CN117009204A - Service call tracking-based health evaluation system of credit giving system - Google Patents
Service call tracking-based health evaluation system of credit giving system Download PDFInfo
- Publication number
- CN117009204A CN117009204A CN202311102965.7A CN202311102965A CN117009204A CN 117009204 A CN117009204 A CN 117009204A CN 202311102965 A CN202311102965 A CN 202311102965A CN 117009204 A CN117009204 A CN 117009204A
- Authority
- CN
- China
- Prior art keywords
- abnormal
- determining
- credit
- health
- user
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 230000036541 health Effects 0.000 title claims abstract description 205
- 238000011156 evaluation Methods 0.000 title claims abstract description 45
- 230000002159 abnormal effect Effects 0.000 claims abstract description 148
- 238000012216 screening Methods 0.000 claims abstract description 36
- 238000005457 optimization Methods 0.000 claims abstract description 15
- 238000000034 method Methods 0.000 claims description 127
- 230000004044 response Effects 0.000 claims description 10
- 238000004590 computer program Methods 0.000 claims description 9
- 238000012795 verification Methods 0.000 claims description 8
- 230000000903 blocking effect Effects 0.000 claims description 6
- 230000005856 abnormality Effects 0.000 claims description 5
- 238000012545 processing Methods 0.000 abstract description 3
- 238000012423 maintenance Methods 0.000 abstract description 2
- 238000013475 authorization Methods 0.000 description 8
- 230000008569 process Effects 0.000 description 4
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 230000004075 alteration Effects 0.000 description 1
- 230000002547 anomalous effect Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000012790 confirmation Methods 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 238000002620 method output Methods 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 230000000750 progressive effect Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/30—Monitoring
- G06F11/34—Recording or statistical evaluation of computer activity, e.g. of down time, of input/output operation ; Recording or statistical evaluation of user activity, e.g. usability assessment
- G06F11/3466—Performance evaluation by tracing or monitoring
- G06F11/3476—Data logging
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/30—Monitoring
- G06F11/3003—Monitoring arrangements specially adapted to the computing system or computing system component being monitored
- G06F11/302—Monitoring arrangements specially adapted to the computing system or computing system component being monitored where the computing system component is a software system
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/30—Monitoring
- G06F11/3065—Monitoring arrangements determined by the means or processing involved in reporting the monitored data
- G06F11/3072—Monitoring arrangements determined by the means or processing involved in reporting the monitored data where the reporting involves data filtering, e.g. pattern matching, time or event triggered, adaptive or policy-based reporting
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/30—Monitoring
- G06F11/34—Recording or statistical evaluation of computer activity, e.g. of down time, of input/output operation ; Recording or statistical evaluation of user activity, e.g. usability assessment
- G06F11/3452—Performance evaluation by statistical analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/30—Monitoring
- G06F11/34—Recording or statistical evaluation of computer activity, e.g. of down time, of input/output operation ; Recording or statistical evaluation of user activity, e.g. usability assessment
- G06F11/3466—Performance evaluation by tracing or monitoring
- G06F11/3495—Performance evaluation by tracing or monitoring for systems
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2201/00—Indexing scheme relating to error detection, to error correction, and to monitoring
- G06F2201/865—Monitoring of software
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- General Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Quality & Reliability (AREA)
- General Physics & Mathematics (AREA)
- Computer Hardware Design (AREA)
- Computing Systems (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Bioinformatics & Computational Biology (AREA)
- Evolutionary Biology (AREA)
- Probability & Statistics with Applications (AREA)
- Life Sciences & Earth Sciences (AREA)
- Mathematical Physics (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Financial Or Insurance-Related Operations Such As Payment And Settlement (AREA)
Abstract
The invention provides a credit system health evaluation system based on service call tracking, which belongs to the technical field of data operation and maintenance processing, and specifically comprises the following steps: a user data screening system; an anomaly module evaluation system; using a health assessment system; a health assessment system; wherein the user data screening system is responsible for determining screening user data; the abnormal module evaluation system is responsible for determining the health degree of the service module according to the screening user data, and determining whether the abnormal module exists or not according to the type and the health degree of the service module; the usage health evaluation system is responsible for determining the usage health of the credit giving system according to the screening user data; the health degree evaluation system is responsible for evaluating the health degree of the credit giving system through the health degree and weight of the service module and the use health degree of the credit giving system, and outputting optimization suggestions according to the health degree of the credit giving system, so that the operation reliability of the credit giving system is further improved.
Description
Technical Field
The invention belongs to the technical field of data operation and maintenance, and particularly relates to a credit giving system health degree evaluation system based on service call tracking.
Background
In order to obtain the trust information of the user, the trust system often needs to call a plurality of service modules to obtain the trust application information of the user, so how to realize tracking statistics on the call conditions of different service modules and realize health assessment of the trust system become a technical problem to be solved urgently.
In order to realize the tracking and statistics of the call of the service module, the invention patent CN115834699A (a service call chain tracking realization method and system) displays and analyzes the call through a call chain, counts time consumption, success rate, failure rate and the like of an interface and service call, can analyze the execution condition of the call chain in real time, quickly positions abnormal service nodes and improves the running performance of software, but has the following technical problems:
the evaluation of the overall system running state according to the calling monitoring conditions of different service modules is neglected, specifically, when the verification of the credit information is carried out, a plurality of service modules are often required to be called, and if the evaluation of the overall running state of the credit system through the abnormal analysis conditions of the service modules cannot be considered, the accurate evaluation of the overall running state of the credit management system cannot be realized.
When the abnormal analysis of the service module is carried out, the determination of the differentiated abnormal service module and the determination of the weight of the service module are not considered in combination with the type of the service module, specifically, when the credit giving processing is carried out, the requirements of the delay amount and the reliability of the service module for identity verification are obviously higher than those of the service module for user information filling, so if the evaluation of the differentiated abnormal service module cannot be carried out, the accurate evaluation of the whole operation state of the credit giving management system cannot be accurately realized.
Aiming at the technical problems, the invention provides a credit giving system health degree evaluation system based on service call tracking.
Disclosure of Invention
In order to achieve the purpose of the invention, the invention adopts the following technical scheme:
according to one aspect of the invention, a trust system health assessment method based on service call tracking is provided.
A trust system health evaluation method based on service call tracking is characterized by comprising the following steps:
s11, acquiring user data of a credit system through an operation log, determining abnormal users by utilizing the network connection state of users of the credit system and the card-on state of user terminals, and taking the user data without the abnormal users as screening user data;
S12, determining call data of different service modules of the credit giving system according to the screening user data, evaluating the health degree of the service modules according to the call data of the service modules, determining whether an abnormal module exists according to the type and the health degree of the service modules, if so, determining that the health degree of the credit giving system is abnormal, and if not, entering the next step;
s13, determining the service data of the credit application number, the credit application information completion number, the credit information problem number and the credit approval completion number of the credit system according to the screening user data, determining the service health degree of the credit system according to the service data, determining whether the credit system is abnormal or not according to the service health degree, if so, determining that the health degree of the credit system is abnormal, and if not, entering the next step;
s14, determining the weight of the service module through the type of the service module, evaluating the health of the credit giving system through the health degree and the weight of the service module and the use health degree of the credit giving system, and outputting optimization suggestions according to the health degree of the credit giving system.
The further technical scheme is that the network connection state of the user is determined according to the network connection state data of the user, and particularly according to the response time of the user responding to the instruction data of the credit giving system.
The further technical scheme is that the blocking state of the user terminal is determined according to the accumulated execution time of the calling method of the main thread when the user terminal executes the credit application system when the user terminal is successfully executed.
The further technical scheme is that the method for confirming the stuck state of the user terminal comprises the following steps:
acquiring the accumulated execution time of different calling methods of the user terminal, determining an abnormal calling method according to the accumulated execution time, determining whether the user terminal is stuck according to the number of the abnormal calling methods, if so, determining that the user terminal is stuck, determining the stuck state according to the number of the abnormal calling methods, and if not, entering the next step;
acquiring the execution times of different calling methods of the user terminal, determining the abnormal calling methods according to the execution times, determining whether the user terminal is blocked according to the number of the abnormal calling methods, if so, determining that the user terminal is blocked, and determining the blocked state according to the number of the abnormal calling methods and the number of the abnormal calling methods, if not, entering the next step;
Determining a time-abnormal calling method according to the number of times that the execution time of different calling methods of the user terminal is longer than the set time, determining whether the user terminal is stuck according to the number of the time-abnormal calling methods, if so, determining that the user terminal is stuck, and determining the stuck state according to the number of the abnormal calling methods, the number of the abnormal calling methods and the number of the time-abnormal calling methods, if not, entering the next step;
and acquiring the average value of the execution times and the accumulated execution time of different calling methods of the user terminal, and determining the stuck state by combining the number of the abnormal calling methods, the number of the abnormal calling methods and the number of the abnormal calling methods.
The service module comprises, but is not limited to, an identity verification 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.
The further technical scheme is that the method outputs optimization suggestions according to the health degree of the credit giving system, and specifically comprises the following steps:
When the health degree of the trust system meets the requirement, the output of optimization suggestions is not needed;
and when the health degree of the trust system does not meet the requirement, determining the service module to be optimized according to the health degree of the service module of the trust system.
In a second aspect, the present invention provides a service call tracking-based health assessment system for a trust system, and the method for evaluating health of a trust system based on service call tracking specifically includes:
a user data screening system; an anomaly module evaluation system; using a health assessment system; a health assessment system;
the user data screening system is responsible for acquiring user data of the credit system through the operation log, determining abnormal users by utilizing the network connection state of users of the credit system and the blocking state of user terminals, and taking the user data without the abnormal users as screening user data;
the abnormal module evaluation system is responsible for determining call data of different service modules of the trust system according to the screening user data, evaluating the health degree of the service modules according to the call data of the service modules, and determining whether the abnormal modules exist or not according to the types and the health degrees of the service modules;
The use health evaluation system is responsible for determining the use data of the credit application number, the credit application information completion number, the credit information problem number and the credit approval completion number of the credit system according to the screening user data, determining the use health of the credit system according to the use data, and determining whether the credit system is abnormal according to the use health;
the health evaluation system is responsible for determining the weight of the service module through the type of the service module, evaluating the health of the credit giving system through the health of the service module, the weight and the use health of the credit giving system, and outputting optimization suggestions according to the health of the credit giving system.
In a third aspect, the present invention provides a computer system comprising: a communicatively coupled memory and processor, and a computer program stored on the memory and capable of running on the processor, characterized by: and executing the credit system health evaluation method based on service call tracking when the processor runs the computer program.
In a fourth aspect, the present invention provides a computer storage medium having a computer program stored thereon, which when executed in a computer causes the computer to perform a method for evaluating the health of a trusted system based on service invocation tracking as described above.
The invention has the beneficial effects that:
the abnormal user is determined through the network connection state of the user of the credit giving system and the blocking state of the user terminal, so that the abnormal user is identified from the network connection state of the user and the blocking state of the user terminal, the error judgment of the user data of the abnormal user on the running state of the service module is avoided, and the accuracy of the judgment of the running state of the credit giving system is improved.
The evaluation of the health degree of the service module is carried out according to the call data of the service module, so that the evaluation of the running state of the service module from the angles of the call data of different service modules of the credit giving system is realized, the accurate identification of the abnormal module is ensured, and meanwhile, the basis is laid for comprehensively carrying out the evaluation of the health degree of the credit giving system.
The service system is used for determining the service health degree of the credit system according to the service data of the screening user data, so that the operation state of the credit system is estimated from the actual service data of the credit system, the problem that different service modules are normal but the operation state is abnormal after combination is avoided, and the comprehensiveness of health degree estimation is ensured.
The health degree of the credit giving system is evaluated through the health degree and the weight of the service module and the use health degree of the credit giving system, so that the health degree of the credit giving system is evaluated from the running condition of the service module and the actual use condition of the credit giving system, the importance difference of different service modules is fully considered, and the accuracy of the health degree evaluation of the credit giving system is further ensured.
Additional features and advantages will be set forth in the description which follows, and in part will be apparent from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and drawings.
In order to make the above objects, features and advantages of the present invention more comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
The above and other features and advantages of the present invention will become more apparent by describing in detail exemplary embodiments thereof with reference to the attached drawings;
FIG. 1 is a flow chart of a method for evaluating the health of a trusted system based on service invocation tracking;
FIG. 2 is a flow chart of a method of determination of an anomalous user;
Fig. 3 is a flow chart of a method of confirmation of a stuck state of a user terminal;
FIG. 4 is a flow chart of a method of evaluation of the health of a service module;
FIG. 5 is a flow chart of a method of determining usage health of a trusted system;
FIG. 6 is a framework diagram of a trust system health assessment system based on service invocation tracking.
Detailed Description
In order to make the technical solutions in the present specification better understood by those skilled in the art, the technical solutions in the embodiments of the present specification will be clearly and completely described below with reference to the drawings in the embodiments of the present specification, and it is obvious that the described embodiments are only some embodiments of the present specification, not all embodiments. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments herein without making any inventive effort, shall fall within the scope of the present disclosure.
In order to solve the above-mentioned problems, according to one aspect of the present invention, as shown in fig. 1, there is provided a method for evaluating health of a trusted system based on service invocation tracking, which is characterized by specifically comprising:
S11, acquiring user data of a credit system through an operation log, determining abnormal users by utilizing the network connection state of users of the credit system and the card-on state of user terminals, and taking the user data without the abnormal users as screening user data;
in this embodiment, since the call abnormality of the service module is caused by abnormality of the user in addition to the problem of the service module, the data caused by abnormality of the user must be excluded, so that the health degree of the trusted system can be obtained more truly.
Specifically, the network connection state of the user is determined according to the network connection state data of the user, and specifically, the response time of the user to the instruction data of the trust system is determined.
It should be noted that, when the response time of the user responding to the instruction data of the trust system reflects the quality of the network connection state of the user, the determination of the preset time threshold can be performed through 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 between the corresponding time and the preset time threshold.
Specifically, the stuck state of the user terminal is determined according to the accumulated execution time of the calling method of the main thread when the user terminal executes the credit application system when the user terminal executes successfully.
The execution time of different calling methods is determined, and when the calling methods are not executed normally all the time, a jam condition is likely to exist, so that accurate detection of the jam is realized by accumulating the determination of the execution time.
As shown in fig. 2, the method for determining the abnormal user is as follows:
s21, acquiring network connection state data of the user, determining response time of a user terminal of the user according to the network connection state data, determining whether the user is an abnormal user according to the response time, if so, determining that the user is the abnormal user, and if not, entering step S22;
it can be understood that when the response time is greater than the set time or more, the user is determined to be an abnormal user, and the response time needs to be continuously read for a plurality of times in the actual operation process, so that the judgment of the abnormal user is realized.
S22, acquiring accumulated execution time of different calling methods of a user terminal of the user in executing a main thread of a credit application system, determining whether the user is an abnormal user according to the accumulated execution time of the calling methods, if so, determining that the user is the abnormal user, and if not, entering step S23;
It can be understood that when the accumulated execution time is greater than the set time threshold and above, the user is determined to be an abnormal user, and in the actual operation process, the accumulated execution time of a plurality of calling methods needs to be continuously read, so as to realize the judgment of the abnormal user.
S23, determining whether the user is an abnormal user or not according to the definition of the face image when the user terminal of the user performs face recognition verification, if so, determining that the user is the abnormal user, and if not, entering step S24;
when the definition of the face image of the user is poor, the final credit verification is not passed due to the reason of the user, so that the final credit verification is required to be eliminated to accurately judge the health degree of the credit verification system.
S24, confirming the network connection state of the user through the network connection state data of the user, confirming the cartoon state of the user terminal by combining the execution times of different calling methods of the user and the execution times of the calling methods with the execution times longer than the set time through the accumulated execution times of different calling methods of the user terminal, and confirming the abnormal user through the cartoon state and the network connection state.
It should be noted that the value of the network connection state is between 0 and 1.
In addition, it can be understood that when executing the calling method, the user may submit for multiple times due to the existence of the stuck state, so that multiple times of execution exist, and thus, the stuck state can be accurately confirmed by identifying the execution times with longer existing star time.
As shown in fig. 3, the method for confirming the stuck state of the ue includes:
acquiring the accumulated execution time of different calling methods of the user terminal, determining an abnormal calling method according to the accumulated execution time, determining whether the user terminal is stuck according to the number of the abnormal calling methods, if so, determining that the user terminal is stuck, determining the stuck state according to the number of the abnormal calling methods, and if not, entering the next step;
acquiring the execution times of different calling methods of the user terminal, determining the abnormal calling methods according to the execution times, determining whether the user terminal is blocked according to the number of the abnormal calling methods, if so, determining that the user terminal is blocked, and determining the blocked state according to the number of the abnormal calling methods and the number of the abnormal calling methods, if not, entering the next step;
Determining a time-abnormal calling method according to the number of times that the execution time of different calling methods of the user terminal is longer than the set time, determining whether the user terminal is stuck according to the number of the time-abnormal calling methods, if so, determining that the user terminal is stuck, and determining the stuck state according to the number of the abnormal calling methods, the number of the abnormal calling methods and the number of the time-abnormal calling methods, if not, entering the next step;
and acquiring the average value of the execution times and the accumulated execution time of different calling methods of the user terminal, and determining the stuck state by combining the number of the abnormal calling methods, the number of the abnormal calling methods and the number of the abnormal calling methods.
In this embodiment, the abnormal user is determined by the network connection state of the user of the trust system and the stuck state of the user terminal, so that the abnormal user is identified from the network connection state of the user and the stuck state of the user terminal, erroneous judgment of the operation state of the service module by the user data of the abnormal user is avoided, and the accuracy of the judgment of the operation state of the trust system is improved.
S12, determining call data of different service modules of the credit giving system according to the screening user data, evaluating the health degree of the service modules according to the call data of the service modules, determining whether an abnormal module exists according to the type and the health degree of the service modules, if so, determining that the health degree of the credit giving system is abnormal, and if not, entering the next step;
it should be noted that, the service module includes, but is not limited to, an 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.
Specifically, as shown in fig. 4, the method for evaluating the health of the service module is as follows:
s31, determining the calling failure times of the service modules according to the calling data of different service modules of the trust system, determining whether the service modules are abnormal or not according to the calling failure times of the service modules, if so, determining that the service modules are abnormal, and if not, entering step S32;
when the number of call failures of the service module is large, it is determined that the service module is abnormal, so that the abnormal module is determined by counting the number of call failures, and in the actual operation process, the abnormal module of the service module can be determined by the ratio of call failures in the number of call persons, and the like.
S32, determining the call failure rate of the service module according to call data of different service modules of the trust system, determining whether the call delay of the service module needs to be evaluated or not according to the call failure rate and the call failure times of the service module, if so, entering step S33, and if not, entering step S34;
it can be understood that when the call failure rate is high or the call failure times are high, if the call delay of the service module is also long, it can be determined that the service module has an exception.
S33, determining whether the service module is abnormal or not according to the calling times that the calling delay of the service module in the set time exceeds the set time, if so, determining that the service module is abnormal, and if not, entering step S34;
s34, determining the abnormal quantity of the call delay of the service module through the call delay average value, the call times of the call delay exceeding the set time and the number of people with the call delay exceeding the set time in the set time, and determining the health degree of the service module through the abnormal quantity of the call delay, the call failure rate, the call failure times and the call failure number of the service module.
It should be noted that, determining whether an abnormal module exists according to the type and the health degree of the service module specifically includes:
determining whether the service module is abnormal or not according to the health degree of the service module, if so, determining that the service module belongs to the abnormal module, and if not, entering the next step;
and determining the weight of the service module through the type of the service module, and determining whether the service module is an abnormal module according to the weight of the service module and the health degree of the service module.
In this embodiment, the health degree of the service module is evaluated according to the call data of the service module, so that the evaluation of the running states of the service module from the angles of the call data of different service modules of the trust system is realized, the accurate identification of the abnormal module is ensured, and meanwhile, a foundation is laid for comprehensively evaluating the health degree of the trust system.
S13, determining the service data of the credit application number, the credit application information completion number, the credit information problem number and the credit approval completion number of the credit system according to the screening user data, determining the service health degree of the credit system according to the service data, determining whether the credit system is abnormal or not according to the service health degree, if so, determining that the health degree of the credit system is abnormal, and if not, entering the next step;
Specifically, as shown in fig. 5, the method for determining the usage health degree of the trusted system is as follows:
s41, determining the number of credit information questions of the credit system according to the use data, determining whether the health degree of the credit system is abnormal or not according to the number of the credit information questions and the ratio of the number of the credit information questions to the number of the credit application, if so, determining that the use health degree of the credit system is problematic, and if not, entering the next step;
s42, determining the number of approval completion of the credit authorization system and the number of credit authorization application of the credit authorization system according to the use data, determining whether the health degree of the credit authorization system is abnormal or not according to the difference value between the number of approval completion of the credit authorization and the number of credit authorization application of the credit authorization system, if so, determining that the use health degree of the credit authorization system is problematic, and if not, entering the next step;
s43, determining whether the health degree of the credit system is in a critical state or not according to the difference value between the number of the credit approval completed persons and the credit application number of the credit system, if so, entering the next step, and if not, entering the step S45;
S44, determining the number of people finished by the credit application information of the credit system according to the use data, determining whether the health degree of the credit system is abnormal or not according to the difference value between the number of people finished by the credit application information and the number of the credit application people and the difference value between the number of people finished by credit approval and the number of the credit application people of the credit system, if so, determining that the use health degree of the credit system is problematic, and if not, entering the next step;
s45, carrying out evaluation of the application information health degree of the credit system by the difference and the ratio of the number of people finished by the credit application information to the number of people finished by the credit application information, carrying out evaluation of the approval information health degree of the credit system by the difference and the ratio of the number of people finished by the credit approval to the number of people finished by the credit application, and carrying out determination of the use health degree of the credit system by the application information health degree, the approval information health degree, the number of credit information problem number and the ratio of the number of credit information problem number to the number of people applied by the credit application.
When the usage health degree of the credit giving system is smaller than the set health threshold, it is determined that the health degree of the credit giving system is abnormal.
In this embodiment, the determination of the usage health degree of the trust system is performed according to the usage data of the screening user data, so that the evaluation of the operation state of the trust system from the angle of the actual usage data of the trust system is realized, the problem that the operation states of different service modules are abnormal due to normal combination is avoided, and the comprehensiveness of the health degree evaluation is ensured.
S14, determining the weight of the service module through the type of the service module, evaluating the health of the credit giving system through the health degree and the weight of the service module and the use health degree of the credit giving system, and outputting optimization suggestions according to the health degree of the credit giving system.
Specifically, the method for evaluating the health degree of the credit giving system comprises the following steps:
and determining the corrected health degree of the service module through the health degree and the weight of the service module, and evaluating the health degree of the credit giving system according to the maximum value of the corrected health degree of the service module and the use health degree of the credit giving system.
It can be understood that outputting optimization suggestions according to the health degree of the trust system specifically includes:
when the health degree of the trust system meets the requirement, the output of optimization suggestions is not needed;
and when the health degree of the trust system does not meet the requirement, determining the service module to be optimized according to the health degree of the service module of the trust system.
In this embodiment, the health degree of the trust system is evaluated by the health degree and the weight of the service module and the use health degree of the trust system, so that the health degree of the trust system is evaluated according to the running condition of the service module and the actual use condition of the trust system, the importance difference of different service modules is fully considered, and the accuracy of the evaluation of the health degree of the trust system is further ensured.
On the other hand, as shown in fig. 6, the invention provides a credit-giving system health degree evaluation system based on service call tracking, which adopts the credit-giving system health degree evaluation method based on service call tracking, and specifically comprises the following steps:
a user data screening system; an anomaly module evaluation system; using a health assessment system; a health assessment system;
The user data screening system is responsible for acquiring user data of the credit system through the operation log, determining abnormal users by utilizing the network connection state of users of the credit system and the blocking state of user terminals, and taking the user data without the abnormal users as screening user data;
the abnormal module evaluation system is responsible for determining call data of different service modules of the trust system according to the screening user data, evaluating the health degree of the service modules according to the call data of the service modules, and determining whether the abnormal modules exist or not according to the types and the health degrees of the service modules;
the use health evaluation system is responsible for determining the use data of the credit application number, the credit application information completion number, the credit information problem number and the credit approval completion number of the credit system according to the screening user data, determining the use health of the credit system according to the use data, and determining whether the credit system is abnormal according to the use health;
the health evaluation system is responsible for determining the weight of the service module through the type of the service module, evaluating the health of the credit giving system through the health of the service module, the weight and the use health of the credit giving system, and outputting optimization suggestions according to the health of the credit giving system.
In another aspect, as shown in FIG. 5, the present invention provides a computer system comprising: a communicatively coupled memory and processor, and a computer program stored on the memory and capable of running on the processor, characterized by: and executing the credit system health evaluation method based on service call tracking when the processor runs the computer program.
The method for evaluating the health degree of the credit giving system based on service call tracking specifically comprises the following steps:
acquiring user data of the credit system through the operation log, determining abnormal users by utilizing the network connection state of users of the credit system and the cartoon state of user terminals, and taking the user data without the abnormal users as screening user data;
determining call data of different service modules of the trust system according to the screening user data, evaluating the health degree of the service modules according to the call data of the service modules, and entering the next step when no abnormal module is determined according to the type and the health degree of the service modules;
determining the number of the credit information questions of the credit information system according to the use data, and entering the next step when the health degree of the credit information system is determined to be not abnormal according to the number of the credit information questions and the ratio of the number of the credit information questions to the number of the credit information application;
Determining the number of approval completion personnel of the credit giving system and the number of credit giving application personnel of the credit giving system according to the use data, and entering the next step when determining that the health degree of the credit giving system is not abnormal according to the difference value between the number of approval completion personnel of the credit giving system and the number of credit giving application personnel of the credit giving system;
determining the number of the people who finish the credit application information of the credit system according to the use data, and entering the next step when determining that the health degree of the credit system is not abnormal according to the difference value between the number of the people who finish the credit application information and the number of the credit application information and the difference value between the number of the people who finish credit approval and the number of the credit application information of the credit system;
the method comprises the steps of carrying out evaluation of the application information health degree of a credit system by the difference and the ratio of the number of finished credit application information to the number of the credit application, carrying out evaluation of the approval information health degree of the credit system by the difference and the ratio of the number of finished credit application information to the number of credit application, carrying out determination of the use health degree of the credit system by the application information health degree, the approval information health degree, the number of credit information questions and the ratio of the number of credit information questions to the number of credit application by the number of finished credit application information, and carrying out the next step when no abnormality exists in the credit system by the use health degree;
And determining the weight of the service module through the type of the service module, evaluating the health of the credit giving system through the health degree, the weight and the use health degree of the credit giving system of the service module, and outputting optimization suggestions according to the health degree of the credit giving system.
In another aspect, the present invention provides a computer storage medium having a computer program stored thereon, which when executed in a computer, causes the computer to perform a method for evaluating the health of a trusted system based on service invocation tracking as described above.
The method for evaluating the health degree of the credit giving system based on service call tracking specifically comprises the following steps:
acquiring user data of a credit system through an operation log, acquiring accumulated execution time of different calling methods of the user terminal, and determining an abnormal calling method according to the accumulated execution time;
acquiring the execution times of different calling methods of the user terminal, and determining the calling method with abnormal execution through the execution times;
determining a time-abnormal calling method through the execution times of different calling methods of the user terminal, wherein the execution time of the different calling methods is longer than the execution times of the set time;
Acquiring the average value of the execution times and the accumulated execution time of different calling methods of the user terminal, determining the cartoon state by combining the number of the abnormal calling methods, the number of the abnormal calling methods and the number of the time abnormal calling methods, determining abnormal users by utilizing the network connection state of users of a credit giving system and the cartoon state of the user terminal, and taking the user data without the abnormal users as screening user data;
determining call data of different service modules of the credit giving system according to the screening user data, evaluating the health degree of the service modules according to the call data of the service modules, determining whether an abnormal module exists according to the type and the health degree of the service modules, if so, determining that the health degree of the credit giving system is abnormal, and if not, entering the next step;
determining the service health degree of the credit giving system according to the screening user data, determining whether the credit giving application number, the credit giving application information completion number, the credit giving information problem number and the service data of the credit giving approval completion number of the credit giving system exist or not, if so, determining that the health degree of the credit giving system is abnormal, and if not, entering the next step;
And determining the weight of the service module through the type of the service module, evaluating the health of the credit giving system through the health degree, the weight and the use health degree of the credit giving system of the service module, and outputting optimization suggestions according to the health degree of the credit giving system.
In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments. In particular, for apparatus, devices, non-volatile computer storage medium embodiments, the description is relatively simple, as it is substantially similar to method embodiments, with reference to the section of the method embodiments being relevant.
The foregoing describes specific embodiments of the present disclosure. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims can be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
The foregoing is merely one or more embodiments of the present description and is not intended to limit the present description. Various modifications and alterations to one or more embodiments of this description will be apparent to those skilled in the art. Any modification, equivalent replacement, improvement, or the like, which is within the spirit and principles of one or more embodiments of the present description, is intended to be included within the scope of the claims of the present description.
Claims (13)
1. A trust system health evaluation method based on service call tracking is characterized by comprising the following steps:
acquiring user data of the credit system through the operation log, determining abnormal users by utilizing the network connection state of the user of the credit system and the katon state of the user terminal, and taking the user data without the abnormal users as screening user data;
determining call data of different service modules of the credit giving system according to the screening user data, evaluating the health degree of the service modules according to the call data of the service modules, determining whether an abnormal module exists according to the type and the health degree of the service modules, if so, determining that the health degree of the credit giving system is abnormal, and if not, entering the next step;
Determining the service health degree of the credit giving system according to the screening user data, determining whether the credit giving application number, the credit giving application information completion number, the credit giving information problem number and the service data of the credit giving approval completion number of the credit giving system exist or not, if so, determining that the health degree of the credit giving system is abnormal, and if not, entering the next step;
and determining the weight of the service module through the type of the service module, evaluating the health of the credit giving system through the health degree, the weight and the use health degree of the credit giving system of the service module, and outputting optimization suggestions according to the health degree of the credit giving system.
2. The method for evaluating the health of a trusted system based on service invocation tracking as claimed in claim 1, wherein said user's network connection status is determined according to said user's network connection status data, and specifically according to the response time of said user to said trusted system's instruction data.
3. The method for evaluating health of a credit giving system based on service call tracking as claimed in claim 1, wherein the stuck state of the user terminal is determined according to the accumulated execution time of the calling method of the main thread when the user terminal executes the credit giving application system when the execution is successful.
4. The method for evaluating the health of a trusted system based on service invocation tracking as claimed in claim 1, wherein the method for determining the abnormal user is as follows:
acquiring network connection state data of the user, determining response time of a user terminal of the user according to the network connection state data, determining whether the user is an abnormal user according to the response time, if so, determining that the user is the abnormal user, and if not, entering the next step;
acquiring accumulated execution time of different calling methods of a user terminal of the user in executing a main thread of a credit application system, determining whether the user is an abnormal user according to the accumulated execution time of the calling methods, if so, determining that the user is the abnormal user, and if not, entering the next step;
determining whether the user is an abnormal user or not according to the definition of the face image of the user terminal of the user during face recognition verification, if so, determining that the user is the abnormal user, and if not, entering the next step;
and confirming the network connection state of the user through the network connection state data of the user, confirming the stuck state of the user terminal by combining the execution times of different calling methods of the user and the execution times of the calling methods with the execution times longer than the set time through the accumulated execution times of different calling methods of the user terminal of the user, and determining the abnormal user through the stuck state and the network connection state.
5. The method for evaluating the health of a trusted system based on service invocation tracking as claimed in claim 1, wherein the method for confirming the stuck state of the user terminal is as follows:
acquiring the accumulated execution time of different calling methods of the user terminal, determining an abnormal calling method according to the accumulated execution time, determining whether the user terminal is stuck according to the number of the abnormal calling methods, if so, determining that the user terminal is stuck, determining the stuck state according to the number of the abnormal calling methods, and if not, entering the next step;
acquiring the execution times of different calling methods of the user terminal, determining the abnormal calling methods according to the execution times, determining whether the user terminal is blocked according to the number of the abnormal calling methods, if so, determining that the user terminal is blocked, and determining the blocked state according to the number of the abnormal calling methods and the number of the abnormal calling methods, if not, entering the next step;
determining a time-abnormal calling method according to the number of times that the execution time of different calling methods of the user terminal is longer than the set time, determining whether the user terminal is stuck according to the number of the time-abnormal calling methods, if so, determining that the user terminal is stuck, and determining the stuck state according to the number of the abnormal calling methods, the number of the abnormal calling methods and the number of the time-abnormal calling methods, if not, entering the next step;
And acquiring the average value of the execution times and the accumulated execution time of different calling methods of the user terminal, and determining the stuck state by combining the number of the abnormal calling methods, the number of the abnormal calling methods and the number of the abnormal calling methods.
6. The method for evaluating health of a trusted system based on service invocation tracking as claimed in claim 1, wherein said service modules include, but are not limited to, an 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.
7. The method for evaluating the health of a trusted system based on service invocation tracking as claimed in claim 1, wherein the method for evaluating the health of the service module is as follows:
s31, determining the calling failure times of the service modules according to the calling data of different service modules of the trust system, determining whether the service modules are abnormal or not according to the calling failure times of the service modules, if so, determining that the service modules are abnormal, and if not, entering step S32;
s32, determining the call failure rate of the service module according to call data of different service modules of the trust system, determining whether the call delay of the service module needs to be evaluated or not according to the call failure rate and the call failure times of the service module, if so, entering step S33, and if not, entering step S34;
S33, determining whether the service module is abnormal or not according to the calling times that the calling delay of the service module in the set time exceeds the set time, if so, determining that the service module is abnormal, and if not, entering step S34;
s34, determining the abnormal quantity of the call delay of the service module through the call delay average value, the call times of the call delay exceeding the set time and the number of people with the call delay exceeding the set time in the set time, and determining the health degree of the service module through the abnormal quantity of the call delay, the call failure rate, the call failure times and the call failure number of the service module.
8. The method for evaluating the health of a trusted system based on service invocation tracking as claimed in claim 7, wherein determining whether an abnormal module exists or not through the type and health of the service module comprises:
determining whether the service module is abnormal or not according to the health degree of the service module, if so, determining that the service module belongs to the abnormal module, and if not, entering the next step;
and determining the weight of the service module through the type of the service module, and determining whether the service module is an abnormal module according to the weight of the service module and the health degree of the service module.
9. The method for evaluating health of a trusted system based on service invocation tracking as claimed in claim 1, wherein when the usage health of the trusted system is less than a set health threshold, determining that there is an abnormality in the health of the trusted system.
10. The method for evaluating the health degree of a trusted system based on service invocation tracking according to claim 1, wherein the method for evaluating the health degree of the trusted system outputs optimization suggestions according to the health degree of the trusted system comprises the following steps:
when the health degree of the trust system meets the requirement, the output of optimization suggestions is not needed;
and when the health degree of the trust system does not meet the requirement, determining the service module to be optimized according to the health degree of the service module of the trust system.
11. A credit system health assessment system based on service call tracking, which adopts the credit system health assessment method based on service call tracking as claimed in any one of claims 1 to 10, and is characterized by comprising the following specific steps:
a user data screening system; an anomaly module evaluation system; using a health assessment system; a health assessment system;
the user data screening system is responsible for acquiring user data of the credit system through the operation log, determining abnormal users by utilizing the network connection state of users of the credit system and the blocking state of user terminals, and taking the user data without the abnormal users as screening user data;
The abnormal module evaluation system is responsible for determining call data of different service modules of the trust system according to the screening user data, evaluating the health degree of the service modules according to the call data of the service modules, and determining whether the abnormal modules exist or not according to the types and the health degrees of the service modules;
the use health evaluation system is responsible for determining the use data of the credit application number, the credit application information completion number, the credit information problem number and the credit approval completion number of the credit system according to the screening user data, determining the use health of the credit system according to the use data, and determining whether the credit system is abnormal according to the use health;
the health evaluation system is responsible for determining the weight of the service module through the type of the service module, evaluating the health of the credit giving system through the health of the service module, the weight and the use health of the credit giving system, and outputting optimization suggestions according to the health of the credit giving system.
12. A computer system, comprising: a communicatively coupled memory and processor, and a computer program stored on the memory and capable of running on the processor, characterized by: the processor, when executing the computer program, performs a trusted system health assessment method based on service invocation tracking as claimed in any one of claims 1-10.
13. A computer storage medium having stored thereon a computer program which, when executed in a computer, causes the computer to perform a trusted system health assessment method based on service invocation tracking as claimed in any one of claims 1 to 10.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311102965.7A CN117009204A (en) | 2023-08-30 | 2023-08-30 | Service call tracking-based health evaluation system of credit giving system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311102965.7A CN117009204A (en) | 2023-08-30 | 2023-08-30 | Service call tracking-based health evaluation system of credit giving system |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN117009204A true CN117009204A (en) | 2023-11-07 |
Family
ID=88567284
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311102965.7A Pending CN117009204A (en) | 2023-08-30 | 2023-08-30 | Service call tracking-based health evaluation system of credit giving system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117009204A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117591530A (en) * | 2024-01-17 | 2024-02-23 | 杭银消费金融股份有限公司 | Data cross section processing method and system |
-
2023
- 2023-08-30 CN CN202311102965.7A patent/CN117009204A/en active Pending
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117591530A (en) * | 2024-01-17 | 2024-02-23 | 杭银消费金融股份有限公司 | Data cross section processing method and system |
| CN117591530B (en) * | 2024-01-17 | 2024-04-19 | 杭银消费金融股份有限公司 | Data cross section processing method and system |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US10805151B2 (en) | Method, apparatus, and storage medium for diagnosing failure based on a service monitoring indicator of a server by clustering servers with similar degrees of abnormal fluctuation | |
| US10317864B2 (en) | Systems and methods for adaptively updating equipment models | |
| CN117009204A (en) | Service call tracking-based health evaluation system of credit giving system | |
| Rosa et al. | Predicting and mitigating jobs failures in big data clusters | |
| CN109509093B (en) | Transaction security control method and system based on main body portrait | |
| US8448025B2 (en) | Fault analysis apparatus, fault analysis method, and recording medium | |
| US8832839B2 (en) | Assessing system performance impact of security attacks | |
| CN111428945A (en) | Business audit processing method and device and electronic equipment | |
| CN116611797B (en) | Service tracking and monitoring method, system and storage medium | |
| CN113590429A (en) | Server fault diagnosis method and device and electronic equipment | |
| CN112506802B (en) | Test data management method and system | |
| CN116089223B (en) | Service operation monitoring system and monitoring method | |
| CN116668644B (en) | Real-time monitoring-based camera device management method and system | |
| CN113760689A (en) | Interface fault alarm method, device, equipment and storage medium | |
| US8089712B2 (en) | Abnormality determining apparatus | |
| KR102509380B1 (en) | Methods for learning application transactions and predicting and resolving real-time failures through machine learning | |
| CN114971223B (en) | Exponential lifetime type sequential test method, system, equipment, medium and terminal | |
| WO2009110326A1 (en) | Error analysis device, error analysis method, and recording medium | |
| CN116861364B (en) | ERP system-based data processing method and system | |
| CN110083470A (en) | Disk analysis method, apparatus and computer readable storage medium | |
| CN117421254B (en) | Automatic test method and system for reconciliation business | |
| CN116719751B (en) | Method and system for checking service loss in real time | |
| CN112053224B (en) | Service processing monitoring realization method, device and system | |
| Avritzer et al. | Using software aging monitoring and rejuvenation for the assessment of high-availability systems | |
| EP3772834A1 (en) | A method of predicting the time course of a plurality of data relative to a telephony infrastructure for network function virtualization |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |