A service invocation tracking based trust system health assessment system

Abstract

The application provides a kind of based on service call tracking's trust system health degree evaluation system, belong to data operation processing technical field, specifically include: user data screening system;Abnormal module evaluation system;Use health degree evaluation system;Health degree evaluation system;Wherein user data screening system is responsible for the determination of screening user data;Abnormal module evaluation system is responsible for determining the health degree of service module according to screening user data, whether there is abnormal module is determined by the type and health degree of service module;Use health degree evaluation system is responsible for determining the use health degree of trust system according to screening user data;Health degree evaluation system is responsible for evaluating the health degree of trust system through the health degree of service module, weight and the use health degree of trust system, and outputting optimization suggestions according to the health degree of trust system, so as to further improve the reliability of trust system operation.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of data operation and maintenance, and particularly relates to a credit system health degree evaluation system based on service call tracking. BACKGROUND

[0002] In order to realize the acquisition of the credit information of the user, the credit system often needs to call multiple service modules to realize the acquisition of the credit application information of the user, and therefore how to realize the tracking and statistics of the calling conditions of different service modules and realize the health degree evaluation of the credit system become technical problems to be solved urgently.

[0003] In order to realize the tracking and statistics of the calling of the service modules, in the invention patent CN115834699A "a service call chain tracking implementation method and system", the calling chain is used for display and analysis, the interface and service call time consumption, success rate, failure rate and the like are counted, the calling chain link execution condition can be analyzed in real time, the service node with an exception can be quickly located, and the software running performance can be improved, but the following technical problems exist:

[0004] The overall system running state is evaluated according to the monitoring conditions of the calling of different service modules, specifically, when the credit information is audited, multiple service modules are often called, if the overall running condition of the credit system cannot be evaluated through the abnormal analysis of the service modules, the overall running state of the credit management system cannot be accurately evaluated.

[0005] When the abnormal analysis of the service modules is performed, the determination of the differentiated abnormal service modules and the determination of the weight values of the service modules are not considered in combination with the types of the service modules, specifically, when the credit processing is performed, the delay amount and the reliability requirement of the service module for identity verification are obviously higher than those of the service module for user information filling, and therefore if the differentiated abnormal service modules cannot be evaluated, the overall running state of the credit management system cannot be accurately evaluated.

[0006] In view of the above technical problems, the application provides a credit system health degree evaluation system based on service call tracking. SUMMARY

[0007] To achieve the purpose of the application, the application adopts the following technical solutions:

[0008] According to one aspect of the application, a credit system health degree evaluation method based on service call tracking is provided.

[0009] A credit system health degree evaluation method based on service call tracking, characterized in that, specifically comprises:

[0010] S11 acquires user data of the credit system through a running log, and determines an abnormal user by using a network connection state of a user of the credit system and a lag state of a user terminal, and takes the user data excluding the abnormal user as screening user data;

[0011] S12 determines calling data of different service modules of the credit system according to the screening user data, and evaluates a health degree of the service modules according to the calling data of the service modules, and determines whether there is an abnormal module by the type and the health degree of the service modules, if yes, determines that the health degree of the credit system is abnormal, if not, enters the next step;

[0012] S13 determines usage data of a number of credit applications, a number of credit application information completion, a number of credit information problems and a number of credit approval completion of the credit system according to the screening user data, and determines a usage health degree of the credit system through the usage data, and determines whether there is an abnormality in the credit system through the usage health degree, if yes, determines that the health degree of the credit system is abnormal, if not, enters the next step;

[0013] S14 determines a weight of the service module through the type of the service module, and evaluates the health degree of the credit system through the health degree of the service module, the weight and the usage health degree of the credit system, and outputs an optimization suggestion according to the health degree of the credit system.

[0014] Further technical solutions are that the network connection state of the user is determined according to network connection state data of the user, and specifically determined according to a response time of the user responding to instruction data of the credit system.

[0015] Further technical solutions are that the lag state of the user terminal is determined according to a cumulative execution time of a main thread calling method of the user terminal when executing a credit application system when execution is successful.

[0016] Further technical solutions are that the method for confirming the lag state of the user terminal is:

[0017] Cumulative execution times of different calling methods of the user terminal are acquired, an abnormal calling method is determined according to the cumulative execution times, and whether the user terminal has lag is determined according to the number of abnormal calling methods, if yes, it is determined that the user terminal has lag, and the lag state is determined through the number of abnormal calling methods, if not, the next step is entered;

[0018] The execution times of different calling methods of the user terminal are acquired, and the determination of the calling methods with execution exceptions is performed through the execution times, and the determination of whether the user terminal has a lag is performed through the number of the calling methods with execution exceptions, if yes, it is determined that the user terminal has a lag, and the determination of the lag state is performed through the number of the abnormal calling methods, the number of the calling methods with execution exceptions and the number of the calling methods with time exceptions, if no, the next step is entered;

[0019] The determination of the calling methods with time exceptions is performed through the execution times of the different calling methods of the user terminal whose execution times are greater than the set time, and the determination of whether the user terminal has a lag is performed through the number of the calling methods with time exceptions, if yes, it is determined that the user terminal has a lag, and the determination of the lag state is performed through the number of the abnormal calling methods, the number of the calling methods with execution exceptions, the number of the calling methods with time exceptions, if no, the next step is entered;

[0020] The average execution times of the different calling methods of the user terminal, the average cumulative execution times are acquired, and the determination of the lag state is performed in combination with the number of the abnormal calling methods, the number of the calling methods with execution exceptions and the number of the calling methods with time exceptions.

[0021] Further technical solutions are that the service module includes but is not limited to an identity authentication service module, an information filling service module, a user face image acquisition module, a pedestrian information acquisition module, a social security information acquisition module and an automatic approval module.

[0022] Further technical solutions are that an optimization suggestion is output according to the health degree of the credit granting system, and specifically includes:

[0023] When the health degree of the credit granting system meets the requirement, the output of the optimization suggestion is not required;

[0024] When the health degree of the credit granting system does not meet the requirement, the determination of the service module needing optimization is performed according to the health degree of the service module of the credit granting system.

[0025] In the second aspect, the application provides a credit granting system health degree evaluation system based on service calling tracking, adopts the credit granting system health degree evaluation method based on service calling tracking, and specifically includes:

[0026] A user data screening system, an abnormal module evaluation system, a use health degree evaluation system and a health degree evaluation system;

[0027] The user data screening system is responsible for acquiring user data of the credit system through operation logs, and determining abnormal users by using network connection states of users of the credit system and stuttering states of user terminals, so as to take user data excluding the abnormal users as screening user data.

[0028] The abnormal module evaluation system is responsible for determining calling data of different service modules of the credit system according to the screening user data, and evaluating health degrees of the service modules according to the calling data of the service modules, so as to determine whether there is an abnormal module by the type and health degree of the service module.

[0029] The use health degree evaluation system is responsible for determining use data of a number of credit applications, a number of credit application information completion, a number of credit information problems and a number of credit approval completion of the credit system according to the screening user data, and determining a use health degree of the credit system by the use data, so as to determine whether there is an abnormality in the credit system by the use health degree.

[0030] The health degree evaluation system is responsible for determining weights of the service modules by the type of the service module, and evaluating a health degree of the credit system by the health degree, the weight of the service module and the use health degree of the credit system, so as to output an optimization suggestion according to the health degree of the credit system.

[0031] In a third aspect, the present application provides a computer system, comprising a memory and a processor connected in communication, and a computer program stored on the memory and capable of running on the processor, characterized in that the processor executes the computer program to perform the above-mentioned credit system health degree evaluation method based on service calling tracking.

[0032] In a fourth aspect, the present application provides a computer storage medium having a computer program stored thereon, when the computer program is executed in a computer, the computer program causes the computer to perform the above-mentioned credit system health degree evaluation method based on service calling tracking.

[0033] The present application has the following beneficial effects:

[0034] The network connection state of the user of the credit system and the stuttering state of the user terminal are used to determine the abnormal user, so as to realize the identification of the abnormal user from the network connection state of the user and the stuttering state of the user terminal, avoid the false judgment of the running state of the service module due to the user data of the abnormal user, and improve the accuracy of the judgment of the running state of the credit system.

[0035] The health degree of the service module is evaluated according to the calling data of the service module, so that the running state of the service module is evaluated from the perspective of the calling data of different service modules of the trust system, the accurate identification of the abnormal module is ensured, and the foundation for the comprehensive evaluation of the health degree of the trust system is laid.

[0036] The use health degree of the trust system is determined according to the use data of the screened user data, so that the running state of the trust system is evaluated from the perspective of the actual use data of the trust system, the problem that different service modules are normal but the combination causes the abnormal running state is avoided, and the comprehensiveness of the health degree evaluation is ensured.

[0037] The health degree of the trust system is evaluated according to the health degree of the service module, the weight and the use health degree of the trust system, the health degree of the trust system is evaluated from the running state of the service module and the actual use state of the trust system, the difference in importance of different service modules is fully considered, and the accuracy of the evaluation of the health degree of the trust system is further ensured.

[0038] Other features and advantages will be set forth in the following description, and in part will become apparent to those skilled in the art from the description, or can be learned by practice of the application. The objects and other advantages of the application will be realized and attained by the structure particularly pointed out in the written description and claims.

[0039] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the following preferred embodiments are specifically described below, and the accompanying drawings are used for detailed description. BRIEF DESCRIPTION OF DRAWINGS

[0040] The above and other features and advantages of the present application will become more apparent by describing in detail exemplary embodiments thereof with reference to the attached drawings:

[0041] Figure 1 A flowchart of a trust system health degree evaluation method based on service calling tracking;

[0042] Figure 2 A flowchart of a method for determining an abnormal user;

[0043] Figure 3 A flowchart of a method for confirming the lag state of a user terminal;

[0044] Figure 4 A flowchart of a method for evaluating the health degree of a service module;

[0045] Figure 5 A flowchart of a method for determining the use health degree of a trust system;

[0046] Figure 6 is a framework diagram of a service call tracking-based trust system health assessment system. DETAILED DESCRIPTION

[0047] In order for those skilled in the art to better understand the technical solutions in the specification, the technical solutions in the specification will be clearly and completely described below in combination with the drawings in the specification. Obviously, the described embodiments are only some of the embodiments of the specification, not all. Based on the embodiments of the specification, all other embodiments obtained by those skilled in the art without creative labor should be within the scope of protection of the specification.

[0048] To solve the above problems, according to one aspect of the present application, as shown in Figure 1 According to one aspect of the present application, a service call tracking-based trust system health assessment method is provided, characterized in that it specifically comprises:

[0049] S11 obtains user data of the trust system through operation logs, and determines abnormal users by using network connection states of users of the trust system and stuttering states of user terminals, and takes the user data excluding the abnormal users as screening user data;

[0050] In this embodiment, since the call exception of the service module may be caused by the abnormality of the user in addition to the problem of the service module, the data caused by the abnormality of the user must be excluded, so that the health degree of the trust system can be more truly obtained.

[0051] Specifically, the network connection state of the user is determined according to the network connection state data of the user, and specifically determined according to the response time of the user responding to the instruction data of the trust system.

[0052] It should be noted that the response time of the user responding to the instruction data of the trust system reflects the good or bad of the network connection state of the user, and the determination of the preset time threshold value can be performed by the corresponding difficulty of the user responding to the instruction data, and the real running state of the network connection of the user is determined according to the deviation of the corresponding time and the preset time threshold value.

[0053] Specifically, the stuttering state of the user terminal is determined according to the cumulative execution time of the calling method of the main thread of the user terminal when executing the trust application system when the execution is successful.

[0054] The execution time of different calling methods is determined, and when the calling method is not executed normally, there may be a stuttering situation, so the cumulative execution time is determined to accurately detect the stuttering.

[0055] It should be noted that, as Figure 2 As shown, the method for determining the abnormal user is as follows:

[0056] S21 Obtain the network connection status data of the user, determine the response time of the user terminal based on the network connection status data, and determine whether the user is an abnormal user based on the response time. If yes, the user is determined to be an abnormal user; otherwise, proceed to step S22.

[0057] Understandably, when the response time exceeds the set time, the user is identified as an abnormal user. In actual operation, it is necessary to read the response time multiple times to determine abnormal users.

[0058] S22 obtains the cumulative execution time of the user's terminal in executing different calling methods of the main thread of the credit application system, and determines whether the user is an abnormal user based on the cumulative execution time of the calling methods. If yes, the user is determined to be an abnormal user; otherwise, proceed to step S23.

[0059] Understandably, when the cumulative execution time exceeds a set time threshold, the user is identified as an abnormal user. In actual operation, it is necessary to continuously read the cumulative execution time of multiple called methods to determine abnormal users.

[0060] S23 determines whether the user is an abnormal user by the clarity of the facial image when the user terminal performs facial recognition verification. If yes, the user is determined to be an abnormal user; otherwise, proceed to step S24.

[0061] When a user's facial image is not clear, the final credit approval will fail due to the user's fault. Therefore, the user must be excluded in order to accurately determine the health of the credit system.

[0062] S24 confirms the user's network connection status by using the user's network connection status data, confirms the user terminal's lag status by using the cumulative execution time of different calling methods of the user's terminal, and combines the execution count of different calling methods of the user and the number of execution times with execution time greater than a set time, and identifies abnormal users by using the lag status and the network connection status.

[0063] It should be noted that the network connection status value ranges from 0 to 1.

[0064] In addition, it can be understood that, when performing the execution of the calling method, the user can submit multiple times due to the existence of the freezing state, so that multiple executions exist, and thus the identification of the number of executions existing for a long time can accurately confirm the freezing state.

[0065] It should be noted that, as shown in Figure 3 The method for confirming the freezing state of the user terminal comprises the following steps:

[0066] The cumulative execution time of different calling methods of the user terminal is obtained, the abnormal calling method is determined according to the cumulative execution time, and whether the user terminal exists freezing is determined according to the number of abnormal calling methods, if yes, it is determined that the user terminal exists freezing, and the freezing state is determined according to the number of abnormal calling methods, if no, the next step is entered.

[0067] The number of executions of different calling methods of the user terminal is obtained, the execution-abnormal calling method is determined according to the number of executions, and whether the user terminal exists freezing is determined according to the number of execution-abnormal calling methods, if yes, it is determined that the user terminal exists freezing, and the freezing state is determined according to the number of abnormal calling methods and the number of execution-abnormal calling methods, if no, the next step is entered.

[0068] The time-abnormal calling method is determined according to the number of executions of different calling methods of the user terminal whose execution time is greater than a set time, and whether the user terminal exists freezing is determined according to the number of time-abnormal calling methods, if yes, it is determined that the user terminal exists freezing, and the freezing state is determined according to the number of abnormal calling methods, the number of execution-abnormal calling methods and the number of time-abnormal calling methods, if no, the next step is entered.

[0069] The average number of executions of different calling methods of the user terminal, the average cumulative execution time, the number of abnormal calling methods, the number of execution-abnormal calling methods and the number of time-abnormal calling methods are obtained, and the freezing state is determined according to the number of abnormal calling methods, the number of execution-abnormal calling methods and the number of time-abnormal calling methods.

[0070] In the embodiment, the abnormal user is determined according to the network connection state of the user of the trusted system and the freezing state of the user terminal, the abnormal user is identified from the network connection state of the user and the freezing state of the user terminal, the error judgment of the running state of the service module due to the user data of the abnormal user is avoided, and the accuracy of the judgment of the running state of the trusted system is improved.

[0071] S12 determines calling data of different service modules of the credit system according to the screening user data, and performs health degree evaluation of the service modules according to the calling data of the service modules, determines whether there is an abnormal module through the type and health degree of the service modules, if yes, determines that the health degree of the credit system is abnormal, and if not, enters the next step;

[0072] It should be noted that the service modules include but are not limited to an identity authentication service module, an information filling service module, a user face image acquisition module, a pedestrian information acquisition module, a social security information acquisition module and an automatic approval module.

[0073] Specifically, as shown in Figure 4 The method for evaluating the health degree of the service modules is as follows:

[0074] S31 determines the number of calling failures of the service modules according to the calling data of different service modules of the credit system, and determines whether the service modules are abnormal through the number of calling failures of the service modules, if yes, determines that there is an abnormal module, and if not, enters step S32;

[0075] It should be noted that when the number of calling failures of the service modules is large, it can be determined that the service modules are abnormal, and thus the determination of the abnormal module is realized through the statistics of the number of calling failures. In the actual operation process, the abnormality of the service modules can also be determined through the proportion of calling failures in the number of calling persons.

[0076] S32 determines the calling failure rate of the service modules according to the calling data of different service modules of the credit system, and determines whether the calling delay of the service modules needs to be evaluated through the calling failure rate and the number of calling failures of the service modules, if yes, enters step S33, and if not, enters step S34;

[0077] It can be understood that when the calling failure rate is high or the number of calling failures is large, if the calling delay of the service modules is also long at this time, it can be determined that the service modules are abnormal.

[0078] S33 determines whether the service modules are abnormal through the number of calling times of the service modules whose calling delay exceeds the set time within the set time, if yes, determines that there is an abnormal module, and if not, enters step S34;

[0079] S34 determines the call delay abnormality of the service module according to the call delay average of the service module within a set time, the number of calls whose call delay exceeds the set time, and the number of people whose call delay exceeds the set time, and determines the health degree of the service module according to the call delay abnormality, the call failure rate of the service module, the number of call failures, and the number of people whose call fails.

[0080] It should be noted that the type and health degree of the service module are used to determine whether there is an abnormal module, which specifically includes:

[0081] The health degree of the service module is used to determine whether the service module is abnormal, if yes, it is determined that the service module belongs to an abnormal module, if no, it goes to the next step.

[0082] The weight of the service module is determined according to the type of the service module, and it is determined whether the service module is an abnormal module according to the weight of the service module and the health degree of the service module.

[0083] In this embodiment, the health degree of the service module is evaluated according to the call data of the service module, so that the running state of the service module is evaluated from the perspective of the call data of different service modules of the trust system, which not only ensures accurate identification of abnormal modules, but also lays a foundation for comprehensive evaluation of the health degree of the trust system.

[0084] S13 determines the usage data of the number of trust application, the number of trust application information completion, the number of trust information problems, and the number of trust approval completion of the trust system according to the screening user data, determines the usage health degree of the trust system according to the usage data, and determines whether the trust system is abnormal according to the usage health degree, if yes, it is determined that the health degree of the trust system is abnormal, if no, it goes to the next step.

[0085] Specifically, as shown in Figure 5 The method for determining the usage health degree of the trust system is:

[0086] S41 determines the number of trust information problems of the trust system according to the usage data, and determines whether the health degree of the trust system is abnormal according to the number of trust information problems and the ratio of the number of trust information problems to the number of trust applications, if yes, it is determined that the usage health degree of the trust system has a problem, if no, it goes to the next step.

[0087] S42, determining the number of completed credit approval of the credit system and the number of credit application of the credit system according to the use data, and determining whether the health degree of the credit system is abnormal through the difference between the number of completed credit approval and the number of credit application of the credit system, if yes, determining that the use health degree of the credit system has a problem, if not, entering the next step;

[0088] S43, determining whether the health degree of the credit system is in a critical state through the difference between the number of completed credit approval and the number of credit application of the credit system, if yes, entering the next step, if not, entering step S45;

[0089] S44, determining the number of completed credit application information of the credit system according to the use data, and determining whether the health degree of the credit system is abnormal through the difference between the number of completed credit application information and the number of credit application, and the difference between the number of completed credit approval and the number of credit application of the credit system, if yes, determining that the use health degree of the credit system has a problem, if not, entering the next step;

[0090] S45, evaluating the application information health degree of the credit system through the difference and ratio between the number of completed credit application information and the number of credit application, and combining the number of completed credit application information, evaluating the approval information health degree of the credit system through the difference and ratio between the number of completed credit approval and the number of credit application, and combining the number of completed credit approval, and determining the use health degree of the credit system through the application information health degree, the approval information health degree, the number of credit information problems and the ratio between the number of credit information problems and the number of credit application.

[0091] It should be noted that when the use health degree of the credit system is less than the set health threshold, it is determined that the health degree of the credit system is abnormal.

[0092] In the embodiment, the use health degree of the credit system is determined according to the use data of the filtered user data, so as to realize the evaluation of the running state of the credit system from the perspective of the actual use data of the credit system, avoid the problem that different service modules are normal but the combination leads to abnormal running state, and ensure the comprehensiveness of the health degree evaluation.

[0093] S14, determining the weight of the service module through the type of the service module, and evaluating the health degree of the credit system through the health degree of the service module, the weight and the use health degree of the credit system, and outputting an optimization suggestion according to the health degree of the credit system.

[0094] Specifically, the method for evaluating the health degree of the credit system is:

[0095] The health degree of the service module is determined by the health degree of the service module and the weight value, and the health degree of the credit system is evaluated according to the maximum value of the corrected health degree of the service module and the use health degree of the credit system.

[0096] It can be understood that the optimization suggestion is output according to the health degree of the credit system, and specifically includes:

[0097] When the health degree of the credit system meets the requirement, the output of the optimization suggestion is not required;

[0098] When the health degree of the credit system does not meet the requirement, the service module that needs to be optimized is determined according to the health degree of the service module of the credit system.

[0099] In the embodiment, the health degree of the credit system is evaluated by the health degree of the service module, the weight value and the use health degree of the credit system, which realizes the evaluation of the health degree of the credit system from the running condition of the service module and the actual use condition of the credit system, and fully considers the difference in importance of different service modules, further ensuring the accuracy of the evaluation of the health degree of the credit system.

[0100] On the other hand, as Figure 6 shown, the application provides a credit system health degree evaluation system based on service call tracking, which adopts the above-mentioned credit system health degree evaluation method based on service call tracking, and specifically includes:

[0101] A user data screening system, an abnormal module evaluation system, a use health degree evaluation system and a health degree evaluation system.

[0102] The user data screening system is responsible for obtaining user data of the credit system through running logs, and determining abnormal users by using the network connection state of the user of the credit system and the lag state of the user terminal, and taking the user data excluding the abnormal users as screening user data.

[0103] The abnormal module evaluation system is responsible for determining the call data of different service modules of the credit system according to the screening user data, and evaluating the health degree of the service module according to the call data of the service module, and determining whether there is an abnormal module by the type and health degree of the service module.

[0104] The use health degree evaluation system is responsible for determining the use data of the number of credit application, the number of credit application information completion, the number of credit information problems and the number of credit approval completion of the credit system according to the screening user data, and determining the use health degree of the credit system through the use data, and determining whether the credit system is abnormal through the use health degree;

[0105] The health degree evaluation system is responsible for determining the weight of the service module through the type of the service module, and evaluating the health degree of the credit system through the health degree of the service module, the weight and the use health degree of the credit system, and outputting optimization suggestions according to the health degree of the credit system.

[0106] On the other hand, as Figure 5 The application provides a computer system, comprising a memory and a processor connected in communication, and a computer program stored on the memory and capable of running on the processor, characterized in that the processor executes the computer program to perform the above-mentioned credit system health degree evaluation method based on service call tracking.

[0107] The above-mentioned credit system health degree evaluation method based on service call tracking specifically comprises:

[0108] The user data of the credit system is obtained through the running log, and the network connection state of the user of the credit system and the lag state of the user terminal are used to determine abnormal users, and the user data excluding the abnormal users is taken as the screening user data;

[0109] The call data of different service modules of the credit system is determined according to the screening user data, and the health degree of the service module is evaluated according to the call data of the service module, and when there is no abnormal module determined through the type and health degree of the service module, the next step is entered;

[0110] The number of credit information problems of the credit system is determined according to the use data, and when the health degree of the credit system is determined to be normal through the number of credit information problems and the ratio of the number of credit information problems to the number of credit applications, the next step is entered;

[0111] The number of credit approval completion of the credit system and the number of credit applications of the credit system are determined according to the use data, and when the health degree of the credit system is determined to be normal through the difference between the number of credit approval completion and the number of credit applications of the credit system, the next step is entered;

[0112] According to the use data, the number of completed credit application information of the credit system is determined, and when the health degree of the credit system is determined to be normal through the difference between the number of completed credit application information and the number of credit applicants, the difference between the number of completed credit approval and the number of credit applicants of the credit system, the next step is entered;

[0113] According to the use data, the number of completed credit application information of the credit system is determined, and when the health degree of the credit system is determined to be normal through the difference between the number of completed credit application information and the number of credit applicants, the difference between the number of completed credit approval and the number of credit applicants of the credit system, the next step is entered;

[0114] According to the use data, the number of completed credit application information of the credit system is determined, and when the health degree of the credit system is determined to be normal through the difference between the number of completed credit application information and the number of credit applicants, the difference between the number of completed credit approval and the number of credit applicants of the credit system, the next step is entered;

[0115] According to the use data, the number of completed credit application information of the credit system is determined, and when the health degree of the credit system is determined to be normal through the difference between the number of completed credit application information and the number of credit applicants, the difference between the number of completed credit approval and the number of credit applicants of the credit system, the next step is entered;

[0116] The credit system health degree evaluation method based on service call tracking, specifically includes:

[0117] According to the use data, the number of completed credit application information of the credit system is determined, and when the health degree of the credit system is determined to be normal through the difference between the number of completed credit application information and the number of credit applicants, the difference between the number of completed credit approval and the number of credit applicants of the credit system, the next step is entered;

[0118] According to the use data, the number of completed credit application information of the credit system is determined, and when the health degree of the credit system is determined to be normal through the difference between the number of completed credit application information and the number of credit applicants, the difference between the number of completed credit approval and the number of credit applicants of the credit system, the next step is entered;

[0119] According to the use data, the number of completed credit application information of the credit system is determined, and when the health degree of the credit system is determined to be normal through the difference between the number of completed credit application information and the number of credit applicants, the difference between the number of completed credit approval and the number of credit applicants of the credit system, the next step is entered;

[0120] The average execution times of different calling methods of the user terminal are obtained, and the average cumulative execution time is obtained, and the determination of the stall state is combined with the number of abnormal calling methods, the number of calling methods with abnormal execution, and the number of calling methods with time abnormality, and the network connection state of the user of the trusted system and the stall state of the user terminal are used to determine the abnormal user, and the user data excluding the abnormal user is used as the screening user data;

[0121] The calling data of different service modules of the trusted system is determined according to the screening user data, the health degree of the service module is evaluated according to the calling data of the service module, whether there is an abnormal module is determined through the type and health degree of the service module, if yes, it is determined that the health degree of the trusted system is abnormal, if not, the next step is entered;

[0122] The use data of the number of credit applicants, the number of credit application information completion, the number of credit information problems, and the number of credit approval completion of the trusted system is determined according to the screening user data, the use health degree of the trusted system is determined through the use data, and whether the trusted system is abnormal is determined through the use health degree, if yes, it is determined that the health degree of the trusted system is abnormal, if not, the next step is entered;

[0123] The weight of the service module is determined through the type of the service module, and the health degree of the trusted system is evaluated through the health degree, weight of the service module, and use health degree of the trusted system, and the optimization suggestion is output according to the health degree of the trusted system.

[0124] Each embodiment in the specification is described in a progressive manner, and the same and similar parts between each embodiment can be referred to each other, and each embodiment mainly describes the difference from other embodiments. Especially, for the device, equipment, and non-volatile computer storage medium embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the related parts can be referred to the part of the method embodiment.

[0125] The above describes specific embodiments of the specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims can be performed in an order different than the order in the embodiments and still achieve the desired result. In addition, the processes depicted in the figures do not necessarily require the particular order shown or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing can be utilized or can be advantageous.

[0126] The above merely provides one or more embodiments of the present specification and is not intended to limit the present specification. One of ordinary skill in the art can make various modifications and changes to one or more embodiments of the present specification. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of one or more embodiments of the present specification should be included in the scope of claims of the present specification.

Claims

1. A method for assessing the health of a credit system based on service call tracing, characterized in that, Specifically, it includes: User data of the credit granting system is obtained through the operation logs, and abnormal users are identified by using the network connection status of users in the credit granting system and the lag status of user terminals. User data excluding abnormal users is used as the filtered user data. Based on the filtered user data, the call data of different service modules of the credit granting system is determined, and the health of the service modules is evaluated based on the call data of the service modules. The type and health of the service modules are used to determine whether there are any abnormal modules. If so, the health of the credit granting system is determined to be abnormal. If not, proceed to the next step. Based on the filtered user data, the system determines the number of credit applicants, the number of credit application information completions, the number of credit information issues, and the number of credit approvals completed. The system's health is then determined based on the usage data, and the system's health is assessed to determine if any abnormalities exist. If so, the system's health is deemed abnormal; otherwise, the process proceeds to the next step. The weight of the service module is determined by its type, and the health of the credit system is evaluated by the health of the service module, its weight, and the health of the credit system. Optimization suggestions are then output based on the health of the credit system.

2. The method for assessing the health of a credit system based on service call tracing as described in claim 1, characterized in that, The user's network connection status is determined based on the user's network connection status data, specifically based on the response time of the user in response to the instruction data of the credit granting system.

3. The method for assessing the health of a credit system based on service call tracing as described in claim 1, characterized in that, The lag status of the user terminal is determined based on the cumulative execution time of the main thread's call method when the user terminal executes the credit application system.

4. The method for assessing the health of a credit system based on service call tracing as described in claim 1, characterized in that, The method for determining the abnormal user is as follows: Obtain the user's network connection status data, determine the response time of the user's terminal based on the network connection status data, and determine whether the user is an abnormal user based on the response time. If yes, the user is determined to be an abnormal user; otherwise, proceed to the next step. The system obtains the cumulative execution time of different call methods of the user's terminal in the main thread of the credit application system, and determines whether the user is an abnormal user based on the cumulative execution time of the call methods. If yes, the user is determined to be an abnormal user; otherwise, it proceeds to the next step. The clarity of the facial image during facial recognition verification on the user's terminal is used to determine whether the user is an abnormal user. If yes, the user is determined to be an abnormal user; otherwise, proceed to the next step. The network connection status of the user is confirmed by the network connection status data of the user's terminal. The lag status of the user's terminal is confirmed by the cumulative execution time of different calling methods of the user's terminal, combined with the number of executions of different calling methods and the number of executions with execution time exceeding a set time. The abnormal user is identified by the lag status and the network connection status.

5. The method for assessing the health of a credit system based on service call tracing as described in claim 1, characterized in that, The method for confirming the lag status of the user terminal is as follows: The cumulative execution time of different calling methods of the user terminal is obtained, and the abnormal calling methods are determined based on the cumulative execution time. The number of abnormal calling methods is used to determine whether the user terminal is experiencing lag. If so, the user terminal is confirmed to be experiencing lag, and the lag status is determined by the number of abnormal calling methods. If not, proceed to the next step. The execution count of different calling methods of the user terminal is obtained, and the execution count is used to determine the calling methods that have execution errors. The number of calling methods that have execution errors is used to determine whether the user terminal is experiencing lag. If so, the user terminal is confirmed to be experiencing lag. The lag status is determined by the number of abnormal calling methods and the number of calling methods that have execution errors. If not, proceed to the next step. The time-abnormal calling methods are determined by the number of times the execution time of different calling methods on the user terminal exceeds a set time. The number of time-abnormal calling methods is used to determine whether the user terminal is experiencing lag. If so, the user terminal is confirmed to be experiencing lag. The lag status is determined by the number of abnormal calling methods, the number of execution-abnormal calling methods, and the number of time-abnormal calling methods. If not, proceed to the next step. The average number of executions and the average cumulative execution time of different calling methods of the user terminal are obtained, and the lag state is determined by combining the number of abnormal calling methods, the number of calling methods with execution errors, and the number of calling methods with time errors.

6. The method for assessing the health of a credit system based on service call tracing as described in claim 1, characterized in that, The service modules include, but are not limited to, an identity verification service module, an information entry service module, a user facial image acquisition module, a people's bank information acquisition module, a social security information acquisition module, and an automatic approval module.

7. The method for assessing the health of a credit system based on service call tracing as described in claim 1, characterized in that, The method for assessing the health of the service module is as follows: S31 determines the number of times the service module fails to call based on the call data of different service modules of the credit granting system, and determines whether the service module is abnormal based on the number of times the service module fails to call. If so, it is determined that there is an abnormal module; otherwise, proceed to step S32. S32 determines the call failure rate of the service module based on the call data of different service modules of the credit granting system, and determines whether the call delay of the service module needs to be evaluated based on the call failure rate and the number of call failures. If yes, proceed to step S33; otherwise, proceed to step S34. S33 determines whether the service module is abnormal by the number of times the call delay of the service module exceeds the set time within a set time. If yes, it is determined that there is an abnormal module. If no, proceed to step S34. S34 determines the abnormal call latency of the service module by measuring the average call latency of the service module within a set time, the number of calls with call latency exceeding the set time, and the number of people with call latency exceeding the set time. The health of the service module is then determined by measuring the abnormal call latency, the call failure rate of the service module, the number of call failures, and the number of people with call failures.

8. The method for assessing the health of a credit system based on service call tracing as described in claim 7, characterized in that, Determining whether there are abnormal modules by analyzing the type and health status of the service modules specifically includes: The health status of the service module is used to determine whether the service module is abnormal. If it is, the service module is determined to be an abnormal module. If not, proceed to the next step. The weight of the service module is determined by the type of the service module, and whether the service module is an abnormal module is determined based on the weight of the service module and the health of the service module.

9. The method for assessing the health of a credit system based on service call tracing as described in claim 1, characterized in that, When the health status of the credit granting system is lower than the set health threshold, it is determined that the health status of the credit granting system is abnormal.

10. The method for assessing the health of a credit system based on service call tracing as described in claim 1, characterized in that, Based on the health status output optimization suggestions of the credit granting system, the specific suggestions include: When the health of the credit granting system meets the requirements, there is no need to output optimization suggestions; When the health of the credit granting system does not meet the requirements, the service modules that need to be optimized are determined based on the health of the service modules of the credit granting system.

11. A credit system health assessment system based on service call tracing, employing the credit system health assessment method based on service call tracing as described in any one of claims 1-10, characterized in that, Specifically, it includes: User data filtering system; Anomaly module evaluation system; using a health assessment system; health assessment system; The user data filtering system is responsible for obtaining user data from the credit system through the operation log, and using the network connection status of the users in the credit system and the lag status of the user terminal to identify abnormal users, and using the user data excluding abnormal users as the filtered user data. The abnormal module evaluation system is responsible for determining the call data of different service modules of the credit granting system based on the screened user data, and evaluating the health of the service modules based on the call data of the service modules, and determining whether there are abnormal modules through the type and health of the service modules. The usage health assessment system is responsible for determining the number of credit applicants, the number of credit application information completions, the number of credit information issues, and the number of credit approvals completed in the credit granting system based on the screened user data. It also determines the usage health of the credit granting system based on the usage health and whether there are any abnormalities in the credit granting system. The health assessment system is responsible for determining the weight of the service module based on its type, assessing the health of the credit system based on the health of the service module, its weight, and the usage health of the credit system, and outputting optimization suggestions based on the health of the credit system.

12. A computer system, comprising: A memory and processor connected by communication, and a computer program stored in the memory and capable of running on the processor, characterized in that: when the processor runs the computer program, it executes a method for assessing the health of a trust system based on service call tracing as described in any one of claims 1-10.

13. A computer storage medium storing a computer program thereon, which, when executed in a computer, causes the computer to perform a trust system health assessment method based on service call tracing as described in any one of claims 1-10.

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