Port health degree assessment method and device, equipment and storage medium

Abstract

The invention discloses a port health degree assessment method and device, equipment and a storage medium. The method comprises the steps of obtaining multi-dimensional short message port data in a preset time period; based on the short message port data, constructing a port relation graph; aggregating the short message port data based on the port relation graph and the time window to obtain a plurality of aggregated variable data associated with any main port; and based on the multiple pieces of aggregation variable data associated with any main port, utilizing the health degree evaluation model to predict and obtain a health degree evaluation result of any main port. According to the invention, the relationship network between the main port and the sub-port is constructed, the potential risk point and the risk propagation path are identified, the short message port data in the preset time period are aggregated based on the port relationship graph and the time window, and the health degree evaluation result of the main port is predicted according to the plurality of aggregated variable data of the time window. The problem that the health degree condition of the main port cannot be accurately highlighted by the current date data is solved, and the accuracy of port health degree evaluation is improved.

Description

Technical Field

[0001] The present invention relates to the field of computer technology, and in particular to a port health assessment method, device, equipment and storage medium. Background Art

[0002] In recent years, with the rapid development of the Internet and the widespread popularity of smart devices, SMS services are no longer just traditional communication tools, but have evolved into an important bridge for communication between enterprises and users. However, according to statistics, although the overall number of SMS messages sent has increased, the business revenue has declined relatively. The reasons behind this include the crackdown on spam SMS and the emergence of new communication methods in the market. In addition, the public pressure on spam SMS has also caused many companies to become more cautious before sending SMS. Therefore, it is necessary to conduct a health assessment of the SMS main port to plan the most reasonable delivery plan.

[0003] The existing SMS master port health scoring method is based on the scoring method of operation experts. In this method, operation experts evaluate the health of the current master port number based on the basic indicators of the SMS master port on the current date (indicators such as the number of sent messages, the number of complaints, and the ratio of complaints per million) based on business experience. However, the scoring method of operation experts has the following disadvantages:

[0004] (1) Lag: After a text message is sent, complaints may reach a peak on the Nth day and then decline. Therefore, the number of complaints on the current date is related to the number of messages sent on the current date, but it is not accurate enough.

[0005] (2) Biasedness: The sub-port verification and user complaints involved in the case are contagious. The same signature may be sent to multiple main ports, but only the complaint volume and the number of verifications involved in the case for main port A are large, or the complaint volume of other main ports is only temporarily small. In this case, only the increased risk of main port A is reported, and other risky main ports may be missed, which will eventually lead to the main port being restricted or shut down, affecting revenue.

[0006] (3) Subjectivity: Evaluating the health of the primary port based on business experience is subjective and cannot draw on a large amount of historical data as a reference. In addition, the real-time performance is low. Summary of the invention

[0007] Based on this, it is necessary to provide a port health assessment method, device, equipment and storage medium for the above technical problems to solve at least one of the above technical problems.

[0008] The present invention provides a port health assessment method, comprising:

[0009] Obtain multi-dimensional SMS port data within a preset time period;

[0010] Based on the SMS port data, a port relationship diagram corresponding to each main port and each sub-port is constructed;

[0011] Aggregating the SMS port data based on the port relationship diagram and a preset time window to obtain a plurality of aggregate variable data associated with any main port;

[0012] Based on a plurality of aggregate variable data associated with any of the master ports, a health evaluation result corresponding to any of the master ports is predicted using a pre-built health evaluation model.

[0013] Optionally, according to a port health assessment method provided by the present invention, the health assessment model is trained based on the following steps:

[0014] Obtaining the port status associated with multiple main ports and sample data of multiple indicator variables;

[0015] For any indicator variable: binning the indicator variable to obtain multiple binning intervals;

[0016] Based on the sample data corresponding to the indicator variable, determine the weight value corresponding to each binning interval;

[0017] The health assessment model is trained based on the weight value corresponding to each binning interval.

[0018] Optionally, according to a port health assessment method provided by the present invention, the health assessment result corresponding to any of the master ports is predicted based on multiple aggregate variable data associated with any of the master ports using a pre-built health assessment model, including:

[0019] For any primary port:

[0020] Based on the multiple aggregate variable data associated with the master port, multiple target variable data are obtained by screening;

[0021] Performing a joint operation on a plurality of aggregate variable data associated with the primary port to obtain a plurality of derived variable data;

[0022] Binning the target variable data and the derived variable data, and determining the target weight value corresponding to each bin;

[0023] Based on each of the target weight values, the health evaluation model is used to predict and obtain a health evaluation result corresponding to the primary port.

[0024] Optionally, according to a port health assessment method provided by the present invention, the port status includes a normal state, a notification state, a current limiting state and a shutdown state;

[0025] The determining, based on the sample data corresponding to the indicator variable, a weight value corresponding to each binning interval includes:

[0026] Based on the sample data corresponding to the indicator variable, determine the number of samples in each binning interval where the port status is in a normal state, a notification state, a current limiting state, and a shutdown state;

[0027] Determine the number of abnormal samples based on the number of samples in the notification state, the flow limiting state, and the shutdown state and the associated weights;

[0028] Based on the number of samples in a normal state and the number of abnormal samples in each binning interval, a weight value corresponding to each binning interval is determined.

[0029] Optionally, according to a port health evaluation method provided by the present invention, the SMS port data is aggregated based on the port relationship diagram and a preset time window to obtain a plurality of aggregate variable data associated with any main port, including:

[0030] According to the time window, the SMS port data within the preset time period is divided to obtain target division data within multiple time windows;

[0031] In combination with the port relationship diagram, the target partition data in each time window is aggregated to obtain a plurality of aggregate variable data associated with any main port.

[0032] Optionally, according to a port health evaluation method provided by the present invention, constructing a port relationship diagram corresponding to each main port and each sub-port based on the SMS port data includes:

[0033] Determine the sub-port information corresponding to the SMS port data;

[0034] Based on the sub-port information, extract the main port information;

[0035] Based on the association relationship between the sub-port information and the main port information, a port relationship graph corresponding to each main port and each sub-port is constructed.

[0036] Optionally, according to a port health assessment method provided by the present invention, the step of obtaining multi-dimensional SMS port data within a preset time period includes:

[0037] Collect SMS complaint data, sub-port daily sending volume data, sub-port real-name registration data, case-related sub-port verification data and risk signature data within a preset time period, where the case-related sub-port verification data refers to the verification data of the sub-port corresponding to the case-related risk event;

[0038] The SMS complaint data, the sub-port daily sending volume data, the sub-port real-name registration data, the sub-port verification data involved in the case and the neutral signature data are analyzed to form the multi-dimensional SMS port data.

[0039] The present invention also provides a port health evaluation device, comprising:

[0040] An acquisition module is used to acquire multi-dimensional SMS port data within a preset time period;

[0041] A construction module, used to construct a port relationship diagram corresponding to each main port and each sub-port based on the SMS port data;

[0042] An aggregation module, used to aggregate the SMS port data based on the port relationship diagram and a preset time window to obtain a plurality of aggregate variable data associated with any main port;

[0043] The evaluation module is used to predict the health evaluation result corresponding to any of the main ports based on multiple aggregate variable data associated with any of the main ports using a pre-built health evaluation model.

[0044] The present invention also provides a computer device, comprising a memory, a processor, and computer-readable instructions stored in the memory and executable on the processor, wherein the processor implements the above-mentioned port health evaluation method when executing the computer-readable instructions.

[0045] The present invention also provides one or more readable storage media storing computer-readable instructions, and the computer-readable instructions implement the above-mentioned port health evaluation method when executed by a processor.

[0046] The above-mentioned port health assessment method, device, equipment and storage medium include: obtaining multi-dimensional SMS port data within a preset time period; constructing a port relationship diagram corresponding to each main port and each sub-port based on the SMS port data; aggregating the SMS port data based on the port relationship diagram and a preset time window to obtain multiple aggregate variable data associated with any main port; based on the multiple aggregate variable data associated with any of the main ports, using a pre-constructed health assessment model to predict the health assessment result corresponding to any of the main ports. The present invention can identify potential risk points and risk propagation paths by constructing a relationship network between the main port and each sub-port, and take timely measures to prevent the spread of risks. In addition, based on the port relationship diagram and a preset time window, the SMS port data is aggregated to obtain multiple aggregate variable data associated with any main port, so as to predict the health assessment result corresponding to the main port according to the data of each time window, so as to solve the problem that the current date data cannot accurately highlight the health of the main port, and effectively improve the accuracy of port health assessment. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments of the present invention will be briefly introduced below. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative labor.

[0048] Figure 1 It is a flow chart of a method for evaluating port health in one embodiment of the present invention;

[0049] Figure 2 is a schematic diagram of a port relationship diagram provided by an embodiment of the present invention;

[0050] Figure 3 is a structural schematic diagram of a port health evaluation device in one embodiment of the present invention;

[0051] Figure 4 is a schematic diagram of a computer device in one embodiment of the present invention. DETAILED DESCRIPTION

[0052] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0053] The terms used in one or more embodiments of the present invention are only for the purpose of describing specific embodiments, and are not intended to limit one or more embodiments of the present invention. The singular forms of "a", "said" and "the" used in one or more embodiments of the present invention are also intended to include plural forms, unless the context clearly indicates other meanings. It should also be understood that the term "and / or" used in one or more embodiments of the present invention refers to and includes any or all possible combinations of one or more associated listed items.

[0054] In one embodiment, specifically, Figure 1 As shown, Figure 1 FIG. 1 is a flow chart of a method for evaluating port health in an embodiment of the present invention. The method for evaluating port health in an embodiment of the present invention includes the following steps:

[0055] Step S11, obtaining multi-dimensional SMS port data within a preset time period;

[0056] Specifically, the SMS complaint data, sub-port daily sending volume data, sub-port real-name registration data, sub-port verification data and risk signature data within a preset time period are collected, wherein the SMS complaint data includes information such as sub-port information, complaint channel field, and SMS content of terminal user complaints; the sub-port daily sending volume data includes information such as sub-port information and the daily SMS sending volume of the sub-port; the sub-port real-name registration data includes information such as sub-port information, signature of sub-port real-name registration, and port service type. In one embodiment, the port service type can be set to: account registration (business management and service category), account login (business management and service category), advertising promotion (commercial category), notification reminder (business management and service category), public service (public welfare category), etc. The sub-port verification data involved in the case refers to the verification data of the sub-port corresponding to the risk event involved in the case, for example, the sub-port information, SMS signature, SMS content, etc. corresponding to the risk event involved in the case. The risk signature data refers to the SMS signature data with non-compliant signatures such as fraud, gambling, and the company name or logo of the sender not clearly stated in the signature in the SMS content.

[0057] Further, the SMS complaint data, the sub-port daily sending volume data, the sub-port real-name registration data, the case-related sub-port verification data and the neutral signature data are analyzed. In one embodiment, the analysis process specifically includes the following operations:

[0058] SMS signature extraction: Extract SMS signatures based on SMS complaint data. Optionally, extract SMS signatures from SMS complaint data through word segmentation and string matching. SMS signatures are divided into three types: [Signature], [Signature], and {Signature}. The priority is [Signature]>[Signature]>{Signature}, that is, extract SMS signatures with high priority.

[0059] In addition, main port information extraction: Based on the SMS complaint data, sub-port daily sending volume data, sub-port real-name registration data, involved sub-port verification data and risk signature data, the main port information covered by each sub-port information is extracted. The extraction steps are as follows:

[0060] If the subport subPort1 exists in the preset main port data, it is determined that the subport subPort1 is the main port. It should be noted that the main port data is configured with known information of multiple main ports.

[0061] If the subport subPort1 does not exist in the main port data, the last bit of the subport subPort1 is deleted to obtain subPort2, and it is determined again whether the subport subPort2 with the last bit deleted exists in the main port data.

[0062] If it does not exist, continue to delete the last bit of the subport and assign it to subPort2, and then proceed with the judgment process. If it does exist, determine that subPort2 is the master port of subPort1.

[0063] In addition, industry category labeling: using open source AI big models, identify the industry types of SMS content in SMS complaint data, including Internet lending, pornography and terrorism, points redemption, insurance, retail, education marketing, life services, APP promotion, games and medicine, enterprise service marketing, banking, POS, stocks and securities, fund management, telecommunications, dating, travel promotion, real estate, debt collection and other industry types.

[0064] In addition, SMS type labeling: use open source AI large models to perform natural language recognition and identify the SMS types of SMS content in SMS complaint data. For example, SMS types include marketing and notification types.

[0065] In addition, signature compliance verification: the compliance of the SMS signature is comprehensively judged based on the SMS content in the SMS complaint data, the extracted SMS signatures and the sub-port real-name registration data. For example, it is judged whether the sub-port corresponding to the SMS content in the SMS complaint data exists in the sub-port real-name registration data, and whether the SMS signature obtained in the signature extraction stage is consistent with the signature in the sub-port real-name registration data. If both are met, the SMS signature is judged to be compliant, so that a non-compliant signature can be obtained based on the signature compliance verification results.

[0066] In addition, compliance check of mixed use of SMS entities is performed: based on the SMS signature and SMS type extracted from the SMS complaint data, for the same sub-port, if the SMS type is the same and there is only one SMS signature within a preset time (for example, the same month), the sub-port is judged to be compliant, otherwise it is non-compliant.

[0067] In addition, service type compliance check: sub-ports are marked according to the port service type in the sub-port real-name registration basic data table. In one embodiment, the port service type includes 1-account registration, 2-account login, 3-advertising promotion, 4-notification reminder, and 5-public service. The above 5 types of compliant combinations are 124, 3, and 5. If other combinations appear, such as: 123 or 125 or 25, etc., it is determined to be non-compliant, thereby obtaining the main port service type compliance check result.

[0068] In addition, the cancellation specifications in the SMS content are reviewed: the cancellation specifications are determined based on the SMS content in the SMS complaint data, and the cancellation method is judged to be consistent with the preset cancellation words. For example, the preset cancellation words are set to "Reply R to refuse". If they are consistent, the cancellation specifications are judged to have passed. If they are inconsistent or there is no clear cancellation method, the cancellation specifications are judged to have failed.

[0069] In addition, risk ratings are given to the industry types of text messages: for example, industries including Internet lending, pornography and terrorism are classified as severe high risk; industries including points redemption, insurance and retail are classified as high risk; industries including education marketing, life services, APP promotion, games and medicine, corporate service marketing and banking are classified as medium risk; industries including POS machines, stocks and securities, fund management, telecommunications services, dating, travel promotion and real estate are classified as low risk; and industries including debt collection are classified as exemption level.

[0070] In addition, severe risk signature marking: according to the signature of the subport in the subport real-name registration data, determine whether the signature exists in the risk signature data. If not, mark it as no; if it exists, determine the million complaints ratio and the total number of complaints corresponding to the signature; then compare the million complaints ratio with the first preset threshold, and compare the number of complaints with the second preset threshold. If the million complaints ratio is greater than the first preset threshold, or the million complaints ratio is less than or equal to the first preset threshold, the number of complaints is greater than or equal to the second preset threshold, or the signature only has complaints but no sent data, then the signature is determined to be a severe risk signature. For example, if any of the following conditions is met, it is identified as a severe risk signature:

[0071] A: The complaint ratio per million is greater than 1.18.

[0072] B: The complaint ratio per million is less than or equal to 1.18, and the number of complaints is greater than or equal to 27.

[0073] C: The signature only contains complaints but no sent data.

[0074] In addition, silent sub-port mark: based on the registration time of the sub-port in the sub-port real-name registration data and the date in the sending volume data, if the sub-port has not sent any text messages within a preset time (for example, 6 months), the sub-port is determined to be a silent sub-port.

[0075] Furthermore, based on the above analysis results, multi-dimensional SMS port data is formed; for example, the multi-dimensional SMS port data includes the main port to which the sub-port belongs, the compliance verification results of mixed use of entities, the marking results of silent sub-ports, the daily SMS sending volume of the sub-port, whether the sub-port exists in the real-name registration data, the results of the signature extraction stage, the SMS industry category marking results, the SMS type marking results, the signature compliance verification marking results, the unsubscription specification judgment marking results, the SMS industry type risk rating marking results, the complaint channel field in the SMS complaint data, the port verification volume involved in the sub-port verification data, the number of signatures, the severe risk signature marking results and other information.

[0076] Step S12, constructing a port relationship diagram corresponding to each main port and each sub-port based on the SMS port data;

[0077] Specifically, determine the sub-port information corresponding to the SMS port data and the main port information to which the sub-port information belongs; then, based on the association between the sub-port information and the main port information, construct a port relationship diagram corresponding to each main port and each sub-port. In addition, when the same SMS signature has sending records in multiple sub-ports, it proves that the multiple sub-ports have an association relationship. Based on the association relationship between the multiple sub-ports and the association relationship between the sub-port information and the main port information, a port relationship diagram corresponding to each main port and each sub-port can be constructed. The port relationship diagram can be referred to Figure 2 , Figure 2 This is a schematic diagram of a port relationship diagram provided by an embodiment of the present invention, which can show the association between the main port and each sub-port. The sending volume and complaint volume on the sub-port are the results of subsequent data aggregation of SMS port data. By building a port relationship network, potential risk points and risk propagation paths can be identified, and timely measures can be taken to prevent the spread of risks, which can provide early warning of risks to a certain extent.

[0078] Step S13, aggregating the SMS port data based on the port relationship diagram and the preset time window to obtain multiple aggregate variable data associated with any main port;

[0079] It should be noted that the number and value of the time windows can be set according to the specific situation. For example, the time windows are set to 3 days, 7 days, 15 days, and 30 days, respectively. Specifically, the SMS port data within the preset time period is divided according to the time window to obtain the target division data within multiple time windows; combined with the port relationship diagram, the target division data within each time window is aggregated to obtain multiple aggregate variable data associated with any main port. The specific data aggregation process is described in the following embodiment and will not be repeated here.

[0080] Step S14: Based on the multiple aggregate variable data associated with any master port, a pre-built health evaluation model is used to predict and obtain a health evaluation result corresponding to any master port.

[0081] Specifically, for multiple aggregate variable data associated with any main port, the aggregate variable data is screened to select variables with high correlation with the main port status, and multiple target variable data are obtained. Because after flow limiting or shutdown, the sending volume data of the next day will decrease or be 0, but other indicators, such as complaint data, will continue to grow or have a downward trend, so these data will affect the normal judgment results, so they need to be filtered out. In addition, the multiple aggregate variable data associated with the main port are screened for joint operation to obtain multiple derived variable data; for example, the derived variable data includes data such as the complaint ratio per million, the real-name registration rate of sub-ports, the proportion of silent sub-ports, and the growth rate of the complaint ratio per million. Further, each target variable data and each derived variable data are binned, and the target weight value WOE corresponding to each bin is determined; then based on the target weight values ​​corresponding to each target variable data and each derived variable data, the health assessment model is used to predict the health assessment result corresponding to the main port. The training process of the health assessment model is specifically described in the following embodiments and will not be repeated here. By continuously monitoring and optimizing the health of the main port number, the sending of spam text messages and fraudulent messages is effectively reduced, the purity and security of the information received by users is ensured, and the user's communication experience and satisfaction are significantly improved.

[0082] The embodiment of the present invention, through the above scheme, includes: obtaining multi-dimensional SMS port data within a preset time period; constructing a port relationship diagram corresponding to each main port and each sub-port based on the SMS port data; aggregating the SMS port data based on the port relationship diagram and the preset time window to obtain multiple aggregate variable data associated with any main port; based on the multiple aggregate variable data associated with any main port, using a pre-built health assessment model to predict the health assessment result corresponding to any main port. The embodiment of the present invention can identify potential risk points and risk propagation paths by constructing a relationship network between the main port and each sub-port, and take timely measures to prevent the spread of risks. In addition, based on the port relationship diagram and the preset time window, the SMS port data is aggregated to obtain multiple aggregate variable data associated with any main port, so as to predict the health assessment result corresponding to the main port according to the data of each time window, so as to solve the problem that the current date data cannot accurately highlight the health of the main port, and effectively improve the accuracy of the port health assessment.

[0083] In one embodiment of the present invention, based on the port relationship diagram and the preset time window, the SMS port data is aggregated to obtain multiple aggregate variable data associated with any main port, including:

[0084] According to the time window, the SMS port data within the preset time period is divided to obtain the target division data in multiple time windows; combined with the port relationship diagram, the target division data in each time window is aggregated respectively to obtain multiple aggregate variable data associated with any main port.

[0085] Specifically, in order to solve the problem that the current date data cannot accurately highlight the health of the main port, this embodiment divides the SMS port data within the preset time period according to the time window, and obtains the target division data within multiple time windows. In the data aggregation stage, based on the mapreduce technology, a large amount of data is aggregated, and finally the multi-dimensional aggregate variable data associated with the main port is obtained, including the following data:

[0086] (1) Send volume: According to the main port number, the send volume data of all sub-ports associated with the main port number are summarized and the total send volume is obtained after aggregation.

[0087] (2) Number of sub-ports reporting severe risk signatures: The sub-port real-name registration data and the marked severe risk signatures are queried based on the main port, and the number of sub-ports reporting severe risk signatures is obtained after aggregation.

[0088] (3) Severe risk signature sending volume: query the sub-port real-name registration data and the marked severe risk signature according to the main port number, obtain the sub-port with the severe risk signature, and summarize the sending volume data of these sub-ports according to the sub-port daily sending volume data. After aggregation, the sending volume of the severe risk signature is obtained.

[0089] (4) Number of sending sub-ports: query the daily sending volume data of the sub-port according to the main port number, obtain all sub-ports and remove duplicates, and obtain the number of sub-ports with sending records after aggregation.

[0090] (5) Number of sub-ports registered with real names: query the daily sending volume data of the sub-ports according to the main port number, obtain the sub-ports that have been registered with real names and remove duplicates, and after aggregation, obtain the number of sub-ports with sending records and real-name registration.

[0091] (6) Sub-port real-name registration rate: Sub-port real-name registration rate = number of real-name registered sub-ports / number of sending sub-ports.

[0092] (7) Number of silent sub-ports: The data marked as silent sub-ports is queried according to the main port number and deduplication is performed. The number of silent sub-ports is obtained after aggregation.

[0093] (8) Number of sub-ports used by the main body: The data marked as mixed by the main body is obtained according to the main port number query and deduplication is performed. After aggregation, the number of sub-ports used by the main body is obtained.

[0094] (9) Total number of complaints across all channels: Query SMS complaint data based on the main port number and aggregate them to obtain the total number of complaints across all channels.

[0095] (10) Number of complaints from the 12321 channel: Query the SMS complaint data based on the main port number, filter the data whose complaint channel is the 12321 channel, and obtain the total number of complaints corresponding to the 12321 channel after aggregation.

[0096] (11) Number of complaints with severe high risk: query the SMS complaint data based on the main port number, and filter the data with SMS industry risk rating of severe high risk. After aggregation, the total number of complaints with severe high risk is obtained.

[0097] (12) High-label complaint volume: Query SMS complaint data based on the main port number and filter out data with SMS industry risk rating of high risk. After aggregation, the total number of high-label complaints is obtained.

[0098] (13) Number of complaints with medium label: query the SMS complaint data based on the main port number, and filter the data with SMS industry risk rating of medium risk. After aggregation, the total number of complaints with medium label is obtained.

[0099] (14) Low-label complaint volume: Filter data with a low risk rating for the SMS industry type based on the main port number, and obtain the total number of low-risk complaints after aggregation.

[0100] (15) Number of complaints exempted from the label: query the SMS complaint data based on the main port number, and filter the data with SMS industry risk rating as exempted. After aggregation, the total number of complaints exempted from the label is obtained.

[0101] (16) Number of complaints about non-compliance with cancellation regulations: Query SMS complaint data based on the main port number, and filter out data that is judged to be non-compliant with cancellation regulations. After aggregation, the total number of complaints about non-compliance with cancellation regulations is obtained.

[0102] (17) Number of complaints about signature non-compliance: query the SMS complaint data based on the main port number, and filter out the data with signature compliance verification results as non-compliant. After aggregation, the total number of complaints about signature non-compliance is obtained.

[0103] (18) Number of port inspections involved: Query the port inspection data involved according to the main port number, and obtain the total number of complaints about the ports involved after aggregation.

[0104] In other embodiments, based on the information such as the number of sub-ports and signature anomaly data on the port relationship diagram, it is determined whether the port data anomaly has a risk of spreading. For example, when the number of anomaly data associated with the port is large, there is a risk of spreading. Therefore, in combination with the port relationship diagram, the target division data in each time window is aggregated and processed respectively, so as to obtain the data such as the number of spread complaints, spread transmission volume, and spread per million complaints ratio associated with any main port. If the signature corresponding to the sub-port is also transmitted at other main ports, there is actually a certain risk. The spread complaint volume is determined based on the complaint volume of the main port and the complaint volume of the signature corresponding to the sub-port at other main ports. The spread transmission volume is determined based on the transmission volume of the main port and the transmission volume of the signature corresponding to the sub-port at other main ports. The spread per million complaints ratio is determined based on the complaint volume of the main port and the complaint volume and transmission volume of the signature corresponding to the sub-port at other main ports. The aggregate variable data can be referred to as shown in Table 1 below:

[0105] Table 1

[0106]

[0107] Through the above scheme, the embodiment of the present invention realizes the comprehensive association of complaint data, sending behavior patterns and real-name registration information of the main port number, and can quickly and accurately identify the source of potential service quality degradation, abuse or security vulnerabilities, providing operators with immediate risk warning and governance basis.

[0108] In one embodiment of the present invention, the health assessment model is trained based on the following steps:

[0109] Obtain the port status associated with multiple main ports and sample data of multiple indicator variables; for any indicator variable: bin the indicator variable to obtain multiple bin intervals; based on the sample data corresponding to the indicator variable, determine the weight value corresponding to each bin interval; based on the weight value corresponding to each bin interval, train the health assessment model.

[0110] It should be noted that the port status includes normal status, notification status, current limiting status and shutdown status; the port status associated with multiple main ports and the sample data of multiple indicator variables are obtained; the sample data of each indicator variable is obtained by analyzing and aggregating the SMS complaint data, sub-port daily sending volume data, sub-port real-name registration data, involved sub-port verification data and risk signature data within a certain period of time, wherein the data analysis and aggregation process are similar to the data analysis and aggregation process in the above steps, and will not be repeated here. The analyzed data is screened and processed (missing value processing, outlier processing, data cleaning) to obtain the basic variable data for model training, and then based on the basic variable data, the derived variables are constructed. For example, 3 days, 7 days, 15 days, and 30 days are used as time windows to obtain the basic variable data of various indicators (such as: complaint volume, sending volume). According to the joint operation of each time slider data and the data of other indicators, multiple derived variables are obtained. For example, there are 4 types of time sliders, and other indicators include complaint volume, number of sent, million complaints ratio, etc. The number of derived variables generated = 4 types of sliders * other indicators. Million complaint ratio: The data of each slider is statistically calculated based on the time slider method. Million complaint ratio = complaint volume / number of sent. Growth rate: The growth rate of the ratio of complaint volume to sent volume is of great reference significance when the number of sent is ranked in the middle or lower.

[0111] In addition, if the signature corresponding to the sub-port involved in the case verification is also sent on other main ports but not involved in the case verification, there is actually a certain risk, and additional processing can be performed based on the custom risk coefficient. The calculation method is: the number of main port involved in the case verification = the number of main port involved in the case verification + the number of other main ports involved in the case verification * custom coefficient.

[0112] After constructing the derived variables, the variables with high correlation with the main port status are screened out according to the maximum correlation principle. Furthermore, based on the screened basic variable data and the derived variable data, sample data of multiple indicator variables are formed. Optionally, the sample data of the indicator variables include the sending volume associated with the main port, the number of complaints, the complaint ratio per million, the real-name registration rate of sub-ports, the proportion of silent sub-ports, the growth rate of the complaint ratio per million, the spread complaint ratio per million, the number of investigations involved, the compliance rate of sub-ports and other data.

[0113] Further, for any indicator variable: the indicator variable is binned to obtain multiple bin intervals; for example, the sending volume is divided into multiple bin intervals such as 10,000 to 50,000 sending volumes, 50,000 to 300,000 sending volumes, and 300,000 to 1 million sending volumes, and the samples are divided into normal and abnormal (notification, current limiting, and shutdown). It should be noted that in the process of binning, some variables are binned according to the two levels of normal and abnormal, which will cause the weight of the bin to be not accurate enough. When the final evaluation model outputs the results, the degree of distinction of the abnormal level (notification, current limiting, shutdown) is not high. For example: the shutdown state is the most serious state, the shutdown case is the least, and in some variables, the main ports in the shutdown state will be concentrated in the same bin, but due to the low number, the weight is low, which will result in a low final score, affecting the inference result. Therefore, in this embodiment, a weighted operation is performed in the process of calculating the weights of these variables. Optionally, weighting is applied during the binning process, that is, when the main ports in the current limiting and shutdown states are assigned to a certain bin, multiple binning operations are performed to increase the number of abnormal variables in this bin and increase the abnormal proportion of this bin. Specifically: based on the sample data corresponding to the indicator variable, determine the number of samples in each binning interval where the port status is in the normal state, notification state, current limiting state and shutdown state, as well as the normal number in the normal state, that is, count the number of notifications in the notification state, the number of current limiting in the current limiting state, the number of shutdowns in the shutdown state, and the normal number in the normal state in each binning interval; then, based on the number of notifications in the notification state, the number of current limiting in the current limiting state, and the number of shutdowns in the shutdown state in each binning interval, calculate the number of abnormalities in each binning interval according to the preset weight coefficient; then, determine the weight value WOE corresponding to each binning interval based on the number of abnormalities and normal numbers in each binning interval. The calculation formula is as follows:

[0114] Abnormal number = Notification number + Current limiting number * First preset weight coefficient + Shutdown number * Second preset weight coefficient

[0115] WOE = WOE = ln ((abnormal number / abnormal number of all bins) / (normal number / normal number of all bins))

[0116] Furthermore, based on the weight value corresponding to each binning interval, a health assessment model is trained, wherein the health assessment model is a logistic regression algorithm model. In other embodiments, the IV (information value) value of each binning interval is calculated, and then the IV value of each binning interval is screened among the weight values ​​corresponding to each binning interval, and the health assessment model is trained using the weight values ​​corresponding to the binning intervals obtained by the screening.

[0117] Through the above scheme, the embodiment of the present invention realizes the comprehensive association of complaint data, sending behavior patterns and real-name registration information of the main port number, screens the variable data for model training, and trains the health assessment model by adjusting the variable weight coefficient, so as to more accurately assess the health status of the main port.

[0118] In one embodiment of the present invention, based on multiple aggregate variable data associated with any master port, a health evaluation result corresponding to any master port is predicted using a pre-built health evaluation model, including:

[0119] For any primary port:

[0120] Based on multiple aggregate variable data associated with the main port, multiple target variable data are screened and obtained; multiple aggregate variable data associated with the main port are jointly operated to obtain multiple derived variable data; each target variable data and each derived variable data are binned, and the target weight value corresponding to each bin is determined; based on each target weight value, the health assessment result corresponding to the main port is predicted using the health assessment model.

[0121] Specifically, the target variable data is obtained by screening out the multiple aggregate variable data associated with the main port.

[0122] For example, variable data such as the sending volume and the complaint volume associated with the main port are selected; in addition, multiple aggregated variable data associated with the main port are jointly calculated to obtain multiple derived variable data, for example, derived variable data such as the complaint ratio per million, the real-name registration rate of sub-ports, the proportion of silent sub-ports, the growth rate of the complaint ratio per million, and the spread of the complaint ratio per million are calculated. Each target variable data and derived variable data are binned, and the target weight value corresponding to each bin is calculated, wherein the binning process and the calculation process of the target weight value WOE are described in the above embodiment and will not be repeated here; further, based on each target weight value, the health assessment result corresponding to the main port is predicted using the health assessment model. Therefore, based on the health assessment result of the main port, the operator can allocate resources more reasonably, give more support to the main port number with excellent performance, and take targeted improvement measures for the problem port, such as adjusting the sending strategy, strengthening supervision, etc., so as to achieve efficient use of resources and effective control of costs.

[0123] Through the above scheme, the embodiment of the present invention realizes the complaint data, sending data and derived variable data obtained by jointly calculating the aggregated variable data of the main port number in each time window, and uses the health assessment model to predict the health assessment result corresponding to the main port, which effectively solves the problem that date data cannot accurately highlight the health status of the main port, and improves the accuracy of the health assessment of the main port.

[0124] It should be understood that the order of execution of the steps in the above embodiment does not necessarily mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present invention.

[0125] In one embodiment, a port health evaluation device is provided, which corresponds one-to-one to the port health evaluation method in the above embodiment. Figure 3 As shown, Figure 3 1 is a schematic diagram of a structure of a port health evaluation device in an embodiment of the present invention, the port health evaluation device comprising:

[0126] The acquisition module 21 is used to acquire the multi-dimensional SMS port data within a preset time period;

[0127] A construction module 22 is used to construct a port relationship diagram corresponding to each main port and each sub-port based on the SMS port data;

[0128] Aggregation module 23, used to aggregate SMS port data based on the port relationship diagram and a preset time window to obtain multiple aggregate variable data associated with any main port;

[0129] The evaluation module 24 is used to predict the health evaluation result corresponding to any main port based on multiple aggregate variable data associated with any main port using a pre-built health evaluation model.

[0130] For the specific definition of the port health assessment device, please refer to the definition of the port health assessment method above, which will not be repeated here. Each module in the above-mentioned port health assessment device can be implemented in whole or in part by software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0131] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 4 As shown, Figure 4It is a schematic diagram of a computer device in one embodiment of the present invention. The computer device includes a processor, a memory, a network interface and a database connected by a device bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a readable storage medium and an internal memory. The readable storage medium stores an operating device, a computer-readable instruction and a database. The internal memory provides an environment for the operation of the operating device and the computer-readable instructions in the readable storage medium. The database of the computer device is used to store data involved in the port health assessment method. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer-readable instruction is executed by the processor, a port health assessment method is implemented. The readable storage medium provided in this embodiment includes a non-volatile readable storage medium and a volatile readable storage medium.

[0132] In one embodiment, a computer device is provided. The computer device may be a terminal device, and its internal structure diagram may be as follows: Figure 4 As shown. The computer device includes a processor, a memory, and a network interface connected through a device bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a readable storage medium. The readable storage medium stores computer-readable instructions. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer-readable instructions are executed by the processor, a port health assessment method is implemented. The readable storage medium provided in this embodiment includes a non-volatile readable storage medium and a volatile readable storage medium.

[0133] In one embodiment, a computer device is provided, including a memory, a processor, and computer-readable instructions stored in the memory and executable on the processor, and the processor implements the steps of the above-mentioned port health evaluation method when executing the computer-readable instructions.

[0134] In one embodiment, a readable storage medium is provided, the readable storage medium stores computer-readable instructions, and the computer-readable instructions are executed by the processor to implement the steps of the port health assessment method as described above. A person of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment method can be completed by instructing the relevant hardware through computer-readable instructions, and the computer-readable instructions can be stored in a non-volatile readable storage medium or a volatile readable storage medium. When the computer-readable instructions are executed, they may include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0135] Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0136] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the protection scope of the present invention.

Claims

1. A port health assessment method, characterized in that: include: Obtain multi-dimensional SMS port data within a preset time period; Based on the SMS port data, a port relationship diagram corresponding to each main port and each sub-port is constructed; Aggregating the SMS port data based on the port relationship diagram and a preset time window to obtain a plurality of aggregate variable data associated with any main port; Based on a plurality of aggregate variable data associated with any of the master ports, a health evaluation result corresponding to any of the master ports is predicted using a pre-built health evaluation model.

2. The port health evaluation method according to claim 1, characterized in that: The health assessment model is trained based on the following steps: Obtaining the port status associated with multiple main ports and sample data of multiple indicator variables; For any indicator variable: binning the indicator variable to obtain multiple binning intervals; Based on the sample data corresponding to the indicator variable, determine the weight value corresponding to each binning interval; The health assessment model is trained based on the weight value corresponding to each binning interval.

3. The port health evaluation method according to claim 2, characterized in that: The method of using a pre-built health evaluation model to predict a health evaluation result corresponding to any of the master ports based on the plurality of aggregated variable data associated with any of the master ports includes: For any primary port: Based on the multiple aggregate variable data associated with the master port, multiple target variable data are obtained by screening; Performing a joint operation on a plurality of aggregate variable data associated with the primary port to obtain a plurality of derived variable data; Binning the target variable data and the derived variable data, and determining the target weight value corresponding to each bin; Based on each of the target weight values, the health evaluation model is used to predict and obtain a health evaluation result corresponding to the primary port.

4. The port health evaluation method according to claim 2, characterized in that: The port status includes normal status, notification status, current limiting status and shutdown status; The step of determining a weight value corresponding to each binning interval based on the sample data corresponding to the indicator variable includes: Based on the sample data corresponding to the indicator variable, determine the number of samples in each binning interval where the port status is in a normal state, a notification state, a current limiting state, and a shutdown state; Determine the number of abnormal samples based on the number of samples in the notification state, the flow limiting state, and the shutdown state and the associated weights; Based on the number of samples in a normal state and the number of abnormal samples in each binning interval, a weight value corresponding to each binning interval is determined.

5. The port health evaluation method according to claim 1, characterized in that: The method of aggregating the SMS port data based on the port relationship diagram and the preset time window to obtain a plurality of aggregate variable data associated with any main port includes: According to the time window, the SMS port data within the preset time period is divided to obtain target division data within multiple time windows; In combination with the port relationship diagram, the target partition data in each time window is aggregated to obtain a plurality of aggregate variable data associated with any main port.

6. The port health evaluation method according to claim 1, characterized in that: The step of constructing a port relationship diagram corresponding to each main port and each sub-port based on the SMS port data includes: Determine the sub-port information corresponding to the SMS port data; Based on the sub-port information, extract the main port information; Based on the association relationship between the sub-port information and the main port information, a port relationship graph corresponding to each main port and each sub-port is constructed.

7. The port health evaluation method according to claim 1, characterized in that: The obtaining of multi-dimensional SMS port data within a preset time period includes: Collect SMS complaint data, sub-port daily sending volume data, sub-port real-name registration data, case-related sub-port verification data and risk signature data within a preset time period, where the case-related sub-port verification data refers to the verification data of the sub-port corresponding to the case-related risk event; The SMS complaint data, the sub-port daily sending volume data, the sub-port real-name registration data, the sub-port verification data involved in the case and the neutral signature data are analyzed to form the multi-dimensional SMS port data.

8. A port health assessment device, characterized in that: include: An acquisition module is used to acquire multi-dimensional SMS port data within a preset time period; A construction module, used to construct a port relationship diagram corresponding to each main port and each sub-port based on the SMS port data; An aggregation module, used to aggregate the SMS port data based on the port relationship diagram and a preset time window to obtain a plurality of aggregate variable data associated with any main port; The evaluation module is used to predict the health evaluation result corresponding to any of the main ports based on multiple aggregate variable data associated with any of the main ports using a pre-built health evaluation model.

9. A computer device comprising a memory, a processor, and computer-readable instructions stored in the memory and executed on the processor, characterized in that: When the processor executes the computer-readable instructions, the port health evaluation method according to any one of claims 1 to 7 is implemented.

10. A readable storage medium having computer readable instructions stored thereon, characterized in that: When the computer-readable instructions are executed by a processor, the port health evaluation method according to any one of claims 1 to 7 is implemented.

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