CN113762688A - Business analysis system, method and storage medium - Google Patents

Business analysis system, method and storage medium Download PDF

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CN113762688A
CN113762688A CN202110014587.1A CN202110014587A CN113762688A CN 113762688 A CN113762688 A CN 113762688A CN 202110014587 A CN202110014587 A CN 202110014587A CN 113762688 A CN113762688 A CN 113762688A
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service
business
information
rule
ratio
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李超
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Beijing Jingdong Century Trading Co Ltd
Beijing Wodong Tianjun Information Technology Co Ltd
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Beijing Jingdong Century Trading Co Ltd
Beijing Wodong Tianjun Information Technology Co Ltd
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F8/00—Arrangements for software engineering
    • G06F8/40—Transformation of program code
    • G06F8/41—Compilation
    • G06F8/42—Syntactic analysis
    • G06F8/427—Parsing
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/045—Combinations of networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0635—Risk analysis of enterprise or organisation activities

Abstract

The present disclosure provides a service analysis system, a method and a storage medium, which relate to the technical field of computers, wherein the method comprises the following steps: collecting service structure data corresponding to the running service in a service system; transmitting the service structure data by using an asynchronous message mode; obtaining service model information and correlation information between services based on service structure data; and generating a training sample based on the business model information, the associated information and the business daily problem information, and training a business scene model corresponding to the business by using the training sample so as to predict the running state of the business by using the trained business scene model. The method, the system and the storage medium can analyze the relevance, the risk analysis and the like of the service, can visually display the service, can predict the running state of the service, can realize the management of the service requirement, can ensure that the service is reasonable in rule and stable in running, and improve the user experience.

Description

Business analysis system, method and storage medium
Technical Field
The present disclosure relates to the field of computer technologies, and in particular, to a service analysis system, a service analysis method, and a storage medium.
Background
In the pace of rapid internet development, services in a service system typically require rapid iterations. In the initial stage of operation of the service system, developers are familiar with services in the service system, but with continuous iteration of the services, more and more intersections can occur with other services, and the developers can have various problems when iterating the services, so that the normal operation of the services is influenced. At present, a service analysis means is lacked in the development of the service, and developers usually detect, maintain and update the service after the service has a problem, so that the stability of the service and the operation of the whole service system is influenced.
Disclosure of Invention
In view of the above, an object of the present invention is to provide a service analysis system, method and storage medium.
According to a first aspect of the present disclosure, there is provided a traffic analysis system, including: the service data acquisition module is used for acquiring service structure data corresponding to the running service in the service system by using the annotation agent module based on preset service data acquisition rule information; the service data transmission module is used for transmitting the service structure data to the service data processing module in an asynchronous message mode; the service data processing module is used for obtaining service model information and correlation information between services based on the service structure data; and the business prediction module is used for acquiring business daily problem information, generating a training sample based on the business model information, the correlation information and the business daily problem information, and training a business scene model corresponding to the business by using the training sample so as to predict the running state of the business by using the trained business scene model.
Optionally, the service data acquiring module includes: a code acquisition unit for acquiring a code of the service using an annotation agent module; a code annotation unit, configured to construct, based on the business data acquisition rule information, a corresponding abstract syntax tree for the code using an annotation proxy module, and obtain the business structure data based on parsing code syntax information and obtaining call information of code execution by the abstract syntax tree; wherein the service structure data includes: attributes, behaviors, and business relationships.
Optionally, the service data processing module includes: a service grammar rule processing unit, configured to configure the service grammar rule, and analyze and process the service structure data based on the service grammar rule to generate the service model information; and the service analysis processing unit is used for obtaining the correlation information based on the service model information.
Optionally, the association relationship includes: a correlation coefficient; wherein, the service analysis processing unit is configured to calculate the correlation coefficient as:
coefficient ═ ((number of attributes 1 × (rule proportion 1+ number of behaviors 1) × (rule proportion 2)) - (number of attributes 2 × (rule proportion 3+ number of behaviors 3 × (rule proportion 4));
wherein, A and B are services, A is the service that B depends on, targetA and targetB are service system coefficients; the Coefficient is a correlation Coefficient between the service A and the service B; the attribute number 1 is the attribute number depended on by the service B in the service A, and the attribute number 2 is the attribute number which needs to be depended on and corresponds to the attribute number 1 in the service B; the behavior quantity 1 is the behavior quantity depended on by the service B in the service A, and the behavior quantity 2 is the behavior quantity needing to be depended on corresponding to the behavior quantity 1 in the service B; rule ratio 1 is the ratio of the number of attributes 1 to the total number of attributes of service a, rule ratio 2 is the ratio of the number of behaviors 1 to the total number of behaviors of service a, rule ratio 3 is the ratio of the number of attributes 2 to the total number of attributes of service B, and rule ratio 4 is the ratio of the number of behaviors 2 to the total number of behaviors of service B.
Optionally, the association relationship includes: a stability factor; wherein, the service analysis processing unit is configured to calculate the stability coefficient as:
Figure BDA0002886305860000021
wherein, Stability coefficients of the service A and the service B are respectively obtained, and the daily problem proportion is the ratio of the occurrence frequency of the system abnormal problem of the service to the total execution frequency of the service. Increasing the daily problem ratio, wherein the rule ratio 5 is a weighted sum of the ratio 1 of the number of the attributes on which the business needs to depend or is dependent to the total number of the businesses and the ratio 2 of the number of the behaviors on which the business needs to depend or is dependent to the total number of the behaviors of the businesses.
Optionally, the association relationship includes: a risk factor; wherein, the service analysis processing unit is configured to calculate the risk coefficient as:
Risk＝(targetAStability+targetBStability……targetNStability)/count(Target)；
wherein targetA _ Stabilty, targetB _ Stabilty and targetNStabilityThe stability coefficients of service a and service B … …, respectively, and count (target) is the total number of services in the target system.
Optionally, the traffic prediction module includes: the model training unit is used for constructing a business scene model through a TensorFlow framework and training the business scene model by using the training sample; and the service prediction unit is used for establishing a prediction sample according to the service model information and the associated information which need to be predicted, predicting the prediction sample by using the trained service scene model and obtaining the operation state of the service.
Optionally, the scheduling module is configured to generate a work task and schedule the work task; wherein the work tasks include: business analysis tasks and daily problem collection tasks.
Optionally, the service data presentation module is configured to generate service visualization information and/or service matrix grid information based on the service model information and the association information between services, and provide an external access interface for accessing the service visualization information and/or the service matrix grid information.
Optionally, the asynchronous message comprises: kafka messages, jmq messages.
Optionally, the storage module is configured to store data; wherein the data comprises: the service structure data, the service model information, the association information, the service daily problem information and the service running state.
According to a second aspect of the present disclosure, there is provided a traffic analysis method, including: based on preset business data acquisition rule information, using an annotation agent module to acquire business structure data corresponding to the running business in a business system; transmitting the service structure data by using an asynchronous message mode; obtaining service model information and correlation information between services based on the service structure data; acquiring business daily problem information, generating a training sample based on the business model information, the correlation information and the business daily problem information, and training a business scene model corresponding to the business by using the training sample so as to predict the running state of the business by using the trained business scene model.
Optionally, the acquiring, by using the annotation agent module, service structure data corresponding to the running service in the service system based on the preset service data acquisition rule information includes: using an annotation agent module to acquire a code of the service; constructing a corresponding abstract syntax tree for the code by using an annotation agent module based on the business data acquisition rule information; analyzing code syntax information and acquiring code execution calling information based on the abstract syntax tree to acquire the service structure data; wherein the service structure data includes: attributes, behaviors, and business relationships.
Optionally, obtaining service model information and association information between services based on the service structure data includes: configuring the business grammar rule, and analyzing and processing the business structure data based on the business grammar rule to generate the business model information; and obtaining the associated information based on the service model information.
Optionally, the association relationship includes: a correlation coefficient; wherein the obtaining the associated information based on the service model information comprises: calculating the correlation coefficient as:
coefficient ═ ((number of attributes 1 × (rule proportion 1+ number of behaviors 1) × (rule proportion 2)) - (number of attributes 2 × (rule proportion 3+ number of behaviors 3 × (rule proportion 4));
wherein, A and B are services, A is the service that B depends on, targetA and targetB are service system coefficients; the Coefficient is a correlation Coefficient between the service A and the service B; the attribute number 1 is the attribute number depended on by the service B in the service A, and the attribute number 2 is the attribute number which needs to be depended on and corresponds to the attribute number 1 in the service B; the behavior quantity 1 is the behavior quantity depended on by the service B in the service A, and the behavior quantity 2 is the behavior quantity needing to be depended on corresponding to the behavior quantity 1 in the service B; rule ratio 1 is the ratio of the number of attributes 1 to the total number of attributes of service a, rule ratio 2 is the ratio of the number of behaviors 1 to the total number of behaviors of service a, rule ratio 3 is the ratio of the number of attributes 2 to the total number of attributes of service B, and rule ratio 4 is the ratio of the number of behaviors 2 to the total number of behaviors of service B.
Optionally, the association relationship includes: a stability factor; wherein the obtaining the associated information based on the service model information comprises: calculating the stability coefficient as:
Figure BDA0002886305860000051
wherein, Stability coefficients of the service A and the service B are respectively obtained, and the daily problem proportion is the ratio of the occurrence frequency of the system abnormal problem of the service to the total execution frequency of the service. Increasing the daily problem ratio, wherein the rule ratio 5 is a weighted sum of the ratio 1 of the number of the attributes on which the business needs to depend or is dependent to the total number of the businesses and the ratio 2 of the number of the behaviors on which the business needs to depend or is dependent to the total number of the behaviors of the businesses.
Optionally, the association relationship includes: a risk factor; wherein the obtaining the associated information based on the service model information comprises: calculating the risk coefficient as:
Risk＝(targetAStability+targetBStability……targetNStability)/count(Target)；
wherein targetA _ Stabilty, targetB _ Stabilty and targetNStabilityThe stability coefficients of service a and service B … …, respectively, and count (target) is the total number of services in the target system.
Optionally, the training the service scenario model corresponding to the service by using the training sample includes: constructing a business scene model through a TensorFlow frame, and training the business scene model by using the training sample; the predicting the operation state of the service by using the trained service scenario model comprises the following steps: and establishing a prediction sample according to the service model information and the associated information which need to be predicted, and predicting the prediction sample by using the trained service scene model to obtain the operation state of the service.
Optionally, generating a work task and scheduling the work task; wherein the work tasks include: business analysis tasks and daily problem collection tasks.
Optionally, business visualization information and/or business matrix grid information is generated based on the business model information and the association information between the businesses, and an external access interface for accessing the business visualization information and/or the business matrix grid information is provided.
Optionally, a storage module is configured to store data, where the data includes: the service structure data, the service model information, the association information, the service daily problem information and the service running state.
According to a third aspect of the present disclosure, there is provided a traffic analysis system, comprising: a memory; and a processor coupled to the memory, the processor configured to perform the method as described above based on instructions stored in the memory.
According to a fourth aspect of the present disclosure, there is provided a computer readable storage medium storing computer instructions for a processor to perform the traffic analysis method as above.
The business analysis system, the method and the storage medium collect business structure data corresponding to running business in a business system, and obtain business model information and correlation information between businesses based on the business structure data; generating a training sample based on the service model information, the correlation information and the service daily problem information to train a service scene model corresponding to the service, and predicting the operation state of the service by using the service scene model; the method and the system can analyze the service, including service relevance analysis, risk analysis and the like, can visually display the service, can predict the running state of the service, can realize management on the service requirement, can ensure that the service is reasonable in rule and stable in running, and improve user experience.
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In order to more clearly illustrate the embodiments of the present disclosure or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present disclosure, and other drawings can be obtained by those skilled in the art without inventive exercise.
FIG. 1 is a block diagram illustration of one embodiment of a business analysis system according to the present disclosure;
FIG. 2 is a block diagram illustration of another embodiment of a business analysis system according to the present disclosure;
FIG. 3 is a block diagram of an overall framework in one embodiment of a business analysis system according to the present disclosure;
FIG. 4 is a schematic diagram of data flow in one embodiment of a traffic analysis system according to the present disclosure;
FIG. 5 is a schematic diagram of a business data collection module in one embodiment of a business analysis system according to the present disclosure;
FIG. 6 is a data flow diagram of data crawling in one embodiment of a business analysis system according to the present disclosure;
FIG. 7 is a schematic illustration of collected data in one embodiment of a business analysis system according to the present disclosure;
FIG. 8 is a block diagram illustration of a business data processing module in one embodiment of a business analysis system according to the present disclosure;
FIG. 9 is a schematic diagram of an association relationship among services;
FIG. 10 is a block diagram illustration of a traffic prediction module in one embodiment of a traffic analysis system according to the present disclosure;
FIG. 11 is a flow diagram of one embodiment of a business analysis method according to the present disclosure;
FIG. 12 is a block diagram illustration of yet another embodiment of a business analysis system according to the present disclosure.
Detailed Description
The present disclosure now will be described more fully hereinafter with reference to the accompanying drawings, in which exemplary embodiments of the disclosure are shown. The technical solutions in the embodiments of the present disclosure will be clearly and completely described below with reference to the drawings in the embodiments of the present disclosure, and it is obvious that the described embodiments are only a part of the embodiments of the present disclosure, and not all of the embodiments. All other embodiments, which can be derived by one of ordinary skill in the art from the embodiments disclosed herein without making any creative effort, shall fall within the scope of protection of the present disclosure. The technical solution of the present disclosure is described in various aspects below with reference to various figures and embodiments.
At present, a service is maintained by manual memory based on a document maintenance service requirement list, or the service is maintained by using an online document, and if the service requirement is changed, developers update the online document. The existing method for maintaining the services lacks an analysis means for the aspects of service relevance, risk and the like, and can cause the problems of unreasonable service rules, wrong interaction among the services and the like.
As shown in fig. 1, the present disclosure provides a service analysis system, which includes a service data acquisition module 10, a service data transmission module 20, a service data processing module 30, and a service prediction module 40. The business data acquisition module 10 acquires business structure data corresponding to the running business in the business system by using the annotation agent module based on preset business data acquisition rule information. The service system can be various systems, such as an e-commerce system and the like, and services in the e-commerce system include commodity services, order services, payment services, delivery services, customer service services and the like.
The service data transmission module 20 transmits the service configuration data to the service data processing module 30 by means of an asynchronous message. The asynchronous message may be of various types, such as a kafka message, an jmq message, and so on. The service data processing module 30 obtains service model information and association information between services based on the service configuration data.
The service prediction module 40 collects service daily problem information, generates a training sample based on the service model information, the association information and the service daily problem information, and trains a service scene model corresponding to a service by using the training sample so as to predict an operation state of the service by using the trained service scene model, wherein the operation state information of the service includes whether the operation is normal, a specific problem occurring in the operation, a corresponding service rule and the like.
In one embodiment, the business analysis system includes a business data presentation module 50, a scheduling module 60, and a storage module 70. The scheduling module 60 generates and schedules work tasks, including business analysis tasks and daily problem collection tasks.
The service data presentation module 50 generates service visualization information, service matrix grid information, and the like based on the service model information and the association information between services, and provides an external access interface for accessing the service visualization information and the service matrix grid information, where the external access interface includes a web link and the like. The storage module 70 is used for storing various data, including: service structure data, service model information, associated information, service daily problem information, service running state and the like.
In one embodiment, as shown in fig. 3, the overall architecture of the business analysis system of the present disclosure includes an acquisition layer, a transport layer, a processing layer, and a storage layer. And deploying a business system in a business layer, wherein the business in the business system comprises the businesses of commodities, commodity sub-functions, orders, sales promotion and the like.
And deploying a service data acquisition module in the acquisition layer, wherein the service data acquisition module acquires structural data of the service to form visual data. The service structure data collected by the service data collection module is divided into two types: one is the service structure data automatically acquired by the service data acquisition module, and the other is the service structure data manually input at the management end by the manager.
And deploying a service data transmission module in the transmission layer, wherein the service data transmission module transmits the service structure data acquired by the service data acquisition module in an asynchronous message mode. A service data processing module, a scheduling module, a service data display module and a service prediction module are deployed in the processing layer. The service data processing module comprises a service grammar rule processing unit and a service analysis processing unit.
And the service grammar rule processing unit analyzes and analyzes the service structure data according to the annotation configuration rule of the service abstraction. The scheduling module generates a work task, and the scheduling work task is used for analyzing the service structure data to form visual data; or for managing and predicting the service. The work task can be a real-time task or an asynchronous task.
The service data display module is used for forming visual data after the service structure data are analyzed, and providing the visual data to a management end through an open service or an open interface or issuing the visual data to a third party; the service matrix grid is used for displaying services on the global grid line, and specific information (including attributes, behaviors and the like) of each grid can be seen by clicking a specific grid point, and calling relations among the services can also be seen.
The service analysis processing unit performs various analyses on the service structure data to obtain required visual data. After the service is on line, based on a large amount of collected service structure data, deep artificial intelligence learning can be carried out, various service scene models are obtained, and a reference contrast effect is provided for service change. A storage module is deployed in a storage layer and provided with various storage media, wherein mysql stores full data, redis stores hot spot data, hbase stores rule detailed data and the like.
The business analysis system in the above embodiment may perform overall dynamic configuration management business according to business rule abstraction, perform abstraction (attribute and behavior) on the business by using the business rule syntax tree, and perform real-time/asynchronous relevance analysis, stability analysis, reachability analysis, machine learning, business scene model training, and the like on abstract data of the business.
In one embodiment, as shown in fig. 4, a business system introduces a prepared business rule parsing packet bz-annotation. jar through maven or the like for configuring an annotation agent module for a business, where the annotation agent module may be an annotation-agent. Business data acquisition rule information is predefined, and the business data acquisition rule information is an annotation. When the business system is started, loading a starting indication-agent through a spring starter/web container, and acquiring business structure data corresponding to the running business in the business system by using the indication-agent based on an indication.
After the annotation-agent is started, dynamically modifying and adding the annotation. xml configuration file at the management end, and finally, taking the annotation. xml configuration file modified by the management end as the standard. After the business structure data is collected, the business structure data is packaged into message service by an indication-agent, and the business structure data is transmitted through asynchronous messages and stored at a management end (having the functions of safety, overtime, loss and retry). The scheduling module may schedule real-time tasks and offline tasks, for example, service analysis tasks are used to analyze relationships, dependencies, risks, and the like between services; the daily problem collection task is used to calculate risk factors. The business data processing module performs corresponding analysis processing, analyzes result data among businesses to generate visual data, can analyze a relation coefficient, a risk coefficient and a stability coefficient to obtain various indexes of the current business link, and provides reference value for newly added updates.
In one embodiment, as shown in fig. 5, the business data collecting module 10 includes a code acquiring unit 101 and a code annotating unit 102. The code obtaining unit 102 obtains a code of a service, which may be an order service or the like, using the annotation agent module. The service code may be a code obtained by compiling java source codes of the order service, that is, a class file, and the service code may be obtained and analyzed by using an existing method.
The code annotation unit 102 constructs a corresponding abstract syntax tree for the code by using an annotation agent module based on the business data acquisition rule information, analyzes the code syntax information based on the abstract syntax tree and acquires the calling information executed by the code to acquire business structure data; the business structure data includes attributes, behaviors, business relationships, and the like.
An Abstract Syntax Tree (AST) may be a Tree representation of the Abstract Syntax structure of the source code. The abstract syntax tree may include nodes and connection relationships between the nodes. The business data collection rule information includes the type of the business structure data to be collected, including attributes, behaviors, business relations, and the like.
The method comprises the steps of constructing a corresponding abstract syntax tree for a service code by using various existing methods, analyzing syntax information of the service code and calling information executed by the code based on the abstract syntax tree, wherein the calling information is java method calling information of the service code and java method calling information of codes of other services called by the service code. The attribute in the service structure data can be the attribute name of java, the attribute in the service structure data can be the method name of java, and the service relationship in the service structure data can be the attribute and the method name of java for calling other services.
In one embodiment, as shown in fig. 6, an annotation agent module annotation-agent is used to construct a corresponding abstract syntax tree by using a customized business annotation syntax tree rule, and the abstract syntax tree is parsed to obtain key businesses, business relationships, attributes, behaviors, and the like. The association-agent is zero-coupled to the service code (the association-agent needs to be registered first at the management end). As shown in fig. 7, the annotation-agent has an annotation rule scanner built therein for parsing and processing the code of the business. The independent process in the service system is used for processing, and the normal service processing of the service system is not influenced by adopting a mode of running agent-attach by the independent process; security protection and control can be configured to take effect in real time.
In one embodiment, as shown in fig. 8, the business data processing module 30 includes a business syntax rule processing unit 301 and a business analysis processing unit 302. The service grammar rule processing unit 301 configures a service grammar rule, and analyzes and processes the service structure data based on the service grammar rule to generate service model information. The service grammar rule includes processing rules corresponding to attributes, behaviors, service relations and the like in the service structure data, and can be various existing processing rules, and the service model information includes attribute name information used by the service, service function information, calling information between services and the like.
The business analysis processing unit 302 obtains the association information based on the business model information. The business model information is the internal information of the business entities, and the associated information is the relationship information between the business entities. The association relation comprises an association coefficient; the service analysis processing unit 302 calculates the correlation coefficient as:
coefficient ═ ((targetA: (number of attributes 1 × (rule proportion 1+ number of behaviors 1) × (rule proportion 2)) - (number of attributes 2 × (rule proportion 3+ number of behaviors 3 × (rule proportion 4)) (1-1);
wherein, A and B are both services, A is a service on which B depends, that is, A is a service on which B depends, targetA and targetB are service target coefficients, and the service target coefficients can be set according to specific services; coefficient is a correlation Coefficient between a service a and a service B, each service includes attributes and behaviors, for example, the attributes include price, trade name, type, and the like, and the behaviors include ordering, checkout, distribution, and the like.
The correlation coefficient is used for characterizing the dependency quantity (attribute, behavior) minus the dependent quantity (attribute, behavior); attribute number 1 is the number of attributes depended on by service B in service a, and attribute number 2 is the number of attributes corresponding to attribute number 1 in service B, that is, attribute number 2 is the number of attributes required to be depended on in service B; the behavior quantity 1 is the behavior quantity depended on by the service B in the service a, and the behavior quantity 2 is the behavior quantity corresponding to the behavior quantity 1 in the service B, that is, the behavior quantity 2 is the behavior quantity required to be depended on in the service B.
And (3) rule proportion: the attribute quantity, the total attribute quantity of the behavior quantity in the service and the proportion corresponding to the differentiated behavior quantity are referred, for example, the order service only has one behavior "submitOrder ()", and if the "submitOrder ()" is a behavior which is depended or needs to be depended, the proportion of the "submitOrder ()" is 100%. Rule ratio 1 is the ratio of the number of attributes 1 to the total number of attributes of service a, rule ratio 2 is the ratio of the number of behaviors 1 to the total number of behaviors of service a, rule ratio 3 is the ratio of the number of attributes 2 to the total number of attributes of service B, and rule ratio 4 is the ratio of the number of behaviors 2 to the total number of behaviors of service B.
As shown in fig. 9, the association analysis is to analyze the time correlation between two business entities, and the larger the correlation coefficient is, the more intimate the relationship is, the correlation coefficient may be the number of dependents (attributes, behaviors) minus the number of dependents (attributes, behaviors), and the ratio of the attributes and behaviors may be set according to rules. The value interval of the correlation coefficient is between 1 and-1, wherein 1 represents that two services are completely linearly correlated, 1 represents that the two services are completely negatively correlated, and 0 represents that the two services are uncorrelated. The closer the data is to 0, the weaker the correlation is.
In one embodiment, the correlation includes a stability coefficient; wherein, the service analysis processing unit 302 calculates the stability coefficient as:
Figure BDA0002886305860000121
wherein, the Coefficient correlation Coefficient is obtained by a formula (1-1), and the Stability coefficients of the service A and the service B can be obtained respectively; when Stability coefficients Stability of the service A and the service B are respectively calculated, the daily problem proportion is the daily problem proportion of the service A and the service B, and the rule proportion 5 is the proportion rule of the service A and the service B.
The daily problem proportion is the ratio of the occurrence frequency of the system abnormal problem of the service to the total execution frequency of the service in unit time. The daily problem proportion is increased, and the Coefficient correlation Coefficient is combined, so that the calculated data can be more accurate. The rule ratio 5 is a weighted sum of a ratio 1 of the number of attributes that need to be dependent or relied on to the total number of services and a ratio 2 of the number of behaviors that need to be dependent or relied on to the total number of behaviors of services, for example, the ratio 1 is 0.3, the ratio 2 is 0.6, the weighting coefficients are all 0.5, and the rule ratio 5 is 0.45.
The stability analysis refers to stability explanation of a current business entity point, the calculated relevance coefficient and daily business problem analysis are combined to analyze stability, and the stability value is smaller, the stability is more unstable, and the stability value is 1, and the stability is most stable.
The incidence relation comprises a risk coefficient; wherein, the service analysis processing unit 302 calculates the risk coefficient as:
Risk＝(targetAStability+targetBStability……targetNStability)/count(Target) (1-3)。
wherein targetA _ Stabilty, targetB _ Stabilty and targetNStabilityThe stability coefficients calculated by the formula (1-2), count (target), respectively, are the total number of services in the target system.
The risk analysis means how to evaluate the global influence when a service is added, deleted and updated. The greater the risk value, the more unstable, 1 being the most stable. The risk value is a stability analysis value related to the service point, and if there are a plurality of the risk values, an average value is calculated.
In one embodiment, the business daily problem information is collected from the big data platform in real time, and various methods can be used for extracting the business daily problem information corresponding to each business from the log, wherein the business daily problems comprise faults, rule errors and the like. As shown in fig. 10, the traffic prediction module 40 includes a model training unit 401 and a traffic prediction unit 402. The model training unit 401 constructs a business scenario model through a tensrflow framework, and trains the business scenario model using a training sample. Tensorflow is a machine learning platform, and a plurality of open source algorithms can be realized on the platform to carry out deep learning on data.
Corresponding service scene models can be established for all services, the service scene models can be the existing convolutional neural network models and the like, and the existing training sample generation method is used for generating training samples based on service model information, correlation information and service daily problem information. Model training is carried out through a TensorFlow framework, and after data training, a service scene model is obtained for prediction and the like.
The service prediction unit 402 builds a prediction sample according to the service model information and the associated information to be predicted, predicts the prediction sample by using the trained service scene model, obtains the operation state of the service, including whether the operation is normal, the specific problem during the operation and the corresponding service rule, and can adjust and update the service based on the operation state of the service.
Fig. 11 is a schematic flow chart of an embodiment of a service analysis method according to the present disclosure, as shown in fig. 11:
step 1101, collecting service structure data corresponding to the running service in the service system by using the annotation agent module based on the preset service data collection rule information.
Step 1102, the service structure data is transmitted by using an asynchronous message mode.
Step 1103, obtaining the service model information and the association information between the services based on the service structure data.
And 1104, acquiring business daily problem information, generating a training sample based on the business model information, the association information and the business daily problem information, and training a business scenario model corresponding to the business by using the training sample so as to predict the running state of the business by using the trained business scenario model.
In one embodiment, an annotation agent module is used for acquiring codes of the business, and the annotation agent module is used for constructing a corresponding abstract syntax tree for the codes based on business data acquisition rule information; and analyzing the code grammar information and acquiring the calling information executed by the code based on the abstract grammar tree, and acquiring service structure data, wherein the service structure data comprises attributes, behaviors, service relations and the like.
And configuring a business grammar rule, analyzing and analyzing the business structure data based on the business grammar rule, generating business model information, and acquiring association information based on the business model information. Constructing a business scene model through a TensorFlow frame, and training the business scene model by using a training sample; and establishing a prediction sample according to the service model information and the associated information which need to be predicted, and predicting the prediction sample by using the trained service scene model to obtain the operation state of the service.
In one embodiment, a work task is generated and scheduled; the work tasks comprise business analysis tasks, daily problem collection tasks and the like. And generating business visualization information and/or business matrix grid information based on the business model information and the association information between the businesses, and providing an external access interface for accessing the business visualization information and/or the business matrix grid information. Setting a storage module to store data, wherein the data comprises: service structure data, service model information, associated information, service daily problem information, service running state and the like.
In one embodiment, fig. 12 is a block diagram of yet another embodiment of a traffic analysis system according to the present disclosure. As shown in fig. 12, the apparatus may include a memory 1201, a processor 1202, a communication interface 1203, and a bus 1204. The memory 1201 is used for storing instructions, the processor 1202 is coupled to the memory 1201, and the processor 1202 is configured to implement the service analysis method described above based on the instructions stored in the memory 1201.
The memory 1201 may be a high-speed RAM memory, a non-volatile memory (non-volatile memory), or the like, and the memory 1201 may be a memory array. The storage 1201 may also be partitioned, and the blocks may be combined into virtual volumes according to certain rules. Processor 1202 may be a central processing unit CPU, or an application Specific Integrated circuit asic, or one or more Integrated circuits configured to implement the business analysis methods of the present disclosure.
In one embodiment, the present disclosure provides a computer-readable storage medium storing computer instructions that, when executed by a processor, implement a traffic analysis method as in any one of the above embodiments.
In the service analysis system, the service analysis method, and the storage medium provided in the embodiments, service structure data corresponding to a running service is collected in a service system, and service model information and association information between services are obtained based on the service structure data; generating a training sample based on the service model information, the correlation information and the service daily problem information to train a service scene model corresponding to the service, and predicting the operation state of the service by using the service scene model; the method and the system can analyze the service, including service relevance analysis, risk analysis and the like, can visually display the service, can predict the running state of the service, can realize management on the service requirement, can ensure that the service is reasonable in rule and stable in running, and improve user experience.
The method and system of the present disclosure may be implemented in a number of ways. For example, the methods and systems of the present disclosure may be implemented by software, hardware, firmware, or any combination of software, hardware, and firmware. The above-described order for the steps of the method is for illustration only, and the steps of the method of the present disclosure are not limited to the order specifically described above unless specifically stated otherwise. Further, in some embodiments, the present disclosure may also be embodied as programs recorded in a recording medium, the programs including machine-readable instructions for implementing the methods according to the present disclosure. Thus, the present disclosure also covers a recording medium storing a program for executing the method according to the present disclosure.
The description of the present disclosure has been presented for purposes of illustration and description, and is not intended to be exhaustive or limited to the disclosure in the form disclosed. Many modifications and variations will be apparent to practitioners skilled in this art. The embodiment was chosen and described in order to best explain the principles of the disclosure and the practical application, and to enable others of ordinary skill in the art to understand the disclosure for various embodiments with various modifications as are suited to the particular use contemplated.

Claims (23)

1. A business analysis system comprising:
the service data acquisition module is used for acquiring service structure data corresponding to the running service in the service system by using the annotation agent module based on preset service data acquisition rule information;
the service data transmission module is used for transmitting the service structure data to the service data processing module in an asynchronous message mode;
the service data processing module is used for obtaining service model information and correlation information between services based on the service structure data;
and the business prediction module is used for acquiring business daily problem information, generating a training sample based on the business model information, the correlation information and the business daily problem information, and training a business scene model corresponding to the business by using the training sample so as to predict the running state of the business by using the trained business scene model.
2. The system of claim 1, wherein,
the service data acquisition module comprises:
a code acquisition unit for acquiring a code of the service using an annotation agent module;
a code annotation unit, configured to construct, based on the business data acquisition rule information, a corresponding abstract syntax tree for the code using an annotation proxy module, and obtain the business structure data based on parsing code syntax information and obtaining call information of code execution by the abstract syntax tree; wherein the service structure data includes: attributes, behaviors, and business relationships.
3. The system of claim 2, wherein,
the service data processing module comprises:
a service grammar rule processing unit, configured to configure the service grammar rule, and analyze and process the service structure data based on the service grammar rule to generate the service model information;
and the service analysis processing unit is used for obtaining the correlation information based on the service model information.
4. The system of claim 3, the association comprising: a correlation coefficient; wherein the content of the first and second substances,
the service analysis processing unit is configured to calculate the correlation coefficient as:
coefficient ═ ((number of attributes 1 × (rule proportion 1+ number of behaviors 1) × (rule proportion 2)) - (number of attributes 2 × (rule proportion 3+ number of behaviors 3 × (rule proportion 4));
wherein, A and B are services, A is the service that B depends on, targetA and targetB are service system coefficients; the Coefficient is a correlation Coefficient between the service A and the service B; the attribute number 1 is the attribute number depended on by the service B in the service A, and the attribute number 2 is the attribute number which needs to be depended on and corresponds to the attribute number 1 in the service B; the behavior quantity 1 is the behavior quantity depended on by the service B in the service A, and the behavior quantity 2 is the behavior quantity needing to be depended on corresponding to the behavior quantity 1 in the service B; rule ratio 1 is the ratio of the number of attributes 1 to the total number of attributes of service a, rule ratio 2 is the ratio of the number of behaviors 1 to the total number of behaviors of service a, rule ratio 3 is the ratio of the number of attributes 2 to the total number of attributes of service B, and rule ratio 4 is the ratio of the number of behaviors 2 to the total number of behaviors of service B.
5. The system of claim 4, the association comprising: a stability factor; wherein the content of the first and second substances,
the service analysis processing unit is configured to calculate the stability coefficient as:
Figure FDA0002886305850000021
wherein, Stability coefficients of the service A and the service B are respectively obtained, and the daily problem proportion is the ratio of the occurrence frequency of the system abnormal problem of the service to the total execution frequency of the service. Increasing the daily problem ratio, wherein the rule ratio 5 is a weighted sum of the ratio 1 of the number of the attributes on which the business needs to depend or is dependent to the total number of the businesses and the ratio 2 of the number of the behaviors on which the business needs to depend or is dependent to the total number of the behaviors of the businesses.
6. The system of claim 5, the association comprising: a risk factor; wherein the content of the first and second substances,
a service analysis processing unit, configured to calculate the risk coefficient as:
Risk＝(targetAStability+targetBStability......targetNStability)/count(Target)；
wherein targetA _ Stabilty, targetB _ Stabilty and targetNStability: the stability coefficients of the service a and the service b.
7. The system of claim 1, wherein,
the service prediction module comprises:
the model training unit is used for constructing a business scene model through a TensorFlow framework and training the business scene model by using the training sample;
and the service prediction unit is used for establishing a prediction sample according to the service model information and the associated information which need to be predicted, predicting the prediction sample by using the trained service scene model and obtaining the operation state of the service.
8. The system of claim 1, further comprising:
the scheduling module is used for generating a work task and scheduling the work task; wherein the work tasks include: business analysis tasks and daily problem collection tasks.
9. The system of claim 1, further comprising:
and the service data display module is used for generating service visualization information and/or service matrix grid information based on the service model information and the correlation information between the services, and providing an external access interface for accessing the service visualization information and/or the service matrix grid information.
10. The system of claim 1, wherein,
the asynchronous message comprises: kafka messages, jmq messages.
11. The system of any of claims 1 to 10, further comprising:
the storage module is used for storing data; wherein the data comprises: the service structure data, the service model information, the association information, the service daily problem information and the service running state.
12. A method of traffic analysis, comprising:
based on preset business data acquisition rule information, using an annotation agent module to acquire business structure data corresponding to the running business in a business system;
transmitting the service structure data by using an asynchronous message mode;
obtaining service model information and correlation information between services based on the service structure data;
acquiring business daily problem information, generating a training sample based on the business model information, the correlation information and the business daily problem information, and training a business scene model corresponding to the business by using the training sample so as to predict the running state of the business by using the trained business scene model.
13. The method of claim 12, wherein the collecting of the business structure data corresponding to the running business in the business system using the annotation proxy module based on the preset business data collection rule information comprises:
using an annotation agent module to acquire a code of the service;
constructing a corresponding abstract syntax tree for the code by using an annotation agent module based on the business data acquisition rule information;
analyzing code syntax information and acquiring code execution calling information based on the abstract syntax tree to acquire the service structure data;
wherein the service structure data includes: attributes, behaviors, and business relationships.
14. The method of claim 13, obtaining service model information and association information between services based on the service structure data comprises:
configuring the business grammar rule, and analyzing and processing the business structure data based on the business grammar rule to generate the business model information;
and obtaining the associated information based on the service model information.
15. The method of claim 14, the association comprising: a correlation coefficient; wherein the obtaining the associated information based on the service model information comprises:
calculating the correlation coefficient as:
coefficient ═ ((number of attributes 1 × (rule proportion 1+ number of behaviors 1) × (rule proportion 2)) - (number of attributes 2 × (rule proportion 3+ number of behaviors 3 × (rule proportion 4));
wherein, A and B are services, A is the service that B depends on, targetA and targetB are service system coefficients; the Coefficient is a correlation Coefficient between the service A and the service B; the attribute number 1 is the attribute number depended on by the service B in the service A, and the attribute number 2 is the attribute number which needs to be depended on and corresponds to the attribute number 1 in the service B; the behavior quantity 1 is the behavior quantity depended on by the service B in the service A, and the behavior quantity 2 is the behavior quantity needing to be depended on corresponding to the behavior quantity 1 in the service B; rule ratio 1 is the ratio of the number of attributes 1 to the total number of attributes of service a, rule ratio 2 is the ratio of the number of behaviors 1 to the total number of behaviors of service a, rule ratio 3 is the ratio of the number of attributes 2 to the total number of attributes of service B, and rule ratio 4 is the ratio of the number of behaviors 2 to the total number of behaviors of service B.
16. The method of claim 15, the association comprising: a stability factor; wherein the obtaining the associated information based on the service model information comprises:
calculating the stability coefficient as:
Figure FDA0002886305850000051
wherein, Stability coefficients of the service A and the service B are respectively obtained, and the daily problem proportion is the ratio of the occurrence frequency of the system abnormal problem of the service to the total execution frequency of the service. Increasing the daily problem ratio, wherein the rule ratio 5 is a weighted sum of the ratio 1 of the number of the attributes on which the business needs to depend or is dependent to the total number of the businesses and the ratio 2 of the number of the behaviors on which the business needs to depend or is dependent to the total number of the behaviors of the businesses.
17. The method of claim 16, the association comprising: a risk factor; wherein the obtaining the associated information based on the service model information comprises:
calculating the risk coefficient as:
Risk＝(targetAStability+targetBStability......targetNStability)/count(Target)；
wherein targetA _ Stabilty, targetB _ Stabilty and targetNStability: the stability coefficients of the service a and the service b.
18. The method of claim 12, the training a business scenario model corresponding to the business using the training samples comprising:
constructing a business scene model through a TensorFlow frame, and training the business scene model by using the training sample;
the predicting the operation state of the service by using the trained service scenario model comprises the following steps:
and establishing a prediction sample according to the service model information and the associated information which need to be predicted, and predicting the prediction sample by using the trained service scene model to obtain the operation state of the service.
19. The method of claim 12, further comprising:
generating a work task and scheduling the work task;
wherein the work tasks include: business analysis tasks and daily problem collection tasks.
20. The method of claim 12, further comprising:
generating business visualization information and/or business matrix grid information based on business model information and correlation information between businesses, and providing an external access interface for accessing the business visualization information and/or the business matrix grid information.
21. The method of claim 12, wherein,
the storage module is arranged to store the data,
wherein the data comprises: the service structure data, the service model information, the association information, the service daily problem information and the service running state.
22. A business analysis system comprising:
a memory; and a processor coupled to the memory, the processor configured to perform the method of any of claims 12-21 based on instructions stored in the memory.
23. A computer-readable storage medium having stored thereon, non-transitory, computer instructions for execution by a processor to perform the method of any one of claims 12 to 21.
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