CN117609004A - Influence degree evaluation method, device, equipment and medium for financial platform test - Google Patents
Influence degree evaluation method, device, equipment and medium for financial platform test Download PDFInfo
- Publication number
- CN117609004A CN117609004A CN202311351951.9A CN202311351951A CN117609004A CN 117609004 A CN117609004 A CN 117609004A CN 202311351951 A CN202311351951 A CN 202311351951A CN 117609004 A CN117609004 A CN 117609004A
- Authority
- CN
- China
- Prior art keywords
- blasting
- test
- duration
- steady
- service
- 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
- 238000012360 testing method Methods 0.000 title claims abstract description 148
- 238000011156 evaluation Methods 0.000 title claims abstract description 41
- 238000005422 blasting Methods 0.000 claims abstract description 214
- 238000000034 method Methods 0.000 claims abstract description 51
- 230000000739 chaotic effect Effects 0.000 claims abstract description 28
- 238000004458 analytical method Methods 0.000 claims abstract description 9
- 238000012544 monitoring process Methods 0.000 claims description 22
- 238000004590 computer program Methods 0.000 claims description 17
- 238000003860 storage Methods 0.000 claims description 11
- 230000032683 aging Effects 0.000 claims description 3
- 230000008569 process Effects 0.000 abstract description 7
- 238000002474 experimental method Methods 0.000 abstract description 4
- 238000005516 engineering process Methods 0.000 description 11
- 238000013473 artificial intelligence Methods 0.000 description 5
- 230000000694 effects Effects 0.000 description 5
- 238000004880 explosion Methods 0.000 description 5
- 230000003993 interaction Effects 0.000 description 5
- 230000004044 response Effects 0.000 description 5
- 238000001514 detection method Methods 0.000 description 4
- 238000010586 diagram Methods 0.000 description 4
- 238000004519 manufacturing process Methods 0.000 description 4
- 238000012545 processing Methods 0.000 description 4
- 230000008878 coupling Effects 0.000 description 3
- 238000010168 coupling process Methods 0.000 description 3
- 238000005859 coupling reaction Methods 0.000 description 3
- 238000013461 design Methods 0.000 description 3
- 230000006870 function Effects 0.000 description 3
- 230000002427 irreversible effect Effects 0.000 description 3
- 230000002159 abnormal effect Effects 0.000 description 2
- 238000004364 calculation method Methods 0.000 description 2
- 238000004891 communication Methods 0.000 description 2
- 238000010276 construction Methods 0.000 description 2
- 238000011161 development Methods 0.000 description 2
- 230000018109 developmental process Effects 0.000 description 2
- 238000012423 maintenance Methods 0.000 description 2
- 238000003491 array Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004422 calculation algorithm Methods 0.000 description 1
- 238000009960 carding Methods 0.000 description 1
- 238000012790 confirmation Methods 0.000 description 1
- 238000013135 deep learning Methods 0.000 description 1
- 238000009826 distribution Methods 0.000 description 1
- 238000010801 machine learning Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000003058 natural language processing Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/36—Preventing errors by testing or debugging software
- G06F11/3668—Software testing
- G06F11/3672—Test management
- G06F11/3684—Test management for test design, e.g. generating new test cases
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/36—Preventing errors by testing or debugging software
- G06F11/3668—Software testing
- G06F11/3672—Test management
- G06F11/3688—Test management for test execution, e.g. scheduling of test suites
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/36—Preventing errors by testing or debugging software
- G06F11/3668—Software testing
- G06F11/3672—Test management
- G06F11/3692—Test management for test results analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F9/00—Arrangements for program control, e.g. control units
- G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
- G06F9/44—Arrangements for executing specific programs
- G06F9/455—Emulation; Interpretation; Software simulation, e.g. virtualisation or emulation of application or operating system execution engines
- G06F9/45533—Hypervisors; Virtual machine monitors
- G06F9/45558—Hypervisor-specific management and integration aspects
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N7/00—Computing arrangements based on specific mathematical models
- G06N7/08—Computing arrangements based on specific mathematical models using chaos models or non-linear system models
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F9/00—Arrangements for program control, e.g. control units
- G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
- G06F9/44—Arrangements for executing specific programs
- G06F9/455—Emulation; Interpretation; Software simulation, e.g. virtualisation or emulation of application or operating system execution engines
- G06F9/45533—Hypervisors; Virtual machine monitors
- G06F9/45558—Hypervisor-specific management and integration aspects
- G06F2009/45591—Monitoring or debugging support
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- Software Systems (AREA)
- Computer Hardware Design (AREA)
- Quality & Reliability (AREA)
- Artificial Intelligence (AREA)
- Algebra (AREA)
- Nonlinear Science (AREA)
- Computational Mathematics (AREA)
- Data Mining & Analysis (AREA)
- Evolutionary Computation (AREA)
- Mathematical Analysis (AREA)
- Mathematical Optimization (AREA)
- Pure & Applied Mathematics (AREA)
- Computing Systems (AREA)
- Mathematical Physics (AREA)
- Debugging And Monitoring (AREA)
Abstract
The application is applicable to the technical field of financial services, and particularly relates to a method, a device, equipment and a medium for evaluating influence degree of a financial platform test. According to the method, problem analysis is conducted on service components of a financial platform, an affected object set is determined, blasting tests are conducted on N service components, fluctuation of steady-state indexes and working states of all objects in the affected object set are monitored, the blasting tests are repeated by increasing initial blasting duration until the situation that any object in the affected object set breaks down or the fluctuation of the steady-state indexes meets preset conditions is monitored, the initial blasting duration when the blasting tests are stopped is determined to be an evaluation result of the test influence degree, and the evaluation of the blasting duration is achieved, so that in the process of introducing chaotic engineering to conduct blasting experiments, the evaluation result is used as a basis, the platform is prevented from being irreversibly damaged, and the stability of the platform is guaranteed to a certain extent.
Description
Technical Field
The application is applicable to the technical field of financial services, and particularly relates to a method, a device, equipment and a medium for evaluating influence degree of a financial platform test.
Background
Currently, information technology (Information Technology, IT) infrastructure software has become a common public in the industry, and through the development of cloud protogenesis, IT infrastructure software falls to the ground in a cloud protogenesis mode. The cloud native construction scheme which is common to the current industry and is an actual industrial standard is to construct and realize an infrastructure meeting the cloud native standard through Kubernetes (K8S for short) ecology, and along with the increase of business complexity, the micro-servitization of the software architecture design also becomes an irreversible trend.
The containerization of the computing resources based on the K8S containerization environment plus the microservization of the software architecture design thoroughly changes the current IT service: the basic ecology of software design, construction deployment and stable operation is based on the fact that the containerization of K8S and the micro-serviceization of software have become the fact standard of landing a considerable part of IT services, the changes bring the advantages of improving the development efficiency of the software, reducing the total cost of the IT, improving the utilization rate of resources and improving the later maintenance efficiency, but as K8S and the micro-service architecture are both based on a distributed system, the complexity of the inherent characteristics of the distributed system superimposed on the complexity of the software itself HAs led to a sharp rise of the complexity of the final IT service, and for stable IT maintenance personnel with primary responsibility for guaranteeing the service, if the weak points and bottlenecks of the system are still analyzed and positioned by the traditional system carding, component analysis and other methods, the architecture is optimized, the HA is built, and an emergency plan is prepared to maintain, guarantee, improve the system, environment and even service toughness, so that the task of being impossible to be accomplished is becoming more and more. Therefore, the chaotic engineering theory for improving the toughness of a highly complex system is introduced to improve the toughness of the system and the service in the container arrangement (K8S) environment, and the difficulty of maximizing the acquisition effect of the chaotic engineering is how to apply the chaotic engineering in the production environment, and the application of the chaotic engineering in the production environment finally leads to obviously unacceptable abnormal external service, so that the stability of the production service is the final target and can bring negative effects to the falling of the chaotic engineering. Therefore, how to evaluate the influence of the chaotic engineering test on the financial platform so as to guide the platform safety protection work to be done in advance, and the problem that the chaotic engineering test generates irreversible damage to the platform is to be solved urgently.
Disclosure of Invention
In view of this, the embodiments of the present application provide a method, an apparatus, a device, and a medium for evaluating the influence of a financial platform test, so as to evaluate the influence of a chaotic engineering test on a financial platform, to instruct to perform a platform security protection task in advance, and to prevent the problem that the chaotic engineering test generates irreversible damage to the platform.
In a first aspect, an embodiment of the present application provides a method for evaluating an influence degree of a financial platform test, where the method includes:
determining N service components from the financial platform, determining theoretical problems corresponding to faults from configuration information of the N service components, and determining actual problems corresponding to the N service components when faults occur from historical operation data of the financial platform, wherein N is an integer larger than zero;
determining service influence objects corresponding to any service component according to theoretical problems and actual problems of the service component, traversing all service components, and summing the service influence objects of all service components to obtain an influence object set;
acquiring steady state indexes and initial blasting duration of the financial platform, performing blasting tests on the N service components based on the initial blasting duration, and monitoring fluctuation of the steady state indexes and working states of all objects in the influence object set;
If no fault is detected to occur to any object in the influence object set and the fluctuation of the steady-state index does not meet a preset condition, increasing the initial blasting duration to obtain an increased blasting duration, and taking the increased blasting duration as the initial blasting duration;
and returning to execute the step of performing blasting test on the N service components based on the initial blasting duration until the blasting test is stopped when any object in the affected object set is detected to fail or the fluctuation of the steady-state index meets a preset condition, and determining the initial blasting duration when the blasting test is stopped as an evaluation result of the test influence degree.
In a second aspect, an embodiment of the present application provides an influence degree evaluation device for a financial platform test, where the influence degree evaluation device includes:
the component and problem determining module is used for determining N service components from the financial platform, determining theoretical problems corresponding to faults from configuration information of the N service components, determining actual problems corresponding to the N service components when faults occur from historical operation data of the financial platform, wherein N is an integer larger than zero;
the influence object determining module is used for determining service influence objects corresponding to any service component according to the theoretical problem and the actual problem of the service component, traversing all the service components, and summing the service influence objects of all the service components to obtain an influence object set;
The blasting test module is used for acquiring steady-state indexes and initial blasting duration of the financial platform, performing blasting test on the N service components based on the initial blasting duration, and monitoring fluctuation of the steady-state indexes and working states of all objects in the influencing object set;
the blasting duration adjusting module is used for increasing the initial blasting duration to obtain an increased blasting duration if any object in the affected object set is not monitored to be faulty and the fluctuation of the steady-state index does not meet the preset condition, and taking the increased blasting duration as the initial blasting duration;
and the influence degree evaluation module is used for returning to execute the step of performing blasting test on the N service components based on the initial blasting duration until the blasting test is stopped when any object in the influence object set is monitored to be faulty or the fluctuation of the steady-state index meets a preset condition, and determining the initial blasting duration when the blasting test is stopped as an evaluation result of the test influence degree.
In a third aspect, embodiments of the present application provide a computer device, the computer device comprising a processor, a memory, and a computer program stored in the memory and executable on the processor, the processor implementing the impact level assessment method according to the first aspect when executing the computer program.
In a fourth aspect, embodiments of the present application provide a computer readable storage medium storing a computer program which, when executed by a processor, implements the influence degree assessment method according to the first aspect.
Compared with the prior art, the embodiment of the application has the beneficial effects that: according to the method, N service components are determined from a financial platform, theoretical problems corresponding to the N service components when faults occur are determined from configuration information of the N service components, actual problems corresponding to the N service components when faults occur are determined from historical operation data of the financial platform, for any service component, service influence objects corresponding to the service components are determined according to the theoretical problems and the actual problems of the service components, all the service components are traversed, the service influence objects of all the service components are combined to obtain an influence object set, steady state indexes and initial blasting duration of the financial platform are obtained, blasting tests are conducted on the N service components based on the initial blasting duration, fluctuation of the steady state indexes and working states of all the objects in the influence object set are monitored, if the fluctuation of any object in the influence object set does not meet preset conditions, the initial blasting duration is increased, the blasting duration is taken as the initial blasting duration, the blasting test is executed in a returning mode based on the initial blasting duration, the step of performing the blasting test on the N service components is performed until the condition that the fault or fluctuation of any object in the influence object set meets preset conditions is monitored, the initial blasting stop test is stopped, the blasting duration is determined, the result of the test is not being evaluated according to the influence of the experiment duration in a certain condition, and the test is not influenced by the chaotic evaluation, and the test result is achieved in a certain experiment, and the evaluation is not influenced by the platform.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required for the embodiments or the description of the prior art will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present application, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic view of an application environment of a method for evaluating the influence degree of a financial platform test according to an embodiment of the present application;
fig. 2 is a flow chart of a method for evaluating the influence degree of a financial platform test according to a second embodiment of the present disclosure;
fig. 3 is a flow chart of a method for evaluating the influence degree of a financial platform test according to the third embodiment of the present application;
fig. 4 is a schematic structural diagram of a device for evaluating the influence degree of a financial platform test according to a fourth embodiment of the present application;
fig. 5 is a schematic structural diagram of a computer device according to a fifth embodiment of the present application.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular system configurations, techniques, etc. in order to provide a thorough understanding of the embodiments of the present application. It will be apparent, however, to one skilled in the art that the present application may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present application with unnecessary detail.
It should be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
It should also be understood that the term "and/or" as used in this specification and the appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes such combinations.
As used in this specification and the appended claims, the term "if" may be interpreted as "when..once" or "in response to a determination" or "in response to detection" depending on the context. Similarly, the phrase "if a determination" or "if a [ described condition or event ] is detected" may be interpreted in the context of meaning "upon determination" or "in response to determination" or "upon detection of a [ described condition or event ]" or "in response to detection of a [ described condition or event ]".
In addition, in the description of the present application and the appended claims, the terms "first," "second," "third," and the like are used merely to distinguish between descriptions and are not to be construed as indicating or implying relative importance.
Reference in the specification to "one embodiment" or "some embodiments" or the like means that a particular feature, structure, or characteristic described in connection with the embodiment is included in one or more embodiments of the application. Thus, appearances of the phrases "in one embodiment," "in some embodiments," "in other embodiments," and the like in the specification are not necessarily all referring to the same embodiment, but mean "one or more but not all embodiments" unless expressly specified otherwise. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless expressly specified otherwise.
The embodiment of the application can acquire and process the related data based on the artificial intelligence technology. Among these, artificial intelligence (Artificial Intelligence, AI) is the theory, method, technique and application system that uses a digital computer or a digital computer-controlled machine to simulate, extend and extend human intelligence, sense the environment, acquire knowledge and use knowledge to obtain optimal results.
Artificial intelligence infrastructure technologies generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technologies, operation/interaction systems, mechatronics, and the like. The artificial intelligence software technology mainly comprises a computer vision technology, a robot technology, a biological recognition technology, a voice processing technology, a natural language processing technology, machine learning/deep learning and other directions.
It should be understood that the sequence numbers of the steps in the following embodiments do not mean the order of execution, and the execution order of the processes should be determined by the functions and the internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.
In order to illustrate the technical solution of the present application, the following description is made by specific examples.
The method for evaluating the influence degree of the financial platform test provided in the first embodiment of the present application can be applied in an application environment as shown in fig. 1, where a client communicates with a server. The clients include, but are not limited to, palm top computers, desktop computers, notebook computers, ultra-mobile personal computer (UMPC), netbooks, cloud computing devices, personal digital assistants (personal digital assistant, PDA), and other computing devices. The server may be implemented by a stand-alone server or a server cluster formed by a plurality of servers.
Referring to fig. 2, a flow chart of a method for evaluating the influence degree of a financial platform test according to a second embodiment of the present application is shown, where the method for evaluating the influence degree of a financial platform test is applied to a server in fig. 1, and the server is a computer device supporting the financial platform. As shown in fig. 2, the method for evaluating the influence degree of the financial platform test may include the following steps:
step S201, determining N service components from the financial platform, determining theoretical problems corresponding to the N service components when the service components fail from configuration information of the N service components, and determining actual problems corresponding to the N service components when the service components fail from historical operation data of the financial platform.
In the application, the financial platform may refer to a system applied to a financial scene for providing service to a user, where the financial platform is configured with a corresponding service component, and the service component may be invoked by a user of a client, so as to implement a corresponding service, where the service includes computing, querying, storing, downloading and other flow operations set for different services. For example, the financial platform may refer to an insurance application system, an insurance claim system, etc. in the insurance industry, and may also refer to a mobile banking system, an online banking system, etc. in the banking industry.
The platform architecture of the financial platform is already fixed, corresponding service items are fixed, and corresponding service components are also fixed, so that the operation log of the financial platform is analyzed to obtain corresponding service information, business information, component information and other system operation information, and corresponding service components can be determined from the information. Service components are generally directed to a hidden danger, such as an important component or an interface of a corresponding component, which is more commonly used.
Because platform interaction is too complex under the arranging environment, analysis cost is too large or is almost impossible, and service and user use experience are associated more strongly, on the premise of guaranteeing service stability, enough fluctuation space is reserved for internal indexes, therefore, important service components are required to be determined from all used components, and the whole system can be characterized by researching the service components. For example, from the analysis of the interaction requirement of the financial platform, in order to ensure the requirement of user experience, the interaction requirement of the associated system and the like, the key service components, the key page loading condition, the core transaction index and the like can be finally extracted.
When the financial platform is constructed, corresponding configuration information is generated for each service component, and problems occurring under the condition of faults of the corresponding service component, namely theoretical problems, namely problems defined by developers, can be stored in the configuration information.
After the financial platform operates for a period of time, the actual problem corresponding to each service component when the service component fails can be determined by analyzing the historical operation data, and an intersection between the theoretical problem and the actual problem is likely to exist, and of course, different problems exist.
Step S202, for any service component, determining service influence objects corresponding to the service component according to the theoretical problem and the actual problem of the service component, traversing all the service components, and obtaining a union of the service influence objects of all the service components to obtain an influence object set.
In the application, for each theoretical problem and actual problem, correspondingly, the object which can be influenced by each problem can be obtained through table lookup and the like, and for any service component, the object combination of the influences of all the theoretical problems and the actual problems is the service influence object of the service component, and the service influence objects of all the service components are combined to obtain the influence object set.
A service affecting object may refer to that a corresponding object is unavailable due to a corresponding problem of a service component, where the corresponding object cannot be calculated, uploaded, stored, downloaded, and the like. For example, the problem with a service component is that the connection to database a is broken, and then the querying user, modifying user, etc. who need to invoke the database through the financial platform will all be affected.
Step S203, a steady state index and initial blasting duration of the financial platform are obtained, blasting tests are conducted on the N service components based on the initial blasting duration, and fluctuation of the steady state index and working states of all objects in the object set are monitored.
In this application, the steady state indicator may refer to an indicator for managing or monitoring stability of the financial platform, for example, an indicator capable of characterizing stability, such as a memory occupancy rate, response time of each service component, processing time, and the like, if the memory occupancy rate is too high, the usage of the financial platform may be affected, and in a serious case, downtime may be caused.
The initial blasting duration is a duration parameter set according to the requirement, and can also be a duration parameter obtained through experiments. For example, the time required by fluctuation of the steady-state index is obtained through blasting test before all operations, and the time is taken as the initial blasting duration, so that the situation that the number of times of tests is excessive due to repeated test caused by small initial blasting duration can be effectively avoided, namely, the purpose of influencing the steady-state index is achieved by using the initial blasting duration.
Step S204, if the fault of any object in the affected object set is not monitored and the fluctuation of the steady-state index does not meet the preset condition, increasing the initial blasting duration to obtain an increased blasting duration, and taking the increased blasting duration as the initial blasting duration.
In the present application, if the condition for stopping the blasting test is not reached, the blasting duration needs to be increased, and the blasting test is continuously performed based on the increased blasting duration. The preset condition can be set according to the requirement, and specifically is the condition of occurrence of target fluctuation of the steady-state index.
If any object in the affected object set fails, the test is indicated to have affected the normal operation of the financial platform, possibly causing some bad results, so that the blasting test can be stopped at this time.
Optionally, after performing a blasting test on the N service components based on the initial blasting duration and monitoring the fluctuation of the steady-state index and affecting the working states of all objects in the object set, the method further includes:
acquiring a first time when fluctuation of a steady-state index is monitored for the first time and a second time when a primary blasting test starts, and determining the interval between the first time and the second time as the time slice length;
Correspondingly, increasing the initial blasting duration to obtain the increased blasting duration comprises:
and increasing the initial blasting duration by using the time slice length to obtain the increased blasting duration.
The time length of the blasting test is increased along with the increase of the blasting times, the increased step length can be a preset length, the preset length can be a time interval between the starting time of the blasting test and the time when the steady-state index fluctuates, namely, the time slice length is taken as the step length, the blasting time length is increased, the blasting times are reduced, the purpose of rapid iteration is achieved, meanwhile, the step length can be prevented from being overlong, and the accuracy of subsequent evaluation is guaranteed to a certain extent.
Optionally, increasing the initial blasting duration using the time slice length to obtain an increased blasting duration includes:
if the fluctuation of the steady-state index exceeds the preset fluctuation value, taking half of the initial blasting duration as halving blasting duration;
and adding the time slice length of the first preset multiple to the halving blasting duration to obtain the increasing blasting duration, wherein the first preset multiple is smaller than 1.
If the fluctuation of the steady-state index is large, the blasting duration at this time is considered to be close to the final evaluation result, and therefore, in order to obtain a more accurate evaluation result, the blasting duration at this time is halved, and based on the halved blasting duration, the blasting duration starts to be increased. Wherein the first preset multiple may be 1/5.
Optionally, after monitoring the fluctuation of the steady-state index, further comprising:
detecting whether the fluctuation of the steady-state index appears a peak value after the blasting test;
if the fluctuation of the steady-state index after the current blasting test is detected to not have a peak value, determining that the fluctuation of the steady-state index does not meet a preset condition.
The peak value of the steady-state index represents that the corresponding blasting test has the greatest influence effect, so that whether the blasting duration of the blasting test reaches the target can be determined through analysis of the peak value of the steady-state index.
If the fluctuation of the steady-state index does not have a peak value, the blasting test still needs to be performed, namely the fluctuation of the steady-state index does not meet the preset condition.
Optionally, after detecting whether the fluctuation of the steady-state index appears a peak value after the current blasting test, the method further includes:
if the fluctuation of the steady-state index after the blasting test is detected to have a peak value, detecting whether the interval between the occurrence time of the peak value and the second time is larger than the time slice length of a second preset multiple, wherein the second preset multiple is larger than 1;
if the interval between the appearance time of the peak value and the second time is not more than the time slice length of the preset multiple, the fluctuation of the steady-state index is determined to meet the preset condition.
If the interval between the appearance time of the peak value and the second time is greater than the time slice length of the preset multiple, the test time is considered longer, and the requirement cannot be met. If the interval between the appearance time of the peak value and the second time is not greater than the time length of the preset multiple, the duration can be considered to be satisfied, namely the fluctuation of the index satisfies the preset condition.
The second preset multiple may be 5, that is, when the peak value of the fluctuation is reached within the 5 time slices, the explosion radius is considered to be reached, that is, the fluctuation is considered to satisfy the preset condition.
Step S205, returning to execute the step of performing blasting test on the N service components based on the initial blasting duration until the blasting test is stopped when any object in the affected object set is detected to have a fault or the fluctuation of the steady-state index meets the preset condition, and determining the initial blasting duration when the blasting test is stopped as an evaluation result of the test influence degree.
In the application, if the condition of stopping the blasting test cannot be reached, the blasting test needs to be repeatedly executed until the condition is met, if any object in the affected object set fails, the corresponding initial blasting duration can only be used as an evaluation result, the evaluation result may not guide the subsequent chaotic engineering blasting test, if the fluctuation of the steady-state index meets the preset condition, the corresponding initial blasting duration is used as the evaluation result, and the evaluation result can know the subsequent chaotic engineering blasting test so as to improve the safety.
Optionally, after determining the N service components from the financial platform, the method further includes:
acquiring a rollback scheme set corresponding to each service component and aging of a service level protocol of a financial platform;
for any service component, performing time-consuming analysis on each rollback scheme in the rollback scheme set corresponding to the service component to obtain rollback time consumption of the corresponding rollback scheme;
determining a rollback scheme with the minimum rollback time consumption and less rollback time consumption than timeliness in all rollback schemes as a target scheme of a corresponding service component;
accordingly, after monitoring the working states of all the objects in the influencing object set, the method further comprises:
if any object in the affected object set is monitored to fail, after the blasting test is stopped, a target scheme of each service component is started to carry out rollback operation.
In the case that any object in the influencing object set fails, a rollback operation may need to be performed on the financial platform to ensure safe and normal operation of the financial platform.
The rollback operation needs to use a rollback solution, and there may be multiple rollback solutions for the service component, so all rollback solutions need to be analyzed, and a rollback solution with minimum time consumption and less than a preset effective effect is found, so that the rollback operation is performed after the blasting test is stopped.
According to the method, N service components are determined from a financial platform, theoretical problems corresponding to faults are determined from configuration information of the N service components, practical problems corresponding to the N service components when faults occur are determined from historical operation data of the financial platform, for any service component, service influence objects corresponding to the service components are determined according to the theoretical problems and the practical problems of the service components, all the service components are traversed, the service influence objects of all the service components are combined to obtain an influence object set, steady-state indexes and initial blasting duration of the financial platform are obtained, blasting tests are conducted on the N service components based on the initial blasting duration, fluctuation of the steady-state indexes and working states of all the objects in the influence object set are monitored, if the fact that any object in the influence object set fails and the fluctuation of the steady-state indexes does not meet preset conditions are not monitored, the initial blasting duration is increased, the initial blasting duration is obtained, the blasting duration is increased, the blasting duration is used as the initial blasting duration is returned to be executed, the blasting test based on the initial blasting duration is carried out, the blasting test is stopped when the fact that the fluctuation of any object in the influence object set meets preset conditions, the fault or fluctuation of the steady-state indexes is monitored, the initial blasting duration is not met, the test is determined, the test is carried out in a chaotic condition is not to be assessed, and the test is carried out in a certain state, the test is not is carried out in a chaotic condition, and the test is not influenced by the platform is assessed, and the test is not influenced by the test result is evaluated.
Referring to fig. 3, a flow chart of a method for evaluating the influence degree of a financial platform test according to a third embodiment of the present application is shown in fig. 3, where the method for evaluating the influence degree of a financial platform test may include the following steps:
step S301, determining N service components from the financial platform, determining theoretical problems corresponding to the N service components when the service components fail from configuration information of the N service components, and determining actual problems corresponding to the N service components when the service components fail from historical operation data of the financial platform.
Step S302, for any service component, determining service influence objects corresponding to the service component according to the theoretical problem and the actual problem of the service component, traversing all the service components, and obtaining a union set of the service influence objects of all the service components to obtain an influence object set.
Step S303, acquiring steady state indexes and initial blasting duration of the financial platform, performing blasting test on the N service components based on the initial blasting duration, and monitoring fluctuation of the steady state indexes and influencing working states of all objects in the object set.
Step S304, if no fault is detected to occur to any object in the influence object set and the fluctuation of the steady-state index does not meet the preset condition, increasing the initial blasting duration to obtain an increased blasting duration, and taking the increased blasting duration as the initial blasting duration.
Step S305, returning to execute the step of performing blasting test on the N service components based on the initial blasting duration, stopping the blasting test until the situation that any object in the affected object set fails or the fluctuation of the steady-state index meets the preset condition is monitored, and determining the initial blasting duration when the blasting test is stopped as an evaluation result of the test influence degree.
The content of step S301 to step S305 is the same as the content of the above-mentioned part of step S201 to step S205, and reference may be made to the descriptions of step S201 to step S205, which are not repeated here.
And step S306, taking the evaluation result as an alarm threshold value of the corresponding N service components in the monitoring system.
The evaluation result can also be used as a monitoring parameter for monitoring the financial platform, namely, the evaluation result of each service component is used as an alarm threshold value of the corresponding service component.
Step S307, when the monitoring system monitors that the test duration of any one of the N service components in the chaotic engineering test exceeds the alarm threshold, fusing the chaotic engineering test, and generating an alarm prompt.
The method comprises the steps of monitoring the test duration of each service component of the financial platform in real time during normal operation, comparing the test duration with an alarm threshold, and if the test duration exceeds the alarm threshold, indicating that the real-time execution time is too long, possibly causing the conditions of abnormal financial platform, stable index fluctuation and the like, so that an alarm prompt can be generated to prompt a manager to check the financial platform.
In addition, the chaotic engineering test can be fused, so that the test is stopped, and the damage to the financial platform is avoided.
In the case that the minimum explosion radius is required to be provided as a safety measure in the conventional chaotic engineering, if the minimum explosion radius cannot be obtained for some reasons, the related index set is generally found to be too complex in the related hidden danger or in the analysis of explosion points, or the implementation target is a system service which is put into production for a period of time, the minimum explosion radius index cannot be obtained in the case, or for a system with particularly high stability requirement, the stability of the system needs to be ensured by using the steady state index simultaneously, and the implementation of the chaotic engineering is effectively ensured by the confirmation of the steady state index as a whole.
According to the embodiment of the application, N service components are determined from a financial platform, theoretical problems corresponding to faults are determined from configuration information of the N service components, practical problems corresponding to the N service components when faults occur are determined from historical operation data of the financial platform, for any service component, service influence objects corresponding to the service components are determined according to the theoretical problems and the practical problems of the service components, all the service components are traversed, the service influence objects of all the service components are combined to obtain an influence object set, steady-state indexes and initial blasting duration of the financial platform are obtained, the N service components are subjected to blasting test based on the initial blasting duration, fluctuation of the steady-state indexes and working states of all the objects in the influence object set are monitored, if the situation that any object in the influence object set fails and the fluctuation of the steady-state indexes does not meet preset conditions is not monitored, the initial blasting duration is increased, the blasting duration is taken as the initial blasting duration, the blasting duration is returned to be executed, the blasting test steps based on the initial blasting duration are executed, the N service components are stopped when the situation that any object in the influence object set fails or the fluctuation of the steady-state indexes meets preset conditions is monitored, the blasting duration is stopped, the initial blasting duration is determined to be stopped when the fluctuation of the influence object set is monitored, the initial object set is in the influence object set is subjected to the preset conditions, the chaotic condition is monitored, the chaotic condition is not met, the working condition is set, the working conditions is set in the test conditions is set, the chaotic condition is set, the test conditions is set to be tested, the test conditions is subjected to be subjected to the test conditions, and a chaotic condition is subjected to the test condition to be in the test condition, based on the evaluation result, the platform is prevented from being irreversibly damaged, so that the stability of the platform is ensured to a certain extent.
Fig. 4 shows a block diagram of a device for evaluating the influence degree of a financial platform test according to a fourth embodiment of the present application, where the device for evaluating the influence degree is applied to a server in fig. 1, and the server is a computer device supporting the financial platform. For convenience of explanation, only portions relevant to the embodiments of the present application are shown.
Referring to fig. 4, the influence degree evaluating apparatus includes:
the component and problem determining module 41 is configured to determine N service components from the financial platform, determine theoretical problems corresponding to faults from configuration information of the N service components, determine actual problems corresponding to faults of the N service components from historical operation data of the financial platform, and N is an integer greater than zero;
the influencing object determining module 42 is configured to determine, for any service component, a service influencing object corresponding to the service component according to a theoretical problem and an actual problem of the service component, traverse all the service components, and combine the service influencing objects of all the service components to obtain an influencing object set;
The blasting test module 43 is configured to obtain a steady state index and an initial blasting duration of the financial platform, perform blasting test on the N service components based on the initial blasting duration, and monitor fluctuation of the steady state index and influence working states of all objects in the object set;
the blasting duration adjusting module 44 is configured to increase the initial blasting duration if no fault is detected in any object in the affected object set and the fluctuation of the steady-state index does not meet the preset condition, obtain an increased blasting duration, and take the increased blasting duration as the initial blasting duration;
the influence degree evaluation module 45 is configured to return to executing a step of performing a blasting test on the N service components based on an initial blasting duration, stopping the blasting test until it is monitored that any object in the influence object set fails or the fluctuation of the steady-state index satisfies a preset condition, and determining the initial blasting duration when stopping the blasting test as an evaluation result of the test influence degree.
Optionally, the influence degree evaluating device further includes:
the time slice determining module is used for performing blasting test on the N service components based on initial blasting duration, acquiring first time when the fluctuation of the steady-state index is monitored for the first time and second time when the fluctuation of the steady-state index is monitored for the first time after the fluctuation of the steady-state index is monitored and working states of all objects in the object set are influenced, and determining that the interval between the first time and the second time is the time slice length;
Accordingly, the blast-duration adjustment module 44 includes:
and the blasting duration adjusting unit is used for increasing the initial blasting duration by using the time slice length to obtain the increased blasting duration.
Optionally, the blasting duration adjusting unit includes:
the first time length calculation subunit is used for taking half of the initial blasting duration as half-reduction blasting duration if the fluctuation of the steady-state index exceeds a preset fluctuation value;
and the second duration calculation subunit is used for adding the time slice length of the first preset multiple and the halving blasting duration to obtain the increasing blasting duration, and the first preset multiple is smaller than 1.
Optionally, the influence degree evaluating device further includes:
the fluctuation peak value detection module is used for detecting whether the fluctuation of the steady-state index appears in the peak value after the current blasting test after monitoring the fluctuation of the steady-state index;
the first judging module is used for determining that the fluctuation of the steady-state index does not meet the preset condition if the fluctuation of the steady-state index does not have a peak value after the current blasting test is detected.
Optionally, the influence degree evaluating device further includes:
the peak value appearance time module is used for detecting whether the fluctuation of the steady-state index after the current blasting test appears in a peak value or not, and if the fluctuation of the steady-state index after the current blasting test appears in the peak value, detecting whether the interval between the appearance time of the peak value and the second time is larger than the time slice length of a second preset multiple or not, wherein the second preset multiple is larger than 1;
And the second judging module is used for determining that the fluctuation of the steady-state index meets the preset condition if the interval between the occurrence time of the detected peak value and the second time is not more than the time slice length of the preset multiple.
Optionally, the influence degree evaluating device further includes:
the scheme acquisition module is used for acquiring a rollback scheme set corresponding to each service component and timeliness of a service level protocol of the financial platform after N service components are determined from the financial platform;
the rollback time consumption determining module is used for carrying out time consumption analysis on each rollback scheme in the rollback scheme set corresponding to the service component aiming at any service component to obtain rollback time consumption of the corresponding rollback scheme;
the target scheme determining module is used for determining a backspacing scheme with minimum backspacing time consumption and smaller backspacing time consumption than ageing in all backspacing schemes as a target scheme of the corresponding service component;
accordingly, the influence degree evaluation device further includes:
and the rollback operation module is used for starting a target scheme of each service component to perform rollback operation after stopping the blasting test if any object in the influence object set is monitored to have faults after the working states of all the objects in the influence object set are monitored.
Optionally, the influence degree evaluating device further includes:
the alarm threshold determining module is used for taking the evaluation result as an alarm threshold of the corresponding N service components in the monitoring system after determining that the initial blasting duration when the blasting test is stopped is the evaluation result of the test influence degree;
and the fusing alarm module is used for fusing the chaotic engineering test and generating an alarm prompt when the monitoring system monitors that the test duration of any one of the N service components in the chaotic engineering test exceeds an alarm threshold.
It should be noted that, because the content of information interaction and execution process between the modules is based on the same concept as the method embodiment of the present application, specific functions and technical effects thereof may be referred to in the method embodiment section, and details are not repeated herein.
Fig. 5 is a schematic structural diagram of a computer device according to a fifth embodiment of the present application. As shown in fig. 5, the computer device of this embodiment includes: at least one processor (only one shown in fig. 5), a memory, and a computer program stored in the memory and executable on the at least one processor, the processor executing the computer program to perform the steps of any of the various embodiments of the financial platform testing impact level assessment method described above.
The computer device may include, but is not limited to, a processor, a memory. It will be appreciated by those skilled in the art that fig. 5 is merely an example of a computer device and is not intended to be limiting, and that a computer device may include more or fewer components than shown, or may combine certain components, or different components, such as may also include network components, display screens, input devices, and the like.
The processor may be a CPU, but may also be other general purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), off-the-shelf programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory includes a readable storage medium, an internal memory, etc., where the internal memory may be the memory of the computer device, the internal memory providing an environment for the execution of an operating system and computer-readable instructions in the readable storage medium. The readable storage medium may be a hard disk of a computer device, and in other embodiments may be an external storage device of the computer device, for example, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), etc. that are provided on the computer device. Further, the memory may also include both internal storage units and external storage devices of the computer device. The memory is used to store an operating system, application programs, boot loader (BootLoader), data, and other programs such as program codes of computer programs, and the like. The memory may also be used to temporarily store data that has been output or is to be output.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-described division of the functional units and modules is illustrated, and in practical application, the above-described functional distribution may be performed by different functional units and modules according to needs, i.e. the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-described functions. The functional units and modules in the embodiment may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit, where the integrated units may be implemented in a form of hardware or a form of a software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working process of the units and modules in the above device may refer to the corresponding process in the foregoing method embodiment, which is not described herein again. The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the present application implements all or part of the flow of the method of the above-described embodiments, and may be implemented by a computer program to instruct related hardware, where the computer program may be stored in a computer readable storage medium, where the computer program, when executed by a processor, may implement the steps of the method embodiments described above. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, executable files or in some intermediate form, etc. The computer readable medium may include at least: any entity or device capable of carrying computer program code, a recording medium, a computer Memory, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), an electrical carrier signal, a telecommunications signal, and a software distribution medium. Such as a U-disk, removable hard disk, magnetic or optical disk, etc. In some jurisdictions, computer readable media may not be electrical carrier signals and telecommunications signals in accordance with legislation and patent practice.
The present application implementing all or part of the flow of the method of the above embodiment may also be implemented by a computer program product, which when run on a computer device causes the computer device to execute the steps of the method embodiment described above.
In the foregoing embodiments, the descriptions of the embodiments are emphasized, and in part, not described or illustrated in any particular embodiment, reference is made to the related descriptions of other embodiments.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
In the embodiments provided in this application, it should be understood that the disclosed apparatus/computer device and method may be implemented in other ways. For example, the apparatus/computer device embodiments described above are merely illustrative, e.g., the division of modules or units is merely a logical functional division, and there may be additional divisions when actually implemented, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection via some components, devices or units, which may be in electrical, mechanical or other form.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed over a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
The above embodiments are only for illustrating the technical solution of the present application, and are not limiting thereof; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present application, and are intended to be included in the scope of the present application.
Claims (10)
1. The influence degree evaluation method for the financial platform test is characterized by comprising the following steps of:
determining N service components from the financial platform, determining theoretical problems corresponding to faults from configuration information of the N service components, and determining actual problems corresponding to the N service components when faults occur from historical operation data of the financial platform, wherein N is an integer larger than zero;
Determining service influence objects corresponding to any service component according to theoretical problems and actual problems of the service component, traversing all service components, and summing the service influence objects of all service components to obtain an influence object set;
acquiring steady state indexes and initial blasting duration of the financial platform, performing blasting tests on the N service components based on the initial blasting duration, and monitoring fluctuation of the steady state indexes and working states of all objects in the influence object set;
if no fault is detected to occur to any object in the influence object set and the fluctuation of the steady-state index does not meet a preset condition, increasing the initial blasting duration to obtain an increased blasting duration, and taking the increased blasting duration as the initial blasting duration;
and returning to execute the step of performing blasting test on the N service components based on the initial blasting duration until the blasting test is stopped when any object in the affected object set is detected to fail or the fluctuation of the steady-state index meets a preset condition, and determining the initial blasting duration when the blasting test is stopped as an evaluation result of the test influence degree.
2. The influence level assessment method of claim 1, wherein after performing a blast test on the N service components based on an initial blast duration, monitoring fluctuations of the steady-state indicator and operating states of all objects in the set of influence objects, further comprising:
acquiring a first time when the fluctuation of the steady-state index is monitored for the first time and a second time when the initial blasting test starts, and determining that the interval between the first time and the second time is the time slice length;
correspondingly, increasing the initial blasting duration to obtain an increased blasting duration includes:
and increasing the initial blasting duration by using the time slice length to obtain an increased blasting duration.
3. The influence level assessment method according to claim 2, wherein increasing the initial blasting duration using the time slice length, resulting in an increased blasting duration, comprises:
if the fluctuation of the steady-state index exceeds a preset fluctuation value, taking half of the initial blasting duration as halving blasting duration;
and adding the time slice length of the first preset multiple to the halving blasting duration to obtain the increasing blasting duration, wherein the first preset multiple is smaller than 1.
4. The influence degree evaluation method according to claim 2, characterized by further comprising, after monitoring the fluctuation of the steady-state index:
detecting whether the fluctuation of the steady-state index appears a peak value after the blasting test;
if the fluctuation of the steady-state index does not have a peak value after the current blasting test is detected, determining that the fluctuation of the steady-state index does not meet a preset condition.
5. The influence level assessment method according to claim 4, further comprising, after detecting whether or not a peak occurs in fluctuation of the steady-state index after the present blasting test:
if the fluctuation of the steady-state index after the current blasting test is detected to have a peak value, detecting whether the interval between the occurrence time of the peak value and the second time is larger than the time slice length of a second preset multiple, wherein the second preset multiple is larger than 1;
and if the interval between the appearance time of the peak value and the second time is not more than the time slice length of the preset multiple, determining that the fluctuation of the steady-state index meets the preset condition.
6. The influence level assessment method of claim 1, further comprising, after determining N service components from the financial platform:
Acquiring a rollback scheme set corresponding to each service component and aging of a service level protocol of the financial platform;
for any service component, performing time-consuming analysis on each rollback scheme in a rollback scheme set corresponding to the service component to obtain rollback time consumption of the corresponding rollback scheme;
determining that the rollback time consumption is the smallest in all rollback schemes and the rollback time consumption is smaller than the aged rollback scheme as a target scheme corresponding to the service component;
accordingly, after monitoring the working states of all the objects in the influencing object set, the method further comprises:
and if any object in the affected object set is monitored to fail, starting a target scheme of each service component to carry out rollback operation after stopping the blasting test.
7. The influence level evaluation method according to any one of claims 1 to 6, characterized by further comprising, after determining an initial blasting duration at the time of stopping the blasting test as an evaluation result of the test influence level:
taking the evaluation result as an alarm threshold value corresponding to the N service components in the monitoring system;
when the monitoring system monitors that the test duration of any one of the N service components in the chaotic engineering test exceeds the alarm threshold, fusing the chaotic engineering test, and generating an alarm prompt.
8. An influence degree evaluation device for a financial platform test, characterized in that the influence degree evaluation device comprises:
the component and problem determining module is used for determining N service components from the financial platform, determining theoretical problems corresponding to faults from configuration information of the N service components, determining actual problems corresponding to the N service components when faults occur from historical operation data of the financial platform, wherein N is an integer larger than zero;
the influence object determining module is used for determining service influence objects corresponding to any service component according to the theoretical problem and the actual problem of the service component, traversing all the service components, and summing the service influence objects of all the service components to obtain an influence object set;
the blasting test module is used for acquiring steady-state indexes and initial blasting duration of the financial platform, performing blasting test on the N service components based on the initial blasting duration, and monitoring fluctuation of the steady-state indexes and working states of all objects in the influencing object set;
the blasting duration adjusting module is used for increasing the initial blasting duration to obtain an increased blasting duration if any object in the affected object set is not monitored to be faulty and the fluctuation of the steady-state index does not meet the preset condition, and taking the increased blasting duration as the initial blasting duration;
And the influence degree evaluation module is used for returning to execute the step of performing blasting test on the N service components based on the initial blasting duration until the blasting test is stopped when any object in the influence object set is monitored to be faulty or the fluctuation of the steady-state index meets a preset condition, and determining the initial blasting duration when the blasting test is stopped as an evaluation result of the test influence degree.
9. A computer device, characterized in that it comprises a processor, a memory and a computer program stored in the memory and executable on the processor, which processor implements the influence level assessment method according to any one of claims 1 to 7 when executing the computer program.
10. A computer-readable storage medium storing a computer program, characterized in that the computer program, when executed by a processor, implements the influence degree evaluation method according to any one of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311351951.9A CN117609004A (en) | 2023-10-18 | 2023-10-18 | Influence degree evaluation method, device, equipment and medium for financial platform test |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311351951.9A CN117609004A (en) | 2023-10-18 | 2023-10-18 | Influence degree evaluation method, device, equipment and medium for financial platform test |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN117609004A true CN117609004A (en) | 2024-02-27 |
Family
ID=89944916
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311351951.9A Pending CN117609004A (en) | 2023-10-18 | 2023-10-18 | Influence degree evaluation method, device, equipment and medium for financial platform test |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117609004A (en) |
-
2023
- 2023-10-18 CN CN202311351951.9A patent/CN117609004A/en active Pending
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN112162878B (en) | Database fault discovery method and device, electronic equipment and storage medium | |
| CN112668010B (en) | Method, system and computing device for scanning loopholes of industrial control system | |
| CN111984975B (en) | Vulnerability attack detection system, method and medium based on mimicry defense mechanism | |
| CN107992410B (en) | Software quality monitoring method and device, computer equipment and storage medium | |
| CN116340934A (en) | Terminal abnormal behavior detection method, device, equipment and storage medium | |
| CN116628705A (en) | Data security processing method, system, electronic equipment and storage medium | |
| CN110851352A (en) | Fuzzy test system and terminal equipment | |
| CN112559489A (en) | Block chain supervision method and equipment based on cross-chain and storage medium | |
| CN116401113B (en) | Environment verification method, device and medium for heterogeneous many-core architecture acceleration card | |
| CN117609004A (en) | Influence degree evaluation method, device, equipment and medium for financial platform test | |
| CN117691733A (en) | Assessment method and device for information security protection of power distribution automation system | |
| CN116380228A (en) | Method, system, terminal and storage medium for monitoring operation of weighing apparatus | |
| CN111176975B (en) | Test method, device, equipment and computer readable storage medium | |
| CN114881112A (en) | System anomaly detection method, device, equipment and medium | |
| CN115525528A (en) | Page quality detection method and device, electronic equipment and storage medium | |
| CN111934949A (en) | Safety test system based on database injection test | |
| CN112363931A (en) | Web system testing method and device | |
| CN118069493A (en) | Stability testing method, device, equipment and medium of financial service system | |
| CN118502814B (en) | Software modification evaluation method, device, equipment and medium based on byte codes | |
| Di Penta et al. | The evolution and decay of statically detected source code vulnerabilities | |
| CN114745722B (en) | Short message platform security audit verification method and system | |
| CN111708649B (en) | Attack detection method and system for service application system | |
| CN115859282A (en) | Security protection method, device and computer readable storage medium | |
| Rohr et al. | Timing behavior anomaly detection in enterprise information systems | |
| Li et al. | Knowledge-based Defect Prediction and Test Case Generation for Radar Software Fault Prevention |
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 |