset time.

In step 202, the CDR is parsed to obtain the type of the communication information included in the CDR, and at least one type of communication information of each communication number in the CDR is extracted and combined to form a pre-processed CDR.

The CDRs of the preset number of communication numbers that are obtained from the communication service device in the first preset time are generally out of order. In this embodiment, the pre-processed CDRs are formed by using the communication numbers as a dimension, and the pre-processing CDRs are formed. The communication information includes at least one type of communication information corresponding to at least one of the following communication numbers: the communication number is used as the calling number (such as the calling number in the voice service), and the communication number is used as the called number (such as in the voice service) The called number), the communication number is used as the information transmission number (such as the short message transmission number or the data transmission number in the data service), and the communication number is used as the information reception number (such as the short message receiving number or the data receiving number in the data service).

The pre-processed CDR includes only at least one type of communication information of each communication number extracted from the CDR, that is, the pre-processed CDR does not need to include all the information in the CDR; the data of the pre-processed CDR is Each communication number is used as an index to preprocess the data structure of the bill, for example:

Calling number 1 in the voice service: communication information 1, communication information 2, ...;

Calling number 2 in the voice service: communication information 3, communication information 4, ...;

SMS sending number 3: communication information 5, communication information 6, ...;

Data transmission number 4 in the data service: communication information 7, communication information 8, ....

For example, the pre-processing bills indexed by using each communication number as the calling number are shown in Table 1. For the data structure example in Table 1, the calling number, the called number, the communication start time, and the communication duration are shown here. (Second) is a partial example of the type of communication information included in the CDR.

Table 1

Calling number	Called number	Communication start time	Communication duration (seconds)
158xxxx0001	186xxxx0002	2016-01-15 15:32:42	134
158xxxx0001	139xxxx0001	2016-01-15 15:39:02	15
158xxxx0001	139xxxx0002	2016-01-15 15:48:02	123
170xxxx0001	186xxxx0001	2016-01-16 8:30:02	77

170xxxx0001

139xxxx0002

2016-01-17 9:26:02

256

Step 203: Analyze at least one type of communication information of each communication number in the pre-processed bill, and obtain a feature of the corresponding type of communication information of each communication number in the pre-processed bill.

Step 204: Analyze characteristics of corresponding types of communication information of each communication number in the pre-processed bill, and determine whether the feature of the corresponding type of communication information of each communication number matches the preset feature, and if yes, go to step 205, otherwise The process ends.

Step 205: Extract a target communication number that matches the preset feature from the communication number included in the pre-processed bill.

The characteristics of the corresponding type of communication information of each communication number in the pre-processed bill are analyzed, and the characteristics of the corresponding type of communication information are matched with the preset features from the communication numbers included in the pre-processed bill. The target communication number; the preset feature is, for example, a pre-set a priori value.

Compared with the prior art that needs to implement the identification number on the basis of collecting the user tag information, the present embodiment parses the bill of the communication number to obtain the characteristics of the corresponding type of communication information of the communication number, and based on the corresponding type of the communication number. The communication information has characteristics that identify the target communication number that matches the preset feature from each communication number. On the one hand, since the generation and maintenance process of the communication number CDR is generally performed by the operator, the participation of each user is not required. The acquisition speed and efficiency of the communication number CDR are high. On the other hand, since the CDR of the communication number is objective data maintained by the operator, it can truly and completely reflect all communication records of the user within a certain time interval, so The technical solution provided by the embodiment of the present application is based on the CDR of the communication number, and can improve the speed and accuracy of the number identification.

Embodiment 2

This embodiment is based on the first embodiment, and specifically determines how to parse the CDR to obtain the type of the communication information included in the CDR, and extracts at least one type of communication information of each communication number in the CDR and combines to form a pre-processed CDR. The scenario that proposes a solution to the technical solution.

Referring to FIG. 3, the communication number processing method provided in this embodiment includes the following steps:

Step 301: Acquire, from the communication service device, a bill of a preset number of communication numbers in a first preset time.

Step 302: Parsing the CDR to obtain at least one of the following types of communication information included in the CDR: a communication initiation number, a communication response number corresponding to the communication initiation number, a communication start time, and a communication duration.

The communication initiation number may include a communication number as a calling number (such as a calling number in a voice service), and a communication number (such as a short message transmission number or a data transmission number in a data service) as an information transmission number; The communication response number of the number may include a communication number as the called number (such as the called number in the voice service), and a communication number (such as a short message receiving number or a data receiving number in the data service) as the information receiving number; It can be understood by those skilled in the art that the type of communication information included in the CDR is not limited to the above-mentioned communication initiation number, the communication response number corresponding to the communication initiation number, the communication start time, the communication duration, etc., and the type of communication information can also be Including data traffic (upstream traffic and/or downstream traffic), communication location, service type, long-distance type, etc.; this application is not limited thereto.

Step 303: Extract at least one type of communication information associated with each communication initiation number in the CDR to form a communication record of each communication initiation number.

Step 304: Combine the extracted communication records of each communication initiation number to form a pre-processed bill.

Here, the pre-processed CDR only includes at least one type of communication information of each communication number extracted from the CDR, and the pre-processed CDR does not include all the information in the CDR, which can reduce the workload of the communication number processing and improve Communication number processing efficiency.

The CDRs of the preset number of communication numbers that are obtained from the communication service device in the first preset time are generally out of order. The CDRs shown in Table 2 are taken as an example. The communication start time, service type, and communication initiation are used here. The number, the communication response number, the communication place, the long distance type, and the communication duration (seconds) are partial examples of the types of communication information included in the CDR.

Table 2

The communication number processing apparatus parses the CDR shown in Table 2 to obtain at least one of the following types of communication information included in the CDR: a communication initiation number; a communication response number corresponding to the communication initiation number; and a communication start time; Communication duration;

The communication number processing device extracts at least one type of communication information associated with each communication initiation number in the CDR to form a communication record of each communication initiation number; where the communication record of each communication initiation number includes the communication number at the first At least one type of communication information within a preset time;

The communication records of the extracted communication initiation numbers are combined to form a pre-processed CDR; the pre-processed CDRs are statistically formed by using each communication number as a dimension, and the data structure (or display mode) in the pre-processed CDR is used for each communication number. For the index organization, it is assumed that at least one type of communication information corresponding to each communication number is a communication initiation number to form a pre-processed bill, and the data structure of the pre-processed bill can be:

Communication initiation number 1: communication information 1, communication information 2, ...;

Communication initiation number 2: communication information 1, communication information 2, ...;

Taking the pre-processing bill shown in Table 3 as an example, the pre-processing bill shown in Table 3 is obtained by performing the steps 202-204 on the basis of the bill shown in Table 2 by the communication number processing device; The pre-processed CDRs are organized by indexing each communication initiation number.

table 3

Communication origination number	Communication response number	Communication start time	Communication duration (seconds)
158xxxx0001	186xxxx0002	2016-01-15 15:32:42	134
158xxxx0001	186xxxx0007	2016-01-15 15:42:02	97
158xxxx0001	139xxxx0006	2016-01-15 15:48:02	123
158xxxx0001	187xxxx0002	2016-01-15 15:52:07	256
170xxxx0001	186xxxx0001	2016-01-15 15:39:02	15
170xxxx0001	180xxxx0007	2016-01-15 15:51:02	77
170xxxx0001	139xxxx0002	2016-01-16 10:26:02	--

Step 305: Analyze at least one type of communication information of each communication number in the pre-processed bill, and obtain a feature of the corresponding type of communication information of each communication number in the pre-processed bill.

Step 306: Analyze the characteristics of the corresponding type of communication information of each communication number in the pre-processed bill, and determine whether the feature of the corresponding type of communication information of each communication number matches the preset feature, and if yes, go to step 307, otherwise The process ends.

Step 307: Extract a target communication number that matches the preset feature from the communication number included in the pre-processed bill.

This embodiment is directed to how to parse a CDR to obtain the type of communication information included in the CDR, and extract at least one type of communication information of each communication number in the CDR and combine to form a pre-processed CDR. The CDR obtains at least one type of communication information included in the CDR, and extracts at least one type of communication information associated with each communication initiation number in the CDR to form a communication record of each communication initiation number, and extracts each extracted The communication record combination of the communication initiation number forms a pre-processed CDR, and the formed pre-processed CDR includes only at least one type of communication information of each communication number extracted from the CDR, and the pre-processed CDR does not include the CDR All the information in the book can reduce the workload of number identification and improve the speed and efficiency of number identification.

Embodiment 3

The embodiment is based on the first embodiment, and the editing distance of the communication initiation number and the yellow page number is used as the feature of the communication number, and the technical solution for specifically identifying the communication number that meets the preset condition from the plurality of communication numbers is described. The communication number processing method includes the following steps:

1) Acquire a bill of a preset number of communication numbers in the first preset time from the communication service device.

2) Parsing the CDR to obtain the type of communication information included in the CDR, extracting at least one type of communication information of each communication number in the CDR and combining to form a pre-processed CDR.

3) Calculate the edit distance of each communication initiation number and yellow page number in the pre-processed bill separately.

The yellow page number can be one or more; the edit distance refers to the minimum number of editing operations required to convert the yellow page number into the communication initiation number, that is, by adding, reducing, modifying, and moving the number of the yellow page number into communication. The number of operations for initiating a number; in the scenario where the yellow page number is multiple, for each communication initiation number in the pre-processed bill, the edit distance of the communication initiation number and each yellow page number needs to be separately calculated.

4) Based on the edit distance, the similarity between each communication initiation number and the yellow page number in the pre-processed bill is obtained.

At least one of the following methods may be used to obtain each communication in the pre-processed bill based on the edit distance. The similarity between the originating number and the yellow page number:

Method 1, for each communication initiation number in the pre-processed bill, normalizing the calculated communication initiation number and the edit distance of each yellow page number to obtain the communication initiation number and each yellow page number. Similarity; further, the similarity of the communication initiation number to each yellow page number is sorted.

Method 2, for each communication initiation number in the pre-processed bill, calculating a ratio of the edit distance of the communication initiation number to the yellow page number and the preset distance, and calculating the similarity between the calculated ratio communication initiation number and the yellow page number; In a scenario where the yellow page number is multiple, the ratio of the edit distance of the communication initiation number to each yellow page number to the preset distance needs to be separately calculated.

5) determining whether the similarity between each communication initiation number and the yellow page number included in the pre-processed CDR is greater than a first threshold, and if so, extracting the similarity with the yellow page number from each communication initiation number included in the pre-processed CDR is greater than the first A threshold communication initiation number is used as the target communication number; otherwise the process terminates.

The initial value of the first threshold (ie, the similarity threshold) may be calculated by manual setting or training, for example, determining, according to the a priori value, the target number of the target communication number in each communication initiation number included in the pre-processed bill; The similarity between the communication initiation number and the yellow page number is sorted; the communication initiation number of the target number is selected according to the order of decreasing similarity; and the communication initiation number of the selected communication initiation number having the smallest similarity with the yellow page number is similar Degree, determined as the initial value of the first threshold. The first threshold can be continuously updated by training calculation according to actual needs.

In a feasible implementation manner, the communication number processing device sorts the similarity between each communication initiation number and the yellow page number included in the pre-processed bill based on the similarity between each communication initiation number and the yellow page number in the pre-processed bill; The order of the similarity between each communication initiation number and the yellow page number included in the pre-processing CDR is extracted from the communication initiation numbers included in the pre-processed CDR, and the first proportional communication initiation number with the highest similarity is extracted as the target communication number.

In another feasible implementation manner, for any one of the communication initiation numbers included in the pre-processing CDR, the communication number processing device determines, according to the similarity between the communication initiation number and the yellow page number and the first threshold, respectively. The probability that the communication initiation number belongs to the target communication number (such as the fraud number) and the probability of belonging to the normal number category, and the class corresponding to the larger probability value is used as the class to which the communication initiation number belongs; The class is the target communication number class, and it is determined that the communication initiation number is the target communication number, and vice versa, the communication initiation number is determined to be the normal number.

The implementation of this embodiment depends on the cooperation of the user equipment, the server, and the communication service device. Here, the user equipment may be, for example, a smart phone, a fixed phone, a tablet computer, a notebook computer, or a wearable device. The device may be, for example, a service server of an operator, an enterprise gateway, a background server of an application installed on the user equipment, and the like; the communication service device may be, for example, a BSS/OSS or a telecommunication switch; Specifically, it can be a communication application, for example, a Tencent mobile phone housekeeper, a WeChat, a Tencent mailbox, etc., of course, the application is not limited to the communication application, which is not specifically limited in the embodiment of the present application; The server and the communication service device cooperate with each other to implement an optional flowchart of the communication number processing method provided by the embodiment, and the method includes:

Step 401: The user equipment sends an identification indication carrying the to-be-identified communication number to the server, based on the user indication.

For example, referring to FIG. 11a, the user application running on the user equipment is in the receiving user indication state, and the user inputs the to-be-identified communication number in the designated location according to the prompt of the application in the display window of the application installed in the user equipment; The number can be one or more.

Step 402: The server receives the identification indication, and sends a CDR request carrying the to-be-identified communication number to the communication service device according to the identification indication. The CDR request includes the to-be-identified communication number and the first preset time.

Step 403: The communication service device receives the CDR request, and obtains the CDR of the to-be-identified communication number in the first preset time based on the CDR request, and sends the CDR to the server.

Step 404: The server receives the bill of the to-be-identified communication number in the first preset time.

In step 405, the CDR is parsed to obtain the type of the communication information included in the CDR, and at least one type of communication information of each communication number to be identified in the CDR is extracted and combined to form a pre-processed CDR.

Step 406: Calculate an edit distance of each to-be-identified communication number and a yellow page number in the pre-processed bill.

Step 407: Obtain a similarity between each to-be-identified communication number and the yellow page number in the pre-processed bill based on the edit distance.

Step 408: Determine whether the similarity between each to-be-identified communication number and the yellow page number included in the pre-processed CDR is greater than a first threshold. If yes, go to step 409, otherwise the process terminates.

Step 409: Extract, from the to-be-identified communication numbers included in the pre-processed CDR, a communication initiation number whose degree of similarity with the yellow page number is greater than the first threshold, as the target communication number.

Step 410: The server sends an identification response carrying the target communication number to the user equipment based on the identified target communication number, where the identification response is used to perform a dangerous reminder to the user, and the user is reminded that the identified target communication number may be a fraudulent number; Implementation methods include, but are not limited to, reminding by communication applications such as SMS, Flash, WeChat, and Tencent Mobile Manager; the server can also perform dangerous reminding to the user equipment directly through the customer service phone when the target communication number is identified.

At the same time, based on the identified target communication number, the server may also perform a danger reminder to the user who has the communication response number of the communication record of the identified target communication number or the user who is communicating with the identified target communication number. To avoid users being cheated.

After receiving the identification response of the carrying target communication number sent by the server, the user equipment performs a dangerous reminder on the user based on the target communication number; for example, referring to FIG. 11b, the user application running on the user equipment is in a text reminding state, and the user equipment is installed on the user equipment. The display window of the application of the user equipment displays, for example, the following text reminder message "Please be vigilant! The target communication number is a fraudulent number"; the user applications here include, but are not limited to, SMS, Flash, WeChat, Tencent mobile butler, and other communication applications; Of course, the application is not limited to the communication application, which is not specifically limited in the embodiment of the present application.

The embodiment is directed to how to obtain the characteristics of the corresponding type of communication information of each communication number in the pre-processed bill, and extract the scene of the target communication number that matches the preset feature from the communication number included in the pre-processed bill. By pre-processing the CDRs on the basis of the parsing, the editing distances of the communication initiation numbers and the yellow page numbers in the pre-processed CDRs are respectively calculated, and the communication initiation numbers and the yellow page numbers in the pre-processed CDRs are obtained based on the editing distance. The degree of similarity (that is, the communication number is one of the characteristics of the communication initiation number), and the communication initiation number whose degree of similarity with the yellow page number is greater than the preset first threshold is extracted from each communication initiation number included in the pre-processed bill as The destination communication number, or the first generation of the communication initiation number with the highest degree of similarity is extracted as the target communication number based on the ranking of the similarity of the yellow number of each of the communication initiation numbers included in the pre-processing CDR; The similarity between each communication initiation number and the yellow page number in the pre-processed bill is characterized by a first threshold The preset feature, by determining the relative relationship between the similarity between each communication initiation number and the yellow page number included in the pre-processed CDR and the first threshold, extracting the target communication that matches the preset feature from the communication number included in the pre-processed CDR The number enables fast and accurate number identification.

Embodiment 4

The embodiment is based on the first embodiment, and the communication number of the communication initiation number in the unit time is taken as the characteristic of the communication number, and the technical solution for specifically identifying the communication number that meets the preset condition from the plurality of communication numbers is described. The communication number processing method provided includes the following steps:

1) Acquire a bill of a preset number of communication numbers in the first preset time from the communication service device.

2) Parsing the CDR to obtain the type of communication information included in the CDR, extracting at least one type of communication information of each communication number in the CDR and combining to form a pre-processed CDR.

3) Extract the communication start time of each communication number in the pre-processed bill as the communication initiation number.

4) Calculate the number of communication times of each communication initiation number in the pre-processing bill in unit time.

In practice, the number of communications of the communication initiation number in a unit time may include any of the following:

Mode 1, the communication initiation number and the number of communication of the same number in the unit time;

Mode 2: The number of communication times between the communication initiation number and all communication numbers with which it communicates.

5) judging whether the number of communications of each communication initiation number included in the pre-processed CDR is greater than a second threshold, and if so, extracting the number of communications in the unit time from each communication initiation number included in the pre-processed CDR A communication initiation number greater than the second threshold is used as the target communication number; otherwise, the flow is terminated.

The initial value of the second threshold may be calculated by manual setting or training, for example, determining, according to the a priori value, the target number of the target communication number in each communication initiation number included in the pre-processed CDR; and each communication initiation number in the unit time Sorting the number of communication times; selecting the communication initiation number of the target number in the order of decreasing the number of communication times per unit time; corresponding to the communication initiation number of the selected communication initiation number having the smallest number of communication times per unit time The number of communications in a unit time is determined as the initial value of the second threshold. The second threshold can be continuously updated by training calculation according to actual needs.

In a feasible implementation manner, the communication number processing device sorts the communication times of each communication initiation number included in the pre-processed bill in the unit time based on the number of communication times of each communication initiation number in the pre-processed bill. And sorting the number of communication times of each communication initiation number included in the pre-processing CDR in a unit time, and extracting, from each communication initiation number included in the pre-processed CDR, a communication ratio number of the second ratio with the highest communication number as the target Communication number.

The implementation of this embodiment depends on the cooperation of the user equipment, the server, and the communication service device. Here, the user equipment may be, for example, a smart phone, a fixed phone, a tablet computer, a notebook computer, a wearable device (such as smart glasses, a smart watch, etc.). The server may be, for example, a service server of an operator, an enterprise gateway, a back-end server installed in an application of the user equipment, or the like; the communication service device may be, for example, a BSS/OSS or a telecommunication switch; and the application may specifically be a communication application, for example, a Tencent mobile phone. The appliance, the WeChat, the Tencent mailbox, and the like, of course, the application is not limited to the communication application, and is not specifically limited in the embodiment of the present application; the user equipment, the server, and the communication service device shown in FIG. 5 cooperate with each other to implement the implementation. An optional flowchart of the communication number processing method provided by the example includes:

Step 501: When detecting the communication number of the opposite party with the current user, the user equipment (or the application installed on the user equipment) sends an identification indication carrying the communication number of the opposite party to the server.

Step 502: The server receives the identification indication, and sends a CDR request carrying the communication number of the opposite party to the communication service device according to the identification indication. The CDR request includes the communication number of the opposite party and the first preset time.

Step 503: The communication service device receives the bill request, and obtains the bill of the other party communication number in the first preset time based on the bill, and sends the bill to the server.

Step 504: The server receives a bill of the communication number of the other party in the first preset time.

In step 505, the CDR is parsed to obtain the type of the communication information included in the CDR, and at least one type of communication information of the other party's communication number in the CDR is extracted and combined to form a pre-processed CDR.

Step 506: Extract the communication start time of the counterpart communication number in the pre-processed bill as the communication initiation number.

Step 507: Calculate the number of communications of the counterpart communication number in the unit time in the pre-processed bill.

Step 508: Determine whether the communication number of the counterpart communication number included in the pre-processed bill is greater than a second threshold in the unit time, and if yes, go to step 509, otherwise the process terminates.

Step 509: Extract, from the communication number of the other party included in the pre-processing CDR, a communication initiation number whose communication time in the unit time is greater than a second threshold, as the target communication number.

Step 510: The server performs a dangerous reminder to the user based on the identified target communication number, and reminds the user that the identified target communication number may be a fraudulent number; the implementation of the dangerous reminder includes but is not limited to a short message, a flash message, a WeChat, and a Tencent mobile phone. The communication application such as the housekeeper reminds the server; the server can also perform a dangerous reminder to the user equipment directly through the customer service phone when the target communication number is recognized.

At the same time, based on the identified target communication number, the server may also perform a danger reminder to the user having the communication response number of the communication record of the identified target communication number or the user of the communication response number being communicated with the identified target communication number. To avoid users being cheated.

After receiving the identification response of the carrying target communication number sent by the server, the user equipment performs a dangerous reminder on the user based on the target communication number; for example, referring to FIG. 11b, the user equipment displays, for example, the following text reminding information in a display window of an application installed in the user equipment. "Please be vigilant! The target communication number is a fraudulent number"; the user application here includes, but is not limited to, a communication application such as a short message, a flash message, a WeChat, a Tencent mobile phone housekeeper, etc.; of course, the application is not limited to the communication application, and the embodiment of the present application This is not specifically limited.

The embodiment is directed to how to obtain the characteristics of the corresponding type of communication information of each communication number in the pre-processed bill, and extract the scene of the target communication number that matches the preset feature from the communication number included in the pre-processed bill. By obtaining the pre-processed CDRs based on the parsing of the dialog, respectively, the number of communication times of each communication initiation number in the pre-processed CDRs in the unit time is calculated (ie, the communication number is used as One of the characteristics of the communication initiation number), extracting, from each communication initiation number included in the pre-processed bill, a communication initiation number whose communication number in a unit time is greater than a preset second threshold as the target communication number, or based on Pre-processing the number of times of communication in the communication initiation number included in the pre-processing CDRs, and extracting the communication-initiated number of the second ratio, which is the highest number of communication times, as the target communication number; The communication initiation number is characterized by the number of communication times per unit time, and the second threshold is a preset feature, and the relative relationship between the communication times of each communication initiation number included in the pre-processed CDR and the second threshold is determined. The target communication number matching the preset feature is extracted from the communication number included in the pre-processed CDR, and fast and accurate number identification is realized.

Embodiment 5

The embodiment is based on the first embodiment, and is configured to obtain at least one type of communication information of each communication number in the pre-processed bill, and obtain the characteristics of the corresponding type of communication information of each communication number in the pre-processed bill, and The scenario in which the target communication number matching the preset feature is extracted from the communication number included in the pre-processing CDR is proposed, and a technical solution is proposed.

Referring to FIG. 6, the communication number processing method provided in this embodiment includes the following steps:

Step 601: Acquire, from the communication service device, a bill of a preset number of communication numbers in a first preset time.

Step 602: Parse the CDR to obtain the type of the communication information included in the CDR, and extract at least one type of communication information of each communication number in the CDR and combine to form a pre-processed CDR.

Step 603: Extract the communication duration of each communication number in the pre-processed bill as the communication initiation number.

Step 604: Calculate an average communication duration of each communication initiation number in the pre-processed bill.

In practice, the average communication duration of the communication initiation number may include any of the following:

1) The average communication duration of the communication initiation number and the same number;

2) The average communication duration of the communication initiation number and all communication numbers with which it communicates.

Step 605: Determine whether the average communication duration of each communication initiation number included in the pre-processed CDR is greater than a third threshold. If yes, go to step 606, otherwise the process terminates.

The initial value of the third threshold can be calculated manually or by training, for example:

Determining, according to the a priori value, a target number of the target communication numbers in each communication initiation number included in the pre-processed bill;

Sorting the average communication duration of each communication initiation number;

Selecting a target number of communication initiation numbers in the order of decreasing average communication duration;

The average communication duration corresponding to the communication initiation number having the smallest average communication duration in the selected communication initiation number is determined as the initial value of the third threshold.

The third threshold can be continuously updated through training calculation according to actual needs.

Step 606: Extract, from each communication initiation number included in the pre-processed CDR, a communication initiation number whose average communication duration is greater than a third threshold, as the target communication number.

In a feasible implementation manner, the communication number processing device sorts the average communication duration of each communication initiation number included in the pre-processed bill based on the average communication duration of each communication initiation number in the pre-processed bill; The order of the average communication duration of each of the included communication initiation numbers is extracted, and the communication initiation number of the third ratio having the highest average communication duration is extracted from each communication initiation number included in the pre-processed bill as the target communication number.

The embodiment is directed to how to obtain the characteristics of the corresponding type of communication information of each communication number in the pre-processed bill, and extract the scene of the target communication number that matches the preset feature from the communication number included in the pre-processed bill. By obtaining the pre-processed CDRs based on the parsing of the dialog, respectively calculating the average communication duration of each communication initiation number in the pre-processed CDR (ie, the communication number is one of the characteristics of the communication initiation number), from the pre-processing Extracting, by each communication initiation number included in the CDR, a communication initiation number whose average communication duration is greater than a preset third threshold as the target communication number, or sorting based on the average communication duration of each communication initiation number included in the pre-processed CDR The communication initiation number of the third ratio with the highest average communication duration is used as the target communication number. The embodiment of the present application is characterized by the average communication duration of each communication initiation number in the pre-processed bill, and the third threshold is a preset feature. The average communication duration of each communication initiation number included in the pre-processed bill is compared with the third threshold Relationship, from the extracted communication number included in the telephone bill pretreatment target communication number matches a preset characteristics to achieve a rapid and accurate identification number.

Embodiment 6

The embodiment is based on the first embodiment, and is configured to obtain at least one type of communication information of each communication number in the pre-processed bill, and obtain the characteristics of the corresponding type of communication information of each communication number in the pre-processed bill, and The scenario in which the target communication number matching the preset feature is extracted from the communication number included in the pre-processing CDR is proposed, and a technical solution is proposed.

Referring to FIG. 7, the communication number processing method provided in this embodiment includes the following steps:

Step 701: Acquire, from the communication service device, a bill of a preset number of communication numbers in a first preset time.

In step 702, the CDR is parsed to obtain the type of the communication information included in the CDR, and at least one type of communication information of each communication number in the CDR is extracted and combined to form a pre-processed CDR.

Step 703: Extract the attribution of the communication response number corresponding to each communication number in the pre-processed bill as the communication initiation number.

Step 704: Calculate the number of different attributions of the communication response number corresponding to each communication initiation number in the pre-processed bill.

Step 705: Determine whether the number of different attributions of the communication response number corresponding to each communication initiation number included in the pre-processed CDR is greater than a fourth threshold. If yes, go to step 706, otherwise the process terminates.

The initial value of the fourth threshold can be calculated manually or by training, for example:

Determining, according to the a priori value, a target number of the target communication numbers in each communication initiation number included in the pre-processed bill;

Sorting the average communication duration of each communication initiation number;

Selecting a target number of communication initiation numbers according to an order in which the number of different attributions of the corresponding communication response numbers is decreasing;

The number of different attributions of the communication response number corresponding to the communication initiation number having the smallest number of different attributions of the communication response number corresponding to the selected communication initiation number is determined as an initial value of the fourth threshold.

The fourth threshold can be continuously updated through training calculation according to actual needs.

Step 706: Extract, from each communication initiation number included in the pre-processed CDR, a communication initiation number whose number of different attributions of the corresponding communication response number is greater than a fourth threshold, as the target communication number.

In a feasible implementation manner, the communication number processing device is based on the average communication duration of each communication initiation number in the pre-processed bill, and the number of different attributions of the communication response number corresponding to each communication initiation number included in the pre-processed bill. Sorting; sorting the number of different attributions of the communication response number corresponding to each communication initiation number included in the pre-processing CDR, and extracting the difference of the corresponding communication response number from each communication initiation number included in the pre-processed CDR The communication initiation number of the fourth ratio with the highest number of attributions is used as the target communication number.

The embodiment is directed to how to obtain the characteristics of the corresponding type of communication information of each communication number in the pre-processed bill, and extract the scene of the target communication number that matches the preset feature from the communication number included in the pre-processed bill. Obtaining a pre-processed bill based on the parsing of the dialog box, respectively calculating the number of different attributions of the communication response number corresponding to each communication initiation number in the pre-processed bill (ie, the communication number as the communication initiation number) One of the features), from the pre-processing bills included in each pass Extracting, in the letter initiation number, a communication initiation number whose number of different attributions of the corresponding communication response number is greater than a preset third threshold as the target communication number, or based on the communication response corresponding to each communication initiation number included in the pre-processed CDR For the order of the number of different attributions of the number, the communication initiation number of the fourth ratio of the corresponding number of the different communication destinations of the corresponding communication response number is extracted as the target communication number. The embodiment of the present application initiates each communication in the pre-processed bill. The number of different attributions of the communication response number corresponding to the number is characterized, and the fourth threshold is a preset feature, and the number of different attributions of the communication response number corresponding to each communication initiation number included in the pre-processed CDR is determined. The relative relationship of the fourth threshold is obtained by extracting the target communication number matching the preset feature from the communication number included in the pre-processed CDR, thereby realizing fast and accurate number identification.

Example 7

This embodiment is based on the foregoing embodiment, and proposes a solution to solve the scenario of how to extract the target communication number that matches the preset feature from the communication number included in the pre-processed CDR.

Referring to FIG. 8, the communication number processing method provided in this embodiment includes the following steps:

Step 801: Acquire, from the communication service device, a bill of a preset number of communication numbers in a first preset time.

Step 802: Parse the CDR to obtain the type of the communication information included in the CDR, and extract at least one type of communication information of each communication number in the CDR and combine to form a pre-processed CDR.

Step 803: Analyze at least one type of communication information of each communication number in the pre-processed bill, and obtain a feature of the corresponding type of communication information of each communication number in the pre-processed bill.

Step 804: Analyze, by using a machine learning model, features of corresponding types of communication information of each communication number in the pre-processed bill.

Step 805: Determine whether the feature of the corresponding type communication information of each communication number matches the preset feature. If yes, go to step 806, otherwise the process ends.

Step 806: Extract a target communication number that matches the preset feature from the communication number included in the pre-processed bill.

Here, the implementation of the feature of the corresponding type of communication information of each communication number in the pre-processing bill is analyzed by using the machine learning model, including: using the technical solution or technology described in any one of the foregoing embodiments 3 to 6. The combination of scenarios identifies the target communication number.

The machine learning model can adopt any of the following models or combinations: Bayesian classifier model; Support Vector Machine (SVM) classifier model; deep learning model; logic Regression; those skilled in the art can understand that the machine learning model can also include other models not listed herein, and the application is not limited thereto.

This embodiment is directed to how to obtain a scenario in which a target communication number that matches a preset feature is extracted from a communication number included in a pre-processed CDR, and analyzes a corresponding type of communication information of each communication number in the pre-processed CDR by using a machine learning model. The feature has the target communication number matched with the preset feature extracted from the communication number included in the pre-processed CDR, thereby realizing fast and efficient number identification.

Example eight

This embodiment is based on the seventh embodiment, and proposes a solution to solve the scenario in which the machine learning model is trained based on the feedback information of the target communication number on the user side.

Referring to FIG. 9, the communication number processing method provided in this embodiment includes the following steps:

Step 901: Acquire, from the communication service device, a bill of a preset number of communication numbers in a first preset time.

In step 902, the CDR is parsed to obtain the type of the communication information included in the CDR, and at least one type of communication information of each communication number in the CDR is extracted and combined to form a pre-processed CDR.

Step 903: Analyze at least one type of communication information of each communication number in the pre-processed bill, and obtain a feature of the corresponding type of communication information of each communication number in the pre-processed bill.

Step 904: Analyze the characteristics of the corresponding type of communication information of each communication number in the pre-processed bill, and determine whether the feature of the corresponding type of communication information of each communication number matches the preset feature, and if yes, go to step 905, otherwise The process ends.

Step 905: extract a target communication number that matches the preset feature from the communication number included in the pre-processed bill; and identify or identify the user with the communication response number of the communication record with the identified target communication number. The user of the communication response number of the target communication number communication performs a danger reminder.

Step 906: Receive feedback information of the user side for the target communication number.

Receiving feedback information of the user side for the danger reminder carrying the identified target communication number.

Step 907: Determine, according to the feedback information of the target communication number by the user side, whether the target communication number is a security number, and if yes, go to step 908, otherwise the process ends.

Step 908: Determine an error rate of the machine learning model based on the number of target communication numbers that are fed back to the security number by the user side among the identified target communication numbers.

Step 909: Determine whether the error rate of the machine learning model is greater than a fifth threshold, and if yes, go to step Step 910, otherwise the process ends.

Step 910: Retrain the machine learning model based on the communication record of the security number in the pre-processed bill.

Here, based on the communication record of the security number in the pre-processed bill, a feasible implementation of the machine learning model to retraining includes:

Parsing at least one type of communication information of the communication record of the security number in the pre-processed bill, and obtaining characteristics of at least one type of communication information of the security number;

The feature of the at least one type of communication information based on the security number updates the threshold used by the machine learning model to identify the target communication number.

In this embodiment, for the scenario in which the machine learning model is trained based on the feedback information of the target communication number on the user side, the error rate of the machine learning model is determined according to the number of target communication numbers in the target communication number that are fed back to the security number by the user side, and When the error rate of the machine learning model is greater than the fifth threshold, the machine learning model is retrained based on the communication record of the security number in the preprocessed bill; since the retraining is based on the communication record of the security number in the preprocessed bill Therefore, the machine learning model obtained by retraining has a higher accuracy rate. Thus, using the machine learning model obtained by retraining to identify the target communication number can improve the speed and accuracy of the number identification.

Example nine

This embodiment is based on any of the foregoing embodiments, and proposes a solution to the response processing scenario when the target communication number is identified.

Referring to FIG. 10, the communication number processing method provided in this embodiment includes the following steps:

Step 1001: Acquire, from the communication service device, a bill of a preset number of communication numbers in a first preset time.

In step 1002, the CDR is parsed to obtain the type of the communication information included in the CDR, and at least one type of communication information of each communication number in the CDR is extracted and combined to form a pre-processed CDR.

Step 1003: Analyze at least one type of communication information of each communication number in the pre-processed bill, and obtain a feature of the corresponding type of communication information of each communication number in the pre-processed bill.

Step 1004: Analyze characteristics of corresponding types of communication information of each communication number in the pre-processed bill, and determine whether the feature of the corresponding type of communication information of each communication number matches the preset feature. If yes, go to step 1005; otherwise, The process ends.

Step 1005: Extract a target that matches the preset feature from the communication number included in the pre-processed bill Communication number.

Step 1006: Determine a degree of matching between a feature of the corresponding type of communication information of the target communication number and the preset feature.

The degree of matching between the feature of the corresponding type of communication information of the target communication number and the preset feature can also be understood as the degree of difference between the feature and the preset feature of the corresponding type of communication information of the target communication number; For example, the similarity between the target communication number and the yellow page number is as follows. The similarity between the target communication number and the yellow page number is greater than the first threshold. Here, the matching degree refers to the difference between the similarity between the target communication number and the yellow page number and the first threshold. size.

Step 1007: Determine a dangerous level of the target communication number according to the matching degree of the feature of the corresponding type of communication information of the target communication number with the preset feature.

The degree of matching is positively related to the level of danger; different levels of risk can correspond to the degree of matching within different data ranges.

Step 1008: Respond to the communication behavior of the target communication number based on the risk level of the target communication number.

The real-time degree of response processing is positively related to the hazard level; it is assumed that the defined hazard level includes: high risk, low risk; the hazard level here can be used to characterize the probability that the target communication number is a communication number that meets certain conditions, such as danger The level can be used to characterize the probability that the target communication number is a fraudulent number.

When the communication number processing device determines that the dangerous level of the target communication number is low risk, the manner of responding to the communication behavior of the target communication number may include: performing a danger reminder to the user having the communication response number of the communication record with the target communication number The user is reminded that the target communication number is a fraudulent number; here, the danger reminder includes a voice reminder and/or a text reminder; the voice reminder is, for example, a voice recording or a customer service telephone reminder; and the text reminder is, for example, a text message or a flash message.

Referring to FIG. 11b, the communication number processing means performs an after-the-life danger reminder to the user having the communication response number of the communication record of the target communication number, on the user device having the communication response number of the communication record with the target communication number, in the user application The display window displays the following text reminder message "Please be vigilant! The target communication number is a fraudulent number"; the user applications here include but are not limited to: SMS, Flash, WeChat, Tencent mobile butler, etc.; of course, the application does not It is limited to the communication application, which is not specifically limited in the embodiment of the present application.

When the communication number processing device determines that the dangerous level of the target communication number is high risk, the manner of responding to the communication behavior of the target communication number may include: communicating with the target communication number The user of the communication response number of the letter performs an immediate danger reminder (including but not limited to a text reminder such as a short message or a flash message, or a voice reminder such as a voice recording or a customer service telephone reminder), that is, the user is communicating with the target communication number. In the process of communication, the user is reminded that the target communication number is a fraudulent number; or, the ongoing communication with the target communication number is directly intercepted, and the user is reminded of danger afterwards.

In this embodiment, for the response processing scenario when the target communication number is identified, the risk level of the target communication number is determined based on the matching degree of the feature of the corresponding type of communication information of the target communication number and the preset feature, based on the risk level of the target communication number. Respond to the communication behavior of the target communication number, and remind the user who communicates with the target communication number to be vigilant and avoid fraud.

Example ten

The present embodiment is applicable to a scenario in which it is necessary to identify a communication number that satisfies a preset condition from among a plurality of communication numbers, for example, for identification of a whole network number in a communication network, or for a user indication, based on any of the above embodiments. Identifying the identification of the communication number, or in the scene of identifying the communication number for communicating with the current user; the type of communication service includes but is not limited to any one of the following service types or combinations: voice call; short message; flash message; data service (such as WeChat), this application is not limited to this.

Referring to FIG. 12, a communication number processing apparatus (a fraudulent number identification system based on bill analysis) provided in this embodiment includes: an online identification system and an offline training system.

The online identification system extracts features according to the bill records collected by the operator; uses the machine learning model to determine whether a certain phone number is a fraudulent phone; then, the user is reminded/returned to the user to avoid being deceived, and will be reminded/ The results of the return visit are fed back to the offline training system, and the machine learning model is adjusted accordingly. The offline training system extracts the corresponding features by using the historical bill data and the feedback result of the reminder/return visit in the online identification system; using these features, The machine learning model is retrained and adjusted; the trained machine learning model is synchronized to the fraudulent phone recognition engine in the online training system.

Specifically, the online identification system can identify the fraud number according to the user's call bill record; the online identification system can be further divided into three modules: a bill collection module, a fraudulent phone recognition engine, and a deceived user reminder system;

CDR collection module: mainly responsible for the collection of user call records, and pre-processing the collected CDRs to obtain the following four columns of information:

Table 4

Calling number	Called number	Call time	Call duration (seconds)
158XXXX0001	186XXXX0002	2016-01-15 15:36:42	134
001XX86	139XXXX0001	2016-01-15 15:39:02	15
138XXXX0001	139XXXX0002	2016-01-15 15:38:02	123

Fraud phone identification engine: This is the core of the online identification system; the collected bills are cleaned, the features are extracted, and the features extracted from the trained machine learning model dialog are used to identify whether the number is a fraudulent phone; It can be divided into three parts: bill cleaning, feature extraction and fraud number identification;

1) Bill cleaning is to remove the "dirty" data in the bill. The so-called "dirty" data is some abnormal data, such as missing content, abnormal values, and so on.

2) Feature extraction: After cleaning the CDRs, some features are extracted to prepare for the identification of the next scam number. The features include: the similarity of the calling number, the average call duration, and the distance of the adjacent CDRs. Call interval, etc.

The similarity feature between the calling number and the yellow page number (ie, the similarity between the above-mentioned communication initiation number and the yellow page number): the fraud number is mostly the calling number, and the fraudster changes the calling number to the number on the yellow page by changing the numbering software. Similar numbers, such as 001XX86, +0109XX88, 08XXX10010 (China Unicom's customer service phone number is 10010), etc., calculate the edit distance of the substring of these numbers and the number on the yellow page (edit distance indicates the yellow page number, for example, by adding, reducing, modifying, moving The number of operations that the operation becomes the calling number).

The number of calls per unit time (that is, the number of communication times of the above communication initiation number in the unit time): The fraudsters usually make a lot of calls every hour, and most of these calls are during working hours, that is, Monday to Friday 08. :00:00--18:00:00, during this time, the number of calls is evenly distributed; during non-working hours, the number of calls made by the phone is generally small, basically 0.

The average call duration (that is, the average communication duration mentioned above): that is, the average number of calls per call for the fraudulent number. When the general user receives the fraudulent call, the call will be quickly hanged, so the average call duration of the fraud is short, no more than 20s.

The distribution of the attribution of the called number in time (unit: day) (ie, the number of different attributions of the communication response number corresponding to the above-mentioned communication initiation number): the fraudster is usually fraudulently by city, therefore, The called numbers in these bills usually belong to a certain city, and the number of the cities belonging to the called number within a certain period of time is taken as the feature.

3) Identification of fraudulent telephones: Using the extracted features described above, machine learning models are used to identify fraud.

The deceived user reminds the system: telling the victim of the fraudulent call to receive a call that is a fraudulent call, preventing the victim from being deceived; and submitting the information of the victim's feedback to the offline training system.

2. Offline training system

When it is found that the error rate of the machine learning model fed back by the deceived user reminder system is higher than the domain value, the offline training system extracts the characteristics of the relevant historical bills, retrains the machine learning model, and adjusts the Bayesian classifier (here can also Use other machine learning algorithms, such as svm classifier, logistic regression, deep learning, etc.); offline training system can be divided into three parts:

a) Extract historical bills: Extract historical bills from the most recent period of time, especially if the feedback result is wrong.

b) Feature extraction: Extract features from historical CDRs to provide data for the next model retraining.

c) Model retraining: Using the features extracted in b), training the Bayesian classifier to obtain new parameters, and updating the trained machine learning model to the online recognition system.

In this way, the online identification system and the offline training system form a complete closed loop. The offline training system will decide whether to retrain and update the fraudulent number identification model in the online identification system according to the result of the voice return visit.

The communication number processing device provided in this embodiment has the following advantages: 1) no need for the user's tag information, only the bill record is required; 2) speeding up the recognition speed and accuracy of the fraud number; 3) more accurate identification Fraud number; enables the operator to identify fraudulent calls during the user's call.

Embodiment 11

Corresponding to the description of the foregoing embodiment, the embodiment further describes a communication number processing device, which can be used to execute the communication number processing method in the embodiment of the present application, and the communication number processing device can be implemented in various manners. For example, implementing all components of the device in a user device such as a smart phone, a landline phone, a tablet computer, a notebook computer, a wearable device (such as smart glasses, a smart watch, etc.), or in a network device such as an enterprise gateway or a carrier gateway. All components of the device are implemented, or components in the device are implemented in a coupled manner on the user equipment side or the network side described above, or the communication number processing device may also be a client application or a background server of the user application, for example, when the user application When the Tencent mobile phone manager is in charge, the corresponding communication number processing device may be a client or a background server of the Tencent mobile phone housekeeper; see FIG. 13, the communication number processing device includes:

The obtaining module 1301 is configured to acquire, from the communication service device, a bill of a preset number of communication numbers in a first preset time;

The pre-processing module 1302 is configured to parse the CDR to obtain the type of the communication information included in the CDR, and extract at least one type of communication information of each communication number in the CDR and combine to form a pre-processed CDR;

The parsing module 1303 is configured to parse at least one type of communication information of each communication number in the pre-processed bill, and obtain a feature of the corresponding type of communication information of each communication number in the pre-processed bill;

The extracting module 1304 is configured to extract, from the communication number included in the pre-processed bill, a target communication number that matches the preset feature.

Compared with the prior art that needs to implement the identification number on the basis of collecting the user tag information, the present embodiment parses the bill of the communication number to obtain the characteristics of the corresponding type of communication information of the communication number, and based on the corresponding type of the communication number. The communication information has characteristics that identify the target communication number that matches the preset feature from each communication number. On the one hand, since the generation and maintenance process of the communication number CDR is generally performed by the operator, the participation of each user is not required. The acquisition speed and efficiency of the communication number CDR are high. On the other hand, since the CDR of the communication number is objective data maintained by the operator, it can truly and completely reflect all communication records of the user within a certain time interval, so The technical solution provided by the embodiment of the present application is based on the CDR of the communication number, and can improve the speed and accuracy of the number identification.

Based on the foregoing embodiment, the pre-processing module 1302 is specifically configured to:

Resolving the CDR to obtain at least one of the following types of communication information included in the CDR: a communication initiation number, a communication response number corresponding to the communication initiation number, a communication start time, and a communication duration;

Extracting at least one type of communication information associated with each communication initiation number in the CDR to form a communication record of each communication initiation number;

The extracted communication records of the respective communication initiation numbers are combined to form a pre-processed bill.

On the basis of the foregoing embodiment, the parsing module 1303 is specifically configured to: separately calculate an edit distance of each communication initiation number and a yellow page number in the pre-processed bill; and obtain each communication initiation number and yellow page in the pre-processed bill based on the edit distance. Number similarity;

The extraction module 1304 is configured to: extract, from each communication initiation number included in the pre-processed CDR, a communication initiation number whose degree of similarity with the yellow page number is greater than a first threshold; or, based on each communication initiation number included in the pre-processed CDR The ordering of the similarity between the middle and the yellow page number extracts the first proportion of the communication initiation number with the highest similarity.

Based on the foregoing embodiment, the parsing module 1303 is specifically configured to: extract each of the pre-processed bills The communication number is used as the communication start time of the communication initiation number; and the number of communication times of each communication initiation number in the pre-processed bill in the unit time is calculated;

The extraction module 1304 is specifically configured to: extract, from each communication initiation number included in the pre-processed CDR, a communication initiation number whose communication time is greater than a second threshold in a unit time; or, based on each communication initiation number included in the pre-processed CDR The order of the number of communication times per unit time is extracted, and the communication ratio number of the second ratio with the highest number of communication times is extracted.

On the basis of the foregoing embodiment, the parsing module 1303 is specifically configured to: extract the communication duration of each communication number in the pre-processed bill as the communication initiation number; and calculate the average communication duration of each communication initiation number in the pre-processed bill;

The extraction module 1304 is configured to: extract, from each communication initiation number included in the pre-processed CDR, a communication initiation number whose average communication duration is greater than a third threshold; or, based on the average communication of each communication initiation number included in the pre-processed CDR The sorting of the duration, extracting the third ratio of the communication initiation number with the highest average communication duration.

On the basis of the foregoing embodiment, the parsing module 1303 is specifically configured to: obtain the attribution of the communication response number corresponding to each communication number in the pre-processed bill as the communication initiation number; calculate each communication initiation number in the pre-processed bill The number of different attributions of the corresponding communication response number;

The extraction module 1304 is configured to: extract, from each communication initiation number included in the pre-processed CDR, a communication initiation number whose number of different attributions of the corresponding communication response number is greater than a fourth threshold; or, based on the pre-processed CDR The order of the number of different attributions of the communication response number corresponding to each communication initiation number is extracted, and the communication generation number of the fourth ratio having the highest number of different attributions of the corresponding communication response number is extracted.

On the basis of the foregoing embodiment, the extraction module 1304 is specifically configured to: use a machine learning model to analyze characteristics of corresponding types of communication information of each communication number in the pre-processed bill, and extract from the communication number included in the pre-processed bill. The target communication number that matches the preset feature.

Example twelve

The present embodiment is based on the eleventh embodiment. Referring to FIG. 14, the communication number processing apparatus of this embodiment also includes the obtaining module 1301, the preprocessing module 1302, the parsing module 1303, and the extracting module 1304 in FIG. The communication number processing device of the embodiment further includes:

The training module 1305 is configured to receive feedback information of the user side for the target communication number, and determine the target. Whether the communication number is a security number; determining an error rate of the machine learning model based on the number of target communication numbers that are fed back to the security number by the user side in the identified target communication number; when the error rate of the machine learning model is greater than the fifth threshold, based on The communication record of the security number in the CDR is preprocessed, and the machine learning model is retrained.

Further, the training module 1305 is specifically configured to: parse at least one type of communication information of the communication record of the security number in the pre-processed bill, and obtain a feature of the at least one type of communication information of the security number; The feature possessed by the at least one type of communication information updates the threshold used by the machine learning model to identify the target communication number.

Based on the above embodiment, the device further includes:

The response module 1306 is configured to determine a degree of matching between a feature of the corresponding type of communication information of the target communication number and the preset feature, and determine a target according to a matching degree of the feature of the corresponding type of the communication number of the target communication number and the preset feature. The danger level of the communication number; responding to the communication behavior of the target communication number based on the danger level of the target communication number.

In an actual application, the obtaining module 1301, the pre-processing module 1302, the parsing module 1303, the extracting module 1304, the training module 1305, and the response module 1306 may all be configured by a central processing unit (CPU) and a microprocessor (MPU) located in the communication number processing device. ), an application specific integrated circuit (ASIC) or a field programmable gate array (FPGA).

Example thirteen

This embodiment describes a computer readable medium, which may be a ROM (eg, a read only memory, a FLASH memory, a transfer device, etc.), a magnetic storage medium (eg, a magnetic tape, a disk drive, etc.), an optical storage medium (eg, a CD- ROM, DVD-ROM, paper card, paper tape, etc.) and other well-known types of program memory; computer-readable medium storing computer-executable instructions (such as binary executable instructions for projection applications such as Tencent video), when executing instructions Causing at least one processor to perform the following operations:

Acquiring a preset number of communication numbers in the first preset time from the communication service device;

Parsing the CDR to obtain the type of the communication information included in the CDR, extracting at least one type of communication information of each communication number in the CDR and combining to form a pre-processed CDR;

Parsing at least one type of communication information of each communication number in the pre-processed bill, and obtaining characteristics of the corresponding type of communication information of each communication number in the pre-processed bill;

The target communication number matching the preset feature is extracted from the communication number included in the pre-processed bill.

In summary, the communication number processing device parses the bill of the communication number to obtain the characteristics of the corresponding type of communication information of the communication number, and identifies and presets from each communication number based on the characteristics of the corresponding type of communication information of the communication number. The target communication number of the feature matching, on the one hand, is generally responsible for the generation and maintenance process of the communication number CDR, and does not require the participation of each user, and the acquisition speed and efficiency of the communication number CDR are high, on the other hand Because the CDR of the communication number is the objective data maintained by the operator, it can truly and completely reflect all the communication records of the user in a certain time interval. Therefore, the technical solution provided by the embodiment of the present application is processed by the CDR of the communication number. Basic, can improve the speed and accuracy of number identification.

Those skilled in the art will appreciate that embodiments of the present application can be provided as a method, system, or computer program product. Thus, the application can take the form of a hardware embodiment, a software embodiment or an embodiment in combination with software and hardware. Moreover, the application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage and optical storage, etc.) including computer usable program code.

The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the present application. It will be understood that each flow and/or block of the flowchart illustrations and/or FIG. These computer program instructions can be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing device to produce a machine for the execution of instructions for execution by a processor of a computer or other programmable data processing device. Means for implementing the functions specified in one or more of the flow or in a block or blocks of the flow chart.

The computer program instructions can also be stored in a computer readable memory that can direct a computer or other programmable data processing device to operate in a particular manner, such that the instructions stored in the computer readable memory produce an article of manufacture comprising the instruction device. The apparatus implements the functions specified in one or more blocks of a flow or a flow and/or block diagram of the flowchart.

These computer program instructions can also be loaded onto a computer or other programmable data processing device such that a series of operational steps are performed on a computer or other programmable device to produce computer-implemented processing for execution on a computer or other programmable device. The instructions provide steps for implementing the functions specified in one or more of the flow or in a block or blocks of a flow diagram.

The above is only the preferred embodiment of the present application and is not intended to limit the scope of the present application.

Claims (20)

1. A communication number processing method, characterized in that the method comprises:

   Acquiring a preset number of communication numbers in the first preset time from the communication service device;

   Parsing the CDR to obtain the type of the communication information included in the CDR, extracting at least one type of communication information of each communication number in the CDR, and combining to form a pre-processed CDR;

   Parsing at least one type of communication information of each communication number in the pre-processed bill, and obtaining characteristics of corresponding types of communication information of each communication number in the pre-processed bill;

   Extracting a target communication number that matches the preset feature from the communication number included in the pre-processed bill.
2. The method according to claim 1, wherein said parsing said bill obtains a type of communication information included in said bill, and extracting at least one type of each communication number in said bill The communication information is combined and formed into a pre-processed bill, including:

   Parsing the CDR to obtain at least one of the following types of communication information included in the CDR: a communication initiation number, a communication response number corresponding to the communication initiation number, a communication start time, and a communication duration;

   Extracting at least one type of communication information associated with each communication initiation number in the CDR to form a communication record of each communication initiation number;

   The extracted communication records of the respective communication initiation numbers are combined to form the pre-processed bill.
3. The method according to claim 1, wherein the parsing at least one type of communication information of each communication number in the pre-processed bill obtains a corresponding type of communication of each communication number in the pre-processed bill The characteristics of the information, including:

   Calculating, respectively, an edit distance of each communication initiation number and a yellow page number in the preprocessed bill;

   Obtaining a similarity between each communication initiation number and a yellow page number in the preprocessed bill based on the edit distance;

   Extracting, from the communication number included in the pre-processed CDR, a target communication number that matches the preset feature, including:

   Extracting, from each communication initiation number included in the pre-processed CDR, a communication initiation number whose degree of similarity with the yellow page number is greater than a first threshold;

   Or extracting, according to the ranking of the similarity of the yellow page numbers among the communication initiation numbers included in the pre-processed CDR, extracting the first proportion of the communication initiation number with the highest similarity,

   The edit distance indicates the number of operations in which the yellow page number becomes the communication initiation number.
4. The method according to claim 1, wherein the parsing at least one type of communication information of each communication number in the pre-processed bill obtains a corresponding type of communication of each communication number in the pre-processed bill The characteristics of the information, including:

   Extracting a communication start time of each communication number in the pre-processed bill as a communication initiation number;

   Calculating the number of communications of each communication initiation number in the pre-processed bill in a unit time;

   Extracting, from the communication number included in the pre-processed CDR, a target communication number that matches the preset feature, including:

   Extracting, from each communication initiation number included in the pre-processed CDR, a communication initiation number whose communication time is greater than a second threshold in a unit time;

   Alternatively, based on the order of the number of communication times of each communication initiation number included in the pre-processed CDR in a unit time, the communication initiation number of the second ratio with the highest number of communication times is extracted.
5. The method according to claim 1, wherein the parsing at least one type of communication information of each communication number in the pre-processed bill obtains a corresponding type of communication of each communication number in the pre-processed bill The characteristics of the information, including:

   Extracting a communication duration of each communication number in the pre-processed bill as a communication initiation number;

   Calculating an average communication duration of each communication initiation number in the pre-processed bill;

   Extracting, from the communication number included in the pre-processed CDR, a target communication number that matches the preset feature, including:

   Extracting, from each communication initiation number included in the pre-processed CDR, a communication initiation number whose average communication duration is greater than a third threshold;

   Alternatively, based on the ordering of the average communication durations of the communication initiation numbers included in the pre-processed CDR, the communication initiation number of the third ratio having the highest average communication duration is extracted.
6. The method according to claim 1, wherein the parsing at least one type of communication information of each communication number in the pre-processed bill obtains a corresponding type of communication of each communication number in the pre-processed bill The characteristics of the information, including:

   Obtaining a attribution of a communication response number corresponding to each communication number in the pre-processed bill as a communication initiation number;

   Calculating, by the number of different attributions of the communication response number corresponding to each communication initiation number in the pre-processed bill;

   Extracting, from the communication number included in the pre-processed CDR, a target communication number that matches the preset feature, including:

   Extracting, from each communication initiation number included in the pre-processed CDR, a communication initiation number whose number of different attributions of the corresponding communication response number is greater than a fourth threshold;

   Or, based on the order of the number of different attributions of the communication response numbers corresponding to the communication initiation numbers included in the pre-processed CDR, extracting the fourth proportion of communication with the highest number of different attributions of the corresponding communication response number Initiate the number.
7. The method according to claim 1, wherein the target communication number matching the preset feature is extracted from the communication number included in the pre-processed CDR, including:

   The machine learning model is used to analyze the characteristics of the corresponding type of communication information of each communication number in the pre-processed bill, and the target communication number matching the preset feature is extracted from the communication number included in the pre-processed bill.
8. The method of claim 7, wherein the method further comprises:

   Receiving feedback information of the user side for the target communication number, determining whether the target communication number is a security number;

   Determining an error rate of the machine learning model based on the number of target communication numbers in the identified target communication number that are fed back as a security number by the user side;

   When the error rate of the machine learning model is greater than the fifth threshold, the machine learning model is retrained based on the communication record of the security number in the pre-processed bill.
9. The method according to claim 8, wherein the retraining of the machine learning model based on the communication record of the security number in the pre-processed bill comprises:

   Parsing at least one type of communication information of the communication record of the security number in the pre-processed bill, obtaining characteristics of at least one type of communication information of the security number;

   Updating a threshold used by the machine learning model to identify the target communication number based on characteristics of at least one type of communication information of the security number.
10. The method according to claim 1, wherein after the extracting the target communication number that matches the preset feature from the communication number included in the pre-processed CDR, the method further includes:

    Determining a degree of matching between a feature of the corresponding type of communication information of the target communication number and a preset feature;

    Determining a danger level of the target communication number according to a matching degree of a feature of the corresponding type communication information of the target communication number and a preset feature;

    Responding to the communication behavior of the target communication number based on the danger level of the target communication number.
11. A communication number processing device, characterized in that the device comprises:

    An obtaining module, configured to acquire, from the communication service device, a CDR of a preset number of communication numbers in a first preset time;

    a pre-processing module, configured to parse the CDR to obtain a type of communication information included in the CDR, extract at least one type of communication information of each communication number in the CDR, and combine to form a pre-processed CDR ;

    a parsing module, configured to parse at least one type of communication information of each communication number in the pre-processed bill, and obtain a feature of a corresponding type of communication information of each communication number in the pre-processed bill;

    And an extracting module, configured to extract, from the communication number included in the pre-processed bill, a target communication number that matches the preset feature.
12. The device of claim 11 wherein:

    The preprocessing module is specifically configured to:

    Parsing the CDR to obtain at least one of the following types of communication information included in the CDR: a communication initiation number, a communication response number corresponding to the communication initiation number, a communication start time, and a communication duration;

    Extracting at least one type of communication information associated with each communication initiation number in the CDR to form a communication record of each communication initiation number;

    The extracted communication records of the respective communication initiation numbers are combined to form the pre-processed bill.
13. The device of claim 11 wherein:

    The parsing module is specifically configured to:

    Calculating, respectively, an edit distance of each communication initiation number and a yellow page number in the preprocessed bill;

    Obtaining a similarity between each communication initiation number and a yellow page number in the preprocessed bill based on the edit distance;

    The extraction module is specifically configured to:

    Extracting, from each communication initiation number included in the pre-processed CDR, a communication initiation number whose degree of similarity with the yellow page number is greater than a first threshold; or

    And determining, according to the similarity of the yellow page number, each communication initiation number included in the pre-processed bill Sorting, extracting the first proportion of the communication initiation number with the highest similarity,

    The edit distance indicates the number of operations in which the yellow page number becomes the communication initiation number.
14. The device of claim 11 wherein:

    The parsing module is specifically configured to:

    Extracting a communication start time of each communication number in the pre-processed bill as a communication initiation number;

    Calculating the number of communications of each communication initiation number in the pre-processed bill in a unit time;

    The extraction module is specifically configured to:

    Extracting, from each communication initiation number included in the pre-processed CDR, a communication initiation number whose communication time is greater than a second threshold in a unit time; or

    And extracting, according to the order of the communication times of each communication initiation number included in the pre-processing CDR, the communication protocol number of the second ratio having the highest communication number.
15. The device of claim 11 wherein:

    The parsing module is specifically configured to:

    Extracting a communication duration of each communication number in the pre-processed bill as a communication initiation number;

    Calculating an average communication duration of each communication initiation number in the pre-processed bill;

    The extraction module is specifically configured to:

    Extracting, from each communication initiation number included in the pre-processed CDR, a communication initiation number whose average communication duration is greater than a third threshold; or

    And extracting, according to the order of the average communication duration of each communication initiation number included in the pre-processed CDR, a communication initiation number of a third ratio having the highest average communication duration.
16. The device of claim 11 wherein:

    The parsing module is specifically configured to:

    Obtaining a attribution of a communication response number corresponding to each communication number in the pre-processed bill as a communication initiation number;

    Calculating, by the number of different attributions of the communication response number corresponding to each communication initiation number in the pre-processed bill;

    The extraction module is specifically configured to:

    Extracting, from each communication initiation number included in the pre-processed CDR, a communication initiation number whose number of different attributions of the corresponding communication response number is greater than a fourth threshold; or

    Different communication response numbers corresponding to each communication initiation number included in the pre-processed CDR The order of the number of attributions is extracted, and the communication initiation number of the fourth ratio having the highest number of different attributions of the corresponding communication response number is extracted.
17. The device of claim 11 wherein:

    The extracting module is specifically configured to: use a machine learning model to analyze characteristics of corresponding types of communication information of each communication number in the pre-processed bill, and extract and pre-take the communication number included in the pre-processed bill Set the target communication number for the feature match.
18. The device according to claim 17, wherein the device further comprises:

    Training module for:

    Receiving feedback information of the user side for the target communication number, determining whether the target communication number is a security number;

    Determining an error rate of the machine learning model based on the number of target communication numbers in the identified target communication number that are fed back as a security number by the user side;

    When the error rate of the machine learning model is greater than the fifth threshold, the machine learning model is retrained based on the communication record of the security number in the pre-processed bill.
19. The device of claim 18, wherein

    The training module is specifically configured to:

    Parsing at least one type of communication information of the communication record of the security number in the pre-processed bill, obtaining characteristics of at least one type of communication information of the security number;

    Updating a threshold used by the machine learning model to identify the target communication number based on characteristics of at least one type of communication information of the security number.
20. The device according to claim 11, wherein the device further comprises:

    a response module, configured to determine a degree of matching between a feature of the corresponding type of communication information of the target communication number and a preset feature; and a matching degree between a feature of the corresponding type of communication information of the target communication number and a preset feature Determining a risk level of the target communication number; and responding to a communication behavior of the target communication number based on a danger level of the target communication number.

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