Risk number management and control method and electronic equipment
By determining the regional risk score and threshold in the fraud detection model, identifying the silent remote-local risk area, and calculating the trajectory risk coefficient of the target number, the problem of not being able to identify the new fraud model in the prior art is solved, and higher detection accuracy and risk prevention effects are achieved.
Patent Information
- Application Number
- CN202411717635.3
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-11-27
- Publication Date
- 2025-02-28
AI Technical Summary
The existing fraud detection model cannot fully cover all types of fraud, especially the new fraud model, which makes it difficult for high-risk silent users to be identified and intercepted, and the false detection rate is high.
By determining the regional risk scores and thresholds for multiple areas, identifying the silent remote activation risk area, calculating the trajectory risk coefficient of the target number, judging the number risk level, and performing different controls based on the level.
It improves the accuracy and timeliness of fraud detection, effectively prevents risks, and ensures the efficiency and security of the system.
Smart Images
Figure CN119545362A_ABST
Abstract
Description
Technical Field
[0001] The present application belongs to the field of communication technology, and in particular relates to a risk number control method and electronic equipment. Background Art
[0002] Existing fraud detection models are usually trained based on a set of fixed features and rules, most of which are derived from the analysis of historical data. However, this approach has a major problem: its detection range and applicable threshold settings often cannot fully cover all types of fraudulent behaviors. In particular, with the continuous innovation of fraudulent methods, many new fraud patterns have not been included in the detection range of existing models, the detection range is limited, and the features of silent remote users are easily missing. In addition, the features of fraudulent users cannot be distinguished, which will make some high-risk silent remote users difficult to identify and intercept, and when using a larger range of models for detection, the false detection rate will be greatly increased.
[0003] In response to the above problems, a risk number management method and electronic device are proposed in the present application. Summary of the invention
[0004] In order to address the deficiencies of the prior art, the present application provides a risk number management method and electronic device, which can solve the problems of low detection accuracy and difficulty caused by missing features and inability to distinguish features in the prior art in detecting silent, remotely activated users.
[0005] The technical effects to be achieved by this application are achieved through the following solutions:
[0006] In a first aspect, the present application provides a method for risk number management and control, the method comprising:
[0007] determining a plurality of regions, and calculating a regional risk score corresponding to each of the regions;
[0008] Determine a regional risk score threshold, and determine a regional risk level corresponding to each of the regions according to the regional analysis score and the regional risk score threshold;
[0009] Determine a silent remote activation risk area, and detect target numbers in the silent remote activation risk area in real time according to a silent remote activation model;
[0010] Determine the trajectory risk coefficient of the target number based on the historical location of the target number;
[0011] Determine the number risk level corresponding to the target number by using the trajectory risk coefficient;
[0012] Different management and control are performed according to different number risk levels corresponding to the target number.
[0013] In some embodiments, the regional risk level includes:
[0014] Risk-free zone;
[0015] Low risk area;
[0016] Medium risk area;
[0017] High risk area.
[0018] In some embodiments, the regional risk score corresponding to each of the regions is calculated as follows:
[0019] S = w1 / calling ratio + w2 / called ratio + w3 / calling ratio from other markets,
[0020] Wherein, S represents the regional risk score, w1 represents the weight value of the proportion of calling parties in the region, w2 represents the weight value of the proportion of called parties in the region, and w3 represents the weight value of the proportion of calling parties belonging to other cities in the region.
[0021] In some embodiments, determining the regional risk score threshold comprises:
[0022] Determining a risk score threshold for the region by analyzing historical data;
[0023] The historical data includes:
[0024] Numbers that have no voice for more than two consecutive months;
[0025] Numbers with no traffic for more than two consecutive months;
[0026] Numbers that have not received any text messages for more than two consecutive months;
[0027] Numbers activated in a different location during a preset time period;
[0028] The serial number used when the number is activated is inconsistent with the serial number collected and matched within six months.
[0029] In some embodiments, the determining of the silent remote activation risk area includes: determining the area with a high risk level as the silent remote activation risk area.
[0030] In some embodiments, determining the trajectory risk coefficient of the target number based on the historical location of the target number includes:
[0031] Determine the historical location of the target number;
[0032] The trajectory risk coefficient of the target number is calculated based on the number of historical areas and the area risk scores corresponding to the historical areas.
[0033] In some embodiments, the number risk level includes:
[0034] Risk-free number;
[0035] Low risk number;
[0036] Medium risk number;
[0037] High risk number.
[0038] In some embodiments, performing different management and control according to different number risk levels corresponding to the target number includes:
[0039] For high-risk numbers, double stop operation is performed;
[0040] For medium-risk numbers, a prompt message is sent to the supervisor;
[0041] For low-risk numbers, collect the corresponding information and archive it for subsequent use;
[0042] There is no control over risk-free numbers.
[0043] In a second aspect, the present application provides an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements any one of the methods described above when executing the computer program.
[0044] In a third aspect, the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to implement any of the methods described above.
[0045] A risk number management method and electronic device provided by the present application constructs a model threshold of a silent remote activation model through cluster analysis or historical data analysis, identifies potential fraudulent behaviors and abnormal patterns, and divides the degree of danger, and implements real-time monitoring and alarm mechanisms to quickly respond to suspicious activities and ensure the efficiency and security of the system; it achieves the technical effect of improving the accuracy and timeliness of data analysis, thereby effectively preventing risks. BRIEF DESCRIPTION OF THE DRAWINGS
[0046] In order to more clearly illustrate the embodiments of the present application or the existing technical solutions, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.
[0047] Figure 1 This is a flow chart of a risk number control method in one embodiment of the present application;
[0048] Figure 2 This is a specific implementation diagram of the risk number control method in one embodiment of the present application;
[0049] Figure 3 It is a schematic block diagram of an electronic device in an embodiment of the present application. DETAILED DESCRIPTION
[0050] In order to make the purpose, technical solution and advantages of the present application clearer, the technical solution of the present application will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without creative work are within the scope of protection of the present application.
[0051] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in one or more embodiments of the present application should be understood by people with ordinary skills in the field to which the present application belongs. The "first", "second" and similar words used in one or more embodiments of the present application do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word cover the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.
[0052] Disadvantages of existing technology:
[0053] 1. Limitations of detection range:
[0054] Existing fraud detection models are usually trained based on a fixed set of features and rules, which are mostly derived from the analysis of historical data.
[0055] However, this approach has a major problem: its detection range and applicable threshold settings often fail to fully cover all types of fraudulent behaviors. In particular, as fraudulent methods continue to innovate, many new fraud patterns fail to be included in the detection range of existing models. This makes it difficult to identify and intercept some high-risk silent remote users, and when using a wider range of models for detection, the false positive rate will be greatly increased.
[0056] 2. Missing features for silent remote activation of users:
[0057] Silent remote users usually show low activity and no frequent interactions, which makes traditional models lack sufficient basis for anomaly detection. Most existing models rely on explicit data such as user behavior, access frequency, and interaction patterns to judge the risk level of users. However, for silent remote users, these data may not effectively reflect their potential fraud risks. This results in the model being unable to effectively identify these potentially high-risk users, causing security risks.
[0058] 3. Unable to distinguish the characteristics of fraud users:
[0059] Since the existing model lacks targeted analysis of silent remote users, it cannot distinguish between ordinary users and potential fraud users when identifying fraud users. This lack of targeted strategy not only reduces the accuracy of the model, but also may lead to misjudgment and missed judgment.
[0060] 4. Lack of adaptability:
[0061] The diversity and complexity of fraudulent methods mean that models built solely on fixed rules and historical data analysis are often unable to adapt to new forms of fraud. For example, some fraudulent users may use technical means to hide their true activities or conceal their intentions by intermittent low-frequency transactions. Such changes make it impossible to update and adjust traditional models in a timely manner, further increasing the difficulty of risk identification.
[0062] The silent remote activation anti-fraud model provides an effective solution to the above shortcomings. It detects and analyzes users who are silently activated remotely, designs thresholds for each user, and extracts user features based on the user's creation to detect the user separately, thus improving the accuracy of the model. This model is particularly suitable for industries with extremely high data processing requirements such as anti-fraud and communications, and provides strong support for enterprises to improve their competitiveness and cope with complex environments.
[0063] Therefore, it is necessary to adopt the risk number management method provided by this application to achieve the management of risk numbers.
[0064] The data analysis technology implementation solution based on the silent off-site activation model mainly includes
[0065] There are four key steps: signaling data collection, data preprocessing, model building and real-time monitoring.
[0066] First, by connecting to signaling data, we collect multi-source data such as user behavior, location, and call information from different locations to ensure the comprehensiveness and real-time nature of the data. Then, we clean, integrate, and feature engineer the collected data to integrate and merge key data and extract key information.
[0067] Then, through cluster analysis or based on historical data analysis, model thresholds for the silent off-site activation model are constructed to identify potential fraudulent behaviors and abnormal patterns and classify the degree of danger.
[0068] Finally, implement real-time monitoring and alarm mechanisms to quickly respond to suspicious activities and ensure the efficiency and security of the system.
[0069] Various non-limiting implementations of the present application are described in detail below in conjunction with the accompanying drawings.
[0070] First, refer to Figure 1 , the risk number control method of the present application is described in detail. The present application provides a risk number control method, the method comprising:
[0071] S1: determining a plurality of regions, and calculating a regional risk score corresponding to each of the regions;
[0072] S2: Determine a regional risk score threshold, and determine a regional risk level corresponding to each of the regions according to the regional analysis score and the regional risk score threshold;
[0073] S3: Determine a silent remote activation risk area, and detect target numbers in the silent remote activation risk area in real time according to a silent remote activation model;
[0074] S4: determining a trajectory risk coefficient of the target number based on the historical location of the target number;
[0075] S5: judging the number risk level corresponding to the target number according to the trajectory risk coefficient;
[0076] S6: Perform different management and control according to different number risk levels corresponding to the target number.
[0077] The method of the present application constructs a model threshold for the silent off-site activation model through cluster analysis or historical data analysis, identifies potential fraudulent behaviors and abnormal patterns, divides the degree of danger, and implements real-time monitoring and alarm mechanisms to quickly respond to suspicious activities and ensure the efficiency and security of the system; it achieves the technical effect of improving the accuracy and timeliness of data analysis, thereby effectively preventing risks.
[0078] Before calculating the regional risk score corresponding to each of the regions, signaling data collection may be performed. Specifically, signaling data collection is to ensure the comprehensiveness and real-time nature of the data, and includes the following steps:
[0079] Multi-source data connection: By establishing an interface with communication operators or other data providers, real-time collection of signaling data such as user behavior, location and call information can be achieved.
[0080] Data type determination: The collected information includes but is not limited to the user's call records, SMS sending and receiving, Internet behavior, location information (such as base station signals), etc. In addition, the user's device information and network status can also be collected.
[0081] Update collected information in real time: Configure scheduled tasks to collect data at the moment it is generated to ensure the timeliness of the data.
[0082] Data storage: Store the collected data in a high-performance database (such as a hive database) to support fast reading and writing and subsequent analysis.
[0083] like Figure 2 As shown, illustratively, the risk number management method of the present application may include the following steps:
[0084] 1) Read signaling data and obtain relevant signaling data;
[0085] 2) Preprocessing the acquired signaling data;
[0086] 3) Analyze the model threshold based on the acquired signaling data;
[0087] 4) Carry out risk assessment based on the model and divide each area into corresponding risk levels. After the division, each area corresponds to a high-risk area, a medium-risk area, a low-risk area, and a no-risk area;
[0088] 5) In real time, the target number’s trajectory risk coefficient is divided according to its activity trajectory in the risk area, and the target number is divided into corresponding levels, such as high-risk number, medium-risk number, low-risk number and no-risk number;
[0089] 6) Carry out real-time risk management and control on numbers of different levels.
[0090] In some embodiments, the collected signaling data is preprocessed to clean, integrate and extract key information to improve data quality, including the following steps:
[0091] 1) Data cleaning: Process missing values, detect outliers, and delete invalid data for the original data. Use statistical methods (such as mean filling, median filling, etc.) to handle missing values.
[0092] 2) Data integration: Integrate data from different sources to ensure data consistency.
[0093] 3) Feature extraction: Extract valuable features from the raw data. For example, the frequency, duration, caller-to-caller ratio, and discreteness of user activities can be calculated through timestamps; for example, features such as interaction patterns between users and changes in geographic locations can also be considered.
[0094] 4) Data Standardization: Standardize or normalize the features so that subsequent models can better learn and recognize patterns.
[0095] In some embodiments, model building is also included, which is to identify potential fraudulent behavior and abnormal patterns, which can be achieved by following the steps below:
[0096] Threshold setting: Through historical data analysis, the decision threshold of the model is set to identify and mark high-risk areas.
[0097] From the perspective of technical solution implementation, we first need to obtain the signaling data of user calls. Generally, we rely on the server architecture of each operator and use scripting languages such as Shell and SQL to process the acquired data according to its characteristics.
[0098] By reprocessing the acquired feature data, we can obtain model feature data based on numbers that have no voice, traffic, or text messages for more than a continuous period of time (for example, two months, three months, etc.), numbers that are activated in a different location within a predetermined time, numbers that use a serial number that is inconsistent with the matching serial number collected within half a year, etc.
[0099] In some embodiments, the regional risk level includes:
[0100] Risk-free zone;
[0101] Low risk area;
[0102] Medium risk area;
[0103] High risk area.
[0104] In some embodiments, the regional risk score corresponding to each of the regions is calculated as follows:
[0105] S = w1 / calling ratio + w2 / called ratio + w3 / calling ratio from other markets,
[0106] Wherein, S represents the regional risk score, w1 represents the weight value of the proportion of calls in the region, w2 represents the weight value of the proportion of calls in the region, and w3 represents the weight value of the proportion of calls from other cities in the region. For example, the weight value can be set based on historical data or experience, and the setting of the weight value can reflect the relative importance of each feature to the risk.
[0107] In some embodiments, determining the regional risk score threshold comprises:
[0108] Determining a risk score threshold for the region by analyzing historical data;
[0109] The historical data includes:
[0110] Numbers that have no voice for more than two consecutive months;
[0111] Numbers with no traffic for more than two consecutive months;
[0112] Numbers that have not received any text messages for more than two consecutive months;
[0113] Numbers activated in a different location during a preset time period;
[0114] The serial number used when the number is activated is inconsistent with the serial number collected and matched within six months.
[0115] According to the regional risk score S and regional risk threshold (T_1, T_2), the region is divided into multiple levels:
[0116] No risk: (S <T_1 / 2);
[0117] Low risk: (T_1 / 2 <S<T_1);
[0118] Medium risk: (T_1 <S<T_2);
[0119] High risk: (S>T_2).
[0120] Based on this algorithm, the risk areas for silent remote activation can be analyzed.
[0121] In some embodiments, the determining of the silent remote activation risk area includes: determining the area with a high risk level as the silent remote activation risk area.
[0122] In some embodiments, determining the trajectory risk coefficient of the target number based on the historical location of the target number includes:
[0123] Determine the historical location of the target number;
[0124] The trajectory risk coefficient of the target number is calculated based on the number of historical areas and the area risk scores corresponding to the historical areas.
[0125] In some embodiments, the number risk level includes:
[0126] Risk-free number;
[0127] Low risk number;
[0128] Medium risk number;
[0129] High risk number.
[0130] In some embodiments, performing different management and control according to different number risk levels corresponding to the target number includes:
[0131] For high-risk numbers, double stop operation is performed;
[0132] For medium-risk numbers, a prompt message is sent to the supervisor;
[0133] For low-risk numbers, collect the corresponding information and archive it for subsequent use;
[0134] There is no control over risk-free numbers.
[0135] Specifically, the double stop operation refers to stopping calls and stopping traffic networking; issuing a prompt message can be a reminder to supervisors that risk numbers need to be manually judged to further confirm whether they are risky numbers, and then take corresponding measures.
[0136] In some embodiments, the silent remote activation number information detected by the model is divided into number risk levels according to the silent remote activation risk area. The silent remote activation area trajectory is determined by multiple queries based on the historical area of the target number and the number of historical areas, and the trajectory risk coefficient of the target number is determined based on the area risk score corresponding to the historical area and the number of historical areas.
[0137] Exemplarily, when the number track risk coefficient is greater than or equal to 0.8, it indicates a high-risk number, when 0.8> the number track risk coefficient is greater than or equal to 0.5, it indicates a medium-risk number, when 0.5> the number track risk coefficient is greater than or equal to 0.2, it indicates a low-risk number, and when 0.2> the number track risk coefficient is greater than or equal to 0.5, it indicates a risk-free number. The thresholds here are exemplary, and other threshold settings known to those skilled in the art may also be applied here, without limitation.
[0138] The method of the present application adopts a unique silent remote activation model to analyze the characteristics of signaling data based on the existing anti-fraud model.
[0139] This application proposes a new sampling stratification and grading method to control risky numbers, and calculates the risk value based on the regional risk level and number activity trajectory of the numbers detected by the model, and divides the numbers into different risk levels according to the risk value for separate control.
[0140] It should be noted that the method of one or more embodiments of the present application can be performed by a single device, such as a computer or server. The method of this embodiment can also be applied to a distributed scenario and completed by multiple devices cooperating with each other. In the case of such a distributed scenario, one of the multiple devices can only perform one or more steps in the method of one or more embodiments of the present application, and the multiple devices will interact with each other to complete the described method.
[0141] It should be noted that the above describes specific embodiments of the present application. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
[0142] Based on the same inventive concept, corresponding to any of the above-mentioned embodiments and methods, the present application also discloses an electronic device;
[0143] Specifically, Figure 3 The hardware structure diagram of an electronic device for a risk number control method provided in this embodiment is shown, and the device may include: a processor 410, a memory 420, an input / output interface 430, a communication interface 440, and a bus 450. The processor 410, the memory 420, the input / output interface 430, and the communication interface 440 are connected to each other in communication within the device through the bus 450.
[0144] The processor 410 can be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of the present application.
[0145] The memory 420 may be implemented in the form of ROM (Read Only Memory), RAM (Random Access Memory), static storage device, dynamic storage device, etc. The memory 420 may store an operating system and other application programs. When the technical solution provided in the embodiment of the present application is implemented by software or firmware, the relevant program code is stored in the memory 420 and is called and executed by the processor 410.
[0146] The input / output interface 430 is used to connect the input / output module to realize information input and output. The input / output module can be configured in the device as a component (not shown in the figure), or it can be externally connected to the device to provide corresponding functions. The input device may include a keyboard, a mouse, a touch screen, a microphone, various sensors, etc., and the output device may include a display, a speaker, a vibrator, an indicator light, etc.
[0147] The communication interface 440 is used to connect a communication module (not shown) to realize communication interaction between the device and other devices. The communication module can realize communication through a wired mode (for example, USB, network cable, etc.) or a wireless mode (for example, mobile network, WIFI, Bluetooth, etc.).
[0148] The bus 450 includes a path that transmits information between the various components of the device (eg, the processor 410 , the memory 420 , the input / output interface 430 , and the communication interface 440 ).
[0149] It should be noted that, although the above device only shows the processor 410, the memory 420, the input / output interface 430, the communication interface 440 and the bus 450, in the specific implementation process, the device may also include other components necessary for normal operation. In addition, it can be understood by those skilled in the art that the above device may also only include the components necessary for implementing the embodiment of the present application, and does not necessarily include all the components shown in the figure.
[0150] The electronic device of the above-mentioned embodiment is used to implement the corresponding risk number control method in any of the above-mentioned embodiments, and has the beneficial effects of the corresponding method embodiment, which will not be repeated here.
[0151] Based on the same inventive concept, corresponding to any of the above-mentioned embodiment methods, one or more embodiments of the present application also provide a non-transitory computer-readable storage medium, wherein the non-transitory computer-readable storage medium stores computer instructions, and the computer instructions are used to enable the computer to execute the risk number management method described in any of the above embodiments.
[0152] The computer-readable medium of this embodiment includes permanent and non-permanent, removable and non-removable media, and information storage can be implemented by any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, read-only compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, tape disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device.
[0153] The computer instructions stored in the storage medium of the above embodiments are used to enable the computer to execute the risk number control method described in any of the above embodiments, and have the beneficial effects of the corresponding method embodiments, which will not be repeated here.
[0154] Those skilled in the art should understand that the discussion of any of the above embodiments is merely illustrative and is not intended to imply that the scope of the present application (including the claims) is limited to these examples. In line with the concept of the present application, the technical features in the above embodiments or different embodiments may be combined, the steps may be implemented in any order, and there are many other variations of different aspects of one or more embodiments of the present application as described above, which are not provided in detail for the sake of simplicity.
[0155] In addition, to simplify the description and discussion, and in order not to make one or more embodiments of the present application difficult to understand, the known power / ground connections to the integrated circuit (IC) chip and other components may or may not be shown in the provided drawings. In addition, the device can be shown in the form of a block diagram to avoid making one or more embodiments of the present application difficult to understand, and this also takes into account the fact that the details of the implementation of these block diagram devices are highly dependent on the platform on which one or more embodiments of the present application will be implemented (that is, these details should be fully within the scope of understanding of those skilled in the art). In the case of elaborating specific details (e.g., circuits) to describe exemplary embodiments of the present application, it is obvious to those skilled in the art that one or more embodiments of the present application can be implemented without these specific details or when these specific details are changed. Therefore, these descriptions should be considered illustrative rather than restrictive.
[0156] Although the present application has been described in conjunction with specific embodiments of the present application, many replacements, modifications and variations of these embodiments will be apparent to those skilled in the art from the foregoing description. For example, other memory architectures (e.g., dynamic RAM (DRAM)) may use the embodiments discussed.
[0157] One or more embodiments of the present application are intended to cover all such substitutions, modifications and variations that fall within the broad scope of the appended claims. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of one or more embodiments of the present application should be included in the scope of protection of the present application.
Claims
1. A risk number management method, characterized in that: The method comprises: determining a plurality of regions, and calculating a regional risk score corresponding to each of the regions; Determine a regional risk score threshold, and determine a regional risk level corresponding to each of the regions according to the regional analysis score and the regional risk score threshold; Determine a silent remote activation risk area, and detect target numbers in the silent remote activation risk area in real time according to a silent remote activation model; Determine the trajectory risk coefficient of the target number based on the historical location of the target number; Determine the number risk level corresponding to the target number by using the trajectory risk coefficient; Different management and control are performed according to different number risk levels corresponding to the target number.
2. The risk number control method according to claim 1, characterized in that: The regional risk levels include: Risk-free zone; Low risk area; Medium risk area; High risk area.
3. The risk number control method according to claim 2, characterized in that: The regional risk score corresponding to each region is calculated as follows: S = w1 / calling ratio + w2 / called ratio + w3 / calling ratio from other markets, Wherein, S represents the regional risk score, w1 represents the weight value of the proportion of calling parties in the region, w2 represents the weight value of the proportion of called parties in the region, and w3 represents the weight value of the proportion of calling parties belonging to other cities in the region.
4. The risk number control method according to claim 3, characterized in that: The step of determining the regional risk score threshold comprises: Determining a risk score threshold for the region by analyzing historical data; The historical data includes: Numbers that have no voice for more than two consecutive months; Numbers with no traffic for more than two consecutive months; Numbers that have not received any text messages for more than two consecutive months; Numbers activated in a different location during a preset time period; The serial number used when the number is activated is inconsistent with the serial number collected and matched within six months.
5. The risk number control method according to claim 4, characterized in that: The determining of the silent remote activation risk area includes: determining the area with a high risk level as the silent remote activation risk area.
6. The risk number control method according to claim 5, characterized in that: The determining of the trajectory risk coefficient of the target number based on the historical location of the target number includes: Determine the historical location of the target number; The trajectory risk coefficient of the target number is calculated based on the number of historical areas and the area risk scores corresponding to the historical areas.
7. The risk number control method according to claim 1 or 6, characterized in that: The number risk levels include: Risk-free number; Low risk number; Medium risk number; High risk number.
8. The risk number control method according to claim 7, characterized in that: The different number risk levels corresponding to the target number are controlled differently, including: For high-risk numbers, double stop operation is performed; For medium-risk numbers, a prompt message is sent to the supervisor; For low-risk numbers, collect the corresponding information and archive it for subsequent use; There is no control over risk-free numbers.
9. An electronic device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method according to any one of claims 1 to 8 when executing the computer program.
10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to implement the method according to any one of claims 1 to 8.