CN117112383A

CN117112383A - Performance analysis method, device, equipment and storage medium based on artificial intelligence

Info

Publication number: CN117112383A
Application number: CN202311176660.0A
Authority: CN
Inventors: 刘文旭
Original assignee: Ping An Property and Casualty Insurance Company of China Ltd
Current assignee: Ping An Property and Casualty Insurance Company of China Ltd
Priority date: 2023-09-12
Filing date: 2023-09-12
Publication date: 2023-11-24

Abstract

The application belongs to the field of artificial intelligence and the field of financial science and technology, and relates to a performance analysis method based on artificial intelligence, which comprises the following steps: acquiring a target interface of a target service system; acquiring request response time of each target interface in a preset time period; calculating the request response time of each target interface based on the performance analysis model to obtain a performance value corresponding to the target service system; comparing the performance value with a preset threshold value to obtain a corresponding comparison result; and generating a performance analysis result of the target service system based on the comparison result. The application also provides a performance analysis device, computer equipment and a storage medium based on the artificial intelligence. In addition, the application also relates to a blockchain technology, and the performance analysis result can be stored in the blockchain. The application can be applied to the system performance analysis scene in the financial field, improves the processing efficiency of the performance analysis of the target business system, and ensures the accuracy of the performance analysis result of the generated financial business system.

Description

Performance analysis method, device, equipment and storage medium based on artificial intelligence

Technical Field

The application relates to the technical field of artificial intelligence development and the technical field of finance, in particular to a performance analysis method, a performance analysis device, computer equipment and a storage medium based on artificial intelligence.

Background

With the rapid development of science and technology, the emerging technologies such as internet, big data, mobile interconnection and cloud computing in the current information age bring about a small challenge to a data center. In the financial industry, problems of bearing capacity, stability, expansibility, reliability and the like of financial business systems developed by financial enterprises such as insurance companies, banks and the like are increasingly prominent. Therefore, in order to monitor and identify the risk points in the business system, the operation and maintenance service work of the financial business system can be normally and orderly carried out in a high-efficient and coordinated manner, and the performance condition of the financial business system is analyzed in a required manner, so that the stability and the safety of the financial business system are ensured.

However, in the existing performance analysis manner of the financial service system, usually, the performance data of the interface in the financial service system is obtained by using the promethaus software, and then an analyst analyzes the performance data of the interface in the financial service system obtained in the promethaus software, so that the performance analysis manner needs to consume more manpower and material resources, has low processing efficiency, and cannot guarantee the accuracy and the effectiveness of the generated performance analysis result of the financial service system.

Disclosure of Invention

The embodiment of the application aims to provide a performance analysis method, a device, computer equipment and a storage medium based on artificial intelligence, which are used for solving the technical problems that the existing performance analysis mode needs to consume more manpower and material resources, has low processing efficiency and cannot guarantee the accuracy and the effectiveness of the performance analysis result of a generated financial service system.

In order to solve the technical problems, the embodiment of the application provides a performance analysis method based on artificial intelligence, which adopts the following technical scheme:

acquiring a target interface of a target service system; wherein the number of target interfaces includes a plurality;

acquiring request response time of each target interface in a preset time period;

calculating the request response time of each target interface based on a preset performance analysis model to obtain a performance value corresponding to the target service system;

comparing the performance value with a preset threshold value to obtain a corresponding comparison result;

and generating a performance analysis result of the target service system based on the comparison result.

Further, the step of calculating the response time of the request of each target interface based on the preset performance analysis model to obtain the performance value corresponding to the target service system specifically includes:

Invoking the performance calculation model;

acquiring target service types respectively corresponding to the target interfaces;

obtaining target weights respectively corresponding to the service types;

using the performance calculation model, carrying out weighted summation processing on the request response time based on the target weight to obtain a corresponding calculation result;

and taking the calculation result as the performance numerical value of the target service system.

Further, the step of generating the performance analysis result of the target service system based on the comparison result specifically includes:

judging whether the performance value is larger than the preset threshold value or not according to the comparison result;

if the comparison result is that the performance value is larger than the preset threshold value, a first performance analysis result of the target service system performance abnormality is generated;

and if the comparison result is that the performance value is smaller than the preset threshold value, generating a second performance analysis result with normal performance of the target service system.

Further, before the step of obtaining the request response time of each target interface within the preset time period, the method further includes:

calling a preset filter;

recording request response time data of each target interface contained in the target service system based on the filter to obtain corresponding recorded data;

And storing the recorded data.

Further, after the step of obtaining the request response time of each target interface within the preset time period, the method further includes:

screening out first interfaces meeting preset expansion conditions from all the target interfaces;

calling a preset resource adjustment model;

acquiring the designated request response time of the first interface;

processing the specified request response time based on the resource adjustment model, and outputting a corresponding adjustment value;

and carrying out resource adjustment processing on the first interface based on the adjustment value.

acquiring a second interface from all the target interfaces; wherein the second interface is any one interface among all the target interfaces;

acquiring the call times of the associated interface when the second interface processes the request in the preset time period;

acquiring the consumption time of the second interface when the request is processed in the preset time period;

judging whether the calling times of the associated interfaces and the consumed time are in a normal threshold range or not;

And if not, carrying out adjustment processing on the second interface based on a preset processing rule.

Further, after the step of generating the performance analysis result of the target service system based on the comparison result, the method further includes:

calling a preset data display tool;

determining a target display mode corresponding to the request response time;

and displaying the request response time in the data display tool based on the target display mode.

In order to solve the technical problems, the embodiment of the application also provides a performance analysis device based on artificial intelligence, which adopts the following technical scheme:

the first acquisition module is used for acquiring a target interface of a target service system; wherein the number of target interfaces includes a plurality;

the second acquisition module is used for acquiring the request response time of each target interface in a preset time period;

the calculation module is used for calculating the request response time of each target interface based on a preset performance analysis model to obtain a performance value corresponding to the target service system;

the comparison module is used for comparing the performance value with a preset threshold value to obtain a corresponding comparison result;

And the generating module is used for generating a performance analysis result of the target service system based on the comparison result.

In order to solve the above technical problems, the embodiment of the present application further provides a computer device, which adopts the following technical schemes:

In order to solve the above technical problems, an embodiment of the present application further provides a computer readable storage medium, which adopts the following technical schemes:

Compared with the prior art, the embodiment of the application has the following main beneficial effects:

the embodiment of the application firstly obtains a target interface of a target service system; then acquiring request response time of each target interface in a preset time period; calculating the request response time of each target interface based on a preset performance analysis model to obtain a performance value corresponding to the target service system; comparing the performance value with a preset threshold value to obtain a corresponding comparison result; and finally, generating a performance analysis result of the target service system based on the comparison result. According to the embodiment of the application, the request response time of each target interface in the target service system is calculated and processed by using the performance analysis model in a preset time period to obtain the performance value corresponding to the target service system, and further, the performance analysis result of the target service system is generated by comparing the performance value with the preset threshold value and according to the obtained comparison result, so that the automatic performance analysis processing of the target service system is realized, the processing efficiency of the performance analysis of the target service system is improved, and the accuracy of the generated performance analysis result of the financial service system is ensured.

Drawings

In order to more clearly illustrate the solution of the present application, a brief description will be given below of the drawings required for the description of the embodiments of the present application, it being apparent that the drawings in the following description are some embodiments of the present application, and that other drawings may be obtained from these drawings without the exercise of inventive effort for a person of ordinary skill in the art.

FIG. 1 is an exemplary system architecture diagram in which the present application may be applied;

FIG. 2 is a flow chart of one embodiment of an artificial intelligence based performance analysis method in accordance with the present application;

FIG. 3 is a schematic diagram of one embodiment of an artificial intelligence based performance analysis apparatus according to the present application;

FIG. 4 is a schematic structural diagram of one embodiment of a computer device in accordance with the present application.

Detailed Description

Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs; the terminology used in the description of the applications herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application; the terms "comprising" and "having" and any variations thereof in the description of the application and the claims and the description of the drawings above are intended to cover a non-exclusive inclusion. The terms first, second and the like in the description and in the claims or in the above-described figures, are used for distinguishing between different objects and not necessarily for describing a sequential or chronological order.

Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the application. The appearances of such phrases in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Those of skill in the art will explicitly and implicitly appreciate that the embodiments described herein may be combined with other embodiments.

In order to make the person skilled in the art better understand the solution of the present application, the technical solution of the embodiment of the present application will be clearly and completely described below with reference to the accompanying drawings.

As shown in fig. 1, a system architecture 100 may include terminal devices 101, 102, 103, a network 104, and a server 105. The network 104 is used as a medium to provide communication links between the terminal devices 101, 102, 103 and the server 105. The network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, among others.

The user may interact with the server 105 via the network 104 using the terminal devices 101, 102, 103 to receive or send messages or the like. Various communication client applications, such as a web browser application, a shopping class application, a search class application, an instant messaging tool, a mailbox client, social platform software, etc., may be installed on the terminal devices 101, 102, 103.

The terminal devices 101, 102, 103 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smartphones, tablet computers, electronic book readers, MP3 players (Moving Picture Experts Group Audio Layer III, dynamic video expert compression standard audio plane 3), MP4 (Moving Picture Experts Group Audio Layer IV, dynamic video expert compression standard audio plane 4) players, laptop and desktop computers, and the like.

The server 105 may be a server providing various services, such as a background server providing support for pages displayed on the terminal devices 101, 102, 103.

It should be noted that, the performance analysis method based on artificial intelligence provided by the embodiment of the present application is generally executed by a server/terminal device, and accordingly, the performance analysis device based on artificial intelligence is generally disposed in the server/terminal device.

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 number of terminal devices, networks and servers in fig. 1 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.

With continued reference to FIG. 2, a flow chart of one embodiment of an artificial intelligence based performance analysis method in accordance with the present application is shown. The order of the steps in the flowchart may be changed and some steps may be omitted according to various needs. The performance analysis method based on the artificial intelligence provided by the embodiment of the application can be applied to any scene needing performance analysis of a service system, and can be applied to products of the scenes, such as performance analysis of the service system in the field of financial insurance. The artificial intelligence-based performance analysis method comprises the following steps:

Step S201, a target interface of a target service system is obtained; wherein the number of target interfaces includes a plurality.

In this embodiment, the electronic device (e.g., the server/terminal device shown in fig. 1) on which the performance analysis method based on artificial intelligence operates may acquire the target interface through a wired connection manner or a wireless connection manner. It should be noted that the wireless connection may include, but is not limited to, 3G/4G/5G connection, wiFi connection, bluetooth connection, wiMAX connection, zigbee connection, UWB (ultra wideband) connection, and other now known or later developed wireless connection. In the application scenario in the field of financial insurance, the target service system may be an insurance system, for example, a personal insurance system, a vehicle insurance system, an accident insurance system, and the like. The target interfaces may include various types of interfaces for price polling, price quotation, premium calculation, conversion insurance, underwriting, printing, and the like.

Step S202, acquiring request response time of each target interface within a preset time period.

In this embodiment, the request response time refers to an average response time when the target interface responds to the HTTP request within the preset time. The value of the preset time period is not particularly limited, and may be set according to actual use requirements.

And step 203, calculating the request response time of each target interface based on a preset performance analysis model to obtain a performance value corresponding to the target service system.

In this embodiment, the foregoing calculation processing is performed on the request response time of each target interface based on the preset performance analysis model to obtain a specific implementation process of the performance value corresponding to the target service system, which will be described in further detail in the following specific embodiments, which will not be described herein.

And step S204, comparing the performance value with a preset threshold value to obtain a corresponding comparison result.

In this embodiment, the comparison result is that the performance value is greater than the preset threshold, or that the performance value is less than the preset threshold.

Step S205, generating a performance analysis result of the target service system based on the comparison result.

In this embodiment, the foregoing specific implementation process of generating the performance analysis result of the target service system based on the comparison result will be described in further detail in the following specific embodiments, which will not be described herein.

Firstly, acquiring a target interface of a target service system; then acquiring request response time of each target interface in a preset time period; calculating the request response time of each target interface based on a preset performance analysis model to obtain a performance value corresponding to the target service system; comparing the performance value with a preset threshold value to obtain a corresponding comparison result; and finally, generating a performance analysis result of the target service system based on the comparison result. According to the application, the request response time of each target interface in the target service system is calculated and processed by using the performance analysis model in a preset time period to obtain the performance value corresponding to the target service system, and further, the performance analysis result of the target service system is generated by comparing the performance value with the preset threshold value and according to the obtained comparison result, so that the automatic performance analysis processing of the target service system is realized, the processing efficiency of the performance analysis of the target service system is improved, and the accuracy of the generated performance analysis result of the financial service system is ensured.

In some alternative implementations, step S203 includes the steps of:

and calling the performance calculation model.

In this embodiment, the performance calculation model is a model that is built in advance and is used for calculating performance data of the service system.

And obtaining the target service types respectively corresponding to the target interfaces.

In this embodiment, the interface information of the target interface may be obtained, and then the service information corresponding to the target interface may be obtained from the interface information, so as to extract the target service type of the target interface from the service information. In the application scenario in the financial insurance field, the service types corresponding to the interface may include types such as price polling, quotation, premium calculation, transfer and insurance, underwriting, printing, and the like.

And obtaining target weights respectively corresponding to the service types.

In this embodiment, the value of the target weight is not specifically limited, and may be set according to the actual service usage requirement. For different service types, the weight of the service type is determined in advance according to the actual service influence degree, and the higher the service influence degree of the service type is, the greater the weight of the service type is.

And carrying out weighted summation processing on the request response time based on the target weight by using the performance calculation model to obtain a corresponding calculation result.

The performance calculation model is called; then, obtaining target service types respectively corresponding to the target interfaces; then, obtaining target weights respectively corresponding to the service types; subsequently, the performance calculation model is used, and the request response time is subjected to weighted summation processing based on the target weight, so that a corresponding calculation result is obtained; and finally, taking the calculation result as the performance numerical value of the target service system. According to the application, the target weights corresponding to the target service types of the target interfaces are obtained, and then the performance calculation model is used, and the request response time is weighted and summed based on the target weights, so that the performance value of the target service system can be automatically and rapidly generated, the generation efficiency of the performance value of the target service system is improved, and the accuracy of the generated performance value is ensured.

In some alternative implementations of the present embodiment, step S205 includes the steps of:

And judging whether the performance value is larger than the preset threshold value or not according to the comparison result.

And if the comparison result is that the performance value is larger than the preset threshold value, generating a first performance analysis result of the target service system performance abnormality.

In this embodiment, the preset threshold is a value that is pre-configured to identify whether the performance of the service system is in a normal state, and the value of the preset threshold is not specifically limited, and may be set according to the actual use requirement. If the comparison result is that the performance value is larger than the preset threshold value, the current performance value of the target service system exceeds a normal value, the situation that the performance of the target service system is abnormal is indicated, and therefore a first performance analysis result of the performance abnormality of the target service system can be generated.

In this embodiment, if the comparison result is that the performance value is smaller than the preset threshold, it indicates that the current performance value of the target service system is a normal value, and it indicates that the target service system is in a state of normal performance currently, so as to generate a second performance analysis result of normal performance of the target service system.

Judging whether the performance value is larger than the preset threshold value or not according to the comparison result; if the comparison result is that the performance value is larger than the preset threshold value, a first performance analysis result of the target service system performance abnormality is generated; and if the comparison result is that the performance value is smaller than the preset threshold value, generating a second performance analysis result with normal performance of the target service system. According to the application, the comparison result is analyzed, so that the performance analysis result of the target service system matched with the analysis content can be accurately generated according to the obtained analysis content, and the accuracy of the generated performance analysis result is ensured.

In some alternative implementations, before step S202, the electronic device may further perform the following steps:

and calling a preset filter.

In this embodiment, the filter may also be referred to as a filter, where the filter is built in advance in the electronic device. Filters, also called filters, are the most practical of the Servlet technologies by which Web developers manage all Web resources for Web servers: for example, jsp, servlet, still picture file or static html file, etc. are intercepted, thereby realizing some special functions. Such as implementing URL-level rights access control, filtering sensitive words, compressing response information, and some other high-level functions. The filter is mainly used for preprocessing the user request and can also be used for post-processing the HttpServletResponse. Complete flow using Filter: the Filter preprocesses the user request, then gives the request to the Servlet for processing and generating a response, and finally the Filter carries out post-processing on the server response.

And recording request response time data of each target interface contained in the target service system based on the filter to obtain corresponding recorded data.

In this embodiment, by using a filter, recording request response time data of each target interface included in the target service system may be implemented, so as to generate corresponding record data. In the process of recording the request response time data of each target interface contained in the target service system, other interface information of all calls related to the request processed by the synchronous target interface can be recorded together, and the other interface information is associated and stored through a thread number so as to be used for analyzing the performance problem of the service system. Specifically, the other interface information may include information such as a thread number, RT, API address, request start time, request end time, and service flow identifier to which the request belongs.

And storing the recorded data.

In this embodiment, the storage manner of the record data is not limited, and may be set according to actual service requirements, for example, a database storage manner, a blockchain storage manner, a cloud disk storage manner, and the like may be adopted. In storage

The application calls the preset filter; then, based on the filter, recording request response time data of each target interface contained in the target service system to obtain corresponding recorded data; and storing the recorded data later. By using the filter, the application can intelligently record the request response time data of each target interface contained in the target service system to obtain the record data, thereby improving the generation intelligence of the request response time data. And the recorded data is stored to realize the safe storage of the request response time data, so that the request response time of each target interface in a preset time period can be quickly obtained by inquiring the recorded data.

In some alternative implementations, after step S202, the electronic device may further perform the following steps:

and screening the first interfaces which meet preset expansion conditions from all the target interfaces.

In this embodiment, the preset expansion condition refers to a condition that the request response time of the interface is greater than a preset time threshold, and if the request response time of the interface is greater than the time threshold, it indicates that the interface is an interface with a bottleneck for performance that needs to be expanded. The time threshold value is not specifically limited, and may be set according to an actual interface service test result.

And calling a preset resource adjustment model.

In this embodiment, the resource adjustment model is a model that is trained in advance and is used to evaluate how the interface performs resource expansion. The selection of the resource adjustment model is not particularly limited, and for example, a linear regression model, a support vector machine model, a logistic regression model and the like can be adopted.

And acquiring the designated request response time of the first interface.

In this embodiment, the specified request response time of the first interface may be obtained from record data obtained by querying record data obtained by recording request response time data of each of the target interfaces included in the target service system by a filter.

And processing the specified request response time based on the resource adjustment model, and outputting a corresponding adjustment value.

In this embodiment, the specified request response time is input into the resource adjustment model, so that the specified request response time is processed through the resource adjustment model, and a corresponding adjustment value is output.

In this embodiment, a preset resource adjustment table is called, and the adjustment value is used to perform query processing on the resource adjustment table, so as to query a target extension value corresponding to the adjustment value from the resource adjustment table, and then perform resource adjustment processing on the first interface by using the target extension value. The resource adjustment table is a data table which is built in advance and stores corresponding adjustment values and expansion values corresponding to the adjustment values.

The method comprises the steps of screening out first interfaces which meet preset expansion conditions from all target interfaces; then calling a preset resource adjustment model; then acquiring the appointed request response time of the first interface; processing the specified request response time based on the resource adjustment model, and outputting a corresponding adjustment value; and finally, carrying out resource adjustment processing on the first interface based on the adjustment value. After the request response time of each target interface in the preset time period is obtained, the application also intelligently screens the first interface which accords with the preset expansion condition from all the target interfaces, then invokes the resource adjustment model to process the appointed request response time of the first interface, outputs the corresponding adjustment value, and further uses the adjustment value to carry out resource adjustment processing on the first interface so as to ensure that the performance of the first interface is in a normal state, avoid the situation of abnormal performance of the first interface and be beneficial to ensuring the normal operation of the business processing of the first interface.

In some optional implementations of this embodiment, after step S202, the electronic device may further perform the following steps:

And acquiring a second interface from all the target interfaces.

In this embodiment, the second interface is any one of all the target interfaces.

And acquiring the call times of the associated interface when the second interface processes the request in the preset time period.

In this embodiment, the record data obtained by recording the request response time data of each target interface included in the target service system by the filter may be queried, so as to obtain the number of related interface calls when the second interface processes the request in the preset time period from the record data.

And acquiring the consumed time of the second interface when the request is processed in the preset time period.

In this embodiment, similarly, the record data obtained by recording the request response time data of each target interface included in the target service system by the filter may be queried, so as to obtain, from the record data, the consumed time when the second interface processes the request in the preset time period.

And judging whether the calling times of the associated interfaces and the consumed time are in a normal threshold range or not.

In this embodiment, the value of the normal threshold range is not specifically limited, and may be set according to an actual service test result. Wherein the normal threshold range includes a first threshold range corresponding to the number of associated interface calls and includes a second threshold range corresponding to the elapsed time.

In this embodiment, if the number of times of call and the consumed time of the associated interface when the second interface processes the request in the preset time period are both greater than the normal threshold range, the buffer memory is added to the second interface and the associated interface is correspondingly fused, so as to complete the adjustment processing of the second interface, thereby ensuring that the performance of the second interface is in a normal state. And if the calling times and the consumed time of the associated interfaces when the second interface processes the request in the preset time period are smaller than the normal threshold range, the interface logic codes of the second interface are optimized to finish the adjustment processing of the second interface, so that the performance of the second interface is ensured to be in a normal state. The processing mode of optimizing the interface logic code of the second interface can be performed by notifying a developer of modification.

The second interface is obtained from all the target interfaces; then acquiring the calling times of the associated interface when the second interface processes the request in the preset time period; acquiring the consumption time of the second interface when the request is processed in the preset time period; subsequently judging whether the calling times of the associated interfaces and the consumed time are in a normal threshold range or not; and if not, carrying out adjustment processing on the second interface based on a preset processing rule. In the process of monitoring the performance of the target interface, if the related interface calling times and the consumed time when the target interface processes the request in the preset time period are not in the normal threshold range, the application intelligently uses the preset processing rule to adjust the target interface so as to ensure the performance of the target interface to be in a normal state, avoid the situation of abnormal performance of the target interface and be beneficial to ensuring the normal operation of the business processing of the target interface.

In some optional implementations of this embodiment, after step S205, the electronic device may further perform the following steps:

and calling a preset data presentation tool.

In this embodiment, the data presentation tool may specifically be a Kibana tool. Kibana is an open source data analysis and visualization platform that is one of the members of the Elastic Stack designed to cooperate with the Elastic search. Kibana may be used to search, view, and interoperate data in the elastomer search index.

And determining a target display mode corresponding to the request response time.

In this embodiment, the target display modes may include a chart, a table, a map, and the like.

In this embodiment, the request response time may be subjected to data conversion processing according to the target display mode based on the target display mode, and then the request response time after the data conversion processing is displayed in the data display tool. In addition, the link call condition of the target interface can be presented in the data presentation tool.

The method and the device call a preset data display tool; then determining a target display mode corresponding to the request response time; and then displaying the request response time in the data display tool based on the target display mode. After the request response time of each target interface in the preset time period is obtained, the target display mode corresponding to the request response time is intelligently determined, and the request response time is displayed in the data display tool by utilizing the target display mode, so that the intelligent display of the request response time is realized, and the method is beneficial to the follow-up related users to analyze the performance problems of a target service system by inquiring the request response time in the data display tool, so that the working efficiency and the working experience of the users are improved.

It should be understood that the sequence number of each step in the foregoing embodiment does not mean that the execution sequence of each process should be determined by the function and the internal logic, and should not limit the implementation process of the embodiment of the present application.

It should be emphasized that, to further ensure the privacy and security of the performance analysis results, the performance analysis results may also be stored in a blockchain node.

The blockchain is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, consensus mechanism, encryption algorithm and the like. The Blockchain (Blockchain), which is essentially a decentralised database, is a string of data blocks that are generated by cryptographic means in association, each data block containing a batch of information of network transactions for verifying the validity of the information (anti-counterfeiting) and generating the next block. The blockchain may include a blockchain underlying platform, a platform product services layer, an application services layer, and the like.

Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by computer readable instructions stored in a computer readable storage medium that, when executed, may comprise the steps of the embodiments of the methods described above. The storage medium may be a nonvolatile storage medium such as a magnetic disk, an optical disk, a Read-Only Memory (ROM), or a random access Memory (Random Access Memory, RAM).

It should be understood that, although the steps in the flowcharts of the figures are shown in order as indicated by the arrows, these steps are not necessarily performed in order as indicated by the arrows. The steps are not strictly limited in order and may be performed in other orders, unless explicitly stated herein. Moreover, at least some of the steps in the flowcharts of the figures may include a plurality of sub-steps or stages that are not necessarily performed at the same time, but may be performed at different times, the order of their execution not necessarily being sequential, but may be performed in turn or alternately with other steps or at least a portion of the other steps or stages.

With further reference to fig. 3, as an implementation of the method shown in fig. 2, the present application provides an embodiment of an artificial intelligence based performance analysis apparatus, which corresponds to the method embodiment shown in fig. 2, and which is particularly applicable to various electronic devices.

As shown in fig. 3, the artificial intelligence based performance analysis apparatus 300 according to the present embodiment includes: a first acquisition module 301, a second acquisition module 302, a calculation module 303, a comparison module 304, and a generation module 305. Wherein:

a first obtaining module 301, configured to obtain a target interface of a target service system; wherein the number of target interfaces includes a plurality;

a second obtaining module 302, configured to obtain a request response time of each of the target interfaces within a preset period of time;

the calculating module 303 is configured to calculate the response time of the request of each target interface based on a preset performance analysis model, so as to obtain a performance value corresponding to the target service system;

the comparison module 304 is configured to compare the performance value with a preset threshold value to obtain a corresponding comparison result;

and the generating module 305 is configured to generate a performance analysis result of the target service system based on the comparison result.

In this embodiment, the operations performed by the modules or units respectively correspond to the steps of the artificial intelligence-based performance analysis method in the foregoing embodiment one by one, and are not described herein again.

In some alternative implementations of the present embodiment, the computing module 303 includes:

a calling sub-module for calling the performance calculation model;

the first acquisition sub-module is used for acquiring target service types respectively corresponding to the target interfaces;

the second acquisition sub-module is used for acquiring target weights corresponding to the service types respectively;

the processing sub-module is used for carrying out weighted summation processing on the request response time based on the target weight by using the performance calculation model to obtain a corresponding calculation result;

and the determining submodule is used for taking the calculation result as the performance numerical value of the target service system.

In some alternative implementations of the present embodiment, the generating module 305 includes:

the judging submodule is used for judging whether the performance value is larger than the preset threshold value or not according to the comparison result;

The first generation sub-module is used for generating a first performance analysis result of the target service system performance abnormality if the comparison result is that the performance value is larger than the preset threshold value;

and the second generation sub-module is used for generating a second performance analysis result with normal performance of the target service system if the performance value is smaller than the preset threshold value as the comparison result.

In this embodiment, the operations performed by the modules or units are respectively corresponding to the steps of the performance analysis method based on artificial intelligence in the foregoing embodiment, which is not described herein again.

In some optional implementations of the present embodiment, the artificial intelligence based performance analysis apparatus further includes:

the first calling module is used for calling a preset filter;

the recording module is used for recording the request response time data of each target interface contained in the target service system based on the filter to obtain corresponding recording data;

and the storage module is used for storing the record data.

the screening module is used for screening first interfaces which meet preset expansion conditions from all the target interfaces;

the second calling module is used for calling a preset resource adjustment model;

a third obtaining module, configured to obtain a specified request response time of the first interface;

the first processing module is used for processing the specified request response time based on the resource adjustment model and outputting a corresponding adjustment value;

and the first adjusting module is used for carrying out resource adjusting processing on the first interface based on the adjusting value.

a fourth obtaining module, configured to obtain a second interface from all the target interfaces; wherein the second interface is any one interface among all the target interfaces;

A fifth obtaining module, configured to obtain the number of times of related interface call when the second interface processes the request in the preset time period;

a sixth obtaining module, configured to obtain a consumed time of the second interface when the second interface processes the request in the preset time period;

the judging module is used for judging whether the calling times of the associated interfaces and the consumed time are in a normal threshold range or not;

and the second adjusting module is used for adjusting the second interface based on a preset processing rule if not.

the third calling module is used for calling a preset data display tool;

the determining module is used for determining a target display mode corresponding to the request response time;

and the display module is used for displaying the request response time in the data display tool based on the target display mode.

In order to solve the technical problems, the embodiment of the application also provides computer equipment. Referring specifically to fig. 4, fig. 4 is a basic structural block diagram of a computer device according to the present embodiment.

The computer device 4 comprises a memory 41, a processor 42, a network interface 43 communicatively connected to each other via a system bus. It should be noted that only computer device 4 having components 41-43 is shown in the figures, but it should be understood that not all of the illustrated components are required to be implemented and that more or fewer components may be implemented instead. It will be appreciated by those skilled in the art that the computer device herein is a device capable of automatically performing numerical calculations and/or information processing in accordance with predetermined or stored instructions, the hardware of which includes, but is not limited to, microprocessors, application specific integrated circuits (Application Specific Integrated Circuit, ASICs), programmable gate arrays (fields-Programmable Gate Array, FPGAs), digital processors (Digital Signal Processor, DSPs), embedded devices, etc.

The computer equipment can be a desktop computer, a notebook computer, a palm computer, a cloud server and other computing equipment. The computer equipment can perform man-machine interaction with a user through a keyboard, a mouse, a remote controller, a touch pad or voice control equipment and the like.

The memory 41 includes at least one type of readable storage medium including flash memory, hard disk, multimedia card, card memory (e.g., SD or DX memory, etc.), random Access Memory (RAM), static Random Access Memory (SRAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), programmable Read Only Memory (PROM), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the storage 41 may be an internal storage unit of the computer device 4, such as a hard disk or a memory of the computer device 4. In other embodiments, the memory 41 may also be an external storage device of the computer device 4, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash Card (Flash Card) or the like, which are provided on the computer device 4. Of course, the memory 41 may also comprise both an internal memory unit of the computer device 4 and an external memory device. In this embodiment, the memory 41 is typically used to store an operating system and various application software installed on the computer device 4, such as computer readable instructions based on an artificial intelligence performance analysis method. Further, the memory 41 may be used to temporarily store various types of data that have been output or are to be output.

The processor 42 may be a central processing unit (Central Processing Unit, CPU), controller, microcontroller, microprocessor, or other data processing chip in some embodiments. The processor 42 is typically used to control the overall operation of the computer device 4. In this embodiment, the processor 42 is configured to execute computer readable instructions stored in the memory 41 or process data, such as executing computer readable instructions of the artificial intelligence based performance analysis method.

The network interface 43 may comprise a wireless network interface or a wired network interface, which network interface 43 is typically used for establishing a communication connection between the computer device 4 and other electronic devices.

in the embodiment of the application, a target interface of a target service system is firstly obtained; then acquiring request response time of each target interface in a preset time period; calculating the request response time of each target interface based on a preset performance analysis model to obtain a performance value corresponding to the target service system; comparing the performance value with a preset threshold value to obtain a corresponding comparison result; and finally, generating a performance analysis result of the target service system based on the comparison result. According to the embodiment of the application, the request response time of each target interface in the target service system is calculated and processed by using the performance analysis model in a preset time period to obtain the performance value corresponding to the target service system, and further, the performance analysis result of the target service system is generated by comparing the performance value with the preset threshold value and according to the obtained comparison result, so that the automatic performance analysis processing of the target service system is realized, the processing efficiency of the performance analysis of the target service system is improved, and the accuracy of the generated performance analysis result of the financial service system is ensured.

The present application also provides another embodiment, namely, a computer-readable storage medium storing computer-readable instructions executable by at least one processor to cause the at least one processor to perform the steps of the artificial intelligence based performance analysis method as described above.

From the above description of the embodiments, it will be clear to those skilled in the art that the above-described embodiment method may be implemented by means of software plus a necessary general hardware platform, but of course may also be implemented by means of hardware, but in many cases the former is a preferred embodiment. Based on such understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art in the form of a software product stored in a storage medium (e.g. ROM/RAM, magnetic disk, optical disk) comprising instructions for causing a terminal device (which may be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to perform the method according to the embodiments of the present application.

It is apparent that the above-described embodiments are only some embodiments of the present application, but not all embodiments, and the preferred embodiments of the present application are shown in the drawings, which do not limit the scope of the patent claims. This application may be embodied in many different forms, but rather, embodiments are provided in order to provide a thorough and complete understanding of the present disclosure. Although the application has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that modifications may be made to the embodiments described in the foregoing description, or equivalents may be substituted for elements thereof. All equivalent structures made by the content of the specification and the drawings of the application are directly or indirectly applied to other related technical fields, and are also within the scope of the application.

Claims

1. An artificial intelligence based performance analysis method is characterized by comprising the following steps:

2. The performance analysis method based on artificial intelligence according to claim 1, wherein the step of calculating the request response time of each target interface based on a preset performance analysis model to obtain a performance value corresponding to the target service system specifically comprises:

invoking the performance calculation model;

obtaining target weights respectively corresponding to the service types;

3. The artificial intelligence based performance analysis method according to claim 1, wherein the step of generating the performance analysis result of the target service system based on the comparison result specifically comprises:

4. The artificial intelligence based performance analysis method according to claim 1, further comprising, before the step of acquiring the request response time of each of the target interfaces within a preset period of time:

calling a preset filter;

And storing the recorded data.

5. The artificial intelligence based performance analysis method according to claim 1, further comprising, after the step of acquiring the request response time of each of the target interfaces within a preset period of time:

calling a preset resource adjustment model;

acquiring the designated request response time of the first interface;

6. The artificial intelligence based performance analysis method according to claim 1, further comprising, after the step of acquiring the request response time of each of the target interfaces within a preset period of time:

7. The artificial intelligence based performance analysis method according to claim 1, further comprising, after the step of generating the performance analysis result of the target business system based on the comparison result:

calling a preset data display tool;

determining a target display mode corresponding to the request response time;

8. An artificial intelligence based performance analysis device, comprising:

9. A computer device comprising a memory having stored therein computer readable instructions which when executed implement the steps of the artificial intelligence based performance analysis method of any of claims 1 to 7.

10. A computer readable storage medium having stored thereon computer readable instructions which when executed by a processor implement the steps of the artificial intelligence based performance analysis method of any of claims 1 to 7.